Abdollahi, M, Ni, W, Abolhasan, M & Li, S 2021, 'Software-Defined Networking-Based Adaptive Routing for Multi-Hop Multi-Frequency Wireless Mesh', IEEE Transactions on Vehicular Technology, vol. 70, no. 12, pp. 13073-13086.
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While multi-hop multi-frequency mesh has been extensively studied in the past decades, only several deployable and relatively bulky systems have been developed to support small numbers of hops under stationary settings. This paper presents a new Software-Defined Networking (SDN)-based design of multihop multi-frequency mesh. A new lightweight hardware platform is developed to support adaptive routing and frequency selection, by modifying and integrating commercial-off-the-shelf WiFi modules. We also extend the celebrated Dijkstra’s algorithm in support of the new multi-hop multi-frequency platform, where non-overlapping frequency bands are selected together with the routing paths by maintaining N2 Dijkstra processes for N frequency bands. These processes interact to recursively select the optimal upstream node and frequency for each downstream frequency of a node. Mininet-WiFi is used to evaluate the routing of the new system under dense network settings. The results indicate that our system improves the end-to-end throughput by taking background WiFi traffic into account and adaptively selecting the routes and frequencies, as compared to the shortest-path-based routing strategy.
Afzal, MU, Lalbakhsh, A & Esselle, KP 2021, 'Method to Enhance Directional Propagation of Circularly Polarized Antennas by Making Near-Electric Field Phase More Uniform', IEEE Transactions on Antennas and Propagation, vol. 69, no. 8, pp. 4447-4456.
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A new approach to significantly increase the uniformity in aperture phase distribution, through time synchronization in near-electric field, of circularly polarized (CP) antennas is presented. The method uses the phase of the CP electric field vectors, obtained through full-wave numerical simulations, and does not rely on any approximation such as ray tracing. The near-field data is post-processed to extract the relative phase difference that exist due to the unsynchronized rotations of the electric field vectors in a plane parallel to the antenna aperture. The phase delay is compensated with a thin time-synchronizing metasurface (TSM) that has a 2D array of time-delay cells. The method is demonstrated with a prototype made of two-port patch antenna, which is fed through a hybrid junction, and a TSM that is placed at one wavelength spacing above the patch. When TSM is used with patch antenna, its uniform phase area increases manyfold thus increasing far-field directivity from 6.8 dBic to 22 dBic.
Ahmed, F, Afzal, MU, Hayat, T, Esselle, KP & Thalakotuna, DN 2021, 'A dielectric free near field phase transforming structure for wideband gain enhancement of antennas', Scientific Reports, vol. 11, no. 1.
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AbstractThe gain of some aperture antennas can be significantly increased by making the antenna near-field phase distribution more uniform, using a phase-transformation structure. A novel dielectric-free phase transforming structure (DF-PTS) is presented in this paper for this purpose, and its ability to correct the aperture phase distribution of a resonant cavity antenna (RCA) over a much wider bandwidth is demonstrated. As opposed to printed multilayered metasurfaces, all the cells in crucial locations of the DF-PTS have a phase response that tracks the phase error of the RCA over a large bandwidth, and in addition have wideband transmission characteristics, resulting in a wideband antenna system. The new DF-PTS, made of three thin metal sheets each containing modified-eight-arm-asterisk-shaped slots, is significantly stronger than the previous DF-PTS, which requires thin and long metal interconnects between metal patches. The third advantage of the new DF-PTS is, all phase transformation cells in it are highly transparent, each with a transmission magnitude greater than − 1 dB at the design frequency, ensuring excellent phase correction with minimal effect on aperture amplitude distribution. With the DF-PTS, RCA gain increases to 20.1 dBi, which is significantly greater than its 10.7 dBi gain without the DF-PTS. The measured 10-dB return loss bandwidth and the 3-dB gain bandwidth of the RCA with DF-PTS are 46% and 12%, respectively.
Alanazi, F, Gay, V, N., M & Alturki, R 2021, 'Modelling Health Process and System Requirements Engineering for Better e-Health Services in Saudi Arabia', International Journal of Advanced Computer Science and Applications, vol. 12, no. 1, pp. 549-559.
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This systematic review aimed to examine the published works on e-health modelling system requirements and suggest one applicable to Saudi Arabia. PRISMA method was adopted to search, screen and select the papers to be included in this review. Google Scholar was used as the search engine to collect relevant works. From an initial 74 works, 20 were selected after all screening procedures as per PRISMA flow diagram. The 20 selected works were discussed under various sections. The review revealed that goal setting is the first step. Using the goals, a model can be created based on which system requirements can be elicited. Different research used different approaches within this broad framework and applied the procedures to varying healthcare contexts. Based on the findings, an attempt has been made to set the goals and elicit the system requirements for a diabetes self-management model for the entire country in Saudi Arabian context. This is a preliminary model which needs to be tested, improved and then implemented.
Alharbi, AI, Gay, V, AlGhamdi, MJ, Alturki, R & Alyamani, HJ 2021, 'Towards an Application Helping to Minimize Medication Error Rate', Mobile Information Systems, vol. 2021, pp. 1-7.
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Medication errors related to medication administration done by both doctors and nurses can be considered a vital issue around the world. It is believed that systematisation and the introduction of main documents are done manually, which might increase the opportunities to have inaccuracies and errors because of unexpected wrong actions done by medical practitioners. Experts stated that the lack of pharmacological knowledge is one of the key factors, which play an important role in causing such errors. Doctors and nurses may face problems when they move from one unit to another and the medication administration list has changed. However, promoting public health activities and recent AI-enabled applications can provide general information about medication that helps both doctors and nurses administer the right medication. However, such an application can require a lot of time and effort to search and then find a medication. Therefore, this article aims to investigate whether AI-enabled applications can help avoid or at least minimize medication error rates.
Ali, JSM, Siddique, MD, Mekhilef, S, Yang, Y, Siwakoti, YP & Blaabjerg, F 2021, 'Experimental validation of nine-level switched-capacitor inverter topology with high voltage gain.', Int. J. Circuit Theory Appl., vol. 49, no. 8, pp. 2479-2493.
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This paper proposes a new switched-capacitor nine-level (9L) inverter with reduced switch count. In the proposed topology, floating capacitor (FC) is employed as a voltage booster, and it does not need any additional sensors to maintain the voltage across the FC. Due to additional FC, the number of dc sources and voltage stress on switches is reduced. Moreover, the proposed topology can be cascaded to achieve more voltage levels. Various parameters are considered in the comparison of the proposed topology with other recent switched-capacitor topologies. Simulation and experimental results demonstrate the performance with different load and modulation index variations.
Ali, SMN, Sharma, V, Hossain, MJ, Mukhopadhyay, SC & Wang, D 2021, 'Optimized Energy Control Scheme for Electric Drive of EV Powertrain Using Genetic Algorithms', Energies, vol. 14, no. 12, pp. 3529-3529.
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Automotive applications often experience conflicting-objective optimization problems focusing on performance parameters that are catered through precisely developed cost functions. Two such conflicting objectives which substantially affect the working of traction machine drive are maximizing its speed performance and minimizing its energy consumption. In case of an electric vehicle (EV) powertrain, drive energy is bounded by battery dynamics (charging and capacity) which depend on the consumption of drive voltage and current caused by driving cycle schedules, traffic state, EV loading, and drive temperature. In other words, battery consumption of an EV depends upon its drive energy consumption. A conventional control technique improves the speed performance of EV at the cost of its drive energy consumption. However, the proposed optimized energy control (OEC) scheme optimizes this energy consumption by using robust linear parameter varying (LPV) control tuned by genetic algorithms which significantly improves the EV powertrain performance. The analysis of OEC scheme is conducted on the developed vehicle simulator through MATLAB/Simulink based simulations as well as on an induction machine drive platform. The accuracy of the proposed OEC is quantitatively assessed to be 99.3% regarding speed performance which is elaborated by the drive speed, voltage, and current results against standard driving cycles.
Aljarajreh, H, Lu, DD-C, Siwakoti, YP, Aguilera, RP & Tse, CK 2021, 'A Method of Seamless Transitions Between Different Operating Modes for Three-Port DC-DC Converters.', IEEE Access, vol. 9, pp. 59184-59195.
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This paper presents the design of three-port converters (TPCs) for smooth transitions (i.e., fast settling time, and no obvious overshoot/undershoot) of 7 distinctive operating modes, depending on sources and loads scheduling. Two viable converter configurations have been identified and selected for further analysis and design of PV-battery systems. Conventionally, mode transition is achieved by assigning specific switching patterns through feedback signals and appropriate control algorithms. This incurs a delay in the response and unavoidable noise in the circuit. Additionally, in TPCs, three voltage sensors and three current sensors are generally required for decision making in mode selection, where errors in sensors may lead to an inaccurate response. This paper presents a new control strategy where the number of switching patterns is significantly reduced to 3 patterns instead of minimum 5 patterns for existing reported topologies. Therefore, decisions are simplified so that the transition occurs naturally based on the power availability and load demand but not deliberately as in the conventional method. In addition, instead of six sensors, three voltage sensors and only one current sensor are required to achieve all the necessary operations, namely, MPPT, battery protection, and output regulation. Moreover, these sensors do not participate in mode selection decision, which leads to seamless and fast mode transition. In addition, this work considers two bidirectional ports as compared with only one bidirectional port in most reported topologies. This configuration enables both standalone and DC grid-connected applications. Experimental results are reported to verify the proposed solution.
Aljarajreh, H, Lu, DD-C, Siwakoti, YP, Tse, CK & See, KW 2021, 'Synthesis and Analysis of Three-Port DC/DC Converters with Two Bidirectional Ports Based on Power Flow Graph Technique', Energies, vol. 14, no. 18, pp. 5751-5751.
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This paper presents a systematic topological study to derive all possible basic and non-isolated three-port converters (TPCs) using power flow diagrams. Unlike most reported TPCs with one bidirectional port, this paper considers up to two bidirectional ports and provides a comprehensive analytical tool. This tool acts as a framework for all power flow combinations, selection, and design. Some viable converter configurations have been identified and selected for further analysis.
Alsahafi, YA & Gay, V 2021, 'Erratum to ‘An overview of electronic personal health records’ [Health Policy and Technology 7 (2018) 427-432]', Health Policy and Technology, vol. 10, no. 4, pp. 100566-100566.
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Alsmadi, L, Kong, X, Sandrasegaran, K & Fang, G 2021, 'An Improved Indoor Positioning Accuracy Using Filtered RSSI and Beacon Weight', IEEE Sensors Journal, vol. 21, no. 16, pp. 18205-18213.
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Alzahrani, AS, Gay, V, Alturki, R & AlGhamdi, MJ 2021, 'Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications', Journal of Healthcare Engineering, vol. 2021, pp. 1-8.
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Mobile application (app) use is increasingly becoming an essential part of our daily lives. Due to their significant usefulness, people rely on them to perform multiple tasks seamlessly in almost all aspects of everyday life. Similarly, there has been immense progress in artificial intelligence (AI) technology, especially deep learning, computer vision, natural language processing, and robotics. These technologies are now actively being implemented in smartphone apps and healthcare, providing multiple healthcare services. However, several factors affect the usefulness of mobile healthcare apps, and usability is an important one. There are various healthcare apps developed for each specific task, and the success of these apps depends on their performance. This study presents a systematic review of the existing apps and discusses their usability attributes. It highlights the usability models, outlines, and guidelines proposed in previous research for designing apps with improved usability characteristics. Thirty-nine research articles were reviewed and examined to identify the usability attributes, framework, and app design conducted. The results showed that satisfaction, efficiency, and learnability are the most important usability attributes to consider when designing eHealth mobile apps. Surprisingly, other significant attributes for healthcare apps, such as privacy and security, were not among the most indicated attributes in the studies.
Amin, BMR, Taghizadeh, S, Maric, S, Hossain, MJ & Abbas, R 2021, 'Smart Grid Security Enhancement by Using Belief Propagation', IEEE Systems Journal, vol. 15, no. 2, pp. 2046-2057.
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Amin, U, Hossain, MJ, Tushar, W & Mahmud, K 2021, 'Energy Trading in Local Electricity Market With Renewables—A Contract Theoretic Approach', IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 3717-3730.
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Emerging smart grid technologies and increased penetration of renewable energy sources (RESs) direct the power sector to focus on RESs as an alternative to meet both baseload and peak load demands in a cost-efficient way. A key issue in such schemes is the design and analysis of energy trading techniques involving complex interactions between an aggregator and multiple electricity suppliers (ESs) with RESs fulfilling a certain demand. This is challenging because ESs can be of various categories, such as small/medium/large scale, and they are self-interested and generally have different preferences toward trading based on their types and constraints. This article introduces a new contract theoretic framework to tackle this challenge by designing optimal contracts for ESs. To this end, a dynamic pricing scheme is developed such that the aggregator can utilize to incentivize the ESs to contribute to both baseload and peak load demands according to their categories. An algorithm is proposed that can be implemented in a distributed manner by trading partners to enable energy trading. It is shown that the trading strategy under a baseload scenario is feasible, and the aggregator only needs to consider the per unit generation cost of ESs to decide on its strategy. The trading strategy for a peak load scenario, however, is complex and requires consideration of different factors, such as variations in the wholesale price and its effect on the selling price of ESs, and the uncertainty of energy generation from RESs. Simulation results demonstrate the effectiveness of the proposed scheme for energy trading in the local electricity market.
Amirgholipour, S, Jia, W, Liu, L, Fan, X, Wang, D & He, X 2021, 'PDANet: Pyramid density-aware attention based network for accurate crowd counting', Neurocomputing, vol. 451, pp. 215-230.
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Crowd counting, i.e., estimating the number of people in crowded areas, has attracted much interest in the research community. Although many attempts have been reported, crowd counting remains an open real-world problem due to the vast density variations and severe occlusion within the interested crowd area. In this paper, we propose a novel Pyramid Density-Aware Attention based network, abbreviated as PDANet, which leverages the attention, pyramid scale feature, and two branch decoder modules for density-aware crowd counting. The PDANet utilizes these modules to extract features of different scales while focusing on the relevant information and suppressing the misleading information. We also address the variation of crowdedness levels among different images with a Density-Aware Decoder (DAD) modules. For this purpose, a classifier is constructed to evaluate the density level of the input features and then passes them to the corresponding high and low density DAD modules. Finally, we generate an overall density map by considering the summation of low and high crowdedness density maps. Meanwhile, we employ different losses aiming to achieve a precise density map for the input scene. Extensive evaluations conducted on the challenging benchmark datasets well demonstrate the superior performance of the proposed PDANet in terms of the accuracy of counting and generated density maps over the well-known state-of-the-art approaches.
Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2021, 'Soil moisture remote sensing using SIW cavity based metamaterial perfect absorber', Scientific Reports, vol. 11, no. 1, pp. 1-17.
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AbstractContinuous and accurate sensing of water content in soil is an essential and useful measure in the agriculture industry. Traditional sensors developed to perform this task suffer from limited lifetime and also need to be calibrated regularly. Further, maintenance, support, and deployment of these sensors in remote environments provide additional challenges to the use of conventional soil moisture sensors. In this paper, a metamaterial perfect absorber (MPA) based soil moisture sensor is introduced. The ability of MPAs to absorb electromagnetic signals with near 100% efficiency facilitates the design of highly accurate and low-profile radio frequency passive sensors. MPA based sensor can be fabricated from highly durable materials and can therefore be made more resilient than traditional sensors. High resolution sensing is achieved through the creation of physical channels in the substrate integrated waveguide (SIW) cavity. The proposed sensor does not require connection for both electromagnetic signals or for adding a testing sample. Importantly, an external power supply is not needed, making the MPA based sensor the perfect solution for remote and passive sensing in modern agriculture. The proposed MPA based sensor has three absorption bands due to the various resonance modes of the SIW cavity. By changing the soil moisture level, the absorption peak shifts by 10 MHz, 23.3 MHz, and 60 MHz, which is correlated with the water content percentage at the first, second and third absorption bands, respectively. Finally, a $$6 \times 6$$ 6 × 6 cell array with...
Amiri, M, Tofigh, F, Shariati, N, Lipman, J & Abolhasan, M 2021, 'Review on Metamaterial Perfect Absorbers and Their Applications to IoT', IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4105-4131.
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Future Internet of Things (IoT) devices are expected to be fully ubiquitous. To achieve this vision, a new generation of IoT devices needs to be developed, which can operate autonomously. To achieve autonomy, IoT devices must be completely wireless, both in terms of transmission and power. Further, accurate sensing is another crucial parameter of autonomy. Several wireless standards have been developed for improving the efficiency of IoT applications. However, the powering of IoT devices, sensor accuracy, and efficiency of electronic devices are open research problems in literature. With the advent of metamaterial perfect absorbers (MPAs), electromagnetic waves can be used as a source of energy, to enable sensing of the phenomenon and as a carrier for exchanging data. In this article, an extensive application-based investigation has been conducted on design principles and various methods of enhancing MPA characteristics. Moreover, the current applications that benefit from MPA, such as absorption of undesired frequencies, optical switching, energy harvesting, and sensing, are investigated. Finally, some implemented examples of MPA in industrial applications are provided along with possible directions for future work and open research areas.
Amjadipour, M, Bradford, J, Zebardastan, N, Motta, N & Iacopi, F 2021, 'MoS2/Epitaxial graphene layered electrodes for solid-state supercapacitors', Nanotechnology, vol. 32, no. 19, pp. 195401-195401.
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Abstract The potential of transition metal dichalcogenides such as MoS2 for energy storage has been significantly limited so far by the lack of conductivity and structural stability. Employing highly conductive, graphitic materials in combination with transition metal dichalcogenides can address this gap. Here, we explore the use of a layered electrode structure for solid-state supercapacitors, made of MoS2 and epitaxial graphene (EG) on cubic silicon carbide for on-silicon energy storage. We show that the energy storage of the solid-state supercapacitors can be significantly increased by creating layered MoS2/graphene electrodes, yielding a substantial improvement as compared to electrodes using either EG or MoS2 alone. We conclude that the conductivity of EG and the growth morphology of MoS2 on graphene play an enabling role in the successful use of transition metal dichalcogenides for on-chip energy storage.
Ansari, M, Jones, B, Zhu, H, Shariati, N & Guo, YJ 2021, 'A Highly Efficient Spherical Luneburg Lens for Low Microwave Frequencies Realized With a Metal-Based Artificial Medium', IEEE Transactions on Antennas and Propagation, vol. 69, no. 7, pp. 3758-3770.
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IEEE This paper describes a novel spherical lens antenna constructed of planar layers of light-weight foam with equally spaced conducting inclusions of varying sizes on an orthogonal grid. This construction largely overcomes the problems of weight and cost that have tended to make larger low frequency Luneburg lenses impractical. A penalty for this type of design is that some anisotropy exists in the lens’s dielectric. This effect is examined using both ray tracing techniques and full-wave simulation and it is found that the principal consequence is that the focal length of the lens varies in different directions. Methods for mitigating the effect are proposed. A prototype lens antenna intended for cellular use in the band 3.3 – 3.8 GHz with dual linear slant polarized feeds was designed and constructed to confirm the findings. Measured results show a peak gain of 23 dBi which is less than 1 dB lower than the maximum possible directivity from the lens’s cross section area. Scanning loss is less than 0.8 dB over the whole sphere. Simulated and measured performance show excellent agreement over the whole sphere. The overall performance of the prototype lens antenna demonstrates that this type of lens should be very suitable for use in high-gain multibeam antennas at lower microwave frequencies.
Ba, X, Wang, P, Zhang, C, Zhu, JG & Guo, Y 2021, 'Improved Deadbeat Predictive Current Control to Enhance the Performance of the Drive System of Permanent Magnet Synchronous Motors', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-4.
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Bai, J, Liu, Y, Ren, Y, Nie, Z & Guo, YJ 2021, 'Efficient Synthesis of Linearly Polarized Shaped Patterns Using Iterative FFT via Vectorial Least-Square Active Element Pattern Expansion', IEEE Transactions on Antennas and Propagation, vol. 69, no. 9, pp. 6040-6045.
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Banerjee, S, Lyu, J, Huang, Z, Leung, HFF, Lee, TT-Y, Yang, D, Su, S, Zheng, Y & Ling, S-H 2021, 'Light-Convolution Dense Selection U-Net (LDS U-Net) for Ultrasound Lateral Bony Feature Segmentation', Applied Sciences, vol. 11, no. 21, pp. 10180-10180.
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Scoliosis is a widespread medical condition where the spine becomes severely deformed and bends over time. It mostly affects young adults and may have a permanent impact on them. A periodic assessment, using a suitable modality, is necessary for its early detection. Conventionally, the usually employed modalities include X-ray and MRI, which employ ionising radiation and are expensive. Hence, a non-radiating 3D ultrasound imaging technique has been developed as a safe and economic alternative. However, ultrasound produces low-contrast images that are full of speckle noise, and skilled intervention is necessary for their processing. Given the prevalent occurrence of scoliosis and the limitations of scalability of human expert interventions, an automatic, fast, and low-computation assessment technique is being developed for mass scoliosis diagnosis. In this paper, a novel hybridized light-weight convolutional neural network architecture is presented for automatic lateral bony feature identification, which can help to develop a fully-fledged automatic scoliosis detection system. The proposed architecture, Light-convolution Dense Selection U-Net (LDS U-Net), can accurately segment ultrasound spine lateral bony features, from noisy images, thanks to its capabilities of smartly selecting only the useful information and extracting rich deep layer features from the input image. The proposed model is tested using a dataset of 109 spine ultrasound images. The segmentation result of the proposed network is compared with basic U-Net, Attention U-Net, and MultiResUNet using various popular segmentation indices. The results show that LDS U-Net provides a better segmentation performance compared to the other models. Additionally, LDS U-Net requires a smaller number of parameters and less memory, making it suitable for a large-batch screening process of scoliosis without a high computational requirement.
Barzegarkhoo, R, Lee, SS, Khan, SA, Siwakoti, Y & Lu, DD-C 2021, 'A Novel Generalized Common-Ground Switched-Capacitor Multilevel Inverter Suitable for Transformerless Grid-Connected Applications', IEEE Transactions on Power Electronics, vol. 36, no. 9, pp. 10293-10306.
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Recent research on common-ground switched-capacitor transformerless (CGSC-TL) inverters shows some intriguing features, such as integrated voltage boosting ability, possible multilevel output voltage generation, and nullification of the leakage current issue. However, the number of output voltage levels and also the overall voltage boosting ratio of most of the existing CGSC-TL inverters are limited to five and two, respectively. This article presents a generalized circuit configuration of such converters capable of higher voltage gain and output voltage levels generation. A basic five-level (5L) CGSC-TL inverter is first proposed using eight power switches and two self-balanced dc-link capacitors. A generalized extension of the circuit for any output voltage levels and voltage gain is then presented while keeping all the traits of the proposed basic 5L-CGSC-TL inverter. The circuit descriptions, control strategy, design guidelines, comparative study, and the relevant simulation and experimental results for the proposed 5L-CGSC-TL inverters and its seven-level derived topology are presented to validate the effectiveness and feasibility of this proposal.
Barzegarkhoo, R, Lee, SS, Siwakoti, YP, Khan, SA & Blaabjerg, F 2021, 'Design, Control, and Analysis of a Novel Grid-Interfaced Switched-Boost Dual T-Type Five-Level Inverter With Common-Ground Concept.', IEEE Trans. Ind. Electron., vol. 68, no. 9, pp. 8193-8206.
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Barzegarkhoo, R, Mojallali, H, Shahalami, SH & Siwakoti, YP 2021, 'A novel common‐ground switched‐capacitor five‐level inverter with adaptive hysteresis current control for grid‐connected applications', IET Power Electronics, vol. 14, no. 12, pp. 2084-2098.
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Barzegarkhoo, R, Siwakoti, YP, Aguilera, RP, Khan, MNH, Lee, SS & Blaabjerg, F 2021, 'A Novel Dual-Mode Switched-Capacitor Five-Level Inverter With Common-Ground Transformerless Concept', IEEE Transactions on Power Electronics, vol. 36, no. 12, pp. 13740-13753.
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Transformerless (TL) grid-connected photovoltaic (PV) inverters with a common-ground (CG) circuit architecture exhibit some excellent features in removing the leakage current concern and improving the overall efficiency. However, the ability to cope with a wide range of input voltage changes while maintaining the output voltage in a single power conversion stage is a key technological challenge. Considering this, the article at hand proposes a novel dual-mode switched-capacitor five-level (DMSC5L)-TL inverter with a CG feature connected to the grid. The proposed topology is comprised of a single dc source and power diode, three capacitors, four unidirectional, and three bidirectional power switches. Based on the series-parallel switching conversion of the involved switches, the proposed DMSC5L-TL inverter can generate five distinctive output voltage levels during both the boost and buck operation modes with a self-voltage balancing operation for the involved capacitors. A simple dead-beat continuous current controller (DB3C) modulation technique is also used to handle both the active and reactive power exchange while ensuring a fixed switching frequency operation. The proposed circuit description with its DB3C details, the design guidelines with a comparative study, and some experimental results are also given to show the feasibility of the proposed solution for the practical applications.
Barzegarkhoo, R, Siwakoti, YP, Vosoughi, N & Blaabjerg, F 2021, 'Six-Switch Step-Up Common-Grounded Five-Level Inverter With Switched-Capacitor Cell for Transformerless Grid-Tied PV Applications.', IEEE Trans. Ind. Electron., vol. 68, no. 2, pp. 1374-1387.
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Begum, H, Qian, J & Lee, JE-Y 2021, 'Piezoelectric Elliptical Plate Micromechanical Resonator With Low Motional Resistance for Resonant Sensing in Liquid', IEEE Sensors Journal, vol. 21, no. 6, pp. 7339-7347.
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Begum, M, Eskandari, M, Abuhilaleh, M, Li, L & Zhu, J 2021, 'Fuzzy-Based Distributed Cooperative Secondary Control with Stability Analysis for Microgrids', Electronics, vol. 10, no. 4, pp. 399-399.
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This research suggests a novel distributed cooperative control methodology for a secondary controller in islanded microgrids (MGs). The proposed control technique not only brings back the frequency/voltage to its reference values, but also maintains precise active and reactive power-sharing among distributed generation (DG) units by means of a sparse communication system. Due to the dynamic behaviour of distributed secondary control (DSC), stability issues are a great concern for a networked MG. To address this issue, the stability analysis is undertaken systematically, utilizing the small-signal state-space linearized model of considering DSC loops and parameters. As the dynamic behaviour of DSC creates new oscillatory modes, an intelligent fuzzy logic-based parameter-tuner is proposed for enhancing the system stability. Accurate tuning of the DSC parameters can develop the functioning of the control system, which increases MG stability to a greater extent. Moreover, the performance of the offered control method is proved by conducting a widespread simulation considering several case scenarios in MATLAB/Simscape platform. The proposed control method addresses the dynamic nature of the MG by supporting the plug-and-play functionality, and working even in fault conditions. Finally, the convergence and comparison study of the offered control system is shown.
Belotti, Y, McGloin, D & Weijer, CJ 2021, 'Effects of spatial confinement on migratory properties of Dictyostelium discoideum cells', Communicative & Integrative Biology, vol. 14, no. 1, pp. 5-14.
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Berger, PR, Hussain, MM, Iacopi, F, Schulze, J, Ye, P, Rachmady, W, Wen, H-C & Krishnan, S 2021, 'Foreword Special Issue on Low-Temperature Processing of Electronic Materials for Cutting Edge Devices', IEEE Transactions on Electron Devices, vol. 68, no. 7, pp. 3138-3141.
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Buchlak, QD, Esmaili, N, Leveque, J-C, Bennett, C, Farrokhi, F & Piccardi, M 2021, 'Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review', Journal of Clinical Neuroscience, vol. 89, pp. 177-198.
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Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.
Budati, AK & Ling, SSH 2021, 'Guest editorial: Machine Learning in Wireless Networks.', CAAI Trans. Intell. Technol., vol. 6, no. 2, pp. 133-134.
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Cao, Y, Lv, T, Lin, Z & Ni, W 2021, 'Delay-Constrained Joint Power Control, User Detection and Passive Beamforming in Intelligent Reflecting Surface-Assisted Uplink mmWave System', IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 2, pp. 482-495.
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While millimeter-wave (mmWave) communications can enjoy abundant bandwidth resource, their high susceptibility to blockage poses serious challenges to low-latency services. In this paper, a novel intelligent reflecting surface (IRS)-assisted mmWave scheme is proposed to overcome the impact of blockage. The scheme minimizes the user power of a multi-user mmWave system by jointly optimizing the transmit powers of the devices, the multi-user detector at the base station, and the passive beamforming at the IRS, subject to delay requirements. An alternating optimization framework is developed to decompose the joint optimization problem into three subproblems iteratively optimized till convergence. In particular, closed-form expressions are devised for the update of the powers and multi-user detector. The IRS configuration is formulated as a sum-of-inverse minimization (SIMin) fractional programming problem and solved by exploiting the alternating direction method of multipliers (ADMM). The configuration is also interpreted as a latency residual maximization problem, and solved efficiently by designing a new complex circle manifold optimization (CCMO) method. Numerical results corroborate the effectiveness of our scheme in terms of power saving, as compared with a semidefinite relaxation-based alternative.
Cao, Y, Lv, T, Ni, W & Lin, Z 2021, 'Sum-Rate Maximization for Multi-Reconfigurable Intelligent Surface-Assisted Device-to-Device Communications', IEEE Transactions on Communications, vol. 69, no. 11, pp. 7283-7296.
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Chen, C, Liu, B, Liu, Y, Liao, J, Shan, X, Wang, F & Jin, D 2021, 'Heterochromatic Nonlinear Optical Responses in Upconversion Nanoparticles for Super‐Resolution Nanoscopy', Advanced Materials, vol. 33, no. 23, pp. e2008847-2008847.
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AbstractPoint spread function (PSF) engineering by an emitter's response can code higher‐spatial‐frequency information of an image for microscopy to achieve super‐resolution. However, complexed excitation optics or repetitive scans are needed, which explains the issues of low speed, poor stability, and operational complexity associated with the current laser scanning microscopy approaches. Here, the diverse emission responses of upconversion nanoparticles (UCNPs) are reported for super‐resolution nanoscopy to improve the imaging quality and speed. The method only needs a doughnut‐shaped scanning excitation beam at an appropriate power density. By collecting the four‐photon emission of single UCNPs, the high‐frequency information of a super‐resolution image can be resolved through the doughnut‐emission PSF. Meanwhile, the two‐photon state of the same nanoparticle is oversaturated, so that the complementary lower‐frequency information of the super‐resolution image can be simultaneously collected by the Gaussian‐like emission PSF. This leads to a method of Fourier‐domain heterochromatic fusion, which allows the extended capability of the engineered PSFs to cover both low‐ and high‐frequency information to yield optimized image quality. This approach achieves a spatial resolution of 40 nm, 1/24th of the excitation wavelength. This work suggests a new scope for developing nonlinear multi‐color emitting probes in super‐resolution nanoscopy.
Chen, D, Liu, Y, Chen, S-L, Qin, P-Y & Guo, YJ 2021, 'A Wideband High-Gain Multilinear Polarization Reconfigurable Antenna', IEEE Transactions on Antennas and Propagation, vol. 69, no. 7, pp. 4136-4141.
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IEEE In this Communication, a wideband low-profile antenna with switchable multi-linear polarizations (MLPs) is proposed. An odd number of dipoles with trapezoidal-shaped arms printed on both sides of a substrate are adopted as reconfigurable radiators, which provides a much smaller polarization interval than using an adjacent even number of dipoles. PIN diodes with simple DC biasing lines are loaded to reconfigure the polarization states. A circular-contoured artificial magnetic conductor (AMC) reflector using hexagon-patch cells is employed to reduce the antenna profile. The whole multiple dipole structure is rotationally invariant which provides almost rotationally invariant antenna performance for different LPs. In addition, the antenna can be easily re-designed when adjusting the number of dipoles for different LPs. A seven-LP reconfigurable antenna working in 2:85 GHz to 3:40 GHz is used as an example to give the detailed parameters study and performance analysis. Three antennas with 5, 7 and 9 reconfigurable LPs are designed and measured. With 0:035λ height, they achieve the measured overlapped bandwidths of 20:6%, 17:6% and 15:9% for 5, 7 and 9 LPs, respectively, and their measured peak gains are ranging from 8:3 to 8:5 dBi.
Chen, L, Chen, L, Ge, Z, Sun, Y, Hamilton, TJ & Zhu, X 2021, 'A 90-GHz Asymmetrical Single-Pole Double-Throw Switch With >19.5-dBm 1-dB Compression Point in Transmission Mode Using 55-nm Bulk CMOS Technology', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 68, no. 11, pp. 4616-4625.
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The millimeter-wave (mm-wave) single-pole double-throw (SPDT) switch designed in bulk CMOS technology has limited power-handling capability in terms of 1-dB compression point (P1dB) inherently. This is mainly due to the low threshold voltage of the switching transistors used for shunt-connected configuration. To solve this issue, an innovative approach is presented in this work, which utilizes a unique passive ring structure. It allows a relatively strong RF signal passing through the TX branch, while the switching transistors are turned on. Thus, the fundamental limitation for P1dB due to reduced threshold voltage is overcome. To prove the presented approach is feasible in practice, a 90-GHz asymmetrical SPDT switch is designed in a standard 55-nm bulk CMOS technology. The design has achieved an insertion loss of 3.2 dB and 3.6 dB in TX and RX mode, respectively. Moreover, more than 20 dB isolation is obtained in both modes. Because of using the proposed passive ring structure, a remarkable P1dB is achieved. No gain compression is observed at all, while a 19.5 dBm input power is injected into the TX branch of the designed SPDT switch. The die area of this design is only 0.26 mm2.
Chen, L, Liu, H, Hora, J, Zhang, JA, Yeo, KS & Zhu, X 2021, 'A Monolithically Integrated Single-Input Load-Modulated Balanced Amplifier With Enhanced Efficiency at Power Back-Off', IEEE Journal of Solid-State Circuits, vol. 56, no. 5, pp. 1553-1564.
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In this article, the design of a power amplifier (PA) using a simple but effective architecture, namely, load-modulated balanced amplifier (LMBA), is presented. Using this architecture for PA design, it can achieve not only a relatively high saturated output power but also an excellent efficiency enhancement at the power back-off (PBO) region. To prove that the presented approach is feasible in practice, a PA is designed in a 1-μm gallium arsenide (GaAs) HBT process. Operating under a 5-V power supply, the PA can deliver more than 31-dBm saturated output power with 36% collector efficiency (CE) at 5 GHz. Moreover, it also achieves 1.2 and 1.23 times CE enhancement over an idealistic Class-B PA at 6- and 9-dB PBO levels, respectively. Finally, the designed PA supports 64-quadrature amplitude modulation (QAM) with 80 Msys/s at 22-dBm average output power while still maintaining an error vector magnitude (EVM) and adjacent channel power ratio (ACPR) better than -29.5 dB and -29.4 dBc, respectively.
Chen, L, Liu, Y, Yang, S & Guo, YJ 2021, 'Efficient Synthesis of Filter-and-Sum Array With Scanned Wideband Frequency-Invariant Beam Pattern and Space-Frequency Notching', IEEE Signal Processing Letters, vol. 28, pp. 384-388.
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IEEE This work generalizes the Fourier transform (FT)-based frequency-invariant beamforming (FIB) method to the synthesis of scanned frequency-invariant (FI) beam pattern with space-frequency notching for an array with non-isotropic elements. Wideband FI pattern characteristics are described by using multiple reference sub-band FI patterns that are obtained through an iterative single-frequency FT-based synthesis method. By applying fast Fourier transform (FFT) on the combination of these multiple reference sub-band FI patterns, a wideband excitation distribution can be generated. Based on this excitation distribution, we construct a new wideband excitation distribution that is conjugate-symmetric about zero frequency, so that real-valued finite-impulse-response (FIR) filter coefficients can be obtained by applying FFT on the constructed distribution. Two numerical examples are introduced to show the effectiveness and efficiency of the proposed method for synthesizing scanned FI beam patterns with complicated notching requirements.
Chen, X, Lu, Z, Ni, W, Wang, X, Wang, F, Zhang, S & Xu, S 2021, 'Cooling-Aware Optimization of Edge Server Configuration and Edge Computation Offloading for Wirelessly Powered Devices', IEEE Transactions on Vehicular Technology, vol. 70, no. 5, pp. 5043-5056.
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Edge computing (EC) provides an effective means to cope with explosive computation demands of the Internet-of-Things (IoT). This paper presents a new cooling-aware joint optimization of the CPU configuration of the edge servers, and the schedules of wireless power transfer (WPT), offloading and computing for WPT-powered devices, so that the resource-restrained devices can have tasks accomplished in a timely and energy-efficient manner. Alternating optimization is applied to minimize the total energy consumption of WPT, EC, and cooling, while satisfying the computation deadlines of the devices. A key aspect is that semi-closed-form solutions are derived for the WPT power, offloading duration, and CPU frequency by applying the Lagrange duality method. With the solutions, the alternating optimization converges quickly and indistinguishably closely to the lower bound of the energy consumption. The semi-closed-form solutions also reveal the structure underlying the optimal solution to the problem, and can validate the result of the alternating optimization. Extensive simulations show that the proposed algorithm can save up to 90.4% the energy of existing benchmarks in our considered cases.
Chen, X, Wu, K, Bai, A, Masuku, CM, Niederberger, J, Liporace, FS & Biegler, LT 2021, 'Real-time refinery optimization with reduced-order fluidized catalytic cracker model and surrogate-based trust region filter method', Computers & Chemical Engineering, vol. 153, pp. 107455-107455.
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Chen, Y, Westerhausen, MT, Li, C, White, S, Bradac, C, Bendavid, A, Toth, M, Aharonovich, I & Tran, TT 2021, 'Solvent-Exfoliated Hexagonal Boron Nitride Nanoflakes for Quantum Emitters', ACS Applied Nano Materials, vol. 4, no. 10, pp. 10449-10457.
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Quantum emitters in hexagonal boron nitride (hBN) flakes have recently emerged as a promising platform for nanophotonic and quantum applications. The solvent-exfoliation process of these flakes has, however, remained largely unexplored. In this work, we demonstrate a surfactant-assisted exfoliation technique in an aqueous solution to exfoliate a variety of commercially available hBN powders into hBN nanoflakes. We show that the selection of hBN powder greatly impacts the optical properties of the resultant quantum emitters embedded in exfoliated hBN nanoflakes. We find that the sample with the best optical performance also shows the lowest impurity levels in its starting hBN powder. Our study provides further insight into quantum emitter fabrication in hBN and tailoring of their optical properties.
Chen, Y, Xu, X, Li, C, Bendavid, A, Westerhausen, MT, Bradac, C, Toth, M, Aharonovich, I & Tran, TT 2021, 'Bottom‐Up Synthesis of Hexagonal Boron Nitride Nanoparticles with Intensity‐Stabilized Quantum Emitters', Small, vol. 17, no. 17, pp. 1-7.
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AbstractFluorescent nanoparticles are widely utilized in a large range of nanoscale imaging and sensing applications. While ultra‐small nanoparticles (size ≤10 nm) are highly desirable, at this size range, their photostability can be compromised due to effects such as intensity fluctuation and spectral diffusion caused by interaction with surface states. In this article, a facile, bottom‐up technique for the fabrication of sub‐10‐nm hexagonal boron nitride (hBN) nanoparticles hosting photostable bright emitters via a catalyst‐free hydrothermal reaction between boric acid and melamine is demonstrated. A simple stabilization protocol that significantly reduces intensity fluctuation by ≈85% and narrows the emission linewidth by ≈14% by employing a common sol–gel silica coating process is also implemented. This study advances a promising strategy for the scalable, bottom‐up synthesis of high‐quality quantum emitters in hBN nanoparticles.
Cheng, T, Lu, DD-C & Siwakoti, YP 2021, 'A MOSFET SPICE Model With Integrated Electro-Thermal Averaged Modeling, Aging, and Lifetime Estimation.', IEEE Access, vol. 9, pp. 5545-5554.
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Lifetime estimation of power semiconductor devices have been widely investigated to improve the reliability and reduce the cost of maintenance of power converters. However in most reported work, the aging effect is not considered in the lifetime evaluation process due to the omission or limitation of thermal cycle counting method. Additionally, the electrical/thermal simulation and lifetime estimation are usually implemented in different simulators/platforms, for the same reason. Thus, to tackle these problems, a concise but comprehensive MOSFET model that enables electro-thermal modeling, aging and lifetime estimation on LTspice® circuit simulator is proposed in this paper. The idea comes from the fact that, MOSFET on-state resistance R_{ds,on} is not only temperature dependent, but also widely accepted as the device failure precursor. In other words, as it carries critical information about instantaneous temperature and aging progress. Hence, co-simulation can be achieved by constructing electrical, thermal, and aging and lifetime sub-modules exclusively first, and using R_{ds,on} , to build linkages among them. Averaged modeling technique is adopted due to the ease of establishing links among these three sub-modules, and fast simulation speed as compared to a switched converter model. Behavioral models are employed to realize the thermal cycles counting, stress accumulation and degradation evaluation. This paper demonstrates that it is possible to use a single simulation software to monitor performances of devices and circuits, and their lifetime estimation simultaneously. High-stress thermal cycling and long-term random mission profiles are applied to verify the correctness of the model and to mimic a 10-year load respectively. An accelerated aging trend can be observed in the long-term mission profile simulation, which is in agreement with the theory. Facilitated by the employment of averaged circuits, the proposed method is a good simulation/analy...
Chowdhury, L, Kamal, MS, Ripon, SH, Parvin, S, Hussain, OK, Ashour, A & Chowdhury, BR 2021, 'A Biological Data-Driven Mining Technique by Using Hybrid Classifiers With Rough Set', International Journal of Ambient Computing and Intelligence, vol. 12, no. 3, pp. 123-139.
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Biological data classification and analysis are significant for living organs. A biological data classification is an approach that classifies the organs into a particular group based on their features and characteristics. The objective of this paper is to establish a hybrid approach with naive Bayes, apriori algorithm, and KNN classifier that generates optimal classification rules for finding biological pattern matching. The authors create combined association rules by using naïve Bayes and apriori approach with a rough set for next sequence prediction. First, the large DNA sequence is reduced by using k-nearest approach. They apply association rules by using naïve Bayes and apriori approach for the next sequence pattern. The hybrid approach provides more accuracy than single classifier for biological sequence prediction. The optimized hybrid process needs less execution time for rule generation for massive biological data analysis. The results established that the hybrid approach generally outperforms the other association rule generation approach.
Choy, S-M, Cheng, E, Wilkinson, RH, Burnett, I & Austin, MW 2021, 'Quality of Experience Comparison of Stereoscopic 3D Videos in Different Projection Devices: Flat Screen, Panoramic Screen and Virtual Reality Headset', IEEE Access, vol. 9, pp. 9584-9594.
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Cui, Q, Zhu, Z, Ni, W, Tao, X & Zhang, P 2021, 'Edge-Intelligence-Empowered, Unified Authentication and Trust Evaluation for Heterogeneous Beyond 5G Systems', IEEE Wireless Communications, vol. 28, no. 2, pp. 78-85.
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Cuzmar, RH, Pereda, J & Aguilera, RP 2021, 'Phase-Shifted Model Predictive Control to Achieve Power Balance of CHB Converters for Large-Scale Photovoltaic Integration', IEEE Transactions on Industrial Electronics, vol. 68, no. 10, pp. 9619-9629.
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Cascaded H-bridge (CHB) converters are attractive candidates for next generation photovoltaic (PV) inverters. CHB converters present a reduced voltage stress per power switch and a high modularity. Therefore, the plant can be divided in several PV strings that can be connected to each H-bridge cell. However, due to variability on solar irradiance conditions, each PV string may present different maximum available power levels, which difficult the overall converter operation. To address this issue, this article presents a model predictive control (MPC) strategy, which works along with a phase-shifted pulsewidth modulation (PS-PWM) stage; hence, its name phase-shifted MPC (PS-MPC). The novelty of this proposal is the way both interbridge and interphase power imbalance are directly considered into the optimal control problem by a suitable system reference design. Thus, the interphase imbalance power is tackled by enforcing the converter to operate with a proper zero-sequence voltage component. Then, by exploiting the PS-PWM working principle, PS-MPC is able to handle each H-bridge cell independently. This allows the predictive controller to also deal with an interbridge power imbalance using the same control structure. Experimental results on a 3-kW prototype are provided to verify the effectiveness of the proposed PS-MPC strategy.
Dang, Z, Li, L, Ni, W, Liu, R, Peng, H & Yang, Y 2021, 'How does rumor spreading affect people inside and outside an institution', Information Sciences, vol. 574, pp. 377-393.
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In this paper, we study the propagation characteristics of rumors in an institution and develop the rumor dissemination model inside the institution. Different from the traditional model, a person in the proposed model can make a judgement about the message and decide to propagate it or refute it, and most people inside the institution can refute the rumor spreaders and propagate the genuine messages to uninformed people when they have confirmed the message is a rumor. Then, we split all the people into two institutions (inside and outside). Since the rumors and genuine messages from the institution can have a non-negligible impact on people outside the institution, we put forward a new double-institution rumor propagation model, and the model considers the impact of messages on the inside and outside of the institution simultaneously. Based on the two proposed models, the basic reproduction numbers are obtained respectively, and the local and global stability of the rumor-free equilibrium points are discussed separately. We numerically simulate the propagation of rumors in small-world networks. The simulation is carried out to verify the validity of the proposed model, and our model is closer to the reality than traditional models.
Deliri, S, Varesi, K, Siwakoti, YP & Blaabjerg, F 2021, 'A boost type switched‐capacitor multi‐level inverter for renewable energy sources with Self‐Voltage balancing of capacitors', International Journal of Energy Research, vol. 45, no. 10, pp. 15217-15230.
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Deliri, S, Varesi, K, Siwakoti, YP & Blaabjerg, F 2021, 'Generalized diamond‐type single DC‐source switched‐capacitor based multilevel inverter with step‐up and natural voltage balancing capabilities', IET Power Electronics, vol. 14, no. 6, pp. 1208-1218.
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Diao, K, Sun, X, Lei, G, Bramerdorfer, G, Guo, Y & Zhu, J 2021, 'Robust Design Optimization of Switched Reluctance Motor Drive Systems Based on System-Level Sequential Taguchi Method', IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 3199-3207.
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Diao, K, Sun, X, Lei, G, Bramerdorfer, G, Guo, Y & Zhu, J 2021, 'System-Level Robust Design Optimization of a Switched Reluctance Motor Drive System Considering Multiple Driving Cycles', IEEE Transactions on Energy Conversion, vol. 36, no. 1, pp. 348-357.
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In this article, a novel system-level robust design optimization method is presented to improve the performance of switched reluctance motor (SRM) drive systems under multiple operating conditions. Based on typical driving cycles of electric vehicles (EVs), five typical driving modes of the SRM are determined. The optimization objectives in each driving mode are established. The significant parameters of the motor and controller of each driving mode are selected as the optimization variables by using the sensitivity analysis. In order to simplify the optimization process, correlation analysis is performed to determine the coherence of the objective functions of all driving modes. Then, a sequential Taguchi method is applied to find an optimal design which is less sensitive to the noise factors. To verify the effectiveness of the proposed method, an SRM drive system applied in EVs with a 12/10 SRM and angle position control method is investigated. It is found that the proposed method can significantly reduce the torque ripple and improve the comprehensive performance. Finally, a 12/10 SRM is prototyped and tested to validate the simulation results.
Diao, K, Sun, X, Lei, G, Guo, Y & Zhu, J 2021, 'Multimode Optimization of Switched Reluctance Machines in Hybrid Electric Vehicles', IEEE Transactions on Energy Conversion, vol. 36, no. 3, pp. 2217-2226.
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IEEE The belt-driven starter/generator (BSG), as a cost-effective solution, has been widely employed in hybrid electric vehicles (HEVs) to improve the stability and reduce the fuel consumption of the vehicles. It can provide more than 10% reduction in CO2. Electrical machine is the heart of the BSG system, which is functioned both as motor and generator. In order to optimize both aspects of motor and generator simultaneously, this paper presents a new multimode optimization method for the switched reluctance machines. First, the general multimode concept and optimization method are presented. The switched reluctance motor and the switched reluctance generator are the two operation modes. The optimization models are established based on motor and generator functions. Sensitivity analysis, surrogate models and genetic algorithms are employed to improve the efficiency of the multimode optimization. Then, a design example of a segmented-rotor switched reluctance machine (SSRM) is investigated. Seven design variables and four driving modes are considered in the multiobjective optimization model. The Kriging model is employed to approximate the finite element model (FEM) in the optimization. Finally, the optimization results are depicted, and an optimal solution is selected. The comparison between the initial and optimal designs shows that the proposed method can improve the foremost performance of the SSRM under all driving modes.
Dragicevic, T & Vinnikov, D 2021, 'Guest Editorial Special Issue on Topology, Modeling, Control, and Reliability of Bidirectional DC/DC Converters in DC Microgrids', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 2, pp. 1188-1191.
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Du, G, Huang, N, Zhao, Y, Lei, G & Zhu, J 2021, 'Comprehensive Sensitivity Analysis and Multiphysics Optimization of the Rotor for a High Speed Permanent Magnet Machine', IEEE Transactions on Energy Conversion, vol. 36, no. 1, pp. 358-367.
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To improve the reliability of high speed permanent magnet machines (HSPMMs) under multiphysics constraints, including the electromagnetic properties, losses, rotor stress, rotor dynamics, and temperature, the rotor of an HSPMM is optimized to achieve low loss and temperature in this paper. To assess the impact of each rotor design parameter on multiphysics performance, a comprehensive sensitivity analysis of the rotor parameters on multiphysics performance is first implemented. On this basis, a multiphysics optimization process for HSPMM rotor is proposed to obtain the optimal design parameters. A comparison of the multiphysics performances of the initial and optimized design schemes shows that the optimized scheme can achieve much lower rotor loss and temperature. The optimization scheme is verified by comprehensive experimental tests on a 400 kW, 10 000 rpm HSPMM prototype.
Du, H, Gao, H & Jia, W 2021, 'Joint Frequency and DOA Estimation with Automatic Pairing Using the Rayleigh–Ritz Theorem', Computers, Materials & Continua, vol. 67, no. 3, pp. 3907-3919.
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This paper presents a novel scheme for joint frequency and direction of arrival (DOA) estimation, that pairs frequencies and DOAs automatically without additional computations. First, when the property of the Kronecker product is used in the received array signal of the multiple-delay output model, the frequency-angle steering vector can be reconstructed as the product of the frequency steering vector and the angle steering vector. The frequency of the incoming signal is then obtained by searching for the minimal eigenvalue among the smallest eigenvalues that depend on the frequency parameters but are irrelevant to the DOAs. Subsequently, the DOA related to the selected frequency is acquired through some operations on the minimal eigenvector according to the Rayleigh–Ritz theorem, which realizes the natural pairing of frequencies and DOAs. Furthermore, the proposed method can not only distinguish multiple sources, but also effectively deal with other arrays. The effectiveness and superiority of the proposed algorithm are further analyzed by simulations.
Duan, L, Gao, T, Ni, W & Wang, W 2021, 'A hybrid intelligent service recommendation by latent semantics and explicit ratings', International Journal of Intelligent Systems, vol. 36, no. 12, pp. 7867-7894.
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User rating of a service is the explicit behavior of users expressing their preference for the service. Most exciting recommendation methods focus on predicting user-service ratings according to users' historical rating behaviors. However, the behavior of users invoking services is implicit feedback. By analyzing the services called by users, mining their potential semantic representations can also help model users' hidden interests. To this end, how to integrate the implicit feedback and explicit rating of users to provide users with better recommendation experience is a problem to be addressed for service recommendation. In this paper, we propose a novel latent semantic integrated explicit rating (LSIER) scheme to recommend services to users. The LSIER scheme is designed by integrating the probabilistic matrix factorization (PMF) model and the probabilistic latent semantic index (PLSI) model. consists of the two stages: (1) the PMF model is used to generate a user feature matrix and a service feature matrix, and the two feature matrices are updated to complete the missing service score records of the users, and (2) the PLSI model is used to train users access records, where an expectation maximization algorithm is applied to derive the model parameters to realize unsupervised soft clustering of services. When the user gives explicit or implicit feedback to the service, the LSIER scheme can identify the current interest probability distribution of the user according to the category to which the called service belongs, and provide the user with a list of service recommendations with scores. The performance of the proposed LSIER scheme is evaluated using the Netflix data set and the Movielens data set. Experiments show that the scheme can achieve better recommendation accuracy and recall rate than existing methods.
Emami, Y, Wei, B, Li, K, Ni, W & Tovar, E 2021, 'Joint Communication Scheduling and Velocity Control in Multi-UAV-Assisted Sensor Networks: A Deep Reinforcement Learning Approach', IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 10986-10998.
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Recently, Unmanned Aerial Vehicle (UAV) swarm has been increasingly studied to collect data from ground sensors in remote and hostile areas. A key challenge is the joint design of the velocities and data collection schedules of the UAVs, as inadequate velocities and schedules would lead to failed transmissions and buffer overflows of sensors and, in turn, significant packet losses. In this paper, we optimize jointly the velocity controls and data collection schedules of multiple UAVs to minimize data losses, adapting to the battery levels, queue lengths and channel conditions of the ground sensors, and the trajectories of the UAVs. In the absence of the up-to-date knowledge of the ground sensors' states, a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MADRL-SA) is proposed to allow the UAVs to asymptotically minimize the data loss of the system under the outdated knowledge of the network states at individual UAVs. Numerical results demonstrate that the proposed MADRL-SA reduces the packet loss by up to 54% and 46% in the considered simulation setting, as compared to an existing DRL solution with single-UAV and non-learning greedy heuristic, respectively.
Eslahi, H, Hamilton, TJ & Khandelwal, S 2021, 'Small signal model and analog performance analysis of negative capacitance FETs', Solid-State Electronics, vol. 186, pp. 108161-108161.
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Esmaili, N, Buchlak, QD, Piccardi, M, Kruger, B & Girosi, F 2021, 'Multichannel mixture models for time-series analysis and classification of engagement with multiple health services: An application to psychology and physiotherapy utilization patterns after traffic accidents', Artificial Intelligence in Medicine, vol. 111, pp. 101997-101997.
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Background
Motor vehicle accidents (MVA) represent a significant burden on health systems globally. Tens of thousands of people are injured in Australia every year and may experience significant disability. Associated economic costs are substantial. There is little literature on the health service utilization patterns of MVA patients. To fill this gap, this study has been designed to investigate temporal patterns of psychology and physiotherapy service utilization following transport-related injuries.
Method
De-identified compensation data was provided by the Australian Transport Accident Commission. Utilization of physiotherapy and psychology services was analysed. The datasets contained 788 psychology and 3115 physiotherapy claimants and 22,522 and 118,453 episodes of service utilization, respectively. 582 claimants used both services, and their data were preprocessed to generate multidimensional time series. Time series clustering was applied using a mixture of hidden Markov models to identify the main distinct patterns of service utilization. Combinations of hidden states and clusters were evaluated and optimized using the Bayesian information criterion and interpretability. Cluster membership was further investigated using static covariates and multinomial logistic regression, and classified using high-performing classifiers (extreme gradient boosting machine, random forest and support vector machine) with 5-fold cross-validation.
Results
Four clusters of claimants were obtained from the clustering of the time series of service utilization. Service volumes and costs increased progressively from clusters 1 to 4. Membership of cluster 1 was positively associated with nerve damage and negatively associated with severe ABI and spinal injuries. Cluster 3 was positively associated with severe ABI, brain/head injury and psychiatric injury. Cluster 4 was positively associated with internal injuries. The classifiers were capable of cla...
Faisal, SN, Amjadipour, M, Izzo, K, Singer, JA, Bendavid, A, Lin, C-T & Iacopi, F 2021, 'Non-invasive on-skin sensors for brain machine interfaces with epitaxial graphene', Journal of Neural Engineering, vol. 18, no. 6, pp. 066035-066035.
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Abstract Objective. Brain–machine interfaces are key components for the development of hands-free, brain-controlled devices. Electroencephalogram (EEG) electrodes are particularly attractive for harvesting the neural signals in a non-invasive fashion. Approach. Here, we explore the use of epitaxial graphene (EG) grown on silicon carbide on silicon for detecting the EEG signals with high sensitivity. Main results and significance. This dry and non-invasive approach exhibits a markedly improved skin contact impedance when benchmarked to commercial dry electrodes, as well as superior robustness, allowing prolonged and repeated use also in a highly saline environment. In addition, we report the newly observed phenomenon of surface conditioning of the EG electrodes. The prolonged contact of the EG with the skin electrolytes functionalize the grain boundaries of the graphene, leading to the formation of a thin surface film of water through physisorption and consequently reducing its contact impedance more than three-fold. This effect is primed in highly saline environments, and could be also further tailored as pre-conditioning to enhance the performance and reliability of the EG sensors.
Fan, X, Xiang, C, Chen, C, Yang, P, Gong, L, Song, X, Nanda, P & He, X 2021, 'BuildSenSys: Reusing Building Sensing Data for Traffic Prediction With Cross-Domain Learning', IEEE Transactions on Mobile Computing, vol. 20, no. 6, pp. 2154-2171.
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With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by
the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and
cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to
predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of
cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two
major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal
complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more
daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and
time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic
volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain
Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building
sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction
based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the
building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal
attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive
experimental studies demonstrate that BuildSenSys outp...
Farahmandian, S & Hoang, DB 2021, 'Policy-based Interaction Model for Detection and Prediction of Cloud Security Breaches', Journal of Telecommunications and the Digital Economy, vol. 9, no. 2, pp. 92-116.
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The ever-increasing number and gravity of cyberattacks against the cloud's assets, together with the introduction of new technologies, have brought about many severe cloud security issues. The main challenge is finding effective mechanisms for constructing dynamic isolation boundaries for securing cloud assets at different cloud infrastructure levels. Our security architecture tackles these issues by introducing a policy-driven interaction model. The model is governed by cloud system security policies and constrained by cloud interacting entities' locations and levels. Security policies are used to construct security boundaries between cloud objects at their interaction level. The novel interaction model relies on its unique parameters to develop an agile detection and prediction mechanism of security threats against cloud resources. The proposed policy-based interaction model and its interaction security algorithms are developed to protect cloud resources. The model deals with external and internal interactions among entities representing diverse participating elements of different complexity levels in a cloud environment. We build a security controller and simulate various scenarios for testing the proposed interaction model and security algorithms.
Farasat, M, Thalakotuna, DN, Hu, Z & Yang, Y 2021, 'A Review on 5G Sub-6 GHz Base Station Antenna Design Challenges', Electronics, vol. 10, no. 16, pp. 2000-2000.
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Modern wireless networks such as 5G require multiband MIMO-supported Base Station Antennas. As a result, antennas have multiple ports to support a range of frequency bands leading to multiple arrays within one compact antenna enclosure. The close proximity of the arrays results in significant scattering degrading pattern performance of each band while coupling between arrays leads to degradation in return loss and port-to-port isolations. Different design techniques are adopted in the literature to overcome such challenges. This paper provides a classification of challenges in BSA design and a cohesive list of design techniques adopted in the literature to overcome such challenges.
Fatema, I, Kong, X & Fang, G 2021, 'Electricity demand and price forecasting model for sustainable smart grid using comprehensive long short term memory', International Journal of Sustainable Engineering, vol. 14, no. 6, pp. 1714-1732.
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Fernandez, E, Hossain, MJ, Mahmud, K, Nizami, MSH & Kashif, M 2021, 'A Bi-level optimization-based community energy management system for optimal energy sharing and trading among peers', Journal of Cleaner Production, vol. 279, pp. 123254-123254.
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© 2020 Elsevier Ltd The economic and environmental benefits of renewable energy have increased in significance over the past decade. Local energy markets can play a vital role in energy transition by facilitating the rapid proliferation of renewable-based energy resources, thereby increasing the renewable energy hosting capacity of the power grid. This paper proposes an energy management system for a smart locality that facilitates local energy trading involving consumers with renewable energy units, a central storage facility, and a power grid. Two optimization frameworks for sharing surplus onsite produced energy are developed here. The first framework maximizes the combined revenue of sellers and buyers, while the second, a game theoretical model, maximizes consumer utilization at the lower level and the revenue of the common storage facility at the higher level. An intensive study is carried out to investigate the benefits of energy sharing that maximizes overall revenue. The results indicate that the grid pricing scheme is a major factor that determines the revenue sharing between the central storage facility entity and the consumers. The first framework results in optimal resource allocation, while the second framework concentrates only on revenue generation. Results indicate that the energy seller profits are higher if the real-time grid prices are used and if the consumers are not charged according to their willingness to pay.
Gadipudi, N, Elamvazuthi, I, Lu, C-K, Paramasivam, S & Su, S 2021, 'WPO-Net: Windowed Pose Optimization Network for Monocular Visual Odometry Estimation', Sensors, vol. 21, no. 23, pp. 8155-8155.
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Visual odometry is the process of estimating incremental localization of the camera in 3-dimensional space for autonomous driving. There have been new learning-based methods which do not require camera calibration and are robust to external noise. In this work, a new method that do not require camera calibration called the “windowed pose optimization network” is proposed to estimate the 6 degrees of freedom pose of a monocular camera. The architecture of the proposed network is based on supervised learning-based methods with feature encoder and pose regressor that takes multiple consecutive two grayscale image stacks at each step for training and enforces the composite pose constraints. The KITTI dataset is used to evaluate the performance of the proposed method. The proposed method yielded rotational error of 3.12 deg/100 m, and the training time is 41.32 ms, while inference time is 7.87 ms. Experiments demonstrate the competitive performance of the proposed method to other state-of-the-art related works which shows the novelty of the proposed technique.
Gao, G, Yu, Y, Xie, J, Yang, J, Yang, M & Zhang, J 2021, 'Constructing multilayer locality-constrained matrix regression framework for noise robust face super-resolution', Pattern Recognition, vol. 110, pp. 107539-107539.
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Gao, X, Zhang, T, Du, J, An, J, Bu, X & Guo, J 2021, 'A dual-beam lens-free slot-array antenna coupled high-T c superconducting fundamental mixer at the W-band', Superconductor Science and Technology, vol. 34, no. 12, pp. 125006-125006.
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Abstract This paper presents a W-band high-T c superconducting (HTS) Josephson-junction fundamental mixer which is coupled using a dual-beam lens-free slot-array antenna. The antenna features a uniplanar six-element slot array fed by an ungrounded coplanar waveguide line, of which each element is a long slot loaded by four rectangular loops. Highly directional radiation is therefore realized by utilizing the long slots and array synthesis to form a relatively large antenna aperture. The antenna also enables asymmetric dual-beam radiation in opposite directions, which not only reduces the RF coupling losses but greatly facilitates the quasi-optics design for the integration of the HTS mixer into a cryocooler. The electromagnetic simulations show that a coupling efficiency as high as −2.2 dB, a realized gain of 13 dB and a front-to-back ratio of 10 dB are achieved at the frequency of 84 GHz. Using this on-chip antenna, a W-band HTS fundamental mixer module is experimentally developed and characterized for different operating temperatures. The measured conversion gain is −10 dB at 20 K and −14.6 dB at 40 K, respectively. The mixer noise temperature is predicted to be around 780 K at 20 K and 1600 K at 40 K, respectively. It is also analyzed that the mixer performance can be further improved if the Josephson junction parameters were optimized.
Ge, Z, Chen, L, Gomez-Garcia, R & Zhu, X 2021, 'Millimeter-Wave Wide-Band Bandpass Filter in CMOS Technology Using a Two-Layered Highpass-Type Approach With Embedded Upper Stopband', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 5, pp. 1586-1590.
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Ge, Z, Chen, L, Yang, L, Gomez-Garcia, R & Zhu, X 2021, 'On-Chip Millimeter-Wave Integrated Absorptive Bandstop Filter in (Bi)-CMOS Technology', IEEE Electron Device Letters, vol. 42, no. 1, pp. 114-117.
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Geng, L, Lu, Z, Guo, X, Zhang, J, Li, X & He, L 2021, 'Coordinated operation of coupled transportation and power distribution systems considering stochastic routing behaviour of electric vehicles and prediction error of travel demand', IET Generation, Transmission & Distribution, vol. 15, no. 14, pp. 2112-2126.
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Ghabrial, A, Franklin, DR & Zaidi, H 2021, 'A Monte Carlo simulation study of scatter fraction and the impact of patient BMI on scatter in long axial field-of-view PET scanners', Zeitschrift für Medizinische Physik, vol. 31, no. 3, pp. 305-315.
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Ghadi, MJ, Azizivahed, A, Mishra, DK, Li, L, Zhang, J, Shafie-khah, M & Catalão, JPS 2021, 'Application of small-scale compressed air energy storage in the daily operation of an active distribution system', Energy, vol. 231, pp. 120961-120961.
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While compressed air energy storage (CAES) has many applications in the field of generation and transmission power systems based on the state-of-the-art, this paper proposes the application of small-scale CAESs (SCAESs) in form of a storage aggregator in the daily operation of an active distribution system (ADS), joining the distribution system operator (DSO) for the participation in the day-ahead (DA) wholesale market. An innovative two-agent modeling approach is formulated. The first agent is responsible for aggregating SCAES units and the profit maximization of the aggregator is based on the distribution local marginal price. The DSO as the second agent receives the DA scheduling from the independent SCAES aggregator and is thus responsible for the secure operation of the ADS, utilizing solar and dispatchable distributed generation (DG) as well as purchasing power from the wholesale market. Linear programming is used for the formulation and optimization of the SCAES aggregator, while a bi-objective optimization algorithm (with the objectives of minimum operating cost as well as minimum power loss and emissions in different scenarios) is employed for DSO scheduling. The results show that the CAES aggregator can offer a considerable impact for power loss reduction, specifically, when diesel generators are not committed in the system operation (i.e., where emission has very low values between 10,000 and 12000 kg). Additionally, the CAES aggregator could reduce the operation costs of the grid in a wide range of operations, even though for the scenario in which the CAES units are not under the control of the DSO anymore and also are scheduled to maximize their own profit. Moreover, results demonstrated that CAES units can be a significant voltage control device for a distribution grid with different objectives. Finally, some conclusions are duly drawn.
Ghavidel, S, Rajabi, A, Jabbari Ghadi, M, Azizivahed, A, Li, L & Zhang, J 2021, 'Hybrid power plant bidding strategy for voltage stability improvement, electricity market profit maximization, and congestion management', IET Energy Systems Integration, vol. 3, no. 2, pp. 130-141.
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This article models a hybrid power plant (HPP), including a compressed air energy storage (CAES) aggregator with a wind power aggregator (WPA) considering network constraints. Three objective functions are considered including electricity market profit maximization, congestion management, and voltage stability improvement. In order to accurately model the WPA, pitch control curtailment wind power levels are also added to the wind power generator models. To optimize all the mentioned objective functions, a multi-objective Pareto front solution strategy is used. Finally, a fuzzy method is used to find the best compromise solution. The proposed approach is tested on a realistic case study based on an electricity market and wind farm located in Spain, and IEEE 57-bus test system is used to evaluate the network constraint effects on the HPP scheduling for different objective functions.
Gong, Y, Li, Z, Zhang, J, Liu, W, Yin, Y & Zheng, Y 2021, 'Missing Value Imputation for Multi-view Urban Statistical Data via Spatial Correlation Learning', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Gong, Y, Zhang, L, Liu, R, Yu, K & Srivastava, G 2021, 'Nonlinear MIMO for Industrial Internet of Things in Cyber–Physical Systems', IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5533-5541.
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Gong, Z, Zhang, C, Ba, X & Guo, Y 2021, 'Improved Deadbeat Predictive Current Control of Permanent Magnet Synchronous Motor Using a Novel Stator Current and Disturbance Observer', IEEE Access, vol. 9, pp. 142815-142826.
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Guo, K & Guo, Y 2021, 'Corrections to “Design Optimization of Linear-Rotary Motion Permanent Magnet Generator With E-Shaped Stator” [Nov 21 Art. no. 0600705]', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-1.
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Guo, K & Guo, Y 2021, 'Design Optimization of Linear-Rotary Motion Permanent Magnet Generator With E-Shaped Stator', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-5.
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A linear-rotary motion permanent magnet (PM) generator is investigated with six axial modular E-shaped stator sections arranged circumferentially, which can meet the requirement of wave and tidal energies generation. The PM pole is magnetized in radial direction, and the adjacent PM poles with opposite magnetized directions are interlaced by half mover pole pitch both in circumferential and axial directions, which are located on the mover surface. The rotational and rectilinear motions are achieved by one magnetic circuit structure according to the transverse flux principle and electromagnetic induction principle. The optimization design and electromagnetic properties of the proposed motor are calculated by 3-D finite element simulation. Then the optimal structural parameters are obtained. The back electromotive force (back EMF) and harmonics, the amplitudes of the cogging torque and detent force are decreased than those of the initial topology. Since three phases of the nine phase windings generate same initial phase angle of back EMF whether it works in rotational or rectilinear motion, the traditional three phase energy storage system can be used to realize the energy storage of the nine phase windings, which reduces the difficulty of electrical energy storage. The variation of the amplitude of back EMF is hardly affected with different mover positions, which is conducive to improve the efficiency of marine energy power generation.
Guo, K & Guo, Y 2021, 'Electromagnetic Characteristic Analysis of BFSLRM', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-6.
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Flux concentrated structure is adopted in the design of bidirectional flux switching linear-rotary motor, which not only brings high torque/thrust density, but also increases stator iron material magnetic saturation. In order to decrease the magnetic maturation level of the motor, the stator pole width, stator pole axial length, permanent magnet (PM) width, PM axial length and stator yoke height are selected as the analysis variables, which are closely related to the two magnetic saturation regions of the stator section based on the initial analysis. The expressions of flux density in the two magnetically saturated regions of the stator core related with the selected five structure variables are derived by numerical fitting method based on the initial simulation result calculated by finite element method. Then the optimization structure variable values are achieved, a prototype and its test experiment are carried out, which verifies that the torque and thrust densities are improved and the numerical fitting method is efficient and accurate for magnetic saturation calculation.
Guo, YJ, Ansari, M & Fonseca, NJG 2021, 'Circuit Type Multiple Beamforming Networks for Antenna Arrays in 5G and 6G Terrestrial and Non-Terrestrial Networks', IEEE Journal of Microwaves, vol. 1, no. 3, pp. 704-722.
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Guo, YJ, Ansari, M, Ziolkowski, RW & Fonseca, NJG 2021, 'Quasi-Optical Multi-Beam Antenna Technologies for B5G and 6G mmWave and THz Networks: A Review', IEEE Open Journal of Antennas and Propagation, vol. 2, pp. 807-830.
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Multi-beam antennas are critical components in future terrestrial and non-terrestrial wireless communications networks. The multiple beams produced by these antennas will enable dynamic interconnection of various terrestrial, airborne and space-borne network nodes. As the operating frequency increases to the high millimeter wave (mmWave) and terahertz (THz) bands for beyond 5G (B5G) and sixth-generation (6G) systems, quasi-optical techniques are expected to become dominant in the design of high gain multi-beam antennas. This paper presents a timely overview of the mainstream quasi-optical techniques employed in current and future multi-beam antennas. Their operating principles and design techniques along with those of various quasi-optical beamformers are presented. These include both conventional and advanced lens and reflector based configurations to realize high gain multiple beams at low cost and in small form factors. New research challenges and industry trends in the field, such as planar lenses based on transformation optics and metasurface-based transmitarrays, are discussed to foster further innovations in the microwave and antenna research community.
Hamilton, T 2021, 'The best of both worlds', Nature Machine Intelligence, vol. 3, no. 3, pp. 194-195.
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Hasanpour, S, Forouzesh, M, Siwakoti, Y & Blaabjerg, F 2021, 'A New High-Gain, High-Efficiency SEPIC-Based DC–DC Converter for Renewable Energy Applications', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 2, no. 4, pp. 567-578.
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Hasanpour, S, Forouzesh, M, Siwakoti, Y & Blaabjerg, F 2021, 'A Novel Full Soft-Switching High-Gain DC/DC Converter Based on Three-Winding Coupled-Inductor', IEEE Transactions on Power Electronics, vol. 36, no. 11, pp. 12656-12669.
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In this article, a new nonisolated full soft-switching step-up dc/dc converter is introduced with a continuous input current for renewable energy applications. The use of a three-winding coupled-inductor (TWCI) along with a voltage multiplier, enables the proposed converter to enhance the voltage gain with lower turns ratios and duty cycles. Also, a lossless regenerative passive clamp circuit is employed to limit the voltage stress across the power switch. In addition to zero current switching performance at the turn-on instant of the power switch, the turn-off current value is also alleviated by adopting a quasi-resonance operation between the leakage inductor of the TWCI and middle capacitors. Moreover, the current of all diodes reaches zero with a slow slew rate, which leads to the elimination of the reverse recovery problem in the converter. Soft-switching of the power switch and all the diodes in the proposed converter significantly reduces the switching power dissipations. Therefore, the presented converter can provide a high voltage gain ratio with high efficiency. Steady-state analysis, comprehensive comparisons with other related converters, and design considerations are discussed in detail. Finally, a 160 W prototype with 200 V output voltage is demonstrated to justify the theoretical analysis.
Hasanpour, S, Siwakoti, Y & Blaabjerg, F 2021, 'Analysis of a New Soft-Switched Step-Up Trans-Inverse DC/DC Converter Based on Three-Winding Coupled-Inductor', IEEE Transactions on Power Electronics, pp. 1-1.
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Hasanpour, S, Siwakoti, YP, Mostaan, A & Blaabjerg, F 2021, 'New Semiquadratic High Step-Up DC/DC Converter for Renewable Energy Applications', IEEE Transactions on Power Electronics, vol. 36, no. 1, pp. 433-446.
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© 1986-2012 IEEE. In this article, a new semiquadratic high step-up coupled-inductor dc/dc converter (SQHSUCI) with continuous input current and low voltage stress on semiconductor components is presented. The proposed structure employs a coupled-inductor (CI) and two power switches with simultaneous operation to achieve an extremely high voltage conversion ratio in a semiquadratic form. The voltage stress across the main power switch is clamped by two regenerative clamp capacitors. Here, the switching losses of both MOSFETs have been reduced by applying quasi-resonance operation of the circuit created by the leakage inductance of the CI along with the balancing and clamp capacitors. Therefore, by considering the high gain conversion ratio along with low voltage stress on components, the magnetic and semiconductors losses of the SQHSUCI are reduced significantly. Also, the energy stored in the leakage inductance of CI is recycled to the output capacitor. These features make the proposed SQHSUCI more suitable for industrial applications. The operation principle, steady state, and also comparisons with other related converters in continuous conduction mode (CCM) are discussed in detail. Finally, experimental results of a prototype with 20 V input and 200 W-200 V output at 50 kHz switching frequency, verify the theoretical advantages of the proposed strategy.
Hassan, M, Hossain, MJ & Shah, R 2021, 'DC Fault Identification in Multiterminal HVDC Systems Based on Reactor Voltage Gradient', IEEE Access, vol. 9, pp. 115855-115867.
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Hassan, M, Hossain, MJ & Shah, R 2021, 'Impact of Meshed HVDC Grid Operation and Control on the Dynamics of AC/DC Systems', IEEE Systems Journal, vol. 15, no. 4, pp. 5209-5220.
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IEEE The efficacy of long-distance and bulk power transmission largely depends on the efficient control and reliable operation of a multiterminal high-voltage direct current (MT-HVdc) grid, more precisely, a meshed HVdc grid. The capability of enduring the dc grid fault eventually enhances the reliability and improves the dynamic performance of the grid. This article investigates the operation and control of an AC/multiterminal dc (MTDC) system with bipolar topology incorporating the dc grid protection schemes. Based on the scale of a circuit breaker's operating time, the performance of three different protection strategies is compared and analyzed using DIgSILENT PowerFactory. Simulation results explicitly reveal that the dynamic performance of the MTDC grid significantly deteriorates with the slow functioning of the protection schemes, followed by a dc grid fault. Besides, prolonged recovery time causes a substantial loss of power infeed and affects the ac/dc grid's stability. Finally, to assess the frailty of the MTDC grid, a transient energy stability index is proposed considering the voltage variation in the prestate and poststate fault clearing interval. Relevant case studies are performed on the MTDC grid using an analytical approach and nonlinear simulation studies to validate the effectiveness of the proposed index.
Hayat, T, Afzal, MU, Ahmed, F, Zhang, S, Esselle, KP & Vardaxoglou, J 2021, 'The Use of a Pair of 3D-Printed Near Field Superstructures to Steer an Antenna Beam in Elevation and Azimuth', IEEE Access, vol. 9, pp. 153995-154010.
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The paper presents a method to design beam-steering antennas using a pair of 3D printed perforated dielectric structures (PDSs) placed in the near-field region of a base antenna, which has a fixed beam. Detailed designs and quantitative comparison of two beam-steering antenna systems are presented. One antenna system has a conical horn antenna and the other uses a resonant-cavity antenna (RCA) as the base antenna. In both cases, the first PDS transforms the phase distribution of the aperture near field and hence tilts the antenna beam to an offset angle. The second PDS, placed above the first, introduces an additional linear progression to the phase of the near field. The two PDSs are rotated independently to steer the beam in both azimuth and elevation. The PDSs have been 3D-printed using acrylonitrile butadiene styrene (ABS) filaments. Each prototype was fabricated in about 16 hours, weighs 300 grams, and costs approximately 5.5 US Dollars. The measured results show that, at the operating frequency of 11 GHz, the RCA-based system has a peak gain of 17.7 dBi compared to the 16.6 dBi gain obtained with the horn-based system. In a fixed E-plane, the variation in the aperture near-field phase of the horn antenna (115°) is much less than that of the RCA (360°). This reduces the efforts required for phase correction and hence led to the former having a larger 3dB measured gain bandwidth of 1.2 GHz compared with the 0.7 GHz bandwidth of the latter, but at the cost of 35.6% increase in the total height of the antenna system.
He, F, Huang, X, Wang, X, Qiu, S, Jiang, F & Ling, SH 2021, 'A neuron image segmentation method based Deep Boltzmann Machine and CV model', Computerized Medical Imaging and Graphics, vol. 89, pp. 101871-101871.
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He, L, Lu, Z, Zhang, J, Geng, L, Cai, Y & Li, X 2021, 'Economic dispatch of multi-area integrated electricity and natural gas systems considering emission and hourly spinning reserve constraints', International Journal of Electrical Power & Energy Systems, vol. 132, pp. 107177-107177.
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With the increasing growth of gas-fired units and power-to-gas facilities, the power system and natural gas system become an integrated electricity and natural gas system (IENGS), which brings new opportunities to mitigate the associated environmental issues and deal with various uncertainties from electricity load, renewable energy generation, and gas load. Different geographical locations may have many IENGSs which are connected by electric or gas transmission lines, thus formulating multi-area IENGSs. These multi-area IENGSs are not simple addition of individual IENGSs, since renewable energy may distribute unequally between different IENGSs, and excess renewable energy needs to be transmitted from one area to another, but is limited by potential tie-line congestions. To solve these problems, this paper proposes an economic dispatch model for multi-area IENGSs considering tie-line congestion, maximum allowable emission bounds and hourly spinning reserve constraints. The considered multi-area IENGS consists of multiple single-area IENGSs, among which wind energy distribution varies considerably. The maximum allowable emission bounds of carbon dioxide and nitrogen oxygen are separately set based on their different influences on the environment. The hourly spinning reserves are allocated for both the power system and the natural gas system, which are provided by fossil fuel-fired units, power-to-gas facilities, gas pipelines, and natural gas storage facilities. After that, the presented model is converted into a mixed-integer linear programming problem for higher computing efficiency. Case studies for integrated systems are analyzed to demonstrate the effectiveness of the proposed model.
Hesamian, MH, Jia, W, He, X, Wang, Q & Kennedy, PJ 2021, 'Synthetic CT images for semi-sequential detection and segmentation of lung nodules', Applied Intelligence, vol. 51, no. 3, pp. 1616-1628.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Accurately detecting and segmenting lung nodules from CT images play a critical role in the earlier diagnosis of lung cancer and thus have attracted much interest from the research community. However, due to the irregular shapes of nodules, and the low-intensity contrast between the nodules and other lung areas, precisely segmenting nodules from lung CT images is a very challenging task. In this paper, we propose a highly effective and robust solution to this problem by innovatively utilizing the changes of nodule shapes over continuous slices (inter-slice changes) and develop a deep learning based end-to-end system. Different from the existing 2.5D or 3D methods that attempt to explore the inter-slice features, we propose to create a novel synthetic image to depict the unique changing pattern of nodules between slices in distinctive colour patterns. Based on the new synthetic images, we then adopt the deep learning based image segmentation techniques and develop a modified U-Net architecture to learn the unique color patterns formed by nodules. With our proposed approach, the detection and segmentation of nodules can be achieved simultaneously with an accuracy significantly higher than the state of the arts by 10% without introducing high computation cost. By taking advantage of inter-slice information and form the proposed synthetic image, the task of lung nodule segmentation is done more accurately and effectively.
Hieu, NQ, Hoang, DT, Niyato, D & Kim, DI 2021, 'Optimal Power Allocation for Rate Splitting Communications With Deep Reinforcement Learning', IEEE Wireless Communications Letters, vol. 10, no. 12, pp. 2820-2823.
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This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access (RSMA) network. In the network, messages intended for users are split into different parts that are a single common part and respective private parts. This mechanism enables RSMA to flexibly manage interference and thus enhance energy and spectral efficiency. Although possessing outstanding advantages, optimizing power allocation in RSMA is very challenging under the uncertainty of the communication channel and the transmitter has limited knowledge of the channel information. To solve the problem, we first develop a Markov Decision Process framework to model the dynamic of the communication channel. The deep reinforcement algorithm is then proposed to find the optimal power allocation policy for the transmitter without requiring any prior information of the channel. The simulation results show that the proposed scheme can outperform baseline schemes in terms of average sum-rate under different power and QoS requirements.
Hlalele, TG, Zhang, J, Naidoo, RM & Bansal, RC 2021, 'Multi-objective economic dispatch with residential demand response programme under renewable obligation', Energy, vol. 218, pp. 119473-119473.
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Hoang, LM, Zhang, JA, Nguyen, DN, Huang, X, Kekirigoda, A & Hui, K-P 2021, 'Suppression of Multiple Spatially Correlated Jammers', IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 10489-10500.
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Effective suppression of inadvertent or deliberate jamming signals is crucial to ensure reliable wireless communication. However, as demonstrated in this paper, when the transmitted jamming signals are highly correlated, and especially when the correlation coefficient varies, nullifying the jamming signals can be challenging. Unlike existing techniques that often assume uncorrelated jamming signals or non-zero but constant correlation, we analyze the impact of the non-zero and varying correlations between transmitted jamming signals on the suppression of the jamming signals. Specifically, we observe that by varying the correlation coefficients between transmitted jamming signals, jammers can 'virtually change' the jamming channels hence their nullspace, even when these channels do not physically change. This makes most jamming suppression techniques that rely on steering receiving beams towards the nullspace of jamming channels no longer applicable. To tackle the problem, we develop techniques to effectively track the jamming nullspace and correspondingly update receiving beams. Monte Carlo simulations show that our proposed techniques can suppress/nullify jamming signals for all considered scenarios with non-zero and varying correlation coefficients amongst transmitted jamming signals.
Hoang, PM, Tuan, HD, Son, TT & Poor, HV 2021, 'Qualitative HD Image and Video Recovery via High-Order Tensor Augmentation and Completion', IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 3, pp. 688-701.
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IEEE This paper presents a new framework for severely distorted image and video recovery via tensor augmentation and completion. By considering a task of representing a matrix by a high-order-n tensor as that of encoding the matrix two-dimension (2D) indices (i, j) by n-digit words i1i2… in, we then develop a new high order tensor augmentation to cast a third order tensor of color images or video sequences containing missing pixels into a higher order tensor, which likes the ket augmentation of quantum physics, is capable of capturing all correlations and entanglements between entries of the original third order tensor. Accordingly, the resultant high-order tensor is completed by our previously developed parallel matrix factorization via tensor train. Simulations are provided to show the clear advantages of our approach to enhance important metrics of the visual quality such as relative square error and structural similarity index in image and video processing that help to achieve high recovery rates even for high-definition images and videos with 95% missing pixels.
Hoang, TM, Duong, TQ, Tuan, HD, Lambotharan, S & Hanzo, L 2021, 'Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines', IEEE Access, vol. 9, pp. 31595-31607.
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This article presents a framework for converting wireless signals into structured datasets, which can be fed into machine learning algorithms for the detection of active eavesdropping attacks at the physical layer. More specifically, a wireless communication system, which consists of an access point (AP), K legitimate users and an active eavesdropper, is considered. To detect the eavesdropper who breaks into the system during the authentication phase, we first build structured datasets based on different features and then apply sophisticated support vector machine (SVM) classifiers to those structured datasets. To be more specific, we first process the signals received by the AP and then define a pair of statistical features based on the post-processing of the signals. By arranging for the AP to simulate the entire process of transmission and the process of constructing features, we form the so-called artificial training data (ATD). By training SVM classifiers on the ATD, we classify the received signals associated with eavesdropping attacks and nonattacks, thereby detecting the presence of the eavesdropper. Two SVM classifiers are considered, including a classic twin-class SVM (TC-SVM) and a single-class SVM (SC-SVM). While the TC-SVM is preferred in the case of having perfect channel state information (CSI) of all channels, the SC-SVM is preferred in the realistic scenario when we have only the CSI of legitimate users. We also evaluate the accuracy of the trained models depending on the choice of kernel functions, the choice of features and on the eavesdropper's power. Our numerical results show that careful parameter-tuning is required for exceeding an eavesdropper detection probability of 95%.
Hou, S, Ni, W, Wang, M, Liu, X, Tong, Q & Chen, S 2021, 'Bottleneck-Aware Resource Allocation for Service Processes', International Journal of Web Services Research, vol. 18, no. 3, pp. 1-21.
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In 5G systems and beyond, traditional generic service models are no longer appropriate for highly customized and intelligent services. The process of reinventing service models involves allocating available resources, where the performance of service processes is determined by the activity node with the lowest service rate. This paper proposes a new bottleneck-aware resource allocation approach by formulating the resource allocation as a max-min problem. The approach can allocate resources proportional to the workload of each activity, which can guarantee that the service rates of activities within a process are equal or close-to-equal. Based on the business process simulator (i.e., BIMP) simulation results show that the approach is able to reduce the average cycle time and improve resource utilization, as compared to existing alternatives. The results also show that the approach can effectively mitigate the impact of bottleneck activity on the performance of service processes.
Hu, S, Ni, W, Wang, X, Jamalipour, A & Ta, D 2021, 'Joint Optimization of Trajectory, Propulsion, and Thrust Powers for Covert UAV-on-UAV Video Tracking and Surveillance', IEEE Transactions on Information Forensics and Security, vol. 16, pp. 1959-1972.
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Autonomous tracking of suspicious unmanned aerial vehicles (UAVs) by legitimate monitoring UAVs (or monitors) can be crucial to public safety and security. It is non-trivial to optimize the trajectory of a monitor while conceiving its monitoring intention, due to typically non-convex propulsion and thrust power functions. This article presents a novel framework to jointly optimize the propulsion and thrust powers, as well as the 3D trajectory of a solar-powered monitor which conducts covert, video-based, UAV-on-UAV tracking and surveillance. A multi-objective problem is formulated to minimize the energy consumption of the monitor and maximize a weighted sum of distance keeping and altitude changing, which measures the disguising of the monitor. Based on the practical power models of the UAV propulsion, thrust and hovering, and the model of the harvested solar power, the problem is non-convex and intangible for existing solvers. We convexify the propulsion power by variable substitution, and linearize the solar power. With successive convex approximation, the resultant problem is then transformed with tightened constraints and efficiently solved by the proximal difference-of-convex algorithm with extrapolation in polynomial time. The proposed scheme can be also applied online. Extensive simulations corroborate the merits of the scheme, as compared to baseline schemes with partial or no disguising.
Huang, H, Savkin, AV & Ni, W 2021, 'Navigation of a UAV Team for Collaborative Eavesdropping on Multiple Ground Transmitters', IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 10450-10460.
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Huang, H, Zhang, J, Zhang, J, Xu, J & Wu, Q 2021, 'Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification', IEEE Transactions on Multimedia, vol. 23, no. 99, pp. 1666-1680.
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Huang, W & Xu, RYD 2021, 'Gaussian process latent variable model factorization for context-aware recommender systems', Pattern Recognition Letters, vol. 151, pp. 281-287.
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Huang, X, Tuyen Le, A & Guo, YJ 2021, 'ALMS Loop Analyses With Higher-Order Statistics and Strategies for Joint Analog and Digital Self-Interference Cancellation', IEEE Transactions on Wireless Communications, vol. 20, no. 10, pp. 6467-6480.
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Joint analog and digital self-interference cancellation (SIC) is essential for enabling in-band full duplex (IBFD) communications. Analog least mean square (ALMS) loop is a promising low-complexity high-performance analog SIC technique with multi-tap adaptive filtering capability, but its properties on the tap coefficient variation have not been fully understood. In this paper, analysis based on higher-order statistics of the transmitted signal is performed to solve the problem of evaluating the variance of the ALMS loop's weighting coefficient error, which reveals two additional types of irreducible residual self-interference (SI) produced by an ALMS loop if it runs freely. The residual SI channel impulse response in digital baseband is also analysed and its unique properties are investigated. By introducing a simple track and hold control to the ALMS loop's tap coefficients, a joint analog and digital SIC scheme is proposed to stop the tap coefficient variation and achieve very low residual SI close to the IBFD receiver's noise floor. In a coordinated application scenario, the noise figure of the digital SIC algorithm is proved to be only 1.76 dB at most. Simulation results are provided to verify the theoretical analyses.
Huang, X, Tuyen Le, A & Guo, YJ 2021, 'Transmit Beamforming for Communication and Self-Interference Cancellation in Full Duplex MIMO Systems: A Trade-Off Analysis', IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3760-3769.
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The performance of transmit beamforming for both optimized precoding and self-interference cancellation (SIC) in full duplex multiple input multiple output (MIMO) transceivers is analysed in this paper. With sub-space dimension larger than that of the null-space of the self-interference channels, the precoding error is reduced but the interference suppression ratio (ISR) is degraded, resulting in a trade-off between multibeam communication and MIMO SIC. An analytical approach for the ISR evaluation is proposed assuming known eigenvalue distribution of the self-interference channels, and a closed-form ISR expression is derived after applying a uniform distribution approximation. The ISR and precoding error trade-off curves are also formulated. Joint SIC by transmit beamforming and beam-based analog adaptive filters over both propagation and analog domains is proposed to achieve better SIC performance and enable more flexible receive antenna selection. Simulation results verify the theoretical analyses.
Huang, Y, Song, R, Argha, A, Celler, BG, Savkin, AV & Su, SW 2021, 'Human Motion Intent Description Based on Bumpless Switching Mechanism for Rehabilitation Robot', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 673-682.
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Huang, Y, Wang, Q, Jia, W, Lu, Y, Li, Y & He, X 2021, 'See more than once: Kernel-sharing atrous convolution for semantic segmentation', Neurocomputing, vol. 443, pp. 26-34.
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The state-of-the-art semantic segmentation solutions usually leverage different receptive fields via multiple parallel branches to handle objects of different sizes. However, employing separate kernels for individual branches may degrade the generalization of the network to objects with different scales, and the computational cost increases with the increase of the number of branches. To tackle this problem, we propose a novel network structure, namely Kernel-Sharing Atrous Convolution (KSAC), where branches with different receptive fields share the same kernel, i.e., let a single kernel ‘see’ the input feature maps more than once with different receptive fields. Experiments conducted on the benchmark PASCAL VOC 2012 dataset show that our proposed sharing strategy can not only boost the network's generalization and representation abilities but also reduce the computational cost significantly. Specifically, on the validation set, when compared with DeepLabv3+, about 2.7G FLOPs and 12.7G FLOPs are saved for output stride = 16 and 8 respectively. In addition, different from the widely used ASPP structure, our proposed KSAC is able to further improve the mIOU by taking benefit of wider context with larger atrous rates. Finally, our KSAC achieves mIOUs of 88.1%, 45.47% and 80.7% on the PASCAL VOC 2012 test set (Everingham et al., 2009), ADE20K dataset (Zhou et al., 2017) and Cityscapes datasets (Marius et al., 2016), respectively. Our full code will be released on Github: https://github.com/edwardyehuang/iSeg.
Huang, Y, Wu, Q, Xu, J, Zhong, Y & Zhang, Z 2021, 'Unsupervised Domain Adaptation with Background Shift Mitigating for Person Re-Identification', International Journal of Computer Vision, vol. 129, no. 7, pp. 2244-2263.
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Unsupervised domain adaptation has been a popular approach for cross-domain person re-identification (re-ID). There are two solutions based on this approach. One solution is to build a model for data transformation across two different domains. Thus, the data in source domain can be transferred to target domain where re-ID model can be trained by rich source domain data. The other solution is to use target domain data plus corresponding virtual labels to train a re-ID model. Constrains in both solutions are very clear. The first solution heavily relies on the quality of data transformation model. Moreover, the final re-ID model is trained by source domain data but lacks knowledge of the target domain. The second solution in fact mixes target domain data with virtual labels and source domain data with true annotation information. But such a simple mixture does not well consider the raw information gap between data of two domains. This gap can be largely contributed by the background differences between domains. In this paper, a Suppression of Background Shift Generative Adversarial Network (SBSGAN) is proposed to mitigate the gaps of data between two domains. In order to tackle the constraints in the first solution mentioned above, this paper proposes a Densely Associated 2-Stream (DA-2S) network with an update strategy to best learn discriminative ID features from generated data that consider both human body information and also certain useful ID-related cues in the environment. The built re-ID model is further updated using target domain data with corresponding virtual labels. Extensive evaluations on three large benchmark datasets show the effectiveness of the proposed method.
Ibrahim, IA & Hossain, MJ 2021, 'Low Voltage Distribution Networks Modeling and Unbalanced (Optimal) Power Flow: A Comprehensive Review', IEEE Access, vol. 9, pp. 143026-143084.
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The rapid increase of distributed energy resources (DERs) installation at residential and commercial levels can pose significant technical issues on the voltage levels and capacity of the network assets in distribution networks. Most of these issues occur in low-voltage distribution networks (LVDNs) or near customer premises. A lack of understanding of the networks and advanced planning approaches by distribution network providers (DNSPs) has led to rough estimations for maximum DERs penetration levels that LVDNs can accommodate. These issues might under- or over-estimate the actual hosting capacity of the LVDNs. Limited available data on LVDNs' capacity to host DERs makes planning, installing, and connecting new DERs problematic and complex. In addition, the lack of transparency in LVDN data and information leads to model simplifications, such as ignoring the phase imbalance. This can lead to grossly inaccurate results. The main aim of this paper is to enable the understanding of the true extent of local voltage excursions to allow more targeted investment, improve the network's reliability, enhance solar performance distribution, and increase photovoltaic (PV) penetration levels in LVDNs. Therefore, this paper reviews the state-of-the-art best practices in modeling unbalanced LVDNs as accurately as possible to avoid under- or over-estimation of the network's hosting capacity. In addition, several PV system modeling variations are reviewed, showing their limitations and merits as a trade-off between accuracy, computational burden, and data availability. Moreover, the unbalanced power flow representations, solving algorithms, and available tools are explained extensively by providing a comparative study between these tools and the ones most commonly used in Australia. This paper also presents an overview of unbalanced optimal power flow representations with their related objectives, solving algorithms, and tools.
Ibrahim, IA, Sabah, S, Abbas, R, Hossain, MJ & Fahed, H 2021, 'A novel sizing method of a standalone photovoltaic system for powering a mobile network base station using a multi-objective wind driven optimization algorithm', Energy Conversion and Management, vol. 238, pp. 114179-114179.
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A new multi-objective wind driven optimization algorithm is proposed to size a standalone photovoltaic system's components to meet the load demand for a mobile network base station at a 1% loss of load probability or less with a minimum annual total life cost. To improve the sized model's accuracy, a long short-term memory deep learning model is utilized to forecast the hourly performance of a photovoltaic module. The long-term memory model's performance is compared with those obtained by a linear photovoltaic model and an artificial neural network model. The comparison is carried out based on the values of normalized root mean square error, normalized mean bias error, mean absolute percentage error, and the training and testing time. Accordingly, on the values obtained for these statistical errors, the long short-term memory model outperforms better than the linear model and the artificial neural network model based. In addition, a dynamic battery model is utilized to characterize the dynamic charging and discharging process. The findings show that the optimal number of the photovoltaic array and the capacity of the storage battery required to cover the load demand of a mobile network base station are 5.4 kWp and 2640 Ah/48 V, respectively. Besides, the annual total life cycle cost for the sized photovoltaic/battery configuration is 4028.33 AUD/year. The simulation time for the proposed method is 421.25 s. To generalize the sizing results for the mobile network base stations based on Sydney weather conditions, the photovoltaic array and storage battery ratios are calculated as 0.324 and 0.223, respectively. In addition, the cost of an energy unit generated by the optimized system is 0.254 AUD/kWh. Here, the results of the proposed method have been compared with those obtained by developed and recent benchmark published methods. The comparison outcomes show the effectiveness of the proposed method in terms of providing a high availability sized system a...
Irshad, UB, Nizami, MSH, Rafique, S, Hossain, MJ & Mukhopadhyay, SC 2021, 'A Battery Energy Storage Sizing Method for Parking Lot Equipped With EV Chargers', IEEE Systems Journal, vol. 15, no. 3, pp. 4459-4469.
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Islam, MR, Lu, H, Hossain, MJ & Li, L 2021, 'Optimal Coordination of Electric Vehicles and Distributed Generators for Voltage Unbalance and Neutral Current Compensation', IEEE Transactions on Industry Applications, vol. 57, no. 1, pp. 1069-1080.
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© 1972-2012 IEEE. To maximize renewable energy usage to combat climate change, the penetration of electric vehicles (EVs) has increased significantly in developed countries. This can cause serious power quality issues, such as increased voltage imbalance and neutral currents, which severely impact the operation of power systems. Although the power quality issue is not a new problem, it requires an improved strategy for the growing penetration of photovoltaic solar energy and EVs in low-voltage distribution grids and their uncoordinated operation. This article presents a new control strategy to reduce the number of coordinated EVs to mitigate voltage unbalance and compensate for the neutral current. The proposed control strategy consists of two controllers arranged in a hierarchical structure with the central controller at the top layer and the local controller at the bottom layer. It is evident that the proposed control strategy reduces the number of EVs that need to be coordinated, and further, EV coordination is not required if the grid imbalance is less. This new hierarchical control strategy can improve power quality and reduce data processing overhead and computational complexity.
Jafarizadeh, S, Veitch, D, Tofigh, F, Lipman, J & Abolhasan, M 2021, 'Optimal Synchronizability in Networks of Coupled Systems: Topological View', IEEE Transactions on Network Science and Engineering, vol. 8, no. 2, pp. 1517-1530.
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Many engineered and natural systems are modeled as networks of coupled systems. Synchronization is one of their crucial and well-studied behaviors. Uniform coupling strength has been the benchmark practice in the majority of the literature. This paper considers nonuniform coupling strength, and a modified approach to the problem of synchronizability optimization, enabling a reduction to a spectral radius minimization problem, which can reach a unique optimal point on the Pareto Frontier. It is established that adding any edge to a connected graph can only improve synchronizability in this optimal measure. This result is utilized for developing a hierarchy between topologies. It is shown that several proposed structural parameters, including betweenness centrality, do not have any simple relationship to the optimal synchronizability measure.
Jamborsalamati, P, Garmabdari, R, Hossain, J, Lu, J & Dehghanian, P 2021, 'Planning for resilience in power distribution networks: A multi‐objective decision support', IET Smart Grid, vol. 4, no. 1, pp. 45-60.
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Jin, J, Sheng, G, Bi, Y, Song, Y, Liu, X, Chen, X, Li, Q, Deng, Z, Zhang, W, Zheng, J, Coombs, T, Shen, B, Zhu, J, Zhao, Y, Wang, J, Xiang, B, Tang, Y, Ren, L, Xu, Y, Shi, J, Islam, MR, Guo, Y & Zhu, J 2021, 'Applied Superconductivity and Electromagnetic Devices - Principles and Current Exploration Highlights', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-29.
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With regard to the state-of-the-art technologies in the fields of applied superconductivity and electromagnetic devices, research and development highlights are presented. The recent progress and achievement described with principle and technical details include mainly i) applied superconducting materials; ii) superconducting magnets and their applications such as in ITER and Tokamaks; iii) high Tc superconducting (HTS) magnetic levitation and applications; iv) HTS smart grids; v) superconducting and electromagnetic material modelling and characterization; and vi) advanced electromagnetic devices. The applied superconductivity technology and availability are especially focused and verified with the trend of development prospection.
Jin, Z, Sun, X, Cai, Y, Zhu, J, Lei, G & Guo, Y 2021, 'Comprehensive Sensitivity and Cross-Factor Variance Analysis-Based Multi-Objective Design Optimization of a 3-DOF Hybrid Magnetic Bearing', IEEE Transactions on Magnetics, vol. 57, no. 2, pp. 1-4.
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Jones, GT, Siwakoti, YP & Rogers, DJ 2021, 'Active Gate Drive to Increase the Power Capacity of Hard-Switched IGBTs', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 2, pp. 2247-2257.
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Ju, M, Ding, C, Guo, CA, Ren, W & Tao, D 2021, 'IDRLP: Image Dehazing Using Region Line Prior', IEEE Transactions on Image Processing, vol. 30, no. 99, pp. 9043-9057.
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In this work, a novel and ultra-robust single image dehazing method called IDRLP is proposed. It is observed that when an image is divided into n regions, with each region having a similar scene depth, the brightness of both the hazy image and its haze-free correspondence are positively related with the scene depth. Based on this observation, this work determines that the hazy input and its haze-free correspondence exhibit a quasi-linear relationship after performing this region segmentation, which is named as region line prior (RLP). By combining RLP and the atmospheric scattering model (ASM), a recovery formula (RF) can be easily obtained with only two unknown parameters, i.e., the slope of the linear function and the atmospheric light. A 2D joint optimization function considering two constraints is then designed to seek the solution of RF. Unlike other comparable works, this 'joint optimization' strategy makes efficient use of the information across the entire image, leading to more accurate results with ultra-high robustness. Finally, a guided filter is introduced in RF to eliminate the adverse interference caused by the region segmentation. The proposed RLP and IDRLP are evaluated from various perspectives and compared with related state-of-the-art techniques. Extensive analysis verifies the superiority of IDRLP over state-of-the-art image dehazing techniques in terms of both the recovery quality and efficiency. A software release is available at https://sites.google.com/site/renwenqi888/.
Ju, M, Ding, C, Ren, W, Yang, Y, Zhang, D & Guo, YJ 2021, 'IDE: Image Dehazing and Exposure Using an Enhanced Atmospheric Scattering Model', IEEE Transactions on Image Processing, vol. 30, pp. 2180-2192.
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Atmospheric scattering model (ASM) is one of the most widely used model to describe the imaging processing of hazy images. However, we found that ASM has an intrinsic limitation which leads to a dim effect in the recovered results. In this paper, by introducing a new parameter, i.e., light absorption coefficient, into ASM, an enhanced ASM (EASM) is attained, which can address the dim effect and better model outdoor hazy scenes. Relying on this EASM, a simple yet effective gray-world-assumption-based technique called IDE is then developed to enhance the visibility of hazy images. Experimental results show that IDE eliminates the dim effect and exhibits excellent dehazing performance. It is worth mentioning that IDE does not require any training process or extra information related to scene depth, which makes it very fast and robust. Moreover, the global stretch strategy used in IDE can effectively avoid some undesirable effects in recovery results, e.g., over-enhancement, over-saturation, and mist residue, etc. Comparison between the proposed IDE and other state-of-the-art techniques reveals the superiority of IDE in terms of both dehazing quality and efficiency over all the comparable techniques.
Kamal, MS, Northcote, A, Chowdhury, L, Dey, N, Crespo, RG & Herrera-Viedma, E 2021, 'Alzheimer’s Patient Analysis Using Image and Gene Expression Data and Explainable-AI to Present Associated Genes', IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-7.
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Kashif, M, Hossain, MJ, Fernandez, E, Nizami, MSH, Ali, SMN & Sharma, V 2021, 'An Optimal Allocation of Reactive Power Capable End-User Devices for Grid Support', IEEE Systems Journal, vol. 15, no. 3, pp. 3249-3260.
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Keshavarz, R, Lipman, J, Schreurs, DMM-P & Shariati, N 2021, 'Highly Sensitive Differential Microwave Sensor for Soil Moisture Measurement', IEEE Sensors Journal, vol. 21, no. 24, pp. 27458-27464.
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This paper presents a highly sensitive differential soil moisture sensor (DSMS) using a microstrip line loaded with triangular two-turn resonator (T2-SR) and complementary of the rectangular two-turn spiral resonator (CR2-SR), simultaneously. Volumetric Water Content (VWC) or permittivity sensing is conducted by loading the T2-SR side with dielectric samples. Two transmission notches are observed for identical loads relating to T2-SR and CR2-SR. The CR2-SR notch at 4.39 GHz is used as a reference for differential permittivity measurement method. Further, the resonance frequency of T2-SR is measured relative to the reference value. Based on this frequency difference, the permittivity of soil is calculated which is related to the soil VWC. Triangular two-turn resonator (T2-SR) resonance frequency changes from 4 to 2.38 GHz when VWC varies 0% to 30%. The sensor's operation principle is described through circuit model analysis and simulations. To validate the differential sensing concept, prototype of the designed 3-cell DSMS is fabricated and measured. The proposed sensor exhibits frequency shift of 110 MHz for 1% change at the highest soil moisture content (30%) for sandy-type soil. This work proves the differential microwave sensing concept for precision agriculture.
Keshavarz, R, Mohammadi, A & Abdipour, A 2021, 'Linearity improvement of a dual-band Doherty power amplifier using E-CRLH transmission line', AEU - International Journal of Electronics and Communications, vol. 131, pp. 153584-153584.
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Keshavarz, S, Keshavarz, R & Abdipour, A 2021, 'COMPACT ACTIVE DUPLEXER BASED ON CSRR AND INTERDIGITAL LOADED MICROSTRIP COUPLED LINES FOR LTE APPLICATION', Progress In Electromagnetics Research C, vol. 109, pp. 27-37.
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Khan, AA, Abolhasan, M, Ni, W, Lipman, J & Jamalipour, A 2021, 'An End-to-End (E2E) Network Slicing Framework for 5G Vehicular Ad-Hoc Networks', IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp. 7103-7112.
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Network slicing is emerging as a promising solution for end-to-end resource management and orchestration together with Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies. In this paper, a comprehensive network slicing framework is presented to achieve end-to-end (E2E) QoS provisioning among customized services in 5G-driven VANETs. The proposed scheme manages the cooperation of both RAN and Core Network (CN), using SDN, NFV and Edge Computing technologies. Furthermore, a dynamic radio resource slice optimization scheme is formulated mathematically, that handles a mixture of mission-critical and best effort traffic, by delivering the QoS provisioning of Ultra-reliability and low latency. The proposed scheme adjusts the optimal bandwidth slicing and dynamically adapts to instantaneous network load conditions in a way that a targeted performance is guaranteed. The problem is solved using a Genetic Algorithm (GA) and results are compared with the previously proposed 5 G VANET architecture. Simulation reveal that the proposed slicing framework is able to optimize resources and deliver on the key performance metrics for mission critical communication.
Khan, MNH, Siwakoti, YP, Scott, MJ, Li, L, Khan, SA, Lu, DD-C, Barzegarkhoo, R, Sidorski, F, Blaabjerg, F & Hasan, SU 2021, 'A Common Grounded Type Dual-Mode Five-Level Transformerless Inverter for Photovoltaic Applications.', IEEE Trans. Ind. Electron., vol. 68, no. 10, pp. 9742-9754.
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This article presents a novel dual-mode five-level common grounded type (5L-DM-CGT) transformerless inverter topology for a medium-power application with a wide input voltage range (200–400 V). It consists of nine semiconductor switches, two inner flying-capacitors, and a small LC filter at the output side. Due to the direct connection of the negative terminal of the photovoltaic to the neutral point of the grid, there is no leakage current in the 5L-DM-CGT. Depending on the magnitude of the input voltage, the converter can operate in both buck and boost mode to produce the same ac output voltage. The theoretical analysis shows the advantages of the dual-mode inverter for various industrial applications. Finally, the laboratory test results are presented to verify the theoretical analysis. Measurement results show that the proposed inverter rated at 1 kW has around 97±1% efficiency over a wide range of load with a peak efficiency of 98.96% at 130 VA in buck mode and peak efficiency of 99% at 122 VA in boost mode
Khan, SA, Barzegarkhoo, R, Guo, Y, Siwakoti, Y, Khan, MNH, Lu, DD-C & Zhu, J 2021, 'Topology, Modeling and Control Scheme for a new Seven-Level Inverter With Reduced DC-Link Voltage', IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 2734-2746.
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Khawaldeh, HA, Al-Soeidat, M, Farhangi, M, Lu, DD-C & Li, L 2021, 'Efficiency Improvement Scheme for PV Emulator Based on a Physical Equivalent PV-Cell Model', IEEE Access, vol. 9, pp. 83929-83939.
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Recently, a photovoltaic (PV) emulator is proposed which is based on a combination of a constant current source and a one-diode photovoltaic model. Its superior dynamic performance is compatible with that of a real PV system. Although it is power efficient at the maximum power point (MPP), it suffers from high power loss around and at the open-circuit voltage (OCV) operation condition. The PV emulator can be used for PV system analysis and testing, such as maximum power point tracking (MPPT). This paper presents a new switching circuit which is placed in parallel with the diode string to minimize the power loss. The switching circuit consists of a two-switch non-inverting buck-boost DC/DC converter. When the operating point of the PV emulator moves from the current source region to the voltage source region, the converter, which is more efficient, switches in to replace the diode string seamlessly to maintain the circuit operation of the emulator. Experimental results show that in the worst case scenario, i.e. OCV condition, the efficiency and temperature of the proposed solution reach 81.47% and 30.1 °C respectively, as compared with 2.8% and 94.2 °C respectively for the diode string only case. In terms of dynamic response, the proposed PV emulator lags the real PV panel by only 3.5 ms as compared with 120 ms by a commercial emulator under the 30% to 60% insolation change test.
Khawaldeh, HA, Al‐soeidat, M, Lu, DD & Li, L 2021, 'Simple and Fast Dynamic Photovoltaic Emulator based on a Physical Equivalent PV‐cell Model', The Journal of Engineering, vol. 2021, no. 5, pp. 276-285.
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Photovoltaic emulators are a specific type of power electronics system to mimic the behaviour of a photovoltaic (PV) panel or array and facilitate the testing of energy systems. Existing solutions usually require sophisticated hardware design and fast computing. This paper presents a simple, reliable, and effective circuit-based photovoltaic (PV) emulator based on the equivalent PV stacked cells. The PV emulator can be used for solar system testing and analysis, such as maximum power point tracking (MPPT) and partial shading effect. The 𝐼–𝑉 and 𝑃–𝑉 characteristic curves of the emulator have been generated by using an LTspice simulator. It is experimentally investigated and compared with a real PV panel and existing emulator products. The experiment results show good agreement with the mimicked actual PV panel. The proposed PV emulator shows a better dynamic response and shorter settling time than several benchmarked commercial products. The enhancement in the time response is due to the simplicity of the emulator, where a few power diodes and some resisters are used. In addition to simplicity, the PV emulator is very cost-effective.
Kieu, BT, Unanue, IJ, Pham, SB, Phan, HX & Piccardi, M 2021, 'NeuSub: A Neural Submodular Approach for Citation Recommendation', IEEE Access, vol. 9, pp. 148459-148468.
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Kiran, MR, Farrok, O, Islam, MR & Zhu, J 2021, 'Increase in the Power Transfer Capability of Advanced Magnetic Material Based High Frequency Transformer by Using a Novel Distributed Winding Topology', IEEE Transactions on Industry Applications, vol. 57, no. 6, pp. 6306-6317.
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Kiran, MR, Farrok, O, Islam, MR, Zhu, J, Kouzani, AZ & Mahmud, MAP 2021, 'The High Frequency Magnetic-Link With Distributed HTS YBCO Windings for Power Converter Applications', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-5.
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Koli, MNY, Afzal, MU & Esselle, KP 2021, 'Significant Bandwidth Enhancement of Radial-Line Slot Array Antennas Using a Radially Nonuniform TEM Waveguide', IEEE Transactions on Antennas and Propagation, vol. 69, no. 6, pp. 3193-3203.
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IEEE Radial line slot array (RLSA) antennas have attractive features such as high gain, high efficiency, and planar low profile, but their gain bandwidths have been limited to less than 10%. This paper presents a method to significantly increase the gain bandwidth of RLSAs to over 30%. The key to the method is the application of a non-uniform radial TEM waveguide as opposed to the radially uniform TEM waveguide used in conventional RLSAs. Hence, the condition for maximum radiation is satisfied at a wide range of frequencies by different sections of the RLSA. To demonstrate the concept, several circularly polarised RLSA designs and one prototype are presented. The measured results of the prototype demonstrate an unprecedented 3dB gain bandwidth of 27.6%, a peak gain of 27.3 dBic, 3dB axial ratio bandwidth greater than 31.1% and a 10dB return loss bandwidth greater than 34.8%. The overall measured bandwidth of the RLSA in which gain variation and axial ratio are within 3dB and return loss is greater than 10dB is from 9.7 GHz to 12.8 GHz or 27.6%. Its extremely high measured gain bandwidth product per unit area (GBP/A) of 88 indicates excellent overall performance in terms of bandwidth, gain and area.
Koli, MNY, Afzal, MU, Esselle, KP & Mehta, A 2021, 'Use of Narrower Reflection-Canceling Slots to Design Linearly Polarized Radial Line Slot Arrays With Improved Radiation Performance', IEEE Antennas and Wireless Propagation Letters, vol. 20, no. 12, pp. 2275-2279.
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Kumar, A, Esmaili, N & Piccardi, M 2021, 'Topic-Document Inference With the Gumbel-Softmax Distribution', IEEE Access, vol. 9, pp. 1313-1320.
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© 2013 IEEE. Topic modeling is an important application of natural language processing (NLP) that can automatically identify the set of main topics of a given, typically large, collection of documents. In addition to identifying the main topics in the given collection, topic modeling infers which combination of topics is addressed by each individual document (the so-called topic-document inference), which can be useful for their classification and organization. However, the distributional assumptions for this inference are typically restricted to the Dirichlet family which can limit the performance of the model. For this reason, in this paper we propose modeling the topic-document inference with the Gumbel-Softmax distribution, a distribution recently introduced to expand differentiability in deep networks. To set up a performing system, the proposed approach integrates Gumbel-Softmax topic-document inference in a state-of-the-art topic model based on a deep variational autoencoder. Experimental results over two probing datasets show that the proposed approach has been able to outperform the original deep variational autoencoder and other popular topic models in terms of test-set perplexity and two topic coherence measures.
Kusakunniran, W, Charoenpanich, P, Samunyanoraset, P, Suksai, S, Karnjanapreechakorn, S, Wu, Q & Zhang, J 2021, 'Hybrid Learning of Vessel Segmentation in Retinal Images', ECTI Transactions on Computer and Information Technology (ECTI-CIT), vol. 15, no. 1, pp. 1-11.
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This paper aims to develop a technique of vessel segmentation in retinal images. Interpreting the segmented vessels is necessary for the automatic detection of the severe stage of the diabetic retinopathy. Thus, it is important to have the technique for segmenting vessels in an automatic way with high performance, for the sake of further analysis. In this paper, the proposed method is developed based on the double layer combining supervised and non-supervised learning aspects. The first layer is to detect the initial seeds of vessels using the supervised learning. It learns based on three types of features including green intensity, line operators, and Gabor filters. Then, the support vector machine (SVM) is applied as the classification tool. In the second layer, the segmentation results from the first layer is further revised and completed using the non-supervised learning. The morphological operations with the watershed technique are applied on the results obtained from the first layer, to remain with the segmented pixels with high confidential to be vessels. Then, these pixels are used as the initial seeds of foreground in the iterative graph cut. As the result, the more completed and comprehensive foreground (i.e. vessels) can be obtained. The proposed method is evaluated using two well-known datasets including DRIVE and STARE. The experimental results show the promising performance of the proposed method when compared with other existing methods in the literature.
Lalbakhsh, A, Afzal, MU, Hayat, T, Esselle, KP & Mandal, K 2021, 'All-metal wideband metasurface for near-field transformation of medium-to-high gain electromagnetic sources', Scientific Reports, vol. 11, no. 1.
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AbstractElectromagnetic (EM) metasurfaces are essential in a wide range of EM engineering applications, from incorporated into antenna designs to separate devices like radome. Near-field manipulators are a class of metasurfaces engineered to tailor an EM source’s radiation patterns by manipulating its near-field components. They can be made of all-dielectric, hybrid, or all-metal materials; however, simultaneously delivering a set of desired specifications by an all-metal structure is more challenging due to limitations of a substrate-less configuration. The existing near-field phase manipulators have at least one of the following limitations; expensive dielectric-based prototyping, subject to ray tracing approximation and conditions, narrowband performance, costly manufacturing, and polarization dependence. In contrast, we propose an all-metal wideband phase correcting structure (AWPCS) with none of these limitations and is designed based on the relative phase error extracted by post-processing the actual near-field distributions of any EM sources. Hence, it is applicable to any antennas, including those that cannot be accurately analyzed with ray-tracing, particularly for near-field analysis. To experimentally verify the wideband performance of the AWPCS, a shortened horn antenna with a large apex angle and a non-uniform near-field phase distribution is used as an EM source for the AWPCS. The measured results verify a significant improvement in the antenna’s aperture phase distribution in a large frequency band of 25%.
Lalbakhsh, A, Mohamadpour, G, Roshani, S, Ami, M, Roshani, S, Sayem, ASM, Alibakhshikenari, M & Koziel, S 2021, 'Design of a Compact Planar Transmission Line for Miniaturized Rat-Race Coupler With Harmonics Suppression', IEEE Access, vol. 9, pp. 129207-129217.
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This paper presents an elegant yet straightforward design procedure for a compact rat-race coupler (RRC) with an extended harmonic suppression. The coupler's conventional \lambda /4 transmission lines (TLs) are replaced by a specialized TL that offers significant size reduction and harmonic elimination capabilities in the proposed approach. The design procedure is verified through the theoretical, circuit, and electromagnetic (EM) analyses, showing excellent agreement among different analyses and the measured results. The circuit and EM results show that the proposed TL replicates the same frequency behaviour of the conventional one at the design frequency of 1.8 GHz while enables harmonic suppression up to the 7 {\mathrm {th}} harmonic and a size reduction of 74%. According to the measured results, the RRC has a fractional bandwidth of 20%, with input insertion losses of around 0.2 dB and isolation level better than 35 dB. Furthermore, the total footprint of the proposed RRC is only 31.7 mm \times15.9 mm, corresponding to 0.28\,\,\lambda \times 0.14\,\,\lambda , where \lambda is the guided wavelength at 1.8 GHz.
Le, AT, Huang, X & Guo, YJ 2021, 'Analog Self-Interference Cancellation in Dual-Polarization Full-Duplex MIMO Systems', IEEE Communications Letters, vol. 25, no. 9, pp. 3075-3079.
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Full-duplex (FD) technology combined with dual-polarization (DP) multiple-input multiple-output (MIMO) systems is attractive to improve spectral efficiency and to enhance link capacity. Cancelling self-interference (SI) in such DPFD MIMO systems using beamforming techniques is very challenging due to a significant difference of the co-polarization and cross-polarization SI channels. In this letter, an analog adaptive filter structure is proposed to mitigate both co-polarization and cross-polarization SIs in DPFD MIMO systems. Stationary analysis is applied to evaluate the performance of the proposed structure. Simulation results show that about 45 dB to 55 dB of SI cancellation can be achieved regardless of the isolation differences between cross-polarization and co-polarization channels.
Le, AT, Tran, LC, Huang, X, Guo, YJ & Hanzo, L 2021, 'Analog Least Mean Square Adaptive Filtering for Self-Interference Cancellation in Full Duplex Radios', IEEE Wireless Communications, vol. 28, no. 1, pp. 12-18.
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Le, NP, Tran, LC, Huang, X, Choi, J, Dutkiewicz, E, Phung, SL & Bouzerdoum, A 2021, 'Performance Analysis of Uplink NOMA Systems With Hardware Impairments and Delay Constraints Over Composite Fading Channels', IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp. 6881-6897.
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In this paper, we propose a mixture gamma distribution based analytical framework for NOMA wireless systems over composite fading channels. We analyze the outage probability (OP), delay-limited throughput (TP) and effective capacity (EC) in uplink NOMA with imperfect successive interference cancellation (SIC) due to the presence of residual hardware impairments and delay constraints. A mixture gamma distribution is used to approximate the probability density functions of fading channels. Based on this, we obtain closed-form expressions in terms of Meijer-G functions for the OP, the TP and the EC. We also perform asymptotic analysis of these metrics to characterize system behaviors at the high signal-to-noise ratio regime. Moreover, upper-bounds for the EC is derived. Efficacy of NOMA over orthogonal multiple access is analytically examined. Unlike the existing works, our analytical expressions hold for NOMA systems with an arbitrary number of users per cluster over a wide range of channel models, including lognormal-Nakagami-m, KG, η-μ, Nakagami-q (Hoyt), κ-μ, Nakagami-n (Rician), Nakagami-m, and Rayleigh fading channels. This unified analysis facilitates evaluations of impacts of the residual interference, the power allocation among users, the delay quality-of-service exponent as well as the shadowing and small-scale fading parameters on the performance metrics. Simulation results are provided to validate theoretical analysis.
Le, NP, Tran, LC, Huang, X, Dutkiewicz, E, Ritz, C, Phung, SL, Bouzerdoum, A, Franklin, DR & Hanzo, L 2021, 'Energy-Harvesting Aided Unmanned Aerial Vehicles for Reliable Ground User Localization and Communications Under Lognormal-Nakagami-$m$ Fading Channels.', IEEE Trans. Veh. Technol., vol. 70, no. 2, pp. 1632-1647.
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Lee, SS, Siwakoti, YP, Barzegarkhoo, R & Lee, K-B 2021, 'Switched-Capacitor-Based Five-Level T-Type Inverter (SC-5TI) With Soft-Charging and Enhanced DC-Link Voltage Utilization', IEEE Transactions on Power Electronics, vol. 36, no. 12, pp. 13958-13967.
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The emerging switched-capacitor-based multilevel inverters offer interesting merits such as self-balancing of capacitor voltages and boosting of voltage gain. While the switched capacitors (SCs) in these topologies are charged in parallel with the dc source, severe current spikes issue is inevitable, rendering them impractical at high power. This article proposes a novel switched-capacitor-based T-type inverter that mitigates the current spikes by enabling soft charging for its integrated SCs, where both SC in the topological structure charges through a dedicated circuit comprises of an inductor and two switches. The proposed topology is capable of five-level ac voltage generation and when compared to a classical T-type/ANPC (active neutral-point-clamped) inverter, it achieves higher dc-link voltage utilization since its maximum attainable voltage gain is doubled. Theoretical findings of the proposed topology are validated by both the simulation and experimental results.
Lee, SS, Yang, Y & Siwakoti, YP 2021, 'A Novel Single-Stage Five-Level Common-Ground-Boost-Type Active Neutral-Point-Clamped (5L-CGBT-ANPC) Inverter', IEEE Transactions on Power Electronics, vol. 36, no. 6, pp. 6192-6196.
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Lee, SS, Yang, Y, Siwakoti, YP & Lee, K-B 2021, 'A Novel Boost Cascaded Multilevel Inverter.', IEEE Trans. Ind. Electron., vol. 68, no. 9, pp. 8072-8080.
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Lei, G, Bramerdorfer, G, Liu, C, Guo, Y & Zhu, J 2021, 'Robust Design Optimization of Electrical Machines: A Comparative Study and Space Reduction Strategy', IEEE Transactions on Energy Conversion, vol. 36, no. 1, pp. 300-313.
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This article presents a comparative study on different types of robust design optimization methods for electrical machines. Three robust design approaches, Taguchi parameter design, worst-case design and design for six-sigma, are compared for low-dimensional and high-dimensional design optimization scenarios, respectively. For the high-dimensional scenario, the computational burden is normally massive due to the robustness evaluation of a huge number of design candidates. To attempt this challenge, as the second aim of this paper, a space reduction optimization (SRO) strategy is proposed for these robust design approaches, yielding three new robust optimization methods. To illustrate and compare the performance of different robust design optimization methods, a permanent magnet motor with soft magnetic composite cores is investigated with the consideration of material diversities and manufacturing tolerances. 3-D finite element model and thermal network model are employed in the optimization process and the accuracy of both models has been verified by experimental results. Based on the theoretical analysis and optimization results, a detailed comparison is provided for all investigated and proposed robust design optimization methods in terms of different aspects. It shows that the proposed SRO strategy can greatly improve the design optimization effectiveness and efficiency of those three conventional robust design methods.
Lei, G, Bramerdorfer, G, Ma, B, Guo, Y & Zhu, J 2021, 'Robust Design Optimization of Electrical Machines: Multi-Objective Approach', IEEE Transactions on Energy Conversion, vol. 36, no. 1, pp. 390-401.
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This article presents a new method for multi-objective robust design optimization of electrical machines and provides a detailed comparison with so far introduced techniques. First, two robust design approaches, worst-case design and design for six-sigma, are compared with the conventional deterministic approach for multi-objective optimization. Through a case study on a permanent magnet motor, it is found that the reliabilities of motors produced based on robust designs are 100% under the investigated constraints, while the reliabilities of deterministic designs can be lower than 30%. A major disadvantage of robust optimization is the huge computation cost, especially for high-dimensional problems. To attempt this problem, a new multi-objective sequential optimization method (MSOM) with an orthogonal design technique and hypervolume indicator (as a measure of convergence) is proposed for both deterministic and robust design optimization of electrical machines. Through another case study, it is found that the new MSOM can improve motor performance and greatly reduce the computational cost. For the robust optimization, the number of required finite element simulations can be reduced by more than 40%, compared with that required by the conventional approach. The proposed method can be applied to many-objective (robust) design optimization of electrical machines.
Lei, H, Chen, S, Wang, M, He, X, Jia, W & Li, S 2021, 'A New Algorithm for Sketch-Based Fashion Image Retrieval Based on Cross-Domain Transformation', Wireless Communications and Mobile Computing, vol. 2021, pp. 1-14.
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Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.
Li, C, Xie, H-B, Fan, X, Xu, RYD, Van Huffel, S & Mengersen, K 2021, 'Kernelized Sparse Bayesian Matrix Factorization', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 391-404.
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Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensively studied in the machine learning community. Kernelized matrix factorization (KMF) is a powerful tool to incorporate side information into the low-rank approximation model, which has been applied to solve the problems of data mining, recommender systems, image restoration, and machine vision. However, most existing KMF models rely on specifying the rows and columns of the data matrix through a Gaussian process prior and have to tune manually the rank. There are also computational issues of existing models based on regularization or the Markov chain Monte Carlo. In this article, we develop a hierarchical kernelized sparse Bayesian matrix factorization (KSBMF) model to integrate side information. The KSBMF automatically infers the parameters and latent variables including the reduced rank using the variational Bayesian inference. In addition, the model simultaneously achieves low-rankness through sparse Bayesian learning and columnwise sparsity through an enforced constraint on latent factor matrices. We further connect the KSBMF with the nonlocal image processing framework to develop two algorithms for image denoising and inpainting. Experimental results demonstrate that KSBMF outperforms the state-of-the-art approaches for these image-restoration tasks under various levels of corruption.
Li, F, Zheng, J, Zhang, Y-F, Liu, N & Jia, W 2021, 'AMDFNet: Adaptive multi-level deformable fusion network for RGB-D saliency detection', Neurocomputing, vol. 465, pp. 141-156.
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Effective exploration of useful contextual information in multi-modal images is an essential task in salient object detection. Nevertheless, the existing methods based on the early-fusion or the late-fusion schemes cannot address this problem as they are unable to effectively resolve the distribution gap and information loss. In this paper, we propose an adaptive multi-level deformable fusion network (AMDFNet) to exploit the cross-modality information. We use a cross-modality deformable convolution module to dynamically adjust the boundaries of salient objects by exploring the extra input from another modality. This enables incorporating the existing features and propagating more contexts so as to strengthen the model's ability to perceiving scenes. To accurately refine the predicted maps, a multi-scaled feature refinement module is proposed to enhance the intermediate features with multi-level prediction in the decoder part. Furthermore, we introduce a selective cross-modality attention module in the fusion process to exploit the attention mechanism. This module captures dense long-range cross-modality dependencies from a multi-modal hierarchical feature's perspective. This strategy enables the network to select more informative details and suppress the contamination caused by the negative depth maps. Experimental results on eight benchmark datasets demonstrate the effectiveness of the components in our proposed model, as well as the overall saliency model.
Li, K, Lu, N, Zheng, J, Zhang, P, Ni, W & Tovar, E 2021, 'BloothAir', ACM Transactions on Cyber-Physical Systems, vol. 5, no. 3, pp. 1-22.
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Thanks to flexible deployment and excellent maneuverability, autonomous drones have been recently considered as an effective means to act as aerial data relays for wireless ground devices with limited or no cellular infrastructure, e.g., smart farming in a remote area. Due to the broadcast nature of wireless channels, data communications between the drones and the ground devices are vulnerable to eavesdropping attacks. This article develops BloothAir, which is a secure multi-hop aerial relay system based on Bluetooth Low Energy ( BLE ) connected autonomous drones. For encrypting the BLE communications in BloothAir, a channel-based secret key generation is proposed, where received signal strength at the drones and the ground devices is quantized to generate the secret keys. Moreover, a dynamic programming-based channel quantization scheme is studied to minimize the secret key bit mismatch rate of the drones and the ground devices by recursively adjusting the quantization intervals. To validate the design of BloothAir, we build a multi-hop aerial relay testbed by using the MX400 drone platform and the Gust radio transceiver, which is a new lightweight onboard BLE communicator specially developed for the drone. Extensive real-world experiments demonstrate that the BloothAir system achieves a significantly lower secret key bit mismatch rate than the key generation benchmarks, which use the static quantization intervals. In addition, the high randomness of the generated secret keys is verified by the standard NIST test, thereby effectively protecting the BLE communications in BloothAir from the eavesdropping attacks.
Li, K, Ni, W, Tovar, E & Guizani, M 2021, 'Joint Flight Cruise Control and Data Collection in UAV-Aided Internet of Things: An Onboard Deep Reinforcement Learning Approach', IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9787-9799.
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Employing unmanned aerial vehicles (UAVs) as aerial data collectors in Internet-of-Things (IoT) networks is a promising technology for large-scale environment sensing. A key challenge in UAV-aided data collection is that UAV maneuvering gives rise to buffer overflow at the IoT node and unsuccessful transmission due to lossy airborne channels. This article formulates a joint optimization of flight cruise control and data collection schedule to minimize network data loss as a partially observable Markov decision process (POMDP), where the states of individual IoT nodes can be obscure to the UAV. The problem can be optimally solvable by reinforcement learning, but suffers from the curse of dimensionality and becomes rapidly intractable with the growth in the number of IoT nodes. In practice, a UAV-aided IoT network contains a large number of network states and actions in POMDP while the up-to-date knowledge is not available at the UAV. We propose an onboard deep Q -network-based flight resource allocation scheme (DQN-FRAS) to optimize the online flight cruise control of the UAV and data scheduling given outdated knowledge on the network states. Numerical results demonstrate that DQN-FRAS reduces the packet loss by over 51%, as compared to existing nonlearning heuristics.
Li, K, Ni, W, Tovard, E & Jamalipour, A 2021, 'Online Velocity Control and Data Capture of Drones for the Internet of Things: An Onboard Deep Reinforcement Learning Approach', IEEE Vehicular Technology Magazine, vol. 16, no. 1, pp. 49-56.
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Applications of unmanned aerial vehicles (UAVs) for data collection are a promising means to extend Internet of Things (IoT) networks to remote and hostile areas and to locations where there is no access to power supplies. The adequate design of UAV velocity control and communication decision making is critical to minimize the data packet losses at ground IoT nodes that result from overflowing buffers and transmission failures. However, online velocity control and communication decision making are challenging in UAV-enabled IoT networks, due to a UAV?s lack of up-to-date knowledge about the state of the nodes, e.g., the battery energy, buffer length, and channel conditions.
Li, K, Ni, W, Zheng, J, Tovar, E & Guizani, M 2021, 'Confidentiality and Timeliness of Data Dissemination in Platoon-based Vehicular Cyber-Physical Systems', IEEE Network, vol. 35, no. 4, pp. 248-254.
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Li, M, Liu, Y & Guo, YJ 2021, 'Design of Sum and Difference Patterns by Optimizing Element Rotations and Positions for Linear Dipole Array', IEEE Transactions on Antennas and Propagation, vol. 69, no. 5, pp. 3027-3032.
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IEEE This communication presents a novel method of synthesizing both sum and difference patterns by optimizing the element rotations and positions for linear dipole array. The common element rotations and positions are optimized by using the particle swarm optimization (PSO) method to produce sum and difference patterns with reduced sidelobe levels (SLLs) and cross-polarization levels (XPLs), and as steep slope as possible for the difference pattern at the target direction. Such method leads to a sum-and-difference array with sparsely distributed uniform amplitude elements, thus saving many antenna elements and unequal power dividers. Three examples for synthesizing sparse rotated dipole arrays with sum and difference patterns are provided. Synthesis results show that the obtained arrays with uniform amplitudes can produce satisfactory sum and difference patterns while saving about 34.69% ~ 42.27% of the antenna elements when compared with λ/2-spaced arrays occupying the same aperture.
Li, M, Yang, Y, Iacopi, F, Yamada, M & Nulman, J 2021, 'Compact Multilayer Bandpass Filter Using Low-Temperature Additively Manufacturing Solution', IEEE Transactions on Electron Devices, vol. 68, no. 7, pp. 3163-3169.
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This article presented an additively manufactured bandpass filter (BPF) based on a second-order stub-loaded resonator consisting of multimetal layer components. The proposed BPF is fabricated by a low-temperature (140°) additively manufactured electronics (AME) solution that can fabricate conductive and dielectric materials simultaneously with multimetal-layer and flexible interlayer distance. By reducing the interlayer distance, constant inductance and capacitance can be realized in smaller sizes, which helps to achieve device minimization. Taking advantage of this inkjet printing technology, a second-order multimetal layer resonator is proposed. To understand the principle of the BPF, an equivalent circuit with odd- and even-mode analysis is demonstrated. For verification, the frequency response of the circuit's mathematical model is calculated to compare with the electromagnetic simulation results. Good agreement can be achieved among the calculated, simulated, and measured results. The proposed BPF is designed at 12.25 GHz with a bandwidth of 40.8% and a compact size of 2.7 mm \times1.425 mm \times0.585 mm or 0.186\lambda {g} \times 0.098\lambda {g}\times 0.040\lambda {g} , which is suitable for circuit-in-package applications in television programs, radar detection, and satellite communications.
Li, Y, Lei, G, Bramerdorfer, G, Peng, S, Sun, X & Zhu, J 2021, 'Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions', Applied Sciences, vol. 11, no. 4, pp. 1627-1627.
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This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.
Li, Y, Li, Y, Zhu, J, Zhu, L & Liu, C 2021, 'Vibration Estimation in Power Transformers Based on Dynamic Magnetostriction Model and Finite-Element Analysis', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-4.
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This paper presents a modeling approach for estimating the vibration of power transformers based on a magnetostriction model and maxwell stress calculation. The magnetostriction model, accounting for the dynamic hysteresis behavior, is constructed by combining the Becker-Doring crystal magnetostriction model and J-A dynamic hysteresis model. By incorporating the proposed model into the finite-element method (FEM), both the Maxwell stress and the magnetostriction force in each mesh element can be readily obtained simultaneously. To verify the calculation method, the vibration of a three-phase transformer prototype is measured and compared with simulated results. It demonstrated that the proposed method is accurate enough to predict the vibration of power transformers.
Li, Y, Zhu, J, Li, Y, Wang, H & Zhu, L 2021, 'Modeling dynamic magnetostriction of amorphous core materials based on Jiles–Atherton theory for finite element simulations', Journal of Magnetism and Magnetic Materials, vol. 529, pp. 167854-167854.
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Due to its favorable properties of low core loss and high saturation magnetic flux density, amorphous material is widely used as the core material of low and medium frequency transformers. However, its magnetostriction is much higher than that of grain-oriented sheet steel, a very common material for conventional transformers, resulting in high acoustic noises. This paper proposes a comprehensive model of magnetostriction in amorphous material based on the interdependence between magnetostriction and magnetization by combining the isotropic magnetostriction effect and Jiles-Atherton energy balance theory. Incorporated in coupled magneto-mechanical field calculation, the proposed model can correctly simulate the butterfly loops of magnetostriction, magnetic hysteresis loops and vibration displacements. The theoretical results of magnetostriction characteristic are verified by both single sheet test and the experimental results of amorphous transformer prototype.
Lian, J-W, Ban, Y-L & Guo, YJ 2021, 'Wideband Dual-Layer Huygens’ Metasurface for High-Gain Multibeam Array Antennas', IEEE Transactions on Antennas and Propagation, vol. 69, no. 11, pp. 7521-7531.
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A wideband dual-layer Huygens’ unit cell based on offset electric dipole pair (OEDP) is proposed. Different from traditional designs with a combination of electric and magnetic polarizabilities, the proposed Huygens’ unit cell employs electric polarizabilities exclusively. By doing so, it practically avoids the unbalanced resonant frequencies between two polarizabilities, thereby achieving wideband transmission. Based on the proposed unit cell, a wideband and high-gain multibeam array antenna is developed. Firstly, a Rotman lens is designed by using a substrate integrated waveguide (SIW) technology. Then a parallel-fed slot antenna array is connected to the Rotman lens to generate multiple beams. Without using a series-fed slot antenna array, the multibeam array antenna based on Rotman lens can operate within a relatively wide bandwidth (28 GHz to 32 GHz). Secondly, a wideband dual-layer Huygens’ metasurface is developed that serves as a superstrate of the multibeam array antenna for increasing the antenna gain further. A wideband and high-gain multibeam array antenna is finally realized, which is comprised of a Rotman lens, a parallel-fed slot antenna array, and a Huygens’ metasurface. To verify the performance of this design, a prototype is fabricated and its measured results are compared to the simulated counterparts.
Liao, J, Zhou, J, Song, Y, Liu, B, Chen, Y, Wang, F, Chen, C, Lin, J, Chen, X, Lu, J & Jin, D 2021, 'Preselectable Optical Fingerprints of Heterogeneous Upconversion Nanoparticles', Nano Letters, vol. 21, no. 18, pp. 7659-7668.
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The control in optical uniformity of single nanoparticles and tuning their diversity in multiple dimensions, dot to dot, holds the key to unlocking nanoscale applications. Here we report that the entire lifetime profile of the single upconversion nanoparticle (τ2 profile) can be resolved by confocal, wide-field, and super-resolution microscopy techniques. The advances in both spatial and temporal resolutions push the limit of optical multiplexing from microscale to nanoscale. We further demonstrate that the time-domain optical fingerprints can be created by utilizing nanophotonic upconversion schemes, including interfacial energy migration, concentration dependency, energy transfer, and isolation of surface quenchers. We exemplify that three multiple dimensions, including the excitation wavelength, emission color, and τ2 profile, can be built into the nanoscale derivative τ2-dots. Creating a vast library of individually preselectable nanotags opens up a new horizon for diverse applications, spanning from sub-diffraction-limit data storage to high-throughput single-molecule digital assays and super-resolution imaging.
Lin, Z, Lv, T, Ni, W, Zhang, JA & Liu, RP 2021, 'Nested Hybrid Cylindrical Array Design and DoA Estimation for Massive IoT Networks', IEEE Journal on Selected Areas in Communications, vol. 39, no. 4, pp. 919-933.
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IEEE Reducing cost and power consumption while maintaining high network access capability is a key physical-layer requirement of massive Internet of Things (mIoT) networks. Deploying a hybrid array is a cost-and energy-efficient way to meet the requirement, but would penalize system degree of freedom (DoF) and channel estimation accuracy. This is because signals from multiple antennas are combined by a radio frequency (RF) network of the hybrid array. This paper presents a novel hybrid uniform circular cylindrical array (UCyA) for mIoT networks. We design a nested hybrid beamforming structure based on sparse array techniques and propose the corresponding channel estimation method based on the second-order channel statistics. As a result, only a small number of RF chains are required to preserve the DoF of the UCyA. We also propose a new tensor-based two-dimensional (2-D) direction-of-arrival (DoA) estimation algorithm tailored for the proposed hybrid array. The algorithm suppresses the noise components in all tensor modes and operates on the signal data model directly, hence improving estimation accuracy with an affordable computational complexity. Corroborated by a Cramér-Rao lower bound (CRLB) analysis, simulation results show that the proposed hybrid UCyA array and the DoA estimation algorithm can accurately estimate the 2-D DoAs of a large number of IoT devices.
Lin, Z, Lv, T, Ni, W, Zhang, JA, Zeng, J & Liu, RP 2021, 'Joint Estimation of Multipath Angles and Delays for Millimeter-Wave Cylindrical Arrays With Hybrid Front-Ends', IEEE Transactions on Wireless Communications, vol. 20, no. 7, pp. 4631-4645.
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Accurate channel parameter estimation is challenging for wideband millimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much fewer radio frequency (RF) chains than antennas. This article presents a novel joint angle and delay estimation (JADE) approach for wideband mmWave fully-connected hybrid uniform cylindrical arrays. We first design a new hybrid beamformer to reduce the dimension of received signals on the horizontal plane by exploiting the convergence of the Bessel function, and to reduce the active beams in the vertical direction through preselection. The important recurrence relationship of the received signals needed for subspace-based angle and delay estimation is preserved, even with substantially fewer RF chains than antennas. Then, linear interpolation is generalized to reconstruct the received signals of the hybrid beamformer, so that the signals can be coherently combined across the whole band to suppress the beam squint. As a result, efficient subspace-based algorithm algorithms can be developed to estimate the angles and delays of multipath components. The estimated delays and angles are further matched and correctly associated with different paths in the presence of non-negligible noises, by putting forth perturbation operations. Simulations show that the proposed approach can approach the Cramér-Rao lower bound (CRLB) of the estimation with a significantly lower computational complexity than existing techniques.
Liu, B, Wang, F, Chen, C & McGloin, D 2021, 'Single-Pixel Diffuser Camera', IEEE Photonics Journal, vol. 13, no. 6, pp. 1-5.
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We present a compact, diffuser assisted, single-pixel computational camera. A rotating ground glass diffuser is adopted, in preference to a commonly used digital micro-mirror device (DMD), to encode a two-dimensional (2D) image into single-pixel signals. We retrieve images with an 8.8% sampling ratio after the calibration of the pseudo-random pattern of the diffuser under light-emitting diode (LED) illumination. Furthermore, we demonstrate hyperspectral imaging with line array detection by adding a diffraction grating. As the random and fixed patterns of a rotating diffuser placed in the image plane can serve as 2D modulation patterns in single-pixel imaging, we do not need further calibration for spectral imaging case since we use a parallel recovery strategy for images at all wavelengths. The implementation results in a cost-effective single-pixel camera for high-dimensional imaging, with potential for imaging in non-visible wavebands.
Liu, B, Wang, F, Chen, C, Dong, F & McGloin, D 2021, 'Self-evolving ghost imaging', Optica, vol. 8, no. 10, pp. 1340-1340.
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Ghost imaging captures 2D images with a point detector instead of an array sensor. It could therefore solve the challenge of building cameras in wave bands where sensors are difficult and expensive to produce and could open up more routine THz, near-infrared, lifetime, and hyperspectral imaging simply by using single-pixel detectors. Traditionally, ghost imaging retrieves the image of an object offline by correlating measured light intensities with pre-designed illuminating patterns. Here we present a “self-evolving” ghost imaging (SEGI) strategy for imaging objects bypassing offline post-processing. It also offers the capability to image objects in turbid media. By inspecting the optical feedback, we evaluate the illumination patterns by a cost function and generate offspring illumination patterns that mimic the object’s image, bypassing the reconstruction process. At the initial evolving state, the object’s “genetic information” is stored in the patterns. At the following imaging stage, the object’s image ( 48 × 48 p i x e l s ) can be updated at a 40 Hz imaging rate. We numerically and experimentally demonstrate this concept for static and moving objects. The frame-memory effect between the self-evolving illumination patterns provided by the genetic algorithm enables SEGI imaging through turbid media. We furt...
Liu, C, Liu, Q, Wang, S, Wang, Y, Lei, G, Guo, Y & Zhu, J 2021, 'A novel flux switching claw pole machine with soft magnetic composite cores', International Journal of Applied Electromagnetics and Mechanics, vol. 67, no. 2, pp. 183-203.
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This paper proposes a novel flux switching claw pole machine (FSCPM) with soft magnetic composite (SMC) cores. The proposed FSCPM holds advantages of the conventional flux switching permanent magnet machine (FSPMM) and claw pole machine (CPM) with SMC cores. As permanent magnets are installed between the stator claw pole teeth, FSCPM has good flux concentrating ability, and the air gap flux density can be significantly improved. The torque coefficient of FSCPM is relatively high due to the applied claw pole teeth and global winding. FSCPM is mechanically robust because there are no windings or PMs on its rotor. Moreover, the core loss of FSCPM is relatively low for the SMC material has lower core loss at high frequency compared with silicon steels. The topology and operational principle of FSCPM are explained first. Several main dimensions of the machine are optimized to achieve better performance, based on 3D finite element method (FEM). Furthermore, the rotor skewing technology is adopted to reduce the cogging torque and torque ripple.
Liu, C, Wang, D, Wang, S, Niu, F, Wang, Y, Lei, G & Zhu, J 2021, 'Design and Analysis of a New Permanent Magnet Claw Pole Machine With S-Shape Winding', IEEE Transactions on Magnetics, vol. 57, no. 2, pp. 1-5.
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With the continuous improvement of magnetic and mechanical properties of soft magnetic composite (SMC) material, there is a trend to develop novel electrical machines with SMC cores for some special applications. Among these electrical machines, permanent magnet claw pole machine (CPM) has been extensively studied over the past few decades. As linear global winding has been used in this machine, it can be regarded as a linear winding CPM (LWCPM). To improve the performance of LWCPM, a new S-shape winding CPM (SWCPM) is proposed in this article. The main stator structures of the LWCPM and SWCPM are optimized to achieve maximum torque ability. Compared with LWCPM, SWCPM provides higher average torque, power factor, and higher efficiency. The main disadvantage of the proposed SWCPM is its lower flux weakening ability. 3-D finite element model is used to evaluate the performance of the proposed LWCPM and SWCPM. The accuracy of the 3-D finite element model is verified by using a previous prototype.
Liu, D, Chen, Y, Tran, TT & Zhang, G 2021, 'Facile and rapid assembly of high-performance tannic acid thin-film nanofiltration membranes via Fe3+ intermediated regulation and coordination', Separation and Purification Technology, vol. 260, pp. 118228-118228.
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Non-polyamide (non-PA) thin-film composite nanofiltration (TFC-NF) membranes have received tremendous attention in recent years, but their development is strongly hindered by the complicated fabrication process and the trade-off between permeability and selectivity. Here, we report a highly perm-selective non-PA TFC-NF membrane through 2-minute rapid assembly of tannic acid (TA) on hydrolyzed polyacrylonitrile (PAN) substrate via Fe3+ intermediated regulation and coordination. The optimized membrane with a molecular weight cut off of ~390 Da showed high rejections for salts in a sequence of Na2SO4 (90.2%) > MgSO4 (83.4%) > NaCl (50.0%) > MgCl2 (35.2%) and desirable rejections for organic pollutants (e.g. >99.0% dyes, 92.2% streptomycin and 81.8% chloramphenicol) while maintaining a pure water permeability of as high as 13.6 L·m−2·h−1·bar−1, which clearly outperforms the reported non-PA membranes. In addition, the assembled TFC membrane showed excellent antifouling performance and reasonable structural stability against operation pressure and solution alkalinity. These results are highly promising and indicate a great potential for the membrane to be used in practical nanofiltration application, e.g. water purification and wastewater reclamation. Our work outlines the production of novel high-performance non-PA membrane with a fast fabrication process in a green chemistry context.
Liu, D, Wu, Q, Huang, Y, Huang, X & An, P 2021, 'Learning from EPI-Volume-Stack for Light Field image angular super-resolution', Signal Processing: Image Communication, vol. 97, pp. 116353-116353.
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Light Field (LF) image angular super-resolution aims to synthesize a high angular resolution LF image from a low angular resolution one, and is drawing increased attention because of its wide applications. In order to reconstruct a high angular resolution LF image, many learning based LF image angular super-resolution methods have been proposed. However, most existing methods are based on LF Epipolar Plane Image or Epipolar Plane Image volume representation, which underuse the LF image structure. The LF view spatial correlation and neighboring LF views angular correlations which can reflect LF image structure are not fully explored, which reduces LF angular super-resolution quality. In order to alleviate this problem, this paper introduces an Epipolar Plane Image Volume Stack (EPI-VS) representation for LF angular super-resolution. The EPI-VS is constituted by arranging all LF views in a raster order, which benefits in exploring LF view spatial correlation and neighboring LF views angular correlations. Based on such representation, we further propose an LF angular super-resolution network. 3D convolutions are applied in the whole super-resolution network to better accommodate the input EPI-VS data and allow information propagation between two spatial and one directional dimensions of EPI-VS data. Extensive experiments on synthetic and real-world LF scenes demonstrate the effectiveness of the proposed network. Moreover, we also illustrate the superiority of our network by applying it in scene depth estimation task.
Liu, H, Zhu, X, Wang, Y, Men, K & Yeo, KS 2021, 'A 60 GHz 8-Way Combined Power Amplifier in 0.18 μm SiGe BiCMOS', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 6, pp. 1847-1851.
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IEEE A 60 GHz fully-integrated 8-way combined power amplifier (PA) is developed in a standard 0.18 μm SiGe BiCMOS technology. The 8-way power splitter and combiner are co-optimized with transformer based baluns inside the eight differential PA cells, and hence resulting in minimum loss and high gain, linearity and efficiency. The measurement shows that the PA can achieve a gain of 22.2 dB around 60 GHz and 3-dB bandwidth from 53.5 GHz to 66.5 GHz, which covers all the channels specified in IEEE 802.11ad standard. It also attains a 1-dB power compression point (P1dB) of 21.8 dBm and saturated output power (PSAT) of 22.6 dBm, with power-added-efficiency of 10.7% and 12%, respectively.
Liu, L, Guo, Y, Lei, G & Zhu, JG 2021, 'Iron Loss Calculation for High-Speed Permanent Magnet Machines Considering Rotating Magnetic Field and Thermal Effects', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-5.
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Thanks to their many merits such as high power density and fast dynamic response, the high-speed permanent magnet machines (HSPMMs) have attracted increasing industrial and domestic applications. However, the iron loss may become significantly higher at higher operating speed and frequency and it should be carefully considered in the machine design and analysis. In this paper, an advanced iron loss analytical calculation method is applied for HSPMMs in which the influences of rotating magnetic field and thermal field are both considered. A 30 kW, 45000 r/min HSPMM is studied to demonstrate that the proposed model is feasible and advantageous. Analysis results reveal that the predicted iron loss by using the proposed method has satisfactory accuracy with small errors (maximum error of 3.73% and absolute average error of 3.03%) under different operating conditions. The proposed method can also be applied in other electromagnetic devices such as superconducting electrical machines.
Liu, L, Jiang, J, Jia, W, Amirgholipour, S, Wang, Y, Zeibots, M & He, X 2021, 'DENet: A Universal Network for Counting Crowd With Varying Densities and Scales', IEEE Transactions on Multimedia, vol. 23, no. 99, pp. 1060-1068.
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Liu, P, Li, Y, Cheng, W, Gao, X & Huang, X 2021, 'Intelligent Reflecting Surface Aided NOMA for Millimeter-Wave Massive MIMO With Lens Antenna Array', IEEE Transactions on Vehicular Technology, vol. 70, no. 5, pp. 4419-4434.
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Liu, Y, Bai, J, Zheng, J, Liao, H, Ren, Y & Guo, YJ 2021, 'Efficient Shaped Pattern Synthesis for Time Modulated Antenna Arrays Including Mutual Coupling by Differential Evolution Integrated With FFT via Least-Square Active Element Pattern Expansion', IEEE Transactions on Antennas and Propagation, vol. 69, no. 7, pp. 4223-4228.
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IEEE The fast Fourier transform (FFT) via the least-square active element pattern expansion (LSAEPE) is generalized to speed up the computation of array patterns including mutual coupling and platform effect for time-modulated antenna arrays (TMAAs) at the central and sideband frequencies. By integrating the LSAEPE-FFT with differential evolution algorithm (DEA), the resulting DEA-LSAEPE-FFT method can realize efficient shaped pattern synthesis with accurate control of mainlobe shape, sidelobe level (SLL) and sideband level (SBL). Two examples of synthesizing different shaped patterns for different TMAAs mounted on a nonuniform platform or with metal scatters are conducted to validate the effectiveness and robustness of the proposed method. Synthesis results show that the proposed method has much better accuracy performance than the conventional DEA-FFT while costing much less CPU time than that of using DEA combined with direct summation.
Liu, Y, Yang, Y, Wu, P, Ma, X, Li, M, Xu, K-D & Guo, YJ 2021, 'Synthesis of Multibeam Sparse Circular-Arc Antenna Arrays Employing Refined Extended Alternating Convex Optimization', IEEE Transactions on Antennas and Propagation, vol. 69, no. 1, pp. 566-571.
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IEEE A refined extended alternating convex optimization (REACO) method is presented to synthesize multibeam sparse circular-arc antenna arrays with minimum element spacing control by considering real antenna array structure characteristics. This method consists of initial step and a few refining steps. At the initial step, an initial array with dense elements distributed on a circular-arc is considered, and its array manifold vector is described by rotating a simulated isolated element pattern (IEP) without considering element mutual coupling. The collective excitation coefficient vector (CECV) and its energy bound are introduced for each element, and consequently the common element positions for generating desired multibeam patterns can be found by minimizing the number of active CECVs under multiple constraints. This minimization problem is further formulated as performing a sequence of alternating convex optimization (ACO) in which the CECV and an auxiliary weighting vector are alternately chosen as the optimization variables, so that the mimimum element spacing constraint can be easily dealt with. Once the initial optimization step is finished, a few refining steps are performed in which the element positions and excitations are successively updated in each step by renewing the array manifold vector through rotating the simulated nearby active element patterns (AEPs) of the antenna array obtained at the previous step. In such a way, the mutual coupling can be incorporated into the multibeam sparse array synthesis. An example of synthesizing a sparse circular-arc conformal array with 23 beams covering the space from–63.25° to 63.25° is conducted to validate the effectiveness and advantage of the proposed method.
Lotfi, I, Niyato, D, Sun, S, Dinh, HT, Li, Y & Kim, DI 2021, 'Protecting Multi-Function Wireless Systems From Jammers With Backscatter Assistance: An Intelligent Strategy', IEEE Transactions on Vehicular Technology, vol. 70, no. 11, pp. 11812-11826.
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In this paper, we present a novel unified framework to protect multi-function wireless systems from jamming attacks. Examples of such multi-function system include joint radar and communication (JRC) systems and simultaneous wireless information and power transfer (SWIPT) systems. By abstracting the system functionalities as a joint optimization problem of multiple queues, we achieve effective resistance against jammers for the multi-functions simultaneously.We incorporate different antijamming techniques into one framework. Deception mechanism is adopted to lure the jammer to attack and make its actions more predictable, and ambient backscatter technology is used to leverage the jamming signals. Since conventional Markov decision process (MDP) has only one decision epoch at every time slot, it cannot be used to model the deception strategy which needs two decision epochs to leverage the jamming signals. We therefore formulate the problem using an advanced two-step MDP. After that, a deep reinforcement learning algorithm with a prioritized double deep Q-Learning architecture is proposed to learn optimal strategies in different system states. We show that by jointly considering the multi-functions of the system with potential jamming attacks during design phase, significant improvement can be achieved for both of the system functionalities.
Lu, Z, Shi, L, Geng, L, Zhang, J, Li, X & Guo, X 2021, 'Non-cooperative game pricing strategy for maximizing social welfare in electrified transportation networks', International Journal of Electrical Power & Energy Systems, vol. 130, pp. 106980-106980.
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This paper proposes a non-cooperative game pricing strategy framework by the approach of profit-sharing and user equilibrium principles, - to maximize the social welfare of the electrified transportation system stakeholders consisting of electricity wholesalers, fast charging stations, and electric vehicle users. Electricity wholesalers propose profit-sharing contracts to sell electricity to each fast charging station. Fast charging stations compete with each other to develop the optimal retail price while considering their electricity selling revenue and the traveling cost of electric vehicle users for the purpose of maximal social welfare. Non-cooperative game competition between fast charging stations is formulated as a generalized Nash game. Wardrop user equilibrium principle is applied for path selection for electric vehicle users. A Newton-type fixed-point algorithm is developed to solve the generalized Nash equilibrium point. Meanwhile, the nonlinear program is solved by the commercial solver KNITRO. A case study demonstrates the effectiveness of the proposed pricing strategy in maximizing the total profits of the fast charging station retailers, wholesalers, and electric vehicle users.
Luo, H, Wang, P, Chen, H & Xu, M 2021, 'Object Detection Method Based on Shallow Feature Fusion and Semantic Information Enhancement', IEEE Sensors Journal, vol. 21, no. 19, pp. 21839-21851.
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Luong, NC, Lu, X, Hoang, DT, Niyato, D & Kim, DI 2021, 'Radio Resource Management in Joint Radar and Communication: A Comprehensive Survey', IEEE Communications Surveys & Tutorials, vol. 23, no. 2, pp. 780-814.
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Joint radar and communication (JRC) has recently attracted substantial attention. The first reason is that JRC allows individual radar and communication systems to share spectrum bands and thus improves the spectrum utilization. The second reason is that JRC enables a single hardware platform, e.g., an autonomous vehicle or a UAV, to simultaneously perform the communication function and the radar function. As a result, JRC is able to improve the efficiency of resources, i.e., spectrum and energy, reduce the system size, and minimize the system cost. However, there are several challenges to be solved for the JRC design. In particular, sharing the spectrum imposes the interference caused by the systems, and sharing the hardware platform and energy resource complicates the design of the JRC transmitter and compromises the performance of each function. To address the challenges, several resource management approaches have been recently proposed, and this paper presents a comprehensive literature review on resource management for JRC. First, we give fundamental concepts of JRC, important performance metrics used in JRC systems, and applications of the JRC systems. Then, we review and analyze resource management approaches, i.e., spectrum sharing, power allocation, and interference management, for JRC. In addition, we present security issues to JRC and provide a discussion of countermeasures to the security issues. Finally, we highlight important challenges in the JRC design and discuss future research directions related to JRC.
Luu, HM, van Walsum, T, Franklin, D, Pham, PC, Vu, LD, Moelker, A, Staring, M, VanHoang, X, Niessen, W & Trung, NL 2021, 'Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion', Medical Physics, vol. 48, no. 6, pp. 2877-2890.
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PurposeEfficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ‐specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter.MethodsThe proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC‐visually lossless, is applied to compress the image. We demonstrate the proposed method on three‐dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak‐signal‐to‐noise ratio (), structural similarity (), and compression ratio () metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images.
Lyu, B, Ramezani, P, Hoang, DT, Gong, S, Yang, Z & Jamalipour, A 2021, 'Optimized Energy and Information Relaying in Self-Sustainable IRS-Empowered WPCN', IEEE Transactions on Communications, vol. 69, no. 1, pp. 619-633.
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This paper proposes a hybrid-relaying scheme empowered by a self-sustainable intelligent reflecting surface (IRS) in a wireless powered communication network (WPCN), to simultaneously improve the performance of downlink energy transfer (ET) from a hybrid access point (HAP) to multiple users and uplink information transmission (IT) from users to the HAP. We propose time-switching (TS) and power-splitting (PS) schemes for the IRS, where the IRS can harvest energy from the HAP's signals by switching between energy harvesting and signal reflection in the TS scheme or adjusting its reflection amplitude in the PS scheme. For both the TS and PS schemes, we formulate the sum-rate maximization problems by jointly optimizing the IRS's phase shifts for both ET and IT and network resource allocation. To address each problem's non-convexity, we propose a two-step algorithm to obtain the near-optimal solution with high accuracy. To show the structure of resource allocation, we also investigate the optimal solutions for the schemes with random phase shifts. Through numerical results, we show that our proposed schemes can achieve significant system sum-rate gain compared to the baseline scheme without IRS.
Lyu, J, Ling, SH, Banerjee, S, Zheng, JY, Lai, KL, Yang, D, Zheng, YP, Bi, X, Su, S & Chamoli, U 2021, 'Ultrasound volume projection image quality selection by ranking from convolutional RankNet', Computerized Medical Imaging and Graphics, vol. 89, pp. 101847-101847.
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Periodic inspection and assessment are important for scoliosis patients. 3D ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. With the generation of a 3D ultrasound volume projection spine image using our Scolioscan system, a series of 2D coronal ultrasound images are produced at different depths with different qualities. Selecting a high quality image from these 2D images is the crucial task for further scoliosis measurement. However, adjacent images are similar and difficult to distinguish. To learn the nuances between these images, we propose selecting the best image automatically, based on their quality rankings. Here, the ranking algorithm we use is a pairwise learning-to-ranking network, RankNet. Then, to extract more efficient features of input images and to improve the discriminative ability of the model, we adopt the convolutional neural network as the backbone due to its high power of image exploration. Finally, by inputting the images in pairs into the proposed convolutional RankNet, we can select the best images from each case based on the output ranking orders. The experimental result shows that convolutional RankNet achieves better than 95.5% top-3 accuracy, and we prove that this performance is beyond the experience of a human expert.
Lyu, X, Ren, C, Ni, W, Tian, H, Cui, Q & Liu, RP 2021, 'Online Learning of Optimal Proactive Schedule Based on Outdated Knowledge for Energy Harvesting Powered Internet-of-Things', IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 1248-1262.
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Lyu, X, Ren, C, Ni, W, Tian, H, Liu, RP & Tao, X 2021, 'Distributed Online Learning of Cooperative Caching in Edge Cloud', IEEE Transactions on Mobile Computing, vol. 20, no. 8, pp. 2550-2562.
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Mekhilef, S, Yang, Y, Siwakoti, Y, Lam, C & Sathik, J 2021, 'Guest editorial: Modelling, methodologies and control techniques of DC/AC power conversion topologies for small‐ and large‐scale photovoltaic power systems', IET Power Electronics, vol. 14, no. 12, pp. 2027-2030.
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Metia, S, Nguyen, HAD & Ha, QP 2021, 'IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with Extended Fractional-Order Kalman Filtering', Sensors, vol. 21, no. 16, pp. 5313-5313.
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This paper presents the development of high-performance wireless sensor networks for local monitoring of air pollution. The proposed system, enabled by the Internet of Things (IoT), is based on low-cost sensors collocated in a redundant configuration for collecting and transferring air quality data. Reliability and accuracy of the monitoring system are enhanced by using extended fractional-order Kalman filtering (EFKF) for data assimilation and recovery of the missing information. Its effectiveness is verified through monitoring particulate matters at a suburban site during the wildfire season 2019–2020 and the Coronavirus disease 2019 (COVID-19) lockdown period. The proposed approach is of interest to achieve microclimate responsiveness in a local area.
Mishra, DK, Ghadi, MJ, Azizivahed, A, Li, L & Zhang, J 2021, 'A review on resilience studies in active distribution systems', Renewable and Sustainable Energy Reviews, vol. 135, pp. 110201-110201.
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The world has been experiencing natural disasters and man-made attacks on power system networks over the past few decades. These occurrences directly affect electricity infrastructures, thereby resulting in immense economic loss. The electric infrastructure is the backbone and one of the most essential components of human life. Thus, a resilient infrastructure must be constructed to cope with events of high-impact, low-possibility. Moreover, achieving resilience in the active distribution system (ADS) has been a vital research field of planning and operation of electric power systems. The incorporation of recent breakthrough technologies, such as micro- and smart grids, can make the distribution system become considerably resilient through planning-operation activities prior, during, and after an extreme event. This study offers the concepts premised on a systematic review of available literature by distinguishing characteristics between reliability and resiliency. Thereafter, the most relevant proceedings in conformity with an overview of the major blackouts, hardening and its guidelines, weather-related scenarios, taxonomies, and remedial actions are discussed. In addition, this research presents the planning, operational, and planning-operational attributes in response to catastrophes. Furthermore, a case study is conducted to support the review work, where the reliability and resilience of the ADS (IEEE 33-bus test system) are evaluated as performance indices with and without the addition of PV units. The performed research is laying out the importance of the distributed generation, such as PV, in the context of resilience, with the inclusion of different faults.
Mishra, DK, Ghadi, MJ, Li, L, Hossain, MJ, Zhang, J, Ray, PK & Mohanty, A 2021, 'A review on solid-state transformer: A breakthrough technology for future smart distribution grids', International Journal of Electrical Power & Energy Systems, vol. 133, pp. 107255-107255.
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The modern power systems have now prompted the practice of power electronics-based converters for power conversion purposes, which has emerged a solid-state device named as solid-state transformer (SST). It provides the isolation between low/medium-voltage ports with a high-frequency transformer (HFT), and in addition, it facilitates controlling the active and reactive power automatically through power converters. With this objective, the SST is projected as an essential device for smart/microgrids, particularly in multi-microgrid systems, to enhance modern distribution systems' resiliency. This study explores how it is beneficial to the distribution systems in regards to the reduction of size, controllability, reliability, resiliency, and end-use applications. Moreover, the different component types and broad applications have also been discussed pertaining to their characteristics. Lastly, the conclusion gives a brief summary, and the possible direction of future research is presented, which will be useful for researchers and engineers working in future microgrids and smart grids. This review will guide to select appropriate components to develop an SST for a particular application.
Mora, A, Cardenas, R, Aguilera, RP, Angulo, A, Lezana, P & Lu, DD-C 2021, 'Predictive Optimal Switching Sequence Direct Power Control for Grid-Tied 3L-NPC Converters', IEEE Transactions on Industrial Electronics, vol. 68, no. 9, pp. 8561-8571.
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A model predictive control (MPC) strategy based on optimal switching sequence concepts is presented for direct power control of grid-connected three-level neutral-point clamped converters. The proposed control strategy explicitly considers the modulator in its formulation along with the model of the system. Through two well-formulated optimal control problems, the proposed strategy is shown to optimally achieve control of the average trajectory of the active and reactive powers as well as the dc-link capacitor voltages without using weighting factors to tradeoff both control targets. Experimental results demonstrate this strategy produces improved steady-state performance with a well-defined output voltage spectrum and fixed-switching frequency while maintaining the inherent fast dynamic responses of MPC strategies.
Nan, Y, Huang, X, Gao, X & Guo, YJ 2021, '3-D Terahertz Imaging Based on Piecewise Constant Doppler Algorithm and Step- Frequency Continuous-Wave Signaling', IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 8, pp. 6771-6783.
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Nasir, AA, Tuan, HD, Ngo, HQ, Duong, TQ & Poor, HV 2021, 'Cell-Free Massive MIMO in the Short Blocklength Regime for URLLC', IEEE Transactions on Wireless Communications, vol. 20, no. 9, pp. 5861-5871.
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This paper considers cell-free massive MIMO (cfm-MIMO) for downlink ultra reliable and low-latency communication (URLLC). At the time of writing, cfm-MIMO has only been considered for communication in the long blocklength regime (LBR), whose throughput is determined by the Shannon capacity with the interference treated as Gaussian noise. Conjugate beamforming (CB) is often used as it requires only local channel state information (CSI) for implementation but its design is based on a large-scale nonconvex problem, which is computationally intractable. The rate function in URLLC is much more complex than the Shannon rate function. The paper proposes a special class of CB, which admits a low-scale optimization formulation for computational tractability. Accordingly, a new path-following algorithm, which generates a sequence of better feasible points and converges at least to a locally optimal solution, is developed for optimizing URLLC rates and cfm-MIMO energy efficiency. Furthermore, the paper also develops improper Gaussian signaling to improve both the Shannon rate and URLLC rate.
Nasir, AA, Tuan, HD, Nguyen, HH, Debbah, M & Poor, HV 2021, 'Resource Allocation and Beamforming Design in the Short Blocklength Regime for URLLC', IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 1321-1335.
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Providing ultra reliable and low-latency communication (URLLC) is considered one of the major challenges for wireless communication networks. This article considers a downlink URLLC system in which a base station (BS) serves multiple single-antenna users in the short blocklength regime. With the objective of maximizing the users' minimum rate, three different optimization problems are considered: (i) joint design of bandwidth and power allocation for the case of a single-antenna BS; (ii) beamforming design for the case of a multiple-antenna BS; and (iii) design of power allocation with regularized zero-forcing beamforming for the case of a multiple-antenna BS. In the short blocklength regime, the achievable rate is a complicated function of bandwidth and power allocation coefficients or beamforming vectors, which makes these max-min rate optimization problems challenging to solve. This work develops path-following algorithms, which generate a sequence of improved feasible points and converge at least to a locally optimal solution, to solve these three optimization problems. Performance of the proposed algorithms is analyzed through extensive simulations under various settings of transmit power budget, number of users, total bandwidth, transmission time, and number of transmit antennas at the BS. Simulation results clearly demonstrate the merits of the proposed algorithms.
Nayak, A, Rayguru, MM, Mishra, S & Hossain, MJ 2021, 'A Quantitative Approach for Convergence Analysis of a Singularly Perturbed Inverter-Based Microgrid', IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 3016-3030.
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Ngo, CQ, Chai, R, Jones, TW & Nguyen, HT 2021, 'The Effect of Hypoglycemia on Spectral Moments in EEG Epochs of Different Durations in Type 1 Diabetes Patients', IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 8, pp. 2857-2865.
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The potential of using an electroencephalogram (EEG) to detect hypoglycemia in patients with type 1 diabetes (T1D) has been investigated in both time and frequency domains. Under hyperinsulinemic hypoglycemic clamp conditions, we have shown that the brain's response to hypoglycemic episodes could be described by the centroid frequency and spectral gyration radius evaluated from spectral moments of EEG signals. The aim of this paper is to investigate the effect of hypoglycemia on spectral moments in EEG epochs of different durations and to propose the optimal time window for hypoglycemia detection without using clamp protocols. The incidence of hypoglycemic episodes at night time in five T1D adolescents was analyzed from selected data of ten days of observations in this study. We found that hypoglycemia is associated with significant changes (P < 0.05) in spectral moments of EEG segments in different lengths. Specifically, the changes were more pronounced on the occipital lobe. We used effect size as a measure to determine the best EEG epoch duration for the detection of hypoglycemic episodes. Using Bayesian neural networks, this study showed that 30 second segments provide the best detection rate of hypoglycemia. In addition, Clarke's error grid analysis confirms the correlation between hypoglycemia and EEG spectral moments of this optimal time window, with 86% of clinically acceptable estimated blood glucose values. These results confirm the potential of using EEG spectral moments to detect the occurrence of hypoglycemia.
Ngo, QT, Phan, KT, Xiang, W, Mahmood, A & Slay, J 2021, 'On Edge Caching in Satellite — IoT Networks', IEEE Internet of Things Magazine, vol. 4, no. 4, pp. 107-112.
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Nguyen, HT, Hoang, DT, Luong, NC, Niyato, D & Kim, DI 2021, 'A Hierarchical Game Model for OFDM Integrated Radar and Communication Systems', IEEE Transactions on Vehicular Technology, vol. 70, no. 5, pp. 5077-5082.
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This paper studies the spectrum allocation problem between spectrum service providers (SSPs) and terminals equipped with orthogonal frequency division multiplexing (OFDM) integrated radar and communication (IRC) systems. In particular, IRC-equipped terminals such as autonomous vehicles need to buy spectrum for their radar functions, e.g., sensing and detecting distant vehicles, and communication functions, e.g., transmitting sensing data to road-side units. The terminals determine their spectrum demands from the SSPs subject to their IRC performance requirements, while the SSPs compete with each other on the service prices to attract terminals. Taking into account the complicated interactions, a hierarchical Stackelberg game is proposed to reconcile the spectrum demand and service price, where the SSPs are the leaders and the terminals are the followers. Due to the spectrum constraints of the SSPs, we model the lower-layer subgame among the terminals as a generalized Nash equilibrium problem. An iterative searching algorithm is then developed that guarantees the convergence to the Stackelberg equilibrium. Numerical results demonstrate the effectiveness of our proposed scheme in terms of social welfare compared to baseline schemes.
Nguyen, HT, Tuan, HD, Niyato, D, Kim, DI & Vincent Poor, H 2021, 'Improper Gaussian Signaling for D2D Communication Coexisting MISO Cellular Networks', IEEE Transactions on Wireless Communications, vol. 20, no. 8, pp. 5186-5198.
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Nguyen, LD, Tuan, HD, Duong, TQ, Poor, HV & Hanzo, L 2021, 'Energy-Efficient Multi-Cell Massive MIMO Subject to Minimum User-Rate Constraints', IEEE Transactions on Communications, vol. 69, no. 2, pp. 914-928.
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The capability of massive multiple-input multiple-output (mMIMO) systems supporting the throughput requirement of as many users as possible is investigated. The bottleneck of serving small numbers of users by a large number of transmit antennas in conventional mMIMO is unblocked by a new time-fraction-wise beamforming technique, which focuses signal transmission in fractions of a time slot. Based on this time-fraction-wise signal transmission, a new user service scheduling scheme for multi-cell mMIMO, whose cell-edge users suffer not only poor channel conditions but also multi-cell interference, is proposed to support a large user-population. We demonstrate that the numbers of users served by our multi-cell mMIMO within a time-slot may be as high as twice the number of its transmit antennas.
Nguyen, LV, Phung, MD & Ha, QP 2021, 'Iterative Learning Sliding Mode Control for UAV Trajectory Tracking', Electronics, vol. 10, no. 20, pp. 2474-2474.
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This paper presents a novel iterative learning sliding mode controller (ILSMC) that can be applied to the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) subject to model uncertainties and external disturbances. Here, the proposed ILSMC is integrated in the outer loop of a controlled system. The control development, conducted in the discrete-time domain, does not require a priori information of the disturbance bound as with conventional SMC techniques. It only involves an equivalent control term for the desired dynamics in the closed loop and an iterative learning term to drive the system state toward the sliding surface to maintain robust performance. By learning from previous iterations, the ILSMC can yield very accurate tracking performance when a sliding mode is induced without control chattering. The design is then applied to the attitude control of a 3DR Solo UAV with a built-in PID controller. The simulation results and experimental validation with real-time data demonstrate the advantages of the proposed control scheme over existing techniques.
Nguyen, NHT, Perry, S, Bone, D, Le, HT & Nguyen, TT 2021, 'Two-stage convolutional neural network for road crack detection and segmentation', Expert Systems with Applications, vol. 186, pp. 115718-115718.
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Nguyen, N-T, Nguyen, DN, Hoang, DT, Van Huynh, N, Dutkiewicz, E, Nguyen, N-H & Nguyen, Q-T 2021, 'Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-Based IoT Networks', IEEE Transactions on Wireless Communications, vol. 20, no. 10, pp. 6835-6851.
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This article studies the strategic interactions between an IoT service provider (IoTSP) which consists of heterogeneous IoT devices and its energy service provider (ESP). To that end, we propose an economic framework using the Stackelberg game to maximize the network throughput and energy efficiency of both the IoTSP and ESP. To obtain the Stackelberg equilibrium (SE), we apply a backward induction technique which first derives a closed-form solution for the ESP (follower). Then, to tackle the non-convex optimization problem for the IoTSP (leader), we leverage the block coordinate descent and convex-concave procedure techniques to design two partitioning schemes (i.e., partial adjustment (PA) and joint adjustment (JA)) to find the optimal energy price and service time that constitute local SEs. Numerical results reveal that by jointly optimizing the energy trading and time allocation for IoT devices, one can achieve significant improvements in terms of the IoTSP's profit compared with those of conventional transmission methods (up to 38.7 folds). Different tradeoffs between the ESP's and IoTSP's profits and complexities of the PA/JA schemes can also be numerically tuned. Simulations also show that the obtained local SEs approach the optimal social welfare when the benefit per transmitted bit exceeds a given threshold.
Nguyen-Ky, T, Tuan, HD, Savkin, A, Do, MN & Van, NTT 2021, 'Real-Time EEG Signal Classification for Monitoring and Predicting the Transition Between Different Anaesthetic States', IEEE Transactions on Biomedical Engineering, vol. 68, no. 5, pp. 1450-1458.
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Quantitative identification of the transitions between anaesthetic states is very essential for optimizing patient safety and quality care during surgery but poses a very challenging task. The state-of-the-art monitors are still not capable of providing their manifest variables, so the practitioners must diagnose them based on their own experience. The present paper proposes a novel real-time method to identify these transitions. Firstly, the Hurst method is used to pre-process the de-noised electro-encephalograph (EEG) signals. The maximum of Hurst's ranges is then accepted as the EEG real-time response, which induces a new real-time feature under moving average framework. Its maximum power spectral density is found to be very differentiated into the distinct transitions of anaesthetic states and thus can be used as the quantitative index for their identification.
Ni, W, Song, S-P & Jiang, Y-D 2021, 'Association between routine hematological parameters and sudden sensorineural hearing loss: A meta-analysis', Journal of Otology, vol. 16, no. 1, pp. 47-54.
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Ni, Z, Zhang, JA, Huang, X, Yang, K & Yuan, J 2021, 'Uplink Sensing in Perceptive Mobile Networks With Asynchronous Transceivers', IEEE Transactions on Signal Processing, vol. 69, no. 99, pp. 1287-1300.
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Nie, X, Takalkar, MA, Duan, M, Zhang, H & Xu, M 2021, 'GEME: Dual-stream multi-task GEnder-based micro-expression recognition', Neurocomputing, vol. 427, pp. 13-28.
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© 2020 Elsevier B.V. Recognition of micro-expressions remains a topic of concern considering its brief span and low intensity. This issue is addressed through convolutional neural networks (CNNs) by developing multi-task learning (MTL) method to effectively leverage a side task: gender detection. A dual-stream multi-task framework called GEME is introduced that recognises micro-expressions by incorporating unique gender characteristics and subsequently improves the micro-expression recognition accuracy. This research aims to examine how gender differences influence the way micro-expressions are displayed. The current study proves that selecting relevant features of micro-expressions distinctive to the gender and added to the micro-expression features improves the micro-expression recognition accuracy. This network learns gender-specific features and micro-expression features and adds them together to learn the combination of shared and task-specific representations. A multi-class focal loss is used to mitigate the class imbalance issue by down-weighing the easy samples and concentrate more on misclassified samples. The Class-Balanced (CB) focal loss is also implemented for a better class balancing during Leave-One-Subject-Out (LOSO) validations where CB loss re-balances and re-weights the loss. The experimental results on three widely used databases demonstrate the improved performance of the proposed network and achieve comparable results with the state-of-the-art methods.
Nizami, MSH, Hossain, MJ & Mahmud, K 2021, 'A Coordinated Electric Vehicle Management System for Grid-Support Services in Residential Networks', IEEE Systems Journal, vol. 15, no. 2, pp. 2066-2077.
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Nizami, MSH, Hossain, MJ & Mahmud, K 2021, 'A Nested Transactive Energy Market Model to Trade Demand-Side Flexibility of Residential Consumers', IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 479-490.
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Ojo, O 2021, 'In Praise of JESTPE Associate Editors—Part IV', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 6, pp. 6455-6459.
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Palanisamy, A, Siwakoti, YP, Mahajan, A, Long, T, Kashani, OF & Blaabjerg, F 2021, 'A transformerless three‐level three‐phase boost PWM inverter for PV applications', IET Power Electronics, vol. 14, no. 10, pp. 1768-1778.
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Perez, MA, Ceballos, S, Konstantinou, G, Pou, J & Aguilera, RP 2021, 'Modular Multilevel Converters: Recent Achievements and Challenges', IEEE Open Journal of the Industrial Electronics Society, vol. 2, pp. 224-239.
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The modular multilevel converter (MMC) is currently one of the power converter topologies which has attracted more research and development worldwide. Its features, such as high quality of voltages and currents, high modularity and high voltage rating, have made the MMC a very good option for several applications including high-voltage dc (HVdc) transmission, static compensators (STATCOMs), and motor drives. However, its unique features such as the large number of submodules, floating capacitor voltages, and circulating currents require a dedicated control system able to manage the terminal variables, as well as the internal variables with high dynamical performance. In this paper, a review of the research and development achieved during the last years on MMCs is shown, focusing on the challenges and proposed solutions for this power converter still faces in terms of modeling, control, reliability, power topologies, and new applications.
Pham, Q-V, Nguyen, DC, Mirjalili, S, Hoang, DT, Nguyen, DN, Pathirana, PN & Hwang, W-J 2021, 'Swarm intelligence for next-generation networks: Recent advances and applications', Journal of Network and Computer Applications, vol. 191, pp. 103141-103141.
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Phung, MD & Ha, QP 2021, 'Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization', Applied Soft Computing, vol. 107, pp. 107376-107376.
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Qashlan, A, Nanda, P, He, X & Mohanty, M 2021, 'Privacy-Preserving Mechanism in Smart Home Using Blockchain', IEEE Access, vol. 9, pp. 103651-103669.
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The IoT, or Internet of Things has been a major talking point amongst technology enthusiasts in recent years. The internet of thing (IoT) has been emerged and evolved rapidly, making the world's fabric around us smarter and more responsive. The smart home uses one such transformation of IoT, which seems to be the wave of the future. However, with the increasing wide adoption of IoT, data security, and privacy concerns about how our data is collected and shared with others, has also risen. To solve these challenges, an approach to data privacy and security in a smart home using blockchain technology is proposed in this paper. We propose authentication scheme that combines attribute-based access control with smart contracts and edge computing to create a secure framework for IoT devices in smart home systems. The edge server adds scalability to the system by offloading heavy processing activities and using a differential privacy method to aggregate data to the cloud securely and privately. We present several aspects of testing and implementing smart contracts, the differential private stochastic gradient descent algorithm, and system architecture and design. We demonstrate the efficacy of our proposed system by fully examining its security and privacy goals in terms of confidentiality, integrity, and availability. Our framework achieves desired security and privacy goals and is resilient against modification, DoS attacks, data mining and linkage attacks. Finally, we undertake a performance evaluation to demonstrate the proposed scheme's feasibility and efficiency.
Qi, H, Yue, H, Zhang, J & Lo, KL 2021, 'Optimisation of a smart energy hub with integration of combined heat and power, demand side response and energy storage', Energy, vol. 234, pp. 121268-121268.
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Qian, J, Begum, H, Song, Y & Lee, JE-Y 2021, 'Plug-and-play acoustic tweezer enables droplet centrifugation on silicon superstrate with surface multi-layered microstructures', Sensors and Actuators A: Physical, vol. 321, pp. 112432-112432.
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Qian, J, Ren, J, Huang, W, Lam, RHW & Lee, JE-Y 2021, 'Acoustically Driven Manipulation of Microparticles and Cells on a Detachable Surface Micromachined Silicon Chip', IEEE Sensors Journal, vol. 21, no. 10, pp. 11999-12008.
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Rafique, S, Hossain, MJ, Nizami, MSH, Irshad, UB & Mukhopadhyay, SC 2021, 'Energy Management Systems for Residential Buildings With Electric Vehicles and Distributed Energy Resources', IEEE Access, vol. 9, pp. 46997-47007.
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Rahman, ML, Zhang, JA, Huang, X, Guo, YJ & Lu, Z 2021, 'Gaussian-Mixture-Model Based Clutter Suppression in Perceptive Mobile Networks', IEEE Communications Letters, vol. 25, no. 1, pp. 152-156.
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Raj Kafle, Y, Hossain, MJ & Kashif, M 2021, 'Quasi‐Z‐source ‐based bidirectional DC‐DC converters for renewable energy applications', International Transactions on Electrical Energy Systems, vol. 31, no. 4.
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Roselin, AG, Nanda, P, Nepal, S & He, X 2021, 'Intelligent Anomaly Detection for Large Network Traffic With Optimized Deep Clustering (ODC) Algorithm', IEEE Access, vol. 9, no. 99, pp. 47243-47251.
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Rufangura, P, Khodasevych, I, Agrawal, A, Bosi, M, Folland, TG, Caldwell, JD & Iacopi, F 2021, 'Enhanced Absorption with Graphene-Coated Silicon Carbide Nanowires for Mid-Infrared Nanophotonics', Nanomaterials, vol. 11, no. 9, pp. 2339-2339.
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The mid-infrared (MIR) is an exciting spectral range that also hosts useful molecular vibrational fingerprints. There is a growing interest in nanophotonics operating in this spectral range, and recent advances in plasmonic research are aimed at enhancing MIR infrared nanophotonics. In particular, the design of hybrid plasmonic metasurfaces has emerged as a promising route to realize novel MIR applications. Here we demonstrate a hybrid nanostructure combining graphene and silicon carbide to extend the spectral phonon response of silicon carbide and enable absorption and field enhancement of the MIR photon via the excitation and hybridization of surface plasmon polaritons and surface phonon polaritons. We combine experimental methods and finite element simulations to demonstrate enhanced absorption of MIR photons and the broadening of the spectral resonance of graphene-coated silicon carbide nanowires. We also indicate subwavelength confinement of the MIR photons within a thin oxide layer a few nanometers thick, sandwiched between the graphene and silicon carbide. This intermediate shell layer is characteristically obtained using our graphitization approach and acts as a coupling medium between the core and outer shell of the nanowires.
Salehpour, MJ, Alishavandi, AM, Hossain, MJ, Hosseini Rostami, SM, Wang, J & Yu, X 2021, 'A stochastic decentralized model for the privately interactive operation of a multi-carrier energy system', Sustainable Cities and Society, vol. 64, pp. 102551-102551.
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Sang, L, Xu, M, Qian, S & Wu, X 2021, 'Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network', Neurocomputing, vol. 454, pp. 417-429.
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Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem in recommender system. However, existing KG based recommendation methods mainly rely on handcrafted meta-path features or simple triple-level entity embedding, which cannot automatically capture entities’ long-term relational dependencies for the recommendation. Specially, entity embedding learning is not properly designed to combine user-item interaction information with KG context information. In this paper, a two-channel neural interaction method named Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network (KGNCF-RRN) is proposed, which leverages both long-term relational dependencies KG context and user-item interaction for recommendation. (1) For the KG context interaction channel, we propose Residual Recurrent Network (RRN) to construct context-based path embedding, which incorporates residual learning into traditional recurrent neural networks (RNNs) to efficiently encode the long-term relational dependencies of KG. The self-attention network is then applied to the path embedding to capture the polysemy of various user interaction behaviours. (2) For the user-item interaction channel, the user and item embeddings are fed into a newly designed two-dimensional interaction map. (3) Finally, above the two-channel neural interaction matrix, we employ a convolutional neural network to learn complex correlations between user and item. Extensive experimental results on three benchmark datasets show that our proposed approach outperforms existing state-of-the-art approaches for knowledge graph based recommendation.
Sang, L, Xu, M, Qian, S & Wu, X 2021, 'Knowledge graph enhanced neural collaborative recommendation', Expert Systems with Applications, vol. 164, pp. 113992-113992.
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Existing neural collaborative filtering (NCF) recommendation methods suffer from severe sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem. However, pure NCF models can hardly model the high-order connectivity in KG, and ignores complex pairwise correlations between user/item embedding dimensions. To address these problems, we propose a novel Knowledge graph enhanced Neural Collaborative Recommendation (K-NCR) framework, which effectively combines user–item interaction information and auxiliary knowledge information for recommendation task into three parts: (1) For items, the proposed propagating model learns the representation of item entity. It recursively aggregates information from its multi-hop neighbours in KG, and employs an attention mechanism to discriminate the importance of the relation type to mine users’ potential preferences. (2) For users, another heterogeneous attention weights are leveraged to strengthen the embedding learning of users. (3) The user and item embeddings are then fed into a newly designed two-dimensional interaction map with convolutional hidden layers to model the complex pairwise correlations between their embedding dimensions explicitly. Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our K-NCR framework.
Sang, L, Xu, M, Qian, S, Martin, M, Li, P & Wu, X 2021, 'Context-Dependent Propagating-Based Video Recommendation in Multimodal Heterogeneous Information Networks', IEEE Transactions on Multimedia, vol. 23, pp. 2019-2032.
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Santra, SB, Chatterjee, D, Siwakoti, YP & Blaabjerg, F 2021, 'Generalized Switch Current Stress Reduction Technique for Coupled-Inductor-Based Single-Switch High Step-Up Boost Converter', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 2, pp. 1863-1875.
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Sarker, PC, Guo, Y, Lu, HY & Zhu, JG 2021, 'Measurement and Modeling of Rotational Core Loss of Fe-Based Amorphous Magnetic Material Under 2-D Magnetic Excitation', IEEE Transactions on Magnetics, vol. 57, no. 11, pp. 1-8.
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Fe-based amorphous magnetic materials are recently attracting strong interests for constructing high-power density and high-efficiency rotating electrical machines due to their attractive properties, such as low core loss and high magnetic saturation. Accurate measurement and modeling of the rotational core losses of the core magnetic materials, and the corresponding patterns of rotating magnetic flux density ( $B$ ) and magnetic field strength ( $H$ ) are important for the analysis and design of electrical machines. This article presents the measurement of rotational core loss of a Fe-based amorphous magnetic material (amorphous 1k101), and its corresponding modelings under two-dimensional (2-D) circularly and elliptically rotating magnetic fields. In addition, an improved and simplified analogical model of rotational hysteresis loss is proposed for such magnetic materials. The circular and elliptical $B$ loci and the corresponding $H$ loci have been investigated to acquire the perception of anisotropy and permeability of the amorphous materials. The proposed theory and models are experimentally verified.
Sayem, ASM, Simorangkir, RBVB, Esselle, KP, Thalakotuna, DN & Lalbakhsh, A 2021, 'An Electronically-Tunable, Flexible, and Transparent Antenna With Unidirectional Radiation Pattern', IEEE Access, vol. 9, pp. 147042-147053.
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Seifollahi, S, Piccardi, M & Jolfaei, A 2021, 'An Embedding-Based Topic Model for Document Classification', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 20, no. 3, pp. 1-13.
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Topic modeling is an unsupervised learning task that discovers the hidden topics in a collection of documents. In turn, the discovered topics can be used for summarizing, organizing, and understanding the documents in the collection. Most of the existing techniques for topic modeling are derivatives of the Latent Dirichlet Allocation which uses a bag-of-word assumption for the documents. However, bag-of-words models completely dismiss the relationships between the words. For this reason, this article presents a two-stage algorithm for topic modelling that leverages word embeddings and word co-occurrence. In the first stage, we determine the topic-word distributions by soft-clustering a random set of embedded n -grams from the documents. In the second stage, we determine the document-topic distributions by sampling the topics of each document from the topic-word distributions. This approach leverages the distributional properties of word embeddings instead of using the bag-of-words assumption. Experimental results on various data sets from an Australian compensation organization show the remarkable comparative effectiveness of the proposed algorithm in a task of document classification.
Sekuboyina, A, Husseini, ME, Bayat, A, Löffler, M, Liebl, H, Li, H, Tetteh, G, Kukacka, J, Payer, C, Stern, D, Urschler, M, Chen, M, Cheng, D, Lessmann, N, Hu, Y, Wang, T, Yang, D, Xu, D, Ambellan, F, Amiranashvili, T, Ehlke, M, Lamecker, H, Lehnert, S, Lirio, M, Olaguer, NPD, Ramm, H, Sahu, M, Tack, A, Zachow, S, Jiang, T, Ma, X, Angerman, C, Wang, X, Brown, K, Kirszenberg, A, Puybareau, É, Chen, D, Bai, Y, Rapazzo, BH, Yeah, T, Zhang, A, Xu, S, Hou, F, He, Z, Zeng, C, Xiangshang, Z, Liming, X, Netherton, TJ, Mumme, RP, Court, LE, Huang, Z, He, C, Wang, L-W, Ling, SH, Huynh, LD, Boutry, N, Jakubícek, R, Chmelík, J, Mulay, S, Sivaprakasam, M, Paetzold, JC, Shit, S, Ezhov, I, Wiestler, B, Glocker, B, Valentinitsch, A, Rempfler, M, Menze, BH & Kirschke, JS 2021, 'VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images.', Medical Image Anal., vol. 73, pp. 102166-102166.
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Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.
Shahid, I, Thalakotuna, D, Karmokar, DK, Mahon, SJ & Heimlich, M 2021, 'Periodic Structures for Reconfigurable Filter Design: A Comprehensive Review', IEEE Microwave Magazine, vol. 22, no. 11, pp. 38-51.
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Shahid, I, Thalakotuna, DN, Karmokar, DK, Mahon, SJ & Heimlich, M 2021, 'A Compact Reconfigurable 1-D Periodic Structure in GaAs MMIC With Stopband Switching, Dual-Band Operation and Tuning Capabilities', IEEE Access, vol. 9, pp. 142084-142094.
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Shan, X, Wang, F, Wang, D, Wen, S, Chen, C, Di, X, Nie, P, Liao, J, Liu, Y, Ding, L, Reece, PJ & Jin, D 2021, 'Optical tweezers beyond refractive index mismatch using highly doped upconversion nanoparticles', Nature Nanotechnology, vol. 16, no. 5, pp. 531-537.
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Optical tweezers are widely used in materials assembly1, characterization2, biomechanical force sensing3,4 and the in vivo manipulation of cells5 and organs6. The trapping force has primarily been generated through the refractive index mismatch between a trapped object and its surrounding medium. This poses a fundamental challenge for the optical trapping of low-refractive-index nanoscale objects, including nanoparticles and intracellular organelles. Here, we report a technology that employs a resonance effect to enhance the permittivity and polarizability of nanocrystals, leading to enhanced optical trapping forces by orders of magnitude. This effectively bypasses the requirement of refractive index mismatch at the nanoscale. We show that under resonance conditions, highly doping lanthanide ions in NaYF4 nanocrystals makes the real part of the Clausius-Mossotti factor approach its asymptotic limit, thereby achieving a maximum optical trap stiffness of 0.086 pN μm-1 mW-1 for 23.3-nm-radius low-refractive-index (1.46) nanoparticles, that is, more than 30 times stronger than the reported value for gold nanoparticles of the same size. Our results suggest a new potential of lanthanide doping for the optical control of the refractive index of nanomaterials, developing the optical force tag for the intracellular manipulation of organelles and integrating optical tweezers with temperature sensing and laser cooling7 capabilities.
Shen, B, Chen, X, Fu, L, Zhang, M, Chen, Y, Sheng, J, Huang, Z, Wang, W, Zhai, Y, Yuan, Y, Soomro, WA, Guo, Y, Bian, X, Liu, H, Ozturk, Y, Tian, M, Hao, L, Hu, J, Wei, H, Shah, A, Patel, I, Yang, J & Coombs, T 2021, 'A Simplified Model of the Field Dependence for HTS Conductor on Round Core (CORC) Cables', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-5.
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Shen, B, Chen, X, Fu, L, Zhang, M, Jiang, S, Sheng, J, Wang, W, Zhai, Y, Yuan, Y, Gao, S, Soomro, WA, Guo, Y, Wang, S, Li, C, Bian, X, Liu, H, Zheng, Z, Li, C, Zhang, R, Ozturk, Y, Liu, Y, Yang, J & Coombs, T 2021, 'Losses in the Saturated Iron-Core Superconducting Fault Current Limiter For VSC-HVDC System', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-5.
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This paper presents the loss analysis on the saturated iron-core superconducting fault current limiter (SISFCL) for a VSC-HVDC transmission system. The numerical model of SISFCL as well as its loss calculation on superconducting parts were carried out by the finite-element method (FEM) using the H-formulation merged into the commercial package COMSOL. The SISFCL model was established for a practical ±10 kV VSC-HVDC system, and the fault current situation was simulated using the PSCAD with a SISFCL. The capability of fault current limiting was verified using the analysis of electromagnetic characteristics, and the corresponding patterns of magnetic field in the iron-core were studied. During the process of fault current limiting, the instantaneous power losses in the superconducting components were studied with the increasing DC bias current. Even in a DC grid system, results proved there were considerable amounts of losses occurred in the superconducting parts, when the SISFCL encountered the fault currents.
Shen, J, Wang, Y & Zhang, J 2021, 'ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution', Mobile Networks and Applications, vol. 26, no. 1, pp. 13-26.
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Sheng, Z, Tuan, HD, Duong, TQ & Hanzo, L 2021, 'UAV-Aided Two-Way Multi-User Relaying', IEEE Transactions on Communications, vol. 69, no. 1, pp. 246-260.
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Unmanned aerial vehicle (UAV)-aided two-way relaying networks are designed, where a UAV is deployed to assist multiple pairs of users in their information exchange. There are two basic approaches for the user pairs' information exchange within a single time slot via the UAV relay. The first approach is based on full-duplex, where all participants operate in the full-duplex mode to transmit and receive signals simultaneously. However, all transceivers have to operate in the face of severe self-interference, which cannot be completely suppressed. The second approach is based on conventional half-duplex, where the users send their information to the UAV within a certain fraction of the time slot, and the UAV relays them within the remaining fraction to avoid the self-interference. In either approach, the joint bandwidth and power allocation maximizing the sum information exchange throughput under realistic resource and user throughput constraints poses a complex nonconvex problem. New inner approximations are proposed for developing path-following algorithms for their computation. Our numerical results show that the time-fraction-based half-duplex approach clearly outperforms the high-complexity full-duplex approach.
Sheng, Z, Tuan, HD, Nasir, AA, Poor, HV & Dutkiewicz, E 2021, 'Physical Layer Security Aided Wireless Interference Networks in the Presence of Strong Eavesdropper Channels', IEEE Transactions on Information Forensics and Security, vol. 16, pp. 3228-3240.
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Under both long (infinite) and short (finite) blocklength transmissions, this paper considers physical layer security for a wireless interference network of multiple transmitter-user pairs, which is overheard by multiple eavesdroppers (EVs). The EVs are assumed to have better channel conditions than the legitimate users (UEs), making the conventional transmission unsecured. The paper develops a novel time-fraction based transmission, under which the information is transmitted to the UEs within a fraction of the time slot and artificial noise (AN) is transmitted within the remaining fraction to counter the strong EVs' channels. Based on channel distribution information of UEs and EVs, the joint design of transmit beamforming, time fractions and AN power allocation to maximize the worst users' secrecy rate is formulated in terms of nonconvex problems. Path-following algorithms of low complexity and rapid convergence are proposed for their solution. Simulations are provided to demonstrate the viability of the proposed methodology.
Shi, Y, Tuan, HD, Savkin, AV & Poor, HV 2021, 'Model predictive control for on–off charging of electrical vehicles in smart grids', IET Electrical Systems in Transportation, vol. 11, no. 2, pp. 121-133.
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Over the next decade, a massive number of plug‐in electric vehicles (PEVs) will need to be integrated into current power grids. This is likely to give rise to unmanageable fluc-tuations in power demand and unacceptable deviations in voltage. These negative impacts are difficult to mitigate because PEVs connect and disconnect from the grid randomly and each type of PEVs has different charging profiles. This paper presents a solution to these problems that involves coordination of power grid control and PEV charging. The proposed strategy minimises the overall costs of charging and power generation in meeting future increases in PEV charging demand and the operational constraints of the power grid. The solution is based on an on–off PEV charging strategy that is easy and convenient to implement online. The joint coordination problem is formulated by a mixed integer non‐linear programming (MINP) with binary charging and continuous voltage variables and is solved by a highly novel computational algorithm. Its online implementation is based on a new model predictive control method that is free from prior assumptions about PEVs' arrival and charging information. Comprehensive simu-lations are provided to demonstrate the efficiency and practicality of the proposed methods.
Shi, Y, Tuan, HD, Savkin, AV, Lin, C-T, Zhu, JG & Poor, HV 2021, 'Distributed model predictive control for joint coordination of demand response and optimal power flow with renewables in smart grid', Applied Energy, vol. 290, pp. 116701-116701.
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Shi, Z, Xu, M & Pan, Q 2021, '4-D Flight Trajectory Prediction With Constrained LSTM Network', IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 11, pp. 7242-7255.
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The increasing aviation activities pose a challenge to ensure a safe and orderly flight. Trajectory prediction is one of the most important forecasting tasks in Air Traffic Management. Accurate prediction is reasonable for safe and orderly flight tasks in civil aviation monitoring. Points of interests play an important role in most land traffic prediction algorithms due to their abilities in positioning and marking. Compared with land traffic, the sparse way-points and shared airways make it difficult for flight trajectory prediction. A constrained Long Short-Term Memory network for flight trajectory prediction is proposed in this paper. According to the dynamic characteristics of the aircraft, we propose three kinds of constraints to climbing, cruising, and descending/approaching phases, in particular, they are Top of climb, Way-points, and Runway direction, correspondingly. Our model is able to keep long-term dependencies with dynamic physical constraints. Density-Based Spatial Clustering of Applications with Noise and Linear Least Squares are used in data segmentation and preprocessing. Sliding windows help maintain the continuity of trajectory. Four-dimensional spatial-temporal trajectory set consisting of spatial position and timestamps is used to prove the efficiency of our approach. Multiple ADS-B ground stations contribute to our experimental dataset. The widely used Long Short-Term Memory network, Markov Model, weighted Markov Model, Support Vector Machine, and Kalman Filter are used for comparison. Quantitative analysis demonstrates that our model outperforms the above-mentioned state-of-the-art models, and lays a good foundation for decision-making in different scenarios.
Singh, K, Afzal, MU & Esselle, KP 2021, 'Designing Efficient Phase-Gradient Metasurfaces for Near-Field Meta-Steering Systems', IEEE Access, vol. 9, pp. 109080-109093.
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We investigate the aptness of various $4^{th}$ order (90°) rotationally symmetric phase-transforming cells for the upper phase-gradient metasurface, which always receives an oblique incidence wave from the lower metasurface in a Near-Field Meta-Steering system. A comprehensive study on the behavior of various phase-transforming cells and corresponding supercells when a rotating oblique plane wave impinges on them is presented. First, we select the supercell with high transmission in the desired output Floquet modes, for both TE and TM input modes, when an oblique incidence wave is rotated. The selected supercell is then optimized using Floquet analysis in conjunction with particle swarm optimization (PSO). All the undesired modes are successfully suppressed below -32 dB in the optimized supercell, and the predicted broadside radiation pattern is free of spurious grating lobes. A Near-Field Meta-Steering system with an aperture diameter of $7.3\lambda _{0}$ (110mm @ 20 GHz) is presented. It has a pair of optimized phase-gradient metasurfaces and a dipole antenna array. A maximum peak directivity of 24.2 dB is achieved when the beam is in the broadside direction. The proposed steering system is capable of scanning a conical range with an apex angle of 126° when a 6 dB reduction in peak directivity is allowed. For a 3 dB variation in the peak directivity, the corresponding apex angle is 103°.
Song, L-Z, Qin, P-Y & Guo, YJ 2021, 'A High-Efficiency Conformal Transmitarray Antenna Employing Dual-Layer Ultrathin Huygens Element', IEEE Transactions on Antennas and Propagation, vol. 69, no. 2, pp. 848-858.
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Song, L-Z, Qin, P-Y, Chen, S-L & Guo, YJ 2021, 'An Elliptical Cylindrical Shaped Transmitarray for Wide-Angle Multibeam Applications', IEEE Transactions on Antennas and Propagation, vol. 69, no. 10, pp. 7023-7028.
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A transmitarray antenna with an elliptical cylindrical shape is presented for a wide-angle multibeam radiation in this paper. The transmitarray has a cylindrical radiating aperture with an elliptical cross section, namely, elliptical cylindrical shape. Multiple feeds can be placed on the middle horizontal plane to realize multiple beams. Inspired by a two-dimensional (2-D) Ruze lens, the antenna shape and the phase compensation are jointly designed according to the desired maximal beam direction. Innovative methods including a feed refocusing analysis and a virtual focal length are utilized to achieve the phase compensation across the three-dimensional (3-D) aperture for multiple beam radiations with a small scanning loss. In order to validate the proposed antenna, a prototype operating in the millimeter-wave E band has been designed, fabricated and measured. By changing the position of the feeding gain horn along the refocusing arc, the main beam of antenna can be scanned to eleven directions. The measured peak boresight realized gain is 27 dBi at 70.5 GHz and a beam coverage of ±43° with a less than 2.7-dB scanning loss is obtained.
Song, L-Z, Qin, P-Y, Maci, S & Guo, YJ 2021, 'Ultrawideband Conformal Transmitarray Employing Connected Slot-Bowtie Elements', IEEE Transactions on Antennas and Propagation, vol. 69, no. 6, pp. 3273-3283.
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IEEE In contrast to using multi-layer frequency selective surface (FSS) elements or tightly coupled elements, a novel technique is introduced here to achieve ultrawideband planar and conformal transmitarrays, which are based on connected elements like those used in connected arrays. In particular, the elements consist of a horizontally connected slot-bowtie and vertical meander slot-lines. The elements are capable of achieving a 360° phase variation range at the highest frequency 17 GHz with the transmission loss less than 3 dB from 6 GHz to 17 GHz. The transmitarrays have been designed, fabricated and measured in both planar and conformal versions. Stable boresight radiation patterns from 6 GHz to 17 GHz have been obtained for both antennas. Compared to conformal transmitarrays using multi-layer FSS elements, the proposed solution has a much wider bandwidth of radiation patterns. Good agreement has been found between simulation and measurement.
Srinivasan, M, Gopi, S, Kalyani, S, Huang, X & Hanzo, L 2021, 'Airplane-Aided Integrated Next-Generation Networking', IEEE Transactions on Vehicular Technology, vol. 70, no. 9, pp. 9345-9354.
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A high-rate yet low-cost air-to-ground (A2G) communication backbone is conceived for integrating the space and terrestrial network by harnessing the opportunistic assistance of the passenger planes or high altitude platforms (HAPs) as mobile base stations (BSs) and millimetre wave communication. The airliners act as the network-provider for the terrestrial users while relying on satellite backhaul. Three different beamforming techniques relying on a large-scale planar array are used for transmission by the airliner/HAP for achieving a high directional gain, hence minimizing the interference among the users. Furthermore, approximate spectral efficiency (SE) and area spectral efficiency (ASE) expressions are derived and quantified for diverse system parameters.
Sun, JX, Lin, F, Zhou, XY & Zhu, X 2021, 'Design of 74% Fractional Bandwidth Continuous-Mode Doherty Power Amplifier Using Compensation Susceptance', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 6, pp. 1827-1831.
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IEEE This paper presents a novel design method of broadband continuous-mode Doherty power amplifier (CM-DPA) using compensation susceptance. In particular, at power back-off point, the compensation susceptance is added at the current source plane of the carrier PA to make it operates in continuous mode, which thus enhances the back-off efficiency and extends the bandwidth. Meanwhile, considering the influence of the parasitic parameters of the PA in practical design, the designed compensation susceptance at the current source plane is equivalently transformed into the compensation susceptance at the package plane of the carrier PA. Additionally, at saturation point, the load impedances at the package plane of both carrier and peaking PAs can meet the impedance conditions of drain efficiency more than 60%. Based on the proposed method, a 74 fractional bandwidth CM-DPA operating from 1.1 to 2.4 GHz is designed and measured. Under the continuous-wave excitation, the measured 6-dB back-off efficiency is 46.8%-63% and the saturation efficiency is 59.1%-78.8%. The measured small-signal gain and back-off gain are 10-12 dB and 9.1-10 dB, respectively.
Sun, X, Cao, J, Lei, G, Guo, Y & Zhu, J 2021, 'A Composite Sliding Mode Control for SPMSM Drives Based on a New Hybrid Reaching Law With Disturbance Compensation', IEEE Transactions on Transportation Electrification, vol. 7, no. 3, pp. 1427-1436.
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Sun, X, Cao, J, Lei, G, Guo, Y & Zhu, J 2021, 'A Robust Deadbeat Predictive Controller With Delay Compensation Based on Composite Sliding-Mode Observer for PMSMs', IEEE Transactions on Power Electronics, vol. 36, no. 9, pp. 10742-10752.
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This article proposes an improved deadbeat predictive controller for permanent-magnet synchronous motor drive systems. It can eliminate the influence of the parameter mismatch of inductance, resistance, and flux linkage. First, the performance of the conventional predictive current method is investigated to analyze sensitivities of the electric parameters. Then, a composite sliding-mode disturbance observer (SMDO) based on the stator current and lumped disturbance is proposed, which can simultaneously estimate the future current value and lumped disturbance caused by the parameter mismatch of inductance, resistance, and flux linkage. Based on the discrete-time SMDO, currents are estimated and used to replace the sampled values to compensate one-step delay caused by the calculation and sampling delay. Both simulation and experimental performances of the proposed method have been validated and compared with the conventional control methods under different conditions. The comparison results show the superiority of the proposed predictive current control method based on the composite SMDO.
Sun, X, Diao, K, Lei, G, Guo, Y & Zhu, J 2021, 'Direct Torque Control Based on a Fast Modeling Method for a Segmented-Rotor Switched Reluctance Motor in HEV Application', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 1, pp. 232-241.
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Sun, X, Li, T, Yao, M, Lei, G, Guo, Y & Zhu, J 2021, 'Improved Finite-Control-Set Model Predictive Control with Virtual Vectors for PMSHM Drives', IEEE Transactions on Energy Conversion, vol. PP, no. 99, pp. 1-1.
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Finite-control-set model predictive current control (FCS-MPCC) always has large steady-state fluctuation and computational burden. In this paper, a novel FCS-MPCC without a modulator to drive permanent magnet synchronous hub motors (PMSHMs), which combines virtual vectors expansion scheme and duty cycle control was proposed. The lack of a modulator reduces the complexity of the control system. The virtual vectors are synthesized by using active vectors, which improve the accuracy of voltage selection, and further improve PMSHMs steady-state performance and reduce current harmonics. The duty cycle control uses a zero vector to obtain better steady-state performance. However, the duty cycle of the virtual vectors is limited by the synthesis method, and further analysis is needed. A new calculation process is proposed to reduce the amount of calculation. The deadbeat principle is used to get reference voltage which determines sectors. Then, the best voltage vector in the selected sector is determined by the predetermined cost function. The traditional MPCC and the duty cycle MPCC (DCMPCC) are used as a comparison item to compare with the proposed method to illustrate its effectiveness. Results confirm that improved MPCC has good steady-state performance while maintaining a fast dynamic response.
Sun, X, Li, T, Zhu, Z, Lei, G, Guo, Y & Zhu, J 2021, 'Speed Sensorless Model Predictive Current Control Based on Finite Position Set for PMSHM Drives', IEEE Transactions on Transportation Electrification, vol. 7, no. 4, pp. 2743-2752.
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As an efficient control strategy, model predictive current control (MPCC) has rapid response and simple calculation. This paper proposes an improved MPCC scheme for permanent-magnet synchronous hub motor (PMSHM) drives. The mentioned control scheme uses the parameter values at the last moment to obtain the back electromotive force (EMF) and utilizes the obtained back EMF to obtain the predicted current value at the next moment. In the actual application of the motor, to enhance the robustness of the control system, a sliding mode controller is used to replace the conventional PI speed loop, and a finite position phase-locked loop based on the dichotomy is added to achieve sensorless speed control and provide an accurate rotor position angle. To improve the steady-state performance, the method of duty cycle is introduced, and the null vector and the actual vector are used together in the same control cycle. The simulation and experimental results both show the effectiveness of the proposed MPCC scheme, and the steady-state performance of MPCC is greatly improved compared with traditional MPCC.
Sun, X, Shi, Z & Zhu, J 2021, 'Multiobjective Design Optimization of an IPMSM for EVs Based on Fuzzy Method and Sequential Taguchi Method', IEEE Transactions on Industrial Electronics, vol. 68, no. 11, pp. 10592-10600.
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The Taguchi optimization method is an efficient method for motor design optimization. However, it is hard to handle the multiobjective motor optimization problem with big design space for the parameters. To deal with this problem, in this article, a fuzzy method and sequential Taguchi method to optimize an inter permanent magnet synchronous motor (IPMSM) is employed. The fuzzy inference system is introduced to convert the multiple objectives to a single-objective optimization problem. The sequential Taguchi method is used to optimize the structural parameters at multiple levels to improve the accuracy of optimization. After the optimal selection analysis, the best combination of motor structure factors is obtained. By comparing the optimization result of the proposed method with that of the conventional Taguchi optimization method, the effectiveness and superiority of the proposed method are verified.
Sun, X, Shi, Z, Lei, G, Guo, Y & Zhu, J 2021, 'Multi-Objective Design Optimization of an IPMSM Based on Multilevel Strategy', IEEE Transactions on Industrial Electronics, vol. 68, no. 1, pp. 139-148.
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The multiobjective optimization design of interior permanent magnet synchronous motors (IPMSMs) is a challenge due to the high dimension and huge computation cost of finite element analysis. This article presents a new multilevel optimization strategy for efficient multiobjective optimization of an IPMSM. To determine the multilevel optimization strategy, Pearson correlation coefficient analysis and cross-factor variance analysis techniques are employed to evaluate the correlations of design parameters and optimization objectives. A three-level optimization structure is obtained for the investigated IPMSM based on the analysis results, and different optimization parameters and objectives are assigned to different levels. To improve the optimization efficiency, the Kriging model is employed to approximate the finite element analysis for the multiobjective optimization in each level. It is found that the proposed method can provide optimal design schemes with a better performance, such as smaller torque ripple and lower power loss for the investigated IPMSM, while the needed computation cost is reduced significantly. Finally, experimental results based on a prototype are provided to validate the effectiveness of the proposed optimization method. The proposed method can be applied for the efficient multiobjective optimization of other electrical machines with high dimensions.
Sun, X, Wan, B, Lei, G, Tian, X, Guo, Y & Zhu, J 2021, 'Multiobjective and Multiphysics Design Optimization of a Switched Reluctance Motor for Electric Vehicle Applications', IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 3294-3304.
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Switched reluctance motors (SRMs) have attracted much attention in industry due to the advantages of low cost, robust structure, high fault tolerance and high torque density. However, several disadvantages like high torque ripples and coil temperatures hinder their industrialization for some applications requiring high dynamic performance, like electric vehicles (EVs). In this paper, a multiobjective and multiphysics design optimization method considering both thermal and electromagnetic performance is presented for a 12/10 SRM. First, the topology of the SRM is introduced and the optimal parameters are defined. Then, the electromagnetic finite element model (FEM) is introduced and the improved transient lumped-parameter thermal model (TLPTM), considering both axial and radial heat transfer for the SRM, is proposed. Second, the objectives and constraints of the optimization are determined. To improve the optimization efficiency, the sequential subspace optimization strategy is employed to find the optimal solution of this high-dimensional design optimization problem. Finally, to validate the effectiveness of the proposed method, both simulation and experimental results are given and discussed. Compared with the initial design, the optimal solution exhibits lower temperature, higher torque, lower torque ripple and less loss.
Sun, X, Wu, J, Lei, G, Cai, Y, Chen, X & Guo, Y 2021, 'Torque Modeling of a Segmented-Rotor SRM Using Maximum-Correntropy-Criterion-Based LSSVR for Torque Calculation of EVs', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 3, pp. 2674-2684.
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Sun, X, Wu, J, Lei, G, Guo, Y & Zhu, J 2021, 'Torque Ripple Reduction of SRM Drive Using Improved Direct Torque Control With Sliding Mode Controller and Observer', IEEE Transactions on Industrial Electronics, vol. 68, no. 10, pp. 9334-9345.
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The industrial application of the switched reluctance motor (SRM) is limited by its high torque ripples caused by the doubly salient structure. In this article, an improved direct torque control (DTC) with sliding mode controller and observer is developed to reduce the torque ripples of a four-phase SRM. First, a sliding mode controller based on a new reaching law is developed for designing a sliding mode speed controller (SMSC) for the DTC system. An antidisturbance sliding mode observer (ADSMO) is then proposed and combined with the SMSC to build a composite antidisturbance speed control strategy. Moreover, detailed simulation validations are carried out to reveal the effectiveness of the new reaching law, SMSC and ADSMO. Finally, experiments are conducted to verify the performance of the proposed SMSC-ADSMO in a DTC system with a four-phase SRM prototype.
Sun, X, Wu, M, Lei, G, Guo, Y & Zhu, J 2021, 'An Improved Model Predictive Current Control for PMSM Drives Based on Current Track Circle', IEEE Transactions on Industrial Electronics, vol. 68, no. 5, pp. 3782-3793.
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Model predictive current control (MPCC) is a high-performance control strategy for permanent-magnet synchronous motor (PMSM) drives, with the features of quick response and simple computation. However, the conventional MPCC results in high torque and current ripples. This article proposes an improved MPCC scheme for PMSM drives. In the proposed scheme, the back electromotive force is estimated from the previous stator voltage and current, and it is used to predict the stator current for the next period. To further improve the steady state and dynamic performance, the proposed MPCC selects the optimal voltage vector based on a current track circle instead of a cost function. Compared with the calculation of cost function, the prediction of the current track circle is simple and quick. The proposed MPCC is compared with conventional MPCC and a duty-circle based MPCC by simulation and experiment in the aspect of converter output voltage and sensitivity analysis. Results prove the superiority of the proposed MPCC and its effectiveness in reducing the torque and current ripples of PMSM drives.
Sun, Z, Yao, Y, Xiao, J, Zhang, L, Zhang, J & Tang, Z 2021, 'Exploiting textual queries for dynamically visual disambiguation', Pattern Recognition, vol. 110, pp. 107620-107620.
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© 2020 Due to the high cost of manual annotation, learning directly from the web has attracted broad attention. One issue that limits the performance of current webly supervised models is the problem of visual polysemy. In this work, we present a novel framework that resolves visual polysemy by dynamically matching candidate text queries with retrieved images. Specifically, our proposed framework includes three major steps: we first discover and then dynamically select the text queries according to the keyword-based image search results, we employ the proposed saliency-guided deep multi-instance learning (MIL) network to remove outliers and learn classification models for visual disambiguation. Compared to existing methods, our proposed approach can figure out the right visual senses, adapt to dynamic changes in the search results, remove outliers, and jointly learn the classification models. Extensive experiments and ablation studies on CMU-Poly-30 and MIT-ISD datasets demonstrate the effectiveness of our proposed approach.
Suraweera, N, Winter, A, Sorensen, J, Li, S, Johnson, M, Collings, IB, Hanly, SV, Ni, W & Hedley, M 2021, 'Passive Through-Wall Counting of People Walking Using WiFi Beamforming Reports', IEEE Systems Journal, vol. 15, no. 4, pp. 5476-5482.
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This article develops a system for through-wall counting of people walking in a room, based purely on passive reception of the WiFi signals that are generated by devices in that room. We use WiFi compressed beamforming reports, collected using a sniffer node located outside the room. We propose a 2-D discrete Fourier transform (2D DFT) approach for feature extraction. As such, we formulate the counting problem as a multiclass image classification problem. Our proposed system achieves accuracies of 100%, 97.8%, 78.3%, and 93.9% in field trials with zero, one, two, and three people walking inside a room, respectively, even for rooms that were not part of the training set.
Takalkar, MA, Thuseethan, S, Rajasegarar, S, Chaczko, Z, Xu, M & Yearwood, J 2021, 'LGAttNet: Automatic micro-expression detection using dual-stream local and global attentions', Knowledge-Based Systems, vol. 212, pp. 106566-106566.
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© 2020 Elsevier B.V. Research in the field of micro-expressions has gained significance in recent years. Many researchers have concentrated on classifying micro-expressions in different discrete emotion classes, while detecting the presence of micro-expression in the video frames is considered as a pre-requisite step in the recognition process. Hence, there is a need to introduce more advanced detection models for micro-expressions. In order to address this, we propose a dual attention network based micro-expression detection architecture called LGAttNet. LGAttNet is one of the first to utilize a dual attention network grouped with 2-dimensional convolutional neural network to perform frame-wise automatic micro-expression detection. This method divides the feature extraction and enhancement task into two different convolutional neural network modules; sparse module and feature enhancement module. One of the key modules in our approach is the attention network which extracts local and global facial features, namely local attention module and global attention module. The attention mechanism adopts the human characteristic of focusing on the specific regions of micro-movements, which enables the LGAttNet to concentrate on particular facial regions along with the full facial features to identify the micro-expressions in the frames. Experiments performed on widely used publicly available databases demonstrate the robustness and superiority of our LGAttNet when compared to state-of-the-art approaches.
Tang, Q, Yang, J, He, X, Jia, W, Zhang, Q & Liu, H 2021, 'Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion', Computer Vision and Image Understanding, vol. 202, pp. 103086-103086.
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© 2020 Elsevier Inc. Haze removal from nighttime images is more difficult compared with daytime image dehazing due to the uneven illumination, low contrast and severe color distortion. In this paper, following the approaches based on Dark channel prior, we propose a simple yet effective approach using Retinex theory and Taylor series expansion for nighttime image dehazing, referred to as ‘RDT’. Existing nighttime image dehazing methods do not handle color shift and glow removal very well. In order to address these issues, we first propose to decompose the atmospheric light image from the input image based on the Retinex theory. Taylor series expansion is then introduced for the first time to accurately estimate the pointwise transmission map. Finally, during the following processes of image fusion and color transfer, the atmospheric light image and potential haze-free image are adopted to obtain the final haze-free image. The experimental results on benchmark nighttime haze images demonstrate the superior performance of our proposed RDT dehazing method over the state-of-the-art methods.
Tang, Q, Yang, J, Liu, H, Guo, Z & Jia, W 2021, 'Single image deraining using Context Aggregation Recurrent Network', Journal of Visual Communication and Image Representation, vol. 75, pp. 103039-103039.
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Single image deraining is a challenging problem due to the presence of non-uniform rain densities and the ill-posedness of the problem. Moreover, over-/under-deraining can directly impact the performance of vision systems. To address these issues, we propose an end-to-end Context Aggregation Recurrent Network, called CARNet, to remove rain streaks from single images. In this paper, we assume that a rainy image is the linear combination of a clean background image with rain streaks and propose to take advantage of the context information and feature reuse to learn the rain streaks. In our proposed network, we first use the dilation technique to effectively aggregate context information without sacrificing the spatial resolution, and then leverage a gated subnetwork to fuse the intermediate features from different levels. To better learn and reuse rain streaks, we integrate a LSTM module to connect different recurrences for passing the information learned from the previous stages about the rain streaks to the following stage. Finally, to further refine the coarsely derained image, we introduce a refinement module to better preserve image details. As for the loss function, the L1-norm perceptual loss and SSIM loss are adopted to reduce the gridding artifacts caused by the dilated convolution. Experiments conducted on synthetic and real rainy images show that our CARNet achieves superior deraining performance both qualitatively and quantitatively over the state-of-the-art approaches.
Thomas, D, Orgun, M, Hitchens, M, Shankaran, R, Mukhopadhyay, SC & Ni, W 2021, 'A Graph-Based Fault-Tolerant Approach to Modeling QoS for IoT-Based Surveillance Applications', IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3587-3604.
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Thomas, D, Shankaran, R, Sheng, QZ, Orgun, MA, Hitchens, M, Masud, M, Ni, W, Mukhopadhyay, SC & Piran, MJ 2021, 'QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications', IEEE Access, vol. 9, pp. 3065-3096.
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Tran, T & Ha, QP 2021, 'Semi-automatic control of network systems with non-monotonic Lyapunov function', International Journal of Control, vol. 94, no. 8, pp. 2144-2160.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. A partially decentralised scheme for the semi-automatic control of network systems with stabilising agents is presented in this paper. The semi-automatic control of interconnected systems employing the stabilising agent whose installation is segregated from the associated control algorithm has been presented previously. In this development, the quadratic dissipativity constraint (QDC) associated with a non-negative supply rate is newly introduced for the stabilising agent. The closed-loop system having bounded disturbances is input-to-state stabilised with a non-monotonic Lyapunov function when the QDC is used with model predictive controllers. The effectiveness of the QDC for stabilising agents in the presented partially decentralised architecture is demonstrated via simulation studies of a frequency regulation problem in power systems.
Trede, F, Braun, R & Brookes, W 2021, 'Engineering students’ expectations and perceptions of studio-based learning', European Journal of Engineering Education, vol. 46, no. 3, pp. 402-415.
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© 2020, © 2020 SEFI. Studio-based learning is gaining currency in university engineering education programmes. It is widely argued that this practice-oriented, collaborative approach to developing professional, teamwork and interpersonal skills is needed to prepare the future workforce. In this paper, students’ expectations and perceptions of a first-year studio were explored. Data collection included baseline and follow-up interviews. Both included the rich picture method and photo-elicitation. Using critical hermeneutics interpretation, we identified three key themes: teamwork, leadership and reflection. Although studio-based learning was perceived as effortful, slow and at times even frustrating, the move away from didactic lecturing by experts to collaborative learning and building products was welcomed and endorsed by all our participants. The insight gained from this study suggests that more innovative learning and teaching approaches in engineering education may help prepare students for lifelong learning in an uncertain future world of work.
Tuan, HD, Nasir, AA, Savkin, AV, Poor, HV & Dutkiewicz, E 2021, 'MPC-Based UAV Navigation for Simultaneous Solar-Energy Harvesting and Two-Way Communications', IEEE Journal on Selected Areas in Communications, vol. 39, no. 11, pp. 3459-3474.
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The paper is the first work that considers a constrained feedback control strategy to navigate an unmanned aerial vehicle (UAV) from a given starting point to a given terminal point while harvesting solar energy and providing a wireless communication service for ground users. Wireless communication channels are stochastic and cannot be known off-line, making the problem of off-line UAV path planning for wireless communication as considered in most existing works less meaningful. We consider the problem of navigating a solar-powered UAV from a starting point to a terminal point to harvest solar energy while serving the two-way communication between multiple pairs of ground users in a complex terrain. The objective is to jointly optimize the UAV's flight time and its flight path by trading-off between the harvested energy and power consumption subject to the ground users' minimum throughput requirement. We develop a new model predictive control (MPC) technique to address this problem. Namely, based on the well-known statistics of the air-to-ground (A2G) and ground-to-air (G2A) wireless channels, a predictive control model is proposed at each time-instant, which leads to an optimization problem over a receding horizon for the control design. This problem is non-convex due to the involvement of various optimization variables, which is then solved via novel convex iterations. Simulation results show the merits of the proposed algorithm. The results obtained by the proposed algorithm match with the benchmark non-MPC and offline-MPC approaches.
Uddin, MB, Chow, CM, Ling, SH & Su, SW 2021, 'A novel algorithm for automatic diagnosis of sleep apnea from airflow and oximetry signals', Physiological Measurement, vol. 42, no. 1, pp. 015001-015001.
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Abstract Objective. Sleep apnea significantly decreases the quality of life. The apnea hypopnea index (AHI) is the main indicator for sleep apnea diagnosis. This study explored a novel automatic algorithm to diagnose sleep apnea from nasal airflow (AF) and pulse oximetry (SpO2) signals. Approach. Of the 988 polysomnography (PSG) records from the sleep heart health study (SHHS), 45 were randomly selected for the development of an algorithm and the remainder for validation (n = 943). The algorithm detects apnea events by a digitization process, following the determination of the peak excursion (peak-to-trough amplitude) from AF envelope. Hypopnea events were determined from the AF envelope and oxygen desaturation with correction to time lag in SpO2. Total sleep time (TST) was estimated from an optimized percentage of artefact-free total recording time. AHI was estimated from the number of detected events divided by the estimated TST. The estimated AHI was compared to the scored SHHS data for performance evaluation. Main results. The validation showed good agreement between the estimated and scored AHI (intraclass correlation coefficient of 0.95 and mean ±95% limits of agreement of −1.6 ±12.5 events h−1). The diagnostic accuracies were found: 90.7%, 91%, and 96.7% for AHI cut-off ≥5, ≥15, and ≥30 respectively. Significance. The new algorithm is accurate over other existing methods for the automatic diagnosis of sleep apnea. It is applicable to any portable sleep screeners especially for the home diagnosis of sleep apnea.
Van Huynh, N, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2021, 'DeepFake: Deep Dueling-Based Deception Strategy to Defeat Reactive Jammers', IEEE Transactions on Wireless Communications, vol. 20, no. 10, pp. 6898-6914.
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In this paper, we introduce DeepFake, a novel deep reinforcement learning-based deception strategy to deal with reactive jamming attacks. In particular, for a smart and reactive jamming attack, the jammer is able to sense the channel and attack the channel if it detects communications from the legitimate transmitter. To deal with such attacks, we propose an intelligent deception strategy which allows the legitimate transmitter to transmit 'fake' signals to attract the jammer. Then, if the jammer attacks the channel, the transmitter can leverage the strong jamming signals to transmit data by using ambient backscatter communication technology or harvest energy from the strong jamming signals for future use. By doing so, we can not only undermine the attack ability of the jammer, but also utilize jamming signals to improve the system performance. To effectively learn from and adapt to the dynamic and uncertainty of jamming attacks, we develop a novel deep reinforcement learning algorithm using the deep dueling neural network architecture to obtain the optimal policy with thousand times faster than those of the conventional reinforcement algorithms. Extensive simulation results reveal that our proposed DeepFake framework is superior to other anti-jamming strategies in terms of throughput, packet loss, and learning rate.
Van Huynh, N, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2021, 'Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach', IEEE Transactions on Communications, vol. 69, no. 9, pp. 5948-5961.
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In intelligent transportation systems (ITS), vehicles are expected to feature with advanced applications and services which demand ultra-high data rates and low-latency communications. For that, the millimeter wave (mmWave) communication has been emerging as a very promising solution. However, incorporating the mmWave into ITS is particularly challenging due to the high mobility of vehicles and the inherent sensitivity of mmWave beams to dynamic blockages. This article addresses these problems by developing an optimal beam association framework for mmWave vehicular networks under high mobility. Specifically, we use the semi-Markov decision process to capture the dynamics and uncertainty of the environment. The Q-learning algorithm is then often used to find the optimal policy. However, Q-learning is notorious for its slow-convergence. Instead of adopting deep reinforcement learning structures (like most works in the literature), we leverage the fact that there are usually multiple vehicles on the road to speed up the learning process. To that end, we develop a lightweight yet very effective parallel Q-learning algorithm to quickly obtain the optimal policy by simultaneously learning from various vehicles. Extensive simulations demonstrate that our proposed solution can increase the data rate by 47% and reduce the disconnection probability by 29% compared to other solutions.
Varzaneh, MG, Rajaei, A, Forouzesh, M, Siwakoti, YP & Blaabjerg, F 2021, 'A Single-Stage Multi-Port Buck-Boost Inverter', IEEE Transactions on Power Electronics, vol. 36, no. 7, pp. 7769-7782.
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IEEE This paper presents a novel inverter topology with a multi-port structure, which aims to connect two independent DC sources to a three-phase load by using single-stage power conversion. The proposed inverter has been developed to be used in hybrid renewable energy applications such as photovoltaic (PV), fuel cell (FC), and battery energy storage systems. Compared to the conventional hybrid dual-source inverters that use a multi-input DC-DC converter to provide a DC-link voltage at the input of the inverter stage, the proposed dual-source inverter uses an integrated DC-AC power conversion stage. The conventional topologies use bulky electrolytic capacitors at the input of the inverter stage, which leads to lower voltage gain and reliability due to high parasitic ESR/ESL and short lifetime of these capacitors. Moreover, compared to existing multi-port voltage source inverters, the proposed topology uses lower semiconductors, cost, and weight and has higher voltage gain. Besides, the proposed topology draws continuous current from both input ports and there is magnetic isolation between the input sources. The analysis and performance of the proposed inverter are verified through both computer simulations and experimental results of a 600 W - 50 Hz laboratory prototype using the Simple Boost-SPWM modulation method.
Vu, TX, Chatzinotas, S, Nguyen, V-D, Hoang, DT, Nguyen, DN, Renzo, MD & Ottersten, B 2021, 'Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance', IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3710-3722.
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We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M <; N. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD algorithm overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tests all valid antenna subsets. Although approaching (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between key system parameters and the selected antennas. The proposed L-ASPD algorithm is robust against the number of users and their locations, the transmit power of the BS, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD algorithm significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves a better effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD algorithm can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.
Wang, L, Zhang, T, Ye, L, Li, JJ & Su, SW 2021, 'An Efficient Calibration Method for Triaxial Gyroscope', IEEE Sensors Journal, vol. 21, no. 18, pp. 19896-19903.
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This paper presents an efficient servomotor-aided calibration method for the triaxial gyroscope. The entire calibration process only requires approximately one minute, and does not require high-precision equipment. This method is based on the idea that the measurement of the gyroscope should be equal to the rotation speed of the servomotor. A six-observation experimental design is proposed to minimize the maximum variance of the estimated scale factors and biases. In addition, a fast converging recursive linear least square estimation method is presented to reduce computational complexity. The simulation results reflect the robustness of the calibration method under normal and extreme conditions. We experimentally demonstrate the feasibility of the proposed method on a robot arm, and implement the method on a microcontroller. We verify the calibration results of the proposed method by comparing with a traditional turntable approach, and the experiment indicates that the results of these two methods are comparable. By comparing the calibrated low-cost gyroscope reading with the reading from a high-precision gyroscope, we can conclude that our method significantly increases the gyroscope's accuracy.
Wang, M, Zhu, J, Guo, L, Wu, J & Shen, Y 2021, 'Analytical Calculation of Complex Relative Permeance Function and Magnetic Field in Slotted Permanent Magnet Synchronous Machines', IEEE Transactions on Magnetics, vol. 57, no. 3, pp. 1-9.
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Wang, S, Lv, T, Ni, W, Beaulieu, NC & Guo, YJ 2021, 'Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach', IEEE Transactions on Wireless Communications, vol. 20, no. 9, pp. 5672-5688.
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This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users.
Wang, S, Ma, J, Liu, C, Wang, Y, Lei, G, Guo, Y & Zhu, J 2021, 'Design and performance analysis of a novel PM assisted synchronous reluctance machine', International Journal of Applied Electromagnetics and Mechanics, vol. 67, no. 2, pp. 131-140.
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This paper proposes a novel permanent magnet assisted synchronous reluctance (PMAREL) machine, the main structure of this machine is quite similar to that of traditional PMAREL machine, and the main difference is that the grain-oriented silicon steel is used to replace some part of the stator teeth. The rolling direction of the grain-oriented silicon steel is along the radial direction of the machine, thus the advantage of higher permeability and higher kneel point in this material can be used to release the flux saturation problem of the traditional non-grain-oriented steel used in the PMAREL machine when the applied current density is high. Firstly, the structure of both proposed novel and traditional PMAREL machines are optimized and the design parameters are determined. Secondly the electromagnetic and mechanical performance are compared in these two machines which includes the demagnetization analysis, mechanical stress analysis when the rotor at the maximum speed, torque ability, efficiency by using the finite element method (FEM). It can be seen that the problem of stator teeth saturation in the novel PMAREL has been alleviated, and compared with the traditional PMAREL machine, the novel PMAREL has higher efficiency, wider speed range and 7% higher torque ability.
Wang, S, Tao, J, Qiu, X & Burnett, IS 2021, 'Broadband noise insulation of windows using coiled-up silencers consisting of coupled tubes', Scientific Reports, vol. 11, no. 1, pp. 1-9.
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AbstractIt has been demonstrated that a staggered window achieves better noise reduction performance than a traditional single glazing one at middle to high frequencies while maintaining a degree of natural ventilation. There is, however, little improvement in the low frequency range. In contrast, this work proposes to apply coiled-up silencers consisting of coupled tubes on the side walls of staggered windows to obtain noise attenuation in a broad band, especially in the low frequency range. Each element in the silencer consists of two coupled tubes with different cross sections so that noise at more frequencies can be attenuated than that with a uniform cross section. The simulation results show that 8.8 dB overall insertion loss can be obtained between 100 and 500 Hz after applying a combination of silencers designed at 7 different frequencies, and the insertion loss of the staggered window is increased from 6.7 to 15.6 dBA between 100 and 2000 Hz for normal incident traffic noise with the proposed silencers installed. The design is validated by the experiments with a 1:4 scale down model.
Wang, X, Cheng, E & Burnett, IS 2021, 'MSBOTS: a multiple small biological organism tracking system robust against non-ideal detection and segmentation conditions', PeerJ, vol. 9, pp. e11750-e11750.
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Accurately tracking a group of small biological organisms using algorithms to obtain their movement trajectories is essential to biomedical and pharmaceutical research. However, object mis-detection, segmentation errors and overlapped individual trajectories are particularly common issues that restrict the development of automatic multiple small organism tracking research. Extending on previous work, this paper presents an accurate and generalised Multiple Small Biological Organism Tracking System (MSBOTS), whose general feasibility is tested on three types of organisms. Evaluated on zebrafish, Artemia and Daphnia video datasets with a wide variety of imaging conditions, the proposed system exhibited decreased overall Multiple Object Tracking Precision (MOTP) errors of up to 77.59%. Moreover, MSBOTS obtained more reliable tracking trajectories with a decreased standard deviation of up to 47.68 pixels compared with the state-of-the-art idTracker system. This paper also presents a behaviour analysis module to study the locomotive characteristics of individual organisms from the obtained tracking trajectories. The developed MSBOTS with the locomotive analysis module and the tested video datasets are made freely available online for public research use.
Wang, X, Fei, Z, Zhang, JA, Huang, J & Yuan, J 2021, 'Constrained Utility Maximization in Dual-Functional Radar-Communication Multi-UAV Networks', IEEE Transactions on Communications, vol. 69, no. 4, pp. 2660-2672.
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IEEE In this paper, we investigate the network utility maximization problem in a dual-functional radar-communication multi-unmanned aerial vehicle (multi-UAV) network where multiple UAVs serve a group of communication users and cooperatively sense the target simultaneously. To balance the communication and sensing performance, we formulate a joint UAV location, user association, and UAV transmission power control problem to maximize the total network utility under the constraint of localization accuracy. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into three sub-problems, i.e., UAV location optimization, user association and transmission power control. Three mechanisms are then introduced to solve the three sub-problems based on spectral clustering, coalition game, and successive convex approximation, respectively. The spectral clustering result provides an initial solution for user association. Based on the three mechanisms, an overall algorithm is proposed to iteratively solve the whole problem. We demonstrate that the proposed algorithm improves the minimum user data rate significantly, as well as the fairness of the network. Moreover, the proposed algorithm increases the network utility with a lower power consumption and similar localization accuracy, compared to conventional techniques.
Wang, X, Ni, W, Zha, X, Yu, G, Liu, RP, Georgalas, N & Reeves, A 2021, 'Capacity analysis of public blockchain', Computer Communications, vol. 177, pp. 112-124.
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Wang, X, Qin, P-Y & Jin, R 2021, 'Low RCS Transmitarray Employing Phase Controllable Absorptive Frequency-Selective Transmission Elements', IEEE Transactions on Antennas and Propagation, vol. 69, no. 4, pp. 2398-2403.
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Wang, Y, Cui, W, Liu, R, Tian, Y, Ni, W & Zhou, C 2021, 'Silicone Oil–Associated Extensive Intraocular Ossification: A case report', European Journal of Ophthalmology, vol. 31, no. 5, pp. NP53-NP56.
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Background: Intraocular ossification is an uncommon calcium deposition process associated with trauma, chronic inflammation, tumor, and long-standing retinal detachment. This is the first reported extensive intraocular bone formation associated with silicone oil. Case presentation: A 30-year-old Han Chinese man came to us with complaint of red, painful blind right eye. He had a history of ocular trauma, retinal detachment, and two failed retinal reattachment surgeries with silicone oil left in the eye. On examination, conjunctiva congestion, band keratopathy, silicone oil emulsification, and limbus neovascularization were found. B-scan ultrasound and computed tomography scanning demonstrated retinal detachment and calcification of the eyeball wall. Histopathological analysis indicated ossification overlying the choroid. Evisceration was finally operated to relieve the pain. Conclusion: The retention of silicone oil in the eye probably accelerates the ossification. Timely silicone oil removal and evisceration should be recommended if necessary for phthisis bulbi.
Wang, Y, Li, S, Ni, W, Zhao, M, Jamalipour, A & Wu, B 2021, 'Cooperative Three-Dimensional Position Mapping Based on Received Signal Strength Measurements: Algorithm Design and Field Test', IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 10541-10552.
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This paper presents a new approach to accurately and efficiently identifying a large number of wireless devices blindly installed to known three-dimensional installation/fitting points, based on received signal strengths (RSSs) between the devices. The approach is non-trivial because of the factorial, mapping nature of the considered problem, the multiplicative ranging errors of the RSS measurements (with standard deviation of 5-7 dB), and the requirement of high mapping accuracy in many internet-of-things (IoT) applications, e.g., industrial IoT and aircraft. The consideration of a structured environment where the set of candidate node positions is known beforehand shifts the problem of interest from a pure position estimation problem to a position assignment problem. The key idea of the proposed approach is that we interpret the position mapping problem with a probabilistic graphical model, where the factorial nature of mapping (more specifically, the mutual exclusiveness of devices at every installation point) is fully captured. A max-product belief propagation is designed against the probabilistic graph, to estimate the max-marginal position distribution of each device. The Kuhn-Munkres algorithm is applied to preserve the mutual exclusiveness of devices throughout the belief propagation and to decide device locations based on the estimated position distributions. Large-scale simulations and field tests are carried out, showing that the new approach achieves close-to-100% accuracy in simulations with hundreds of blindfolded devices under RSS measurement errors with standard deviation of 5-7 dB. Our approach also achieves 100% accuracy in all field trials with 76 devices inside the cabin of a Fokker 100 airplane, dramatically outperforming baseline techniques.
Wang, Z, Lin, Z, Lv, T & Ni, W 2021, 'Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks With Wireless Power Transfer: A Distributed ADMM Approach', IEEE Internet of Things Journal, vol. 8, no. 18, pp. 14232-14247.
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In multicell massive multiple-input-multiple-output (MIMO) nonorthogonal multiple access (NOMA) networks, base stations with multiple antennas deliver their radio-frequency energy in the downlink, and Internet-of-Things devices use their harvested energy to support uplink data transmission. This article investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer. To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information are considered and their corresponding EE performance is analyzed. Under the Quality-of-Service requirements, an EE maximization problem is formulated, which is nontrivial due to nonconvexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex and then develop a distributed alternating direction method of multipliers-based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.
Wang, Z, Xu, M, Ye, N, Xiao, F, Wang, R & Huang, H 2021, 'Computer Vision-Assisted 3D Object Localization via COTS RFID Devices and a Monocular Camera', IEEE Transactions on Mobile Computing, vol. 20, no. 3, pp. 893-908.
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IEEE In most RFID localization systems, acquiring a reader antenna's position at each sampling time is challenging, especially for those antenna-carrying robot or drone systems with unpredictable trajectories. In this paper, we present RF-MVO that fuses RFID and computer vision for stationary RFID localization in 3D space by attaching a light-weight 2D monocular camera to two reader antennas in parallel. Firstly, the existing monocular visual odometry only recovers a camera/antenna trajectory in the camera view from 2D images. By combining it with RF phase, we design a model to estimate a scale factor for real-world trajectory transformation, along with spatial directions of an RFID tag relative to a virtual antenna array due to the mobility of each antenna. Then we propose a novel RFID localization algorithm that does not require exhaustively searching all possible positions within the pre-specified region. Secondly, to speed up the searching process and improve localization accuracy, we propose a coarse-to-fine optimization algorithm. Thirdly, we introduce the concept of horizontal dilution of precision (HDOP) to measure the confidence level of localization results. Our experiments demonstrate the effectiveness of proposed algorithms and show RF-MVO can achieve 6.23 cm localization error.
Wei, Q, Feng, D & Jia, W 2021, 'UDR: An Approximate Unbiased Difference-Ratio Edge Detector for SAR Images', IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 8, pp. 6688-6705.
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Wei, X, Xu, L, Luo, R, Cheng, M & Zhu, J 2021, 'Model Predictive Power Control of Brushless Doubly-Fed Induction Generator Considering Saturation Effect', Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, vol. 36, no. 17, pp. 3721-3729.
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This paper proposes a model predictive power control strategy based on a dynamic model and analysis of the power distribution, considering the effects of magnetic saturation of the brushless doubly-fed induction generator (BDFIG). This strategy can realize accurate power control, so as to assure the quality of control winding current and reduce the switching frequency by estimating the magnetic inductance based on the saturation effect and correcting the inductance parameters input to the controller each period. Simulation and experiments were conducted on the BDFIG control system to identify the factors influencing the saturation effect, and validate the effectiveness of the proposed control strategy.
White, SJU, Klauck, F, Trong Tran, T, Schmitt, N, Kianinia, M, Steinfurth, A, Heinrich, M, Toth, M, Szameit, A, Aharonovich, I & Solntsev, AS 2021, 'Quantum random number generation using a hexagonal boron nitride single photon emitter', Journal of Optics, vol. 23, no. 1, pp. 01LT01-01LT01.
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Abstract Quantum random number generation (QRNG) harnesses the intrinsic randomness of quantum mechanical phenomena. On-chip photonic circuitry provides a robust and versatile platform that can address and explore fundamental questions in quantum as well as classical physics. Likewise, integrated waveguide-based architectures hold the potential for intrinsically scalable, efficient and compact implementations of photonic QRNG. Here, we harness the quantum emission from the two-dimensional material hexagonal boron nitride an emerging atomically thin medium that can generate single photons on demand while operating at room temperature. By means of a customized splitter arrangement, we achieve true random number generation through the measurement of single photons exiting one of four designated output ports, and subsequently verify the randomness of the sequences in accordance with the National Institute of Standards and Technology benchmark suite. Our results clearly demonstrate the viability and efficiency of this approach to on-chip deterministic random number generators.
Wu, B, Chen, T, Ni, W & Wang, X 2021, 'Multi-Agent Multi-Armed Bandit Learning for Online Management of Edge-Assisted Computing', IEEE Transactions on Communications, vol. 69, no. 12, pp. 8188-8199.
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By orchestrating resources of edge and core network, the delays of edge-assisted computing can decrease. Offloading scheduling is challenging though, especially in the presence of many edge devices with randomly varying link and computing conditions. This paper presents a new online learning-based approach to the offloading scheduling, where multi-agent multi-armed bandit (MA-MAB) learning is designed to exploit the randomly varying conditions and asymptotically minimize the computing delay. We first propose a combinatorial bandit upper confidence bound (CB-UCB) algorithm, where users collectively feed back the observed delays of all edge devices and links. The optimistic bound of the delay is derived to facilitate centralized offloading scheduling for all users. In addition, we put forth a distributed bandit upper confidence bound (DB-UCB) algorithm, where users take random turns to make conflict-free, distributed selections of edge devices. The optimistic confidence bound of each user is developed to allow the user's selection only based on its own observations and decisions. Furthermore, we establish the asymptotic optimality of the proposed algorithms by proving the sublinearity of their regrets, and that the random turns the users take to make decisions do not compromise the asymptotic optimality of the DB-UCB algorithm, as corroborated by numerical simulations.
Wu, J, Huang, Y, Wu, Q, Gao, Z, Zhao, J & Huang, L 2021, 'Dual-Stream Guided-Learning via a Priori Optimization for Person Re-identification', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 17, no. 4, pp. 1-22.
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The task of person re-identification (re-ID) is to find the same pedestrian across non-overlapping camera views. Generally, the performance of person re-ID can be affected by background clutter. However, existing segmentation algorithms cannot obtain perfect foreground masks to cover the background information clearly. In addition, if the background is completely removed, some discriminative ID-related cues (i.e., backpack or companion) may be lost. In this article, we design a dual-stream network consisting of a Provider Stream (P-Stream) and a Receiver Stream (R-Stream). The R-Stream performs an a priori optimization operation on foreground information. The P-Stream acts as a pusher to guide the R-Stream to concentrate on foreground information and some useful ID-related cues in the background. The proposed dual-stream network can make full use of the a priori optimization and guided-learning strategy to learn encouraging foreground information and some useful ID-related information in the background. Our method achieves Rank-1 accuracy of 95.4% on Market-1501, 89.0% on DukeMTMC-reID, 78.9% on CUHK03 (labeled), and 75.4% on CUHK03 (detected), outperforming state-of-the-art methods.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2021, 'Accurate Frequency Estimation With Fewer DFT Interpolations Based on Padé Approximation', IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp. 7267-7271.
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Frequency estimation is a fundamental problem in many areas. The well-known A&M and its variant estimators have established an estimation framework by iteratively interpolating the discrete Fourier transform (DFT) coefficients. In general, previous estimators require two DFT interpolations per iteration, have uneven initial estimation performance against frequencies, and are incompetent for small sample numbers due to low-order approximations involved. Exploiting the iterative estimation framework of A&M, we unprecedentedly introduce the Pad Approximation to frequency estimation, unveil some features about the updating function used for refining the estimation in each iteration, and develop a simple closed-form solution to solving the residual estimation error. Extensive simulation results are provided, validating the superiority of the new estimator over the state-the-art estimators in wide ranges of key parameters.
Wu, K, Zhang, JA, Huang, X, Guo, YJ & Yuan, J 2021, 'Reliable Frequency-Hopping MIMO Radar-Based Communications With Multi-Antenna Receiver', IEEE Transactions on Communications, vol. 69, no. 8, pp. 5502-5513.
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Frequency-hopping (FH) MIMO radar is recently introduced as an underlying system for realizing dual-function radar-communication (DFRC), increasing communication symbol rates to multiples of the radar pulse repetition frequency. As a newly conceived DFRC system, many realistic issues, such as channel estimation and synchronization, are not effectively solved yet. In this paper, we develop a multi-antenna receiver-based downlink communication scheme for the FH-MIMO DFRC, addressing the above issues in multi-path channels. By exploring the unique FH-MIMO radar waveform, we suppress both inter-antenna and inter-hop interference, and introduce minimal constraints on the radar waveform to facilitate DFRC. We then develop accurate estimation methods for timing offset and channel parameters. These methods are further employed to design reliable demodulation methods. We also derive performance bounds for the proposed estimation methods and embedded communications. Simulation results validate the efficacy of our receiving scheme, showing that the performance of estimators and data communications approaches analytical bounds.
Wu, L, Xu, M, Sang, L, Yao, T & Mei, T 2021, 'Noise Augmented Double-Stream Graph Convolutional Networks for Image Captioning', IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 8, pp. 3118-3127.
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Wu, M, Sun, X, Zhu, J, Lei, G & Guo, Y 2021, 'Improved Model Predictive Torque Control for PMSM Drives Based on Duty Cycle Optimization', IEEE Transactions on Magnetics, vol. 57, no. 2, pp. 1-5.
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Wu, S, Pei, Q, Ni, W, Fu, X, Zhang, W, Song, C, Peng, Y, Guo, Q, Dong, J & Yao, M 2021, 'HSPA1A Protects Cells from Thermal Stress by Impeding ESCRT-0–Mediated Autophagic Flux in Epidermal Thermoresistance', Journal of Investigative Dermatology, vol. 141, no. 1, pp. 48-58.e3.
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Wu, S, Yan, Y, Tang, H, Qian, J, Zhang, J, Dong, Y & Jing, X-Y 2021, 'Structured discriminative tensor dictionary learning for unsupervised domain adaptation', Neurocomputing, vol. 442, pp. 281-295.
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Unsupervised domain adaptation aims at learning a classification model robust to data distribution shift between a labeled source domain and an unlabeled target domain. Most existing approaches have overlooked the multi-dimensional nature of visual data, building classification models in vector space. Meanwhile, the issue of limited training samples is rarely considered by previous methods, yet it is ubiquitous in practical visual applications. In this paper, we develop a structured discriminative tensor dictionary learning method (SDTDL), which enables domain matching in tensor space. SDTDL produces disentangled and transferable representations by explicitly separating domain-specific factor and class-specific factor in data. Classification is achieved based on sample reconstruction fidelity and distribution alignment, which is seamlessly integrated into tensor dictionary learning. We evaluate SDTDL on cross-domain object and digit recognition tasks, paying special attention to the scenarios of limited training samples and test beyond training sample set. Experimental results show that our method outperforms existing mainstream shallow approaches and representative deep learning methods by a significant margin.
Xi, Y, Jia, W, Zheng, J, Fan, X, Xie, Y, Ren, J & He, X 2021, 'DRL-GAN: Dual-Stream Representation Learning GAN for Low-Resolution Image Classification in UAV Applications', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, no. 99, pp. 1705-1716.
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CCBY Identifying tiny objects from extremely low resolution (LR) UAV-based remote sensing images is generally considered as a very challenging task, because of very limited information in the object areas. In recent years, there have been very limited attempts to approach this problem. These attempts intend to deal with LR image classification by enhancing either the poor image quality or image representations.In this paper, we argue that the performance improvement in LR image classification is affected by the inconsistency of the information loss and learning priority on Low-Frequency (LF) components and High-Frequency (HF) components.To address this LF-HF inconsistency problem, we propose a Dual-Stream Representation Learning Generative Adversarial Network (DRL-GAN).The core idea is to produce super image representations optimal for LR recognition by simultaneously recovering the missing information in LF and HF components, respectively, under the guidance of high-resolution (HR) images.We evaluate the performance of DRL-GAN on the challenging task of LR image classification.A comparison of the experimental results on the LR benchmark, namely HRSC and CIFAR-10, and our newly collected “WIDER-SHIP” dataset demonstrates the effectiveness of our DRL-GAN, which significantly improves the classification performance, with up to 10% gain on average.
Xia, Y, Ni, W, Wang, X, Yu, Y, Zheng, Q & Huang, X 2021, 'Exploring a molecular switch for dopamine oxidation induced by charge reversal using scanning electrochemical microscopy', Journal of Electroanalytical Chemistry, vol. 895, pp. 115470-115470.
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Xiao, M, Zuo, Y, Li, Y, Zhu, J, Li, Y & Zhu, L 2021, 'Core Loss Calculation of Anode Saturable Reactor in Damping Oscillation State Based on J-A Theory', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-4.
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Anode saturable reactor (ASR) is an important part of the HVDC converter valve. Due to the surge current during the transient processes of thyristors, a sharp increase will occur in core losses and over temperature-rising of the reactor, which may cause premature failure and is difficult to estimate in the design optimization of the reactors. To investigate the single core loss of anode saturable reactor, during such operating conditions, this paper proposes an approach to calculate the core loss from the Jiles-Atherton (J-A) dynamic hysteresis model, which can be applied for various operating conditions with good accuracy. Finally, the model is incorporated into the finite element method (FEM) to investigate the core loss distribution of the iron core during the oscillation process at off-state.
Xiao, Q, Liu, B, Li, Z, Ni, W, Yang, Z & Li, L 2021, 'Progressive Data Augmentation Method for Remote Sensing Ship Image Classification Based on Imaging Simulation System and Neural Style Transfer', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 9176-9186.
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Xie, C, Yang, Q, Huang, Y, Su, S, Xu, T & Song, R 2021, 'A Hybrid Arm-Hand Rehabilitation Robot With EMG-Based Admittance Controller', IEEE Transactions on Biomedical Circuits and Systems, vol. 15, no. 6, pp. 1332-1342.
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Xie, H, Zheng, J, Chai, R & Nguyen, HT 2021, 'Robust tracking control of a differential drive wheeled mobile robot using fast nonsingular terminal sliding mode', Computers & Electrical Engineering, vol. 96, pp. 107488-107488.
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Differential drive wheeled mobile robots (DDWMRs) have been widely used in various applications due to their maneuverability and energy-saving characteristics. Tracking control of such mechanical systems with nonholonomic constraints in the presence of uncertainties and disturbances has attracted much attention. In this paper, a robust control scheme is developed for trajectory tracking control of a DDWMR in the presence of parametric uncertainties and disturbances. To fulfill the controller design, an inner–outer loop control structure is adopted for the DDWMR. For the inner-loop controller, a fast nonsingular terminal sliding mode (FNTSM) control law is applied for robust disturbance rejection in a finite time. Based on the kinematic model of the DDWMR, the outer-loop controller is designed for accurate trajectory tracking control. Finally, experiments are carried out to validate that the proposed control scheme has more robust tracking accuracy and faster response than an existing robust method.
Xie, J, Ma, Z, Chang, D, Zhang, G & Guo, J 2021, 'GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Xie, J, Ma, Z, Lei, J, Zhang, G, Xue, J-H, Tan, Z-H & Guo, J 2021, 'Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Xie, J, Ma, Z, Xue, J-H, Zhang, G, Sun, J, Zheng, Y & Guo, J 2021, 'DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference in Image Recognition', IEEE Transactions on Image Processing, vol. 30, pp. 9208-9219.
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Xu, W, Junejo, AK, Liu, Y, Hussien, MG & Zhu, J 2021, 'An Efficient Antidisturbance Sliding-Mode Speed Control Method for PMSM Drive Systems', IEEE Transactions on Power Electronics, vol. 36, no. 6, pp. 6879-6891.
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Xu, W, Li, X, Zhu, J & Wang, Q 2021, '3-D Modeling and Testing of a Stator-Magnet Transverse-Flux Linear Oscillatory Machine for Direct Compressor Drive', IEEE Transactions on Industrial Electronics, vol. 68, no. 9, pp. 8474-8486.
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A stator-magnet transverse-flux linear oscillatory machine is proposed for direct compressor drive. The robust transverse-flux structure with permanent magnets embedded in the stator yoke and a moving-iron translator can yield high reliability and is relatively simple to fabricate. For electromagnetic performance analysis, a linear model under the no-load condition and a nonlinear model under the loaded condition are developed by taking into account the axial leakage flux and saturation effects of iron core, respectively. The effectiveness and accuracy of the proposed analytical models are verified by comparing the results with those of the finite element analysis and the static experimental tests. Based on the measured static characteristics and damping coefficient, a system kinetic model is developed in the form of coupled equivalent electromechanical circuit, and validated by the results of dynamic test on a prototype. The key indices of the new machine are compared with those of an existing moving-magnet linear oscillatory machine, including the amount of permanent magnet usage, efficiency, and thrust density, etc. The case study results show that the proposed linear oscillatory machine is suitable for linear compressor drives.
Xu, W, Zhang, Y, Du, G, He, M & Zhu, J 2021, 'No-Load Performance Analysis of an Asymmetric-Pole Single-Phase Doubly Salient Permanent Magnet Machine', IEEE Transactions on Industrial Electronics, vol. 68, no. 4, pp. 2907-2918.
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Xu, Y, Afshar, S, Wang, R, Cohen, G, Singh Thakur, C, Hamilton, TJ & van Schaik, A 2021, 'A Biologically Inspired Sound Localisation System Using a Silicon Cochlea Pair', Applied Sciences, vol. 11, no. 4, pp. 1519-1519.
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We present a biologically inspired sound localisation system for reverberant environments using the Cascade of Asymmetric Resonators with Fast-Acting Compression (CAR-FAC) cochlear model. The system exploits a CAR-FAC pair to pre-process binaural signals that travel through the inherent delay line of the cascade structures, as each filter acts as a delay unit. Following the filtering, each cochlear channel is cross-correlated with all the channels of the other cochlea using a quantised instantaneous correlation function to form a 2-D instantaneous correlation matrix (correlogram). The correlogram contains both interaural time difference and spectral information. The generated correlograms are analysed using a regression neural network for localisation. We investigate the effect of the CAR-FAC nonlinearity on the system performance by comparing it with a CAR only version. To verify that the CAR/CAR-FAC and the quantised instantaneous correlation provide a suitable basis with which to perform sound localisation tasks, a linear regression, an extreme learning machine, and a convolutional neural network are trained to learn the azimuthal angle of the sound source from the correlogram. The system is evaluated using speech data recorded in a reverberant environment. We compare the performance of the linear CAR and nonlinear CAR-FAC models with current sound localisation systems as well as with human performance.
Yan, Y, Chen, Z, Varadharajan, V, Hossain, MJ & Town, GE 2021, 'Distributed Consensus-Based Economic Dispatch in Power Grids Using the Paillier Cryptosystem', IEEE Transactions on Smart Grid, vol. 12, no. 4, pp. 3493-3502.
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Yang, B, Xiang, L, Chen, X & Jia, W 2021, 'An Online Chronic Disease Prediction System Based on Incremental Deep Neural Network', Computers, Materials & Continua, vol. 67, no. 1, pp. 951-964.
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Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network. However, due to the complexity of the human body, there are still many challenges to face in that process. One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients, online. This paper presents a novel chronic disease prediction system based on an incremental deep neural network. The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner. With time, the system can predict diabetes more and more accurately by processing the feedback information. Many diabetes prediction studies are based on a common dataset, the Pima Indians diabetes dataset, which has only eight input attributes. In order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources, we have established an in-depth cooperation with a hospital. A Chinese diabetes dataset with 575 diabetics was created. Users’ data collected by different sensors were used to train the network model. We evaluated our system using a real-world diabetes dataset to confirm its effectiveness. The experimental results show that the proposed system can not only continuously monitor the users, but also give early warning of physiological data that may indicate future diabetic ailments.
Yang, D, Zou, Y, Zhang, J & Li, G 2021, 'GID-Net: Detecting human-object interaction with global and instance dependency', Neurocomputing, vol. 444, pp. 366-377.
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© 2020 Elsevier B.V. Since detecting and recognizing individual human or object are not adequate to understand the visual world, learning how humans interact with surrounding objects becomes a core technology. However, convolution operations are weak in depicting visual interactions between the instances since they only build blocks that process one local neighborhood at a time. To address this problem, we learn from human perception in observing HOIs to introduce a two-stage trainable reasoning mechanism, referred to as GID block. GID block breaks through the local neighborhoods and captures long-range dependency of pixels both in global-level and instance-level from the scene to help detecting interactions between instances. Furthermore, we conduct a multi-stream network called GID-Net, which is a human-object interaction detection framework consisting of a human branch, an object branch and an interaction branch. Semantic information in global-level and local-level are efficiently reasoned and aggregated in each of the branches. We have compared our proposed GID-Net with existing state-of-the-art methods on two public benchmarks, including V-COCO and HICO-DET. The results have showed that GID-Net outperforms the existing best-performing methods on both the above two benchmarks, validating its efficacy in detecting human-object interactions.
Yang, T, Ding, C, Ziolkowski, RW & Guo, YJ 2021, 'An Epsilon-Near-Zero (ENZ) Based, Ultra-Wide Bandwidth Terahertz Single-Polarization Single-Mode Photonic Crystal Fiber', Journal of Lightwave Technology, vol. 39, no. 1, pp. 223-232.
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© 1983-2012 IEEE. A novel terahertz (THz) photonic crystal fiber (PCF) that yields single-polarization single-mode (SPSM) propagation over an ultra-wide bandwidth is designed and analyzed. The PCF is based upon a triangle-based lattice of air holes in a high resistivity silicon substrate with three selectively-filled rectangular slots introduced into the core area. Four air holes surrounding the core region are chosen to be loaded with an epsilon-near-zero (ENZ) material. The configuration, and the large loss of the ENZ material establish a large loss difference (LD) between the two fundamental propagating polarization modes, and any higher order modes. When the central slot of the three in the core is filled with a gain material, and the adjacent two slots are air-filled, the LD values between the one desired propagating mode, and all other modes are significantly enhanced. Consequently, essentially only the desired mode will exist in the PCF after a short propagation distance resulting in the SPSM behavior. The optimized design provides large LD values, greater than 9.4 dB/cm, over a SPSM spectrum of 0.64 THz (from 1.10 to 1.74 THz), which, to the best of our knowledge, is the widest SPSM bandwidth achieved to date in the THz regime. The unwanted modes are 30 dB smaller than the wanted mode after a 3.2 cm length of the PCF. This outcome is highly desired for polarization sensitive THz communications, and sensor systems that rely on waveguiding structures.
Yang, T, Ding, C, Ziolkowski, RW & Guo, YJ 2021, 'High Sensitivity Core-Shell Structure (CSS)-Based Fiber Sensor for Monitoring Analytes in Liquids and Gases', Journal of Lightwave Technology, vol. 39, no. 10, pp. 3319-3329.
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Yao, L, Kusakunniran, W, Wu, Q & Zhang, J 2021, 'Gait recognition using a few gait frames', PeerJ Computer Science, vol. 7, pp. e382-e382.
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Gait has been deemed as an alternative biometric in video-based surveillance applications, since it can be used to recognize individuals from a far distance without their interaction and cooperation. Recently, many gait recognition methods have been proposed, aiming at reducing the influence caused by exterior factors. However, most of these methods are developed based on sufficient input gait frames, and their recognition performance will sharply decrease if the frame number drops. In the real-world scenario, it is impossible to always obtain a sufficient number of gait frames for each subject due to many reasons, e.g., occlusion and illumination. Therefore, it is necessary to improve the gait recognition performance when the available gait frames are limited. This paper starts with three different strategies, aiming at producing more input frames and eliminating the generalization error cause by insufficient input data. Meanwhile, a two-branch network is also proposed in this paper to formulate robust gait representations from the original and new generated input gait frames. According to our experiments, under the limited gait frames being used, it was verified that the proposed method can achieve a reliable performance for gait recognition.
Yao, L, Kusakunniran, W, Wu, Q, Zhang, J, Tang, Z & Yang, W 2021, 'Robust gait recognition using hybrid descriptors based on Skeleton Gait Energy Image', Pattern Recognition Letters, vol. 150, pp. 289-296.
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© 2019 Gait features have been widely applied in human identification. The commonly-used representations for gait recognition can be roughly classified into two categories: model-free features and model-based features. However, due to the view variances and clothes changes, model-free features are sensitive to the appearance changes. For model-based features, there is great difficulty in extracting the underlying models from gait sequences. Based on the confidence maps and the part affinity fields produced by a two-branch multi-stage CNN network, a new model-based representation, Skeleton Gait Energy Image (SGEI), has been proposed in this paper. Another contribution is that a hybrid representation has been produced, which uses SGEI to remedy the deficiency of model-free features, Gait Energy Image (GEI) for instance. The experimental performances indicate that our proposed methods are more robust to the cloth changes, and contribute to increasing the robustness of gait recognition in the unconstrained environments with view variances and clothes changes.
Yao, Q, Lu, D-D-C & Lei, G 2021, 'Accurate Online Battery Impedance Measurement Method with Low Output Voltage Ripples on Power Converters', Energies, vol. 14, no. 4, pp. 1064-1064.
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The conventional online battery impedance measurement method works by perturbing the duty cycle of the DC-DC power converter and measuring the response of the battery voltage and current. This periodical duty cycle perturbation will continuously generate large voltage ripples at the output of power converters. These large ripples will not easily be removed due to the high amplitude and wide frequency range and would be a challenge to meet tight output regulation. To solve this problem, this paper presents a new online battery impedance measurement technique by inserting a small switched resistor circuit (SRC) into the converter. The first contribution of this work is that the perturbation source is moved from the main switch to the input-side of the converter, so the ripples are reduced. The analysis and experimental results of the proposed method show a reduction of 16-times compared with the conventional method. The second contribution tackles the possible change of the battery state of charge (SOC) during the online battery measurement process, which will inevitably influence the impedance measurement accuracy. In this proposed method, battery impedance at multiple frequencies can be measured simultaneously using only one perturbation to accelerate measurement speed and minimize possible SOC change. The experimental impedance results coincide with a high-accuracy laboratory battery impedance analyzer.
Yao, Q, Lu, DD-C & Lei, G 2021, 'Rapid Open-Circuit Voltage Measurement Method for Lithium-Ion Batteries Using One-Cycle Bipolar-Current Pulse', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 2, no. 2, pp. 132-141.
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Yao, X, Wu, Q, Zhang, P & Bao, F 2021, 'Weighted Adaptive Image Super-Resolution Scheme Based on Local Fractal Feature and Image Roughness', IEEE Transactions on Multimedia, vol. 23, pp. 1426-1441.
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Image super-resolution aims to reconstruct a high-resolution image from the known low-resolution version. During this process, it should keep the degree of image roughness non-decreasing, which reflects various texture features and appearance. However, this point is not well addressed in the current work. This work argues that reducing roughness during image super-resolution is the key reason causing various problems such as artificial texture and/or edge blur. In this work, keeping the image roughness non-decreasing during super-resolution is being well investigated for the first time to our best knowledge. Image super-resolution is cast as an optimization problem to keep image roughness non-decreasing. In order to tackle this problem, the image super-resolution is approached based on the theory of fractal, where adaptive fractal interpolation function is proposed. In this way, the rational fractal interpolation model is adaptive to every local region. Thus, the roughness of every image region can be best maintained while super-resolution is carried out through fractal interpolation. In this work, the image roughness is reflected by the fractal dimension, which is a key element affecting the construction of fractal interpolation model. That is, the image roughness is measurable using fractal dimension. Mathematically, the overall image super-resolution process can be converted into a fractal interpolation optimization problem where the local fractal dimension is maintained. Although adaptive super-resolution on image segments may best maintain image roughness using the proposed method, it still generates unnecessary block artifacts. To tackle this problem, this work proposes a fine-grained pixel-wise fractal function. Our extensive experimental results demonstrate that the proposed method achieves encouraging performance with the state-of-the-art super-resolution algorithms.
You, X, Wang, C-X, Huang, J, Gao, X, Zhang, Z, Wang, M, Huang, Y, Zhang, C, Jiang, Y, Wang, J, Zhu, M, Sheng, B, Wang, D, Pan, Z, Zhu, P, Yang, Y, Liu, Z, Zhang, P, Tao, X, Li, S, Chen, Z, Ma, X, I, C-L, Han, S, Li, K, Pan, C, Zheng, Z, Hanzo, L, Shen, XS, Guo, YJ, Ding, Z, Haas, H, Tong, W, Zhu, P, Yang, G, Wang, J, Larsson, EG, Ngo, HQ, Hong, W, Wang, H, Hou, D, Chen, J, Chen, Z, Hao, Z, Li, GY, Tafazolli, R, Gao, Y, Poor, HV, Fettweis, GP & Liang, Y-C 2021, 'Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts', Science China Information Sciences, vol. 64, no. 1, p. 110301.
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AbstractThe fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with ad...
Yu, G, Zhang, L, Wang, X, Yu, K, Ni, W, Zhang, JA & Liu, RP 2021, 'A novel Dual-Blockchained structure for contract-theoretic LoRa-based information systems', Information Processing & Management, vol. 58, no. 3, pp. 102492-102492.
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© 2021 LoRa serves as one of the most deployed technologies in Internet-of-Things-based information systems (IoT-IS), and self-motivated deployment is the key to the rollout of LoRa. Proper incentive can play an important role in encouraging the private deployment of LoRa, increasing coverage and promoting effective management of IoT-IS. However, existing incentive mechanisms have the vulnerabilities of insecure centralized architecture and excessive utility loss of LoRa Controllers and Gateways, due to asymmetric information between private owners of gateways and centralized controller (or service providers). Blockchain-based LoRa networks, as a promising solution, have not been comprehensively studied to address the vulnerabilities, let alone the other issues of security, scalability, and flexibility. In this paper, we propose a novel Dual-Chained LoRa-based information system (LoRa-IS) to provide globally cross-validated security. Behaviors, including state-of-the-art contract-theoretic incentive mechanism and new flow control protocol, can be secured with the tamper-resistance of Blockchains. Being part of the proposed incentive mechanism, the new self-driven flow control allows both the Dual-Chain system and the LoRa network to scale. To the best of our knowledge, the proposed system is the first comprehensive Blockchain-based LoRa-IS combined with contract theory. We also provide analysis and simulations, showing that our system can pay fair incentives under information asymmetry. With the new flow control, the system can optimize network coverage while improving the Blockchain scalability and flexibility.
Yu, H, Ye, L, Guo, Y & Su, S 2021, 'An Effective In-Field Calibration Method for Triaxial Magnetometers Based on Local Magnetic Inclination', IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-9.
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Yu, L, Gao, Y, Zhou, J & Zhang, J 2021, 'Parameter-Efficient Deep Neural Networks With Bilinear Projections', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 9, pp. 4075-4085.
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Recent research on deep neural networks (DNNs) has primarily focused on improving the model accuracy. Given a proper deep learning framework, it is generally possible to increase the depth or layer width to achieve a higher level of accuracy. However, the huge number of model parameters imposes more computational and memory usage overhead and leads to the parameter redundancy. In this article, we address the parameter redundancy problem in DNNs by replacing conventional full projections with bilinear projections (BPs). For a fully connected layer with D input nodes and D output nodes, applying BP can reduce the model space complexity from O(D²) to O(2D), achieving a deep model with a sublinear layer size. However, the structured projection has a lower freedom of degree compared with the full projection, causing the underfitting problem. Therefore, we simply scale up the mapping size by increasing the number of output channels, which can keep and even boosts the model accuracy. This makes it very parameter-efficient and handy to deploy such deep models on mobile systems with memory limitations. Experiments on four benchmark data sets show that applying the proposed BP to DNNs can achieve even higher accuracies than conventional full DNNs while significantly reducing the model size.
Yu, P, Ni, W, Yu, G, Zhang, H, Liu, RP & Wen, Q 2021, 'Efficient Anonymous Data Authentication for Vehicular Ad Hoc Networks', Security and Communication Networks, vol. 2021, pp. 1-14.
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Vehicular ad hoc network (VANET) encounters a critical challenge of efficiently and securely authenticating massive on-road data while preserving the anonymity and traceability of vehicles. This paper designs a new anonymous authentication approach by using an attribute-based signature. Each vehicle is defined by using a set of attributes, and each message is signed with multiple attributes, enabling the anonymity of vehicles. First, a batch verification algorithm is developed to accelerate the verification processes of a massive volume of messages in large-scale VANETs. Second, replicate messages captured by different vehicles and signed under different sets of attributes can be dereplicated with the traceability of all the signers preserved. Third, the malicious vehicles forging data can be traced from their signatures and revoked from attribute groups. The security aspects of the proposed approach are also analyzed by proving the anonymity of vehicles and the unforgeability of signatures. The efficiency of the proposed approach is numerically verified, as compared to the state of the art.
Yuan, C, Tao, X, Ni, W, Li, N, Jamalipour, A & Liu, RP 2021, 'Optimal Power Allocation for Superposed Secrecy Transmission in Multicarrier Systems', IEEE Transactions on Vehicular Technology, vol. 70, no. 2, pp. 1332-1346.
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Yuan, X, Feng, Z, Zhang, JA, Ni, W, Liu, RP, Wei, Z & Xu, C 2021, 'Spatio-Temporal Power Optimization for MIMO Joint Communication and Radio Sensing Systems With Training Overhead', IEEE Transactions on Vehicular Technology, vol. 70, no. 1, pp. 514-528.
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Zeng, J, Xiao, C, Li, Z, Ni, W & Liu, RP 2021, 'Dynamic Power Allocation for Uplink NOMA With Statistical Delay QoS Guarantee', IEEE Transactions on Wireless Communications, vol. 20, no. 12, pp. 8191-8203.
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Zhang, A, Rahman, ML, Huang, X, Guo, YJ, Chen, S & Heath, RW 2021, 'Perceptive Mobile Networks: Cellular Networks With Radio Vision via Joint Communication and Radar Sensing', IEEE Vehicular Technology Magazine, vol. 16, no. 2, pp. 20-30.
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Zhang, H & Xu, M 2021, 'Graph neural networks with multiple kernel ensemble attention', Knowledge-Based Systems, vol. 229, pp. 107299-107299.
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Zhang, H & Xu, M 2021, 'Weakly Supervised Emotion Intensity Prediction for Recognition of Emotions in Images', IEEE Transactions on Multimedia, vol. 23, pp. 2033-2044.
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Recognition of emotions in images is attracting increasing research attention. Recent studies show that using local region information helps to improve the recognition performance. Intuitively, emotion intensity maps provide more detailed information than image regions. Inspired by this intuition, we propose an end-to-end deep neural network for image emotion recognition leveraging emotion intensity learning. The proposed network is composed of a first classification stream, an intensity prediction stream and a second classification stream. The intensity prediction stream is built on top of the feature pyramid network to extract multilevel features. The class activation mapping technique is used to generate pseudo intensity maps from the first classification stream to guide the proposed network for emotion intensity learning. The predicted intensity map is integrated into the second classification stream for final emotion recognition. The three streams are trained cooperatively to improve the performance. We evaluate the proposed network for both emotion recognition and sentiment classification on different benchmark datasets. The experimental results demonstrate that the proposed network achieves improved performance compared to previous state-of-the-art approaches.
Zhang, H, Gu, Y, Yao, Y, Zhang, Z, Liu, L, Zhang, J & Shao, L 2021, 'Deep Unsupervised Self-Evolutionary Hashing for Image Retrieval', IEEE Transactions on Multimedia, vol. 23, pp. 3400-3413.
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Zhang, H, Huang, X & Zhang, JA 2021, 'Adaptive Transmission With Frequency-Domain Precoding and Linear Equalization Over Fast Fading Channels', IEEE Transactions on Wireless Communications, vol. 20, no. 11, pp. 7420-7430.
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In this paper, the emerging orthogonal time frequency space (OTFS) modulation is firstly restructured as a precoded orthogonal frequency division multiplexing (OFDM) system, so that the well-established frequency-domain approach can be applied to perform signal in fast fading channels. Then a frequency-domain minimum mean squared error (MMSE) equalizer for OTFS is introduced and its performance is analyzed based on the eigenvalue decomposition of the channel matrix. Inspired by the frequency-domain precoding structure, an adaptive transmission scheme with frequency-domain precoding matrix composed of the eigenvectors of the channel matrix is proposed to improve the system performance under MMSE equalization, and its optimized performance is derived with simple expression. Finally, considering two extreme channel conditions, the lower and upper bounds for the diversity performance of the adaptive transmission scheme are derived. Simulation results show that the proposed adaptive transmission achieves significantly better performance for short signal frames and can work well with imperfect channel state information (CSI). The derived performance bounds can serve as benchmarks for OTFS and other precoded OFDM systems.
Zhang, J, Liu, L, Wang, P & Zhang, J 2021, 'Exploring the auxiliary learning for long-tailed visual recognition', Neurocomputing, vol. 449, pp. 303-314.
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Real-world visual data often exhibits a long-tailed distribution, where some “head” classes have a large number of samples, yet only a few samples are available for “tail” classes. The fundamental problem of learning with the imbalanced data is that insufficient training samples easily lead to the over-fitting of feature extractor and classifier for tail classes, which can be boiled down into a dilemma: on the one hand, we prefer to increase the exposure of tail class samples to avoid the excessive dominance of head classes in the classifier training. On the other hand, oversampling tail classes makes the network prone to over-fitting, since head class samples are often consequently under-represented. To resolve this dilemma, in this paper, we propose an effective auxiliary learning approach. The key idea is to split a network into a classifier part and a feature extractor part, and then employ different training strategies for each part in an auxiliary learning manner. Specifically, to promote the awareness of tail-classes, a class-balanced sampling scheme is utilised for training both the classifier and the feature extractor as the primary task. For the feature extractor, we also introduce an auxiliary training task, which is to train a classifier under the regular random sampling scheme. In this way, the feature extractor is jointly trained from both sampling strategies and thus can take advantage of all training data and avoid the over-fitting issue. Apart from this basic auxiliary task, we further explore the benefits of different types of auxiliary tasks for improving the generality of learned features, including self-supervised learning and class-wise re-weighting. Without using any bells and whistles, our model compares favourably over state-of-the-art solutions.
Zhang, L, Chen, C, Tay, SS, Wen, S, Cao, C, Biro, M, Jin, D & Stenzel, MH 2021, 'Optimizing the Polymer Cloak for Upconverting Nanoparticles: An Evaluation of Bioactivity and Optical Performance', ACS Applied Materials & Interfaces, vol. 13, no. 14, pp. 16142-16154.
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The ability of upconversion nanoparticles (UCNPs) to convert low-energy near-infrared (NIR) light into high-energy visible-ultraviolet light has resulted in their development as novel contrast agents for biomedical imaging. However, UCNPs often succumb to poor colloidal stability in aqueous media, which can be conquered by decorating the nanoparticle surface with polymers. The polymer cloak, therefore, plays an instrumental role in ensuring good stability in biological media. This study aims to understand the relationship between the length and grafting density of the polymer shell on the physicochemical and biological properties of these core-shell UCNPs. Poly(ethylene glycol) methyl ether methacrylate block ethylene glycol methacrylate phosphate (PPEGMEMAn-b-PEGMP3) with different numbers of PEGMEMA repeating units (26, 38, and 80) was prepared and attached to the UCNPs via the phosphate ligand of the poly(ethylene glycol methacrylate phosphate) (PEGMP) block at different polymer densities. The in vitro and in vivo protein corona, cellular uptake in two-dimensional (2D) monolayer and three-dimensional (3D) multicellular tumor spheroid (MCTS) models, and in vivo biodistribution in mice were evaluated. Furthermore, the photoluminescence of single-polymer-coated UCNPs was compared in solid state and cancer cells using laser scanning confocal microscopy (LSCM). Our results showed that the bioactivity and luminescence properties are chain length and grafting density dependent. The UCNPs coated with the longest PPEGMEMA chain, grafted at low brush density, were able to reduce the formation of the protein corona in vitro and in vivo, while these UCNPs also showed the brightest upconversion luminescence in the solid state. Moreover, these particular polymer-coated UCNPs showed enhanced cellular uptake, extended in vivo blood circulation time, and more accumulation in the liver, brain, and heart.
Zhang, M, Li, H, Pan, S, Chang, X, Zhou, C, Ge, Z & Su, S 2021, 'One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 9, pp. 2921-2935.
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One-shot neural architecture search (NAS) has recently become mainstream in the NAS community because it significantly improves computational efficiency through weight sharing. However, the supernet training paradigm in one-shot NAS introduces catastrophic forgetting. To overcome this problem of catastrophic forgetting, we formulate supernet training for one-shot NAS as a constrained continual learning optimization problem such that learning the current architecture does not degrade the validation accuracy of previous architectures. The key to solving this constrained optimization problem is a novelty search based architecture selection (NSAS) loss function that regularizes the supernet training by using a greedy novelty search method to find the most representative subset. We applied the NSAS loss function to two one-shot NAS baselines and extensively tested them on both a common search space and a NAS benchmark dataset. We further derive three variants based on the NSAS loss function, the NSAS with depth constrain (NSAS-C) to improve the transferability, and NSAS-G and NSAS-LG to handle the situation with a limited number of constraints. The experiments on the common NAS search space demonstrate that NSAS and it variants improve the predictive ability of supernet training in one-shot NAS baselines.
Zhang, M, Li, H, Pan, S, Lyu, J, Ling, SSH & Su, SW 2021, 'Convolutional Neural Networks-Based Lung Nodule Classification: A Surrogate-Assisted Evolutionary Algorithm for Hyperparameter Optimization.', IEEE Trans. Evol. Comput., vol. 25, no. 5, pp. 869-882.
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This article investigates deep neural networks (DNNs)-based lung nodule classification with hyperparameter optimization. Hyperparameter optimization in DNNs is a computationally expensive problem, and a surrogate-Assisted evolutionary algorithm has been recently introduced to automatically search for optimal hyperparameter configurations of DNNs, by applying computationally efficient surrogate models to approximate the validation error function of hyperparameter configurations. Different from existing surrogate models adopting stationary covariance functions (kernels) to measure the difference between hyperparameter points, this article proposes a nonstationary kernel that allows the surrogate model to adapt to functions whose smoothness varies with the spatial location of inputs. A multilevel convolutional neural network (ML-CNN) is built for lung nodule classification, and the hyperparameter configuration is optimized by the proposed nonstationary kernel-based Gaussian surrogate model. Our algorithm searches with a surrogate for optimal setting via a hyperparameter importance-based evolutionary strategy, and the experiments demonstrate our algorithm outperforms manual tuning and several well-established hyperparameter optimization methods, including random search, grid search, the tree-structured parzen estimator (TPE) approach, Gaussian processes (GP) with stationary kernels, and the recently proposed hyperparameter optimization via RBF and dynamic (HORD) coordinate search.
Zhang, P, Wu, Q, Yao, X & Xu, J 2021, 'Beyond modality alignment: Learning part-level representation for visible-infrared person re-identification', Image and Vision Computing, vol. 108, pp. 104118-104118.
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Zhang, P, Xu, J, Wu, Q, Huang, Y & Ben, X 2021, 'Learning Spatial-Temporal Representations Over Walking Tracklet for Long-Term Person Re-Identification in the Wild', IEEE Transactions on Multimedia, vol. 23, pp. 3562-3576.
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Long-term person re-identification (re-ID) aims to build identity correspondence of the Target Subject of Interest (TSI) exposed under surveillance cameras over a long time interval. Compared to the conventional short-term re-ID studied by most existing works, it suffers an additional problem: significant dressing change observed with time lapsing. Unfortunately, this variation in long-term person re-ID case contradicts the assumption of prior short-term re-ID approaches, and thus causes significant difficulties if conventional short-term re-ID methods are applied. To address the problem, this paper proposes to learn hybrid feature representation via a two-stream network named SpTSkM, including a spatial-temporal stream and a skeleton motion stream. The former performs directly on image sequences, which tends to learn identity-related spatial-temporal patterns such as body geometric structure and body movement. The latter operates on normalized 3D skeletons by adapting graph convolutional network, which tends to learn pure motion patterns from skeleton sequences. Both streams extract fine-grained level time-gap stable information that is robust to appearance changes in long-term re-ID and meanwhile maintains sufficient discriminability to differentiate different people. The final matching metric is obtained by mixing information of the two streams in a score-level fusion strategy. In addition, we collect a Cloth-Varying vIDeo re-ID (CVID-reID) dataset particularly for long-term re-ID. It contains video tracklets of celebrities posted on the Internet. These videos are snapshots under extremely different scenarios that include highly dynamic background, diverse camera views and abundant cloth variations on each TSI. These factors cause CVID-reID more complicated and closer to practice. Our experiments demonstrate the difficulty of long-term person re-ID and also validate the effectiveness of the proposed SpTSkM, showing the best performance.
Zhang, Q, Ge, L, Zhang, R, Metternicht, GI, Du, Z, Kuang, J & Xu, M 2021, 'Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data', Remote Sensing of Environment, vol. 264, pp. 112575-112575.
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Zhang, T, Du, J & Guo, YJ 2021, 'An 8–10-GHz Low-Loss Image-Reject HTS Mixer Based on Cascaded Josephson Junctions', IEEE Microwave and Wireless Components Letters, vol. 31, no. 8, pp. 945-948.
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A 7.8 to 9.8 GHz low-loss high-Tc superconducting (HTS) image-reject Josephson junction mixer is presented in this paper. Image rejection is achieved using low-loss image rejection filters and a pair of parallel dual-mode resonators. Three cascaded Josephson junctions are monolithically integrated with impedance matching networks and bandpass/lowpass filters on a single chip for low loss and compactness. The implementation of series junction array increased the overall junction equivalent impedance, so that a better impedance matching is achieved. Measurement results of the novel HTS mixer showed a low conversion loss at 5.5 dB, and an image-reject ratio over 30 dB within the desired frequency. The proposed cascaded junction configuration provides a new approach for impedance matching in HTS mixer designs, and the demonstrated mixer performance makes it an ideal candidate for X-band radar systems.
Zhang, T, Yuan, J, Chen, Y-C & Jia, W 2021, 'Self-learning soft computing algorithms for prediction machines of estimating crowd density', Applied Soft Computing, vol. 105, pp. 107240-107240.
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Human needs motivate the improvement of computing paradigms, the emergence of soft computing is more effective in dealing with daily problems, while reducing costs, it also solves the problem of robustness and becomes easier to handle. Examples of this include collecting data on video data from smart sensors and can use that information to predict and monitor the behavior of crowd. Nowadays, with the development of cyber–physical systems and artificial intelligence, the traditional data collection and analysis system faces the risks of low transparency and high data security, making it difficult to obtain an accurate prediction result. Therefore, in this paper a novel prediction machine via self-learning generative adversarial network for soft computing application is proposed, which collects data through a series of high-precision IoT sensor devices and makes preliminary preprocessing, and further solves the crowd prediction problem based on deep learning algorithms and obtains a reliable and accurate prediction result by continuously optimizing internal parameters. The focus of this work is on the accuracy and preprocessing of data collection and crowd prediction algorithms. The prediction algorithm can be used to estimate and monitor the crowd flow in public places, and can prevent crowding, trampling and other traffic jams, such as stations, airports, large exhibitions, tourist attractions and other places. Therefore, the new prediction machine includes video capture, upload and display, data analysis and early warning operations in embedded devices, and automatically predicts crowd density. In terms of constructing the network, first, in order to obtain a clearer generated density map, the feature self-learning module is merged in the generator feature extraction stage. Secondly, in order to avoid the blur of the generated image, an adversarial loss is constructed between the generator and the discriminator, and finally to deal with multiple scales...
Zhang, T, Zhang, Z, Jia, W, He, X & Yang, J 2021, 'Generating Cartoon Images from Face Photos with Cycle-Consistent Adversarial Networks', Computers, Materials & Continua, vol. 69, no. 2, pp. 2733-2747.
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The generative adversarial network (GAN) is first proposed in 2014, and this kind of network model is machine learning systems that can learn to measure a given distribution of data, one of the most important applications is style transfer. Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image. CYCLE-GAN is a classic GAN model, which has a wide range of scenarios in style transfer. Considering its unsupervised learning characteristics, the mapping is easy to be learned between an input image and an output image. However, it is difficult forCYCLE-GANto converge and generate high-quality images. In order to solve this problem, spectral normalization is introduced into each convolutional kernel of the discriminator. Every convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to [0, 1], which promotes the training process of the proposed model. Besides, we use pretrained model (VGG16) to control the loss of image content in the position of l1 regularization. To avoid overfitting, l1 regularization term and l2 regularization term are both used in the object loss function. In terms of Frechet Inception Distance (FID) score evaluation, our proposed model achieves outstanding performance and preserves more discriminative features. Experimental results show that the proposed model converges faster and achieves better FID scores than the state of the art.
Zhang, T, Zhao, Y, Jia, W & Chen, M-Y 2021, 'Collaborative algorithms that combine AI with IoT towards monitoring and control system', Future Generation Computer Systems, vol. 125, pp. 677-686.
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Recently, a new IoT structure known as the Artificial Intelligence of Things (AIoT) comes into play. Crowd counting is a promising field in data analysis of AIoT, however, due to poor transparency and high data security risks, developing a novel network architecture that can precisely elevate the counting of heavy crowd is extremely difficult. In addition, the fusion of IoT and AI also poses several challenges. The focus of this work is on the effective design of IoT framework and deep learning algorithm towards security of smart city. The system can be used to estimate the crowd traffic in public places, and can prevent the occurrence of congestion, stampede and other accidents, such as stations, airports, large-scale exhibitions, tourist attractions and other places. The constructed system contains video collection, upload and display as well as data analysis and early warning operation at the embedded device end, and automatically tracks densely crowd areas by controlling the video monitoring device. Moreover, the cloud platform can be controlled through the network. Our proposed algorithms are composed of two main aspects, i.e., division and focus. Firstly, we propose a novel density-adaptive Gaussian kernel to elevate the quality of density maps. Then, we propose a module based on conditional random fields for feature fusion. Finally, we propose a block segmentation module to predict our segmentation results and extract the context-aware information in segmentation stage. Experiments on our captured data, the Shanghai Tech, UCF_CC_50 and UCF_QNRF datasets demonstrate that our solution has obtained better performance and lower count errors over the state of the art.
Zhang, Y, Wang, Z, Cao, Y, Zhang, L, Wang, G, Dong, F, Deng, R, Guo, B, Zeng, L, Wang, P, Dai, R, Ran, Y, Lyu, W, Miao, P & Su, S 2021, 'The effect of consecutive ambient air pollution on the hospital admission from chronic obstructive pulmonary disease in the Chengdu region, China', Air Quality, Atmosphere & Health, vol. 14, no. 7, pp. 1049-1061.
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Hospitalisation risks for chronic obstructive pulmonary disease (COPD) have been attributed to ambient air pollution worldwide. However, a rise in COPD hospitalisations may indicate a considerable increase in fatality rate in public health. The current study focuses on the association between consecutive ambient air pollution (CAAP) and COPD hospitalisation to offer predictable early guidance towards estimates of COPD hospital admissions in the event of consecutive exposure to air pollution. Big data analytics were collected from 3-year time series recordings (from 2015 to 2017) of both air data and COPD hospitalisation data in the Chengdu region in China. Based on the combined effects of CAAP and unit increase in air pollutant concentrations, a quasi-Poisson regression model was established, which revealed the association between CAAP and estimated COPD admissions. The results show the dynamics and outbreaks in the variations in COPD admissions in response to CAAP. Cross-validation and mean squared error (MSE) are applied to validate the goodness of fit. In both short-term and long-term air pollution exposures, Z test outcomes show that the COPD hospitalisation risk is greater for men than for women; similarly, the occurrence of COPD hospital admissions in the group of elderly people (> 65 years old) is significantly larger than that in lower age groups. The time lag between the air quality and COPD hospitalisation is also investigated, and a peak of COPD hospitalisation risk is found to lag 2 days for air quality index (AQI) and PM10, and 1 day for PM2.5. The big data-based predictive paradigm would be a measure for the early detection of a public health event in post-COVID-19. The study findings can also provide guidance for COPD admissions in the event of consecutive exposure to air pollution in the Chengdu region.
Zhang, Y, Yang, Q, Zhang, L, Ran, Y, Wang, G, Celler, B, Su, S, Xu, P & Yao, D 2021, 'Noise-assisted multivariate empirical mode decomposition based causal decomposition for brain-physiological network in bivariate and multiscale time series', Journal of Neural Engineering, vol. 18, no. 4, pp. 046018-046018.
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Abstract Objective. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) based causal decomposition depicts a cause and effect relationship that is not based on the term of prediction, but rather on the phase dependence of time series. Here, we present the NA-MEMD based causal decomposition approach according to the covariation and power views traced to Hume and Kant: a priori cause-effect interaction is first acquired, and the presence of a candidate cause and of the effect is then computed from the sensory input somehow. Approach. Based on the definition of NA-MEMD based causal decomposition, we show such causal relation is a phase relation where the candidate causes are not merely followed by effects, but rather produce effects. Main results. The predominant methods used in neuroscience (Granger causality, empirical mode decomposition-based causal decomposition) are validated, showing the applicability of NA-MEMD based causal decomposition, particular to brain physiological processes in bivariate and multiscale time series. Significance. We point to the potential use in the causality inference analysis in a complex dynamic process.
Zhang, Y-F, Zheng, J, Li, L, Liu, N, Jia, W, Fan, X, Xu, C & He, X 2021, 'Rethinking feature aggregation for deep RGB-D salient object detection', Neurocomputing, vol. 423, pp. 463-473.
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© 2020 Two-stream UNet based architectures are widely used in deep RGB-D salient object detection (SOD) models. However, UNet only adopts a top-down decoder network to progressively aggregate high-level features with low-level ones. In this paper, we propose to enrich feature aggregation via holistic aggregation paths and an extra bottom-up decoder network. The former aggregates multi-level features holistically to learn abundant feature interactions while the latter aggregates improved low-level features with high-level features, thus promoting their representation ability. Aiming at the two-stream architecture, we propose another early aggregation scheme to aggregate and propagate multi-modal encoder features at each level, thereby improving the encoder capability. We also propose a factorized attention module to efficiently modulate the feature aggregation action for each feature node with multiple learned attention factors. Experimental results demonstrate that all of the proposed components can gradually improve RGB-D SOD results. Consequently, our final SOD model performs favorably against other state-of-the-art methods.
Zhang, Z, Jiang, S, Huang, C, Li, Y & Xu, RYD 2021, 'RGB-IR cross-modality person ReID based on teacher-student GAN model', Pattern Recognition Letters, vol. 150, pp. 155-161.
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Zhang, Z, Wu, Q, Wang, Y & Chen, F 2021, 'Exploring region relationships implicitly: Image captioning with visual relationship attention', Image and Vision Computing, vol. 109, pp. 104146-104146.
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Visual attention mechanism has been widely used by image captioning model in order to dynamically attend to the related visual region based on given language information. Such capability allows a trained model to carry out fine-grained level image understanding and reasoning. However, existing visual attention models only focus on the individual visual region in the image and the alignment between the language representation and related individual visual regions. It does not fully explore the relationships/interactions between visual regions. Furthermore, it does not analyze or explore alignment for related words/phrases (e.g. verb or phrasal verb), which may best describe the relationships/interactions between these visual regions. Thus, it causes the inaccurate or impropriate description to the current image captioning model. Instead of visual region attention commonly addressed by existing visual attention mechanism, this paper proposes the novel visual relationship attention via contextualized embedding for individual regions. It can dynamically explore a related visual relationship existing between multiple regions when generating interaction words. Such relationship exploring process is constrained by spatial relationships and driven by the linguistic context of language decoder. In this work, such new visual relationship attention is designed through a parallel attention mechanism under the learned spatial constraint in order to more precisely map visual relationship information to the semantic description of such relationship in language. Different from existing methods for exploring the visual relationship, it is trained implicitly through an unsupervised approach without using any explicit visual relationship annotations. By integrating the newly proposed visual relationship attention with existing visual region attention, our image captioning model can generate high-quality captions. Solid experiments on the MSCOCO dataset demonstrate the pro...
Zhao, S, Qiu, X, Burnett, I, Rigby, M & Lele, A 2021, 'A lumped-parameter model for sound generation in gas metal arc welding', Mechanical Systems and Signal Processing, vol. 147, pp. 107085-107085.
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It is found that the welding sound generated in Gas Metal Arc Welding (GMAW) can help human welders to improve welding quality; however, little work has been reported on the sound generation and prediction in GMAW processes. To investigate the sound generation in GMAW, this paper proposes a lumped-parameter model to predict the sound signals at the short-circuiting mode in GMAW, where the power source is modeled by a simple resistor–inductor electrical circuit and the metal droplet dynamics is modeled by a mass-spring model. The simulation results of the welding current, arc voltage, and sound signals are found to be in a reasonable agreement with the experimental measurements. Both simulation and experiment results show that the welding current increases from a base value in the arcing phase to a peak value in the short-circuiting phase first and then slumps to the base value; the voltage is close to zero in the short-circuiting phase corresponding to the current peaks and fluctuates in the arcing phase due to the uncertainties in the arc resistance; and an acoustic impulse is formed at each peak current and valley voltage in the short-circuiting phase, indicating that the sound generation is related to the energy release during the arc re-ignition process. The proposed lumped-parameter model can be used to investigate the effect of the input welding parameters on the welding sound.
Zhao, S, Xu, M, Huang, Q & Schuller, BW 2021, 'Introduction to the Special Issue on MMAC: Multimodal Affective Computing of Large-Scale Multimedia Data', IEEE MultiMedia, vol. 28, no. 2, pp. 8-10.
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Zheng, D, Zhang, H, Zhang, JA & Su, SW 2021, 'Stability of Switched Systems with Unstable Subsystems: A Sequence-Based Average Dwell Time Approach', Circuits, Systems, and Signal Processing, vol. 40, no. 11, pp. 5328-5350.
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This paper proposes a new sequence-based approach to resolve the stability problems found in switched systems with unstable subsystems. In existing approaches, the sequence information of switching subsystems is seldom exploited. By exploiting the sequence information, threshold values can be less restrictive and more appropriate for the situation. We study two cases in this paper: (a) all subsystems are unstable, and (b) part of the subsystems are unstable. Both continuous-time and discrete-time systems are studied, and a numerical example is given to show the advantage of our approach.
Zhong, Y, Wang, J, Wu, S, Jiang, T, Huang, Y & Wu, Q 2021, 'Multilocation Human Activity Recognition via MIMO-OFDM-Based Wireless Networks: An IoT-Inspired Device-Free Sensing Approach', IEEE Internet of Things Journal, vol. 8, no. 20, pp. 15148-15159.
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Device-free sensing (DFS) is an emerging technology that empowers wireless communication systems with the ability for not only data communication but also smart sensing. By taking advantage of machine-learning technologies, DFS transforms traditional wireless communication networks into intelligent context-aware networks and will open the doors for a myriad of promising 6G-enabled Internet of Things (IoT) applications, ranging from smart home to smart buildings. Although significant progress has been made for human activity recognition at a single location by leveraging this technology, performance at multiple locations has not been fully explored. As far as multilocation activity sensing is concerned, the performance is compromised along with the change of locations and labor-intensive annotation works caused by multilocation. To tackle this issue, an activity decomposition network (ActNet) is presented to decompose the activity information directly from input samples by using the training data from different locations together. Instead of dealing with different locations separately, our ActNet can assemble data from different locations together for training to mitigate the data limitation issue caused by a single location. To achieve this, a multiple-input-multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) technology-based prototype system is utilized to collect data samples at 24 different locations in a cluttered office environment. Especially, for each location, only ten samples of each activity are used for training. Experiments demonstrate that the average classification accuracy is 94.6% across all locations with ensured robustness produced by our method.
Zhou, I, Makhdoom, I, Shariati, N, Raza, MA, Keshavarz, R, Lipman, J, Abolhasan, M & Jamalipour, A 2021, 'Internet of Things 2.0: Concepts, Applications, and Future Directions', IEEE Access, vol. 9, pp. 70961-71012.
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Applications and technologies of the Internet of Things are in high demand with the increase of network devices. With the development of technologies such as 5G, machine learning, edge computing, and Industry 4.0, the Internet of Things has evolved. This survey article discusses the evolution of the Internet of Things and presents the vision for Internet of Things 2.0. The Internet of Things 2.0 development is discussed across seven major fields. These fields are machine learning intelligence, mission critical communication, scalability, energy harvesting-based energy sustainability, interoperability, user friendly IoT, and security. Other than these major fields, the architectural development of the Internet of Things and major types of applications are also reviewed. Finally, this article ends with the vision and current limitations of the Internet of Things in future network environments.
Zhou, W, Sutton, GJ, Liu, RP, Zhang, JA & Pan, S 2021, 'Deterministic Channel Aggregation for LTE in Unlicensed Spectrum', IEEE Communications Letters, vol. 25, no. 3, pp. 807-811.
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Zhu, G, Li, L, Fu, W, Xue, M, Liu, T & Zhu, J 2021, 'A Novel Neural Network Cell Method for Solving Nonlinear Electromagnetic Problems', Advanced Theory and Simulations, vol. 4, no. 12, pp. 2100216-2100216.
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AbstractEffective analysis of nonlinear electromagnetic fields is essential for the accurate modeling of electromagnetic devices, such as transformers, generators, and motors. This paper proposes a novel approach of coupled neural network (NN) and cell method (CM) or NNCM for solving nonlinear electromagnetic problems with ferromagnetic domains. While the topologically linear relations of the cell complexes are mathematically assembled through a transformation in the Tonti diagram by the CM, and the constitutive nonlinear magnetic relations are dealt with by partially connected NN for the fast prediction of the permeability distribution inside the ferromagnetic domain. Since the construction of NN is directly related to the grid connections, a partially connected NN structure with a small number of neurons can reduce the computational cost of the training process. By using a compact NN, the proposed NNCM can effectively eliminate the time consuming iterations for determining the nonlinear permeability distribution, and improve the computational efficiency significantly. The NNCM is employed to analyze the transient electromagnetic field distribution inside a cylindrical ferromagnetic core. The results are compared with those obtained by the traditional iterative CM, which determines the nonlinear permeability distribution by lengthy numerical iterations, to verify the feasibility and effectiveness of the proposed NNCM.
Zhu, H & Guo, YJ 2021, 'Dual-Band and Tri-Band Balanced-to-Single Ended Power Dividers With Wideband Common-Mode Suppression', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 7, pp. 2332-2336.
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Zhu, J, Yang, Y, Li, M, Mcgloin, D, Liao, S, Nulman, J, Yamada, M & Iacopi, F 2021, 'Additively Manufactured Millimeter-Wave Dual-Band Single-Polarization Shared Aperture Fresnel Zone Plate Metalens Antenna', IEEE Transactions on Antennas and Propagation, vol. 69, no. 10, pp. 6261-6272.
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Fresnel zone plate (FZP) lens antenna, consisting of a set of alternative transparent and opaque concentric rings arranged on curvilinear or flat surfaces, have been widely used in various fields for sensing and communications. Nevertheless, the state-of-art FZP lens antennas are limited to a single band due to the frequency-dependent feature, which hinders their use in multi-band applications. In this work, a shared-aperture dual-band FZP metalens antenna is proposed by merging two single-band FZP metalens antenna operating at distinct frequency bands seamlessly into one. Instead of using conventional metallic conductors, double-screen meta-grids are devised in this work to form the concentric rings. Because the meta-grids show distinct transmission/reflection properties at different frequencies, the performance of one set of concentric rings operating at the one band will not be affected by the other operating at the different band. In addition, to compensate for the phase shift introduced by the meta-grids, an additional dielectric ring layer is added atop the FZP taking advantage of additive manufacturing. Thus, the radiation performance of the dual-band FZP lens antenna is comparable to that of each single FZP metalens antenna. For proof-of-concept, an antenna prototype operating at the dual-band, 75 GHz and 120 GHz with a frequency ratio of 1.6, is fabricated using an integrated additively manufactured electronics (AME) technique. The measured peak gains of 20.3 dBi and 21.9 dBi are achieved at 75 GHz and 120 GHz, respectively.
Zhu, J, Yang, Y, Mcgloin, D, Liao, S & Xue, Q 2021, '3-D Printed All-Dielectric Dual-Band Broadband Reflectarray With a Large Frequency Ratio', IEEE Transactions on Antennas and Propagation, vol. 69, no. 10, pp. 7035-7040.
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This communication proposes a new all-dielectric broadband dual-band reflectarray with a large frequency-ratio using low-cost 3-D printing. In contrast to conventional reflectarrays using metallic resonant cells or dielectric slabs as phasing elements with full metal ground, the proposed design uses air as the phasing element and a stepped dielectric mirror structure as the ground. In this way, the metal ground is removed, which makes the design an all-dielectric one. Taking advantage of the dielectric mirror that only exhibits a bandgap in the pre-designed band while allowing electromagnetic (EM) waves to pass through it at the frequency out of the bandgap region, a dual-band reflectarray is obtained. By properly selecting the bandgap frequency of the dielectric mirror, the dual-band frequency-ratio is scalable and can be very large. Furthermore, instead of using a metallic or dielectric resonator based on resonance, air layers with linear phase response are adopted as the phasing element. Thus, the reflectarray shows broadband and stable performance over the dual-band. Compared with state-of-art works using printed-circuit-boards (PCBs) or micro-fabrication, the proposed design is low-cost and lightweight, and can be rapidly prototyped. For proof-of-concept, a prototype operating at K band and V band with a frequency-ratio of 2.7 is printed and measured.
Zhu, J, Yang, Y, McGloin, D, Liao, S & Xue, Q 2021, 'Sub-Terahertz 3-D Printed All-Dielectric Low-Cost Low-Profile Lens-Integrated Polarization Beam Splitter', IEEE Transactions on Terahertz Science and Technology, vol. 11, no. 4, pp. 433-442.
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Polarizing beam splitters (PBSs) are essential components for polarization-multiplexed communication system. However, conventional PBS, such as Rochon prism and Wollaston prism, must be sufficiently thick to generate enough walk-off distance between two orthogonal polarizations due to the low birefringence of natural material. To reduce this requirement, we propose and demonstrate a three-dimensional printed low-cost low-profile Fresnel-Rochon prism. By adjusting the dimensions of the artificially engineered gratings that comprise the prism, the refractive index tensor along the x- and y-direction can differ, enabling the independent manipulation of the deflection angles of the orthogonal polarized components. Applying the Fresnel principle to this Rochon prism, the thickness can be greatly reduced while the polarization splitting and deflection angles remain unaltered. Meanwhile, the transmission efficiency of the prism is also improved as less energy is dissipated in the lossy printing material. In addition, in contrast to conventional Rochon prisms formed by two right triangle prisms, the proposed Rochon prism enjoys the benefit of simple fabrication without any further assembly procedure. Prototypes operating at 0.14-THz frequency were printed and measured to verify the idea.
Zhu, Q, Dinh, TH, Phung, MD & Ha, QP 2021, 'Hierarchical Convolutional Neural Network With Feature Preservation and Autotuned Thresholding for Crack Detection', IEEE Access, vol. 9, pp. 60201-60214.
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Zhu, Q, Qiu, X, Coleman, P & Burnett, I 2021, 'An experimental study on transfer function estimation using acoustic modelling and singular value decomposition', The Journal of the Acoustical Society of America, vol. 150, no. 5, pp. 3557-3568.
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Transfer functions relating sound source strengths and the sound pressure at field points are important for sound field control. Recently, two modal domain methods for transfer function estimation have been compared using numerical simulations. One is the spatial harmonic decomposition (SHD) method, which models a sound field with a series of cylindrical waves; while the other is the singular value decomposition (SVD) method, which uses prior sound source location information to build an acoustic model and obtain basis functions for sound field modelling. In this paper, the feasibility of the SVD method using limited measurements to estimate transfer functions over densely spaced field samples within a target region is demonstrated experimentally. Experimental results with various microphone placements and system configurations are reported to demonstrate the geometric flexibility of the SVD method compared to the SHD method. It is shown that the SVD method can estimate broadband transfer functions up to 3099 Hz for a target region with a radius of 0.083 m using three microphones, and allow flexibility in system geometry. Furthermore, an application example of acoustic contrast control is presented, showing that the proposed method is a promising approach to facilitating broadband sound zone control with limited microphones.
Zhu, W, Tuan, HD, Dutkiewicz, E, Fang, Y & Hanzo, L 2021, 'A New Class of Structured Beamforming for Content-Centric Fog Radio Access Networks', IEEE Transactions on Communications, vol. 69, no. 11, pp. 7269-7282.
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A multi-user fog radio access network (F-RAN) is designed for supporting content-centric services. The requested contents are partitioned into sub-contents, which are then 'beamformed' by the remote radio heads (RRHs) for transmission to the users. Since a large number of beamformers must be designed, this poses a computational challenge. We tackle this challenge by proposing a new class of regularized zero forcing beamforming (RZFB) for directly mitigating the inter-content interferences, while the 'intra-content interference' is mitigated by successive interference cancellation at the user end. Thus each beamformer is decided by a single real variable (for proper Gaussian signaling) or by a pair of complex variables (for improper Gaussian signaling). Hence the total number of decision variables is substantially reduced to facilitate tractable computation. To address the problem of energy efficiency optimization subject to multiple constraints, such as individual user-rate requirement and the fronthauling constraint of the links between the RRHs and the centralized baseband signal processing unit, as well as the total transmit power budget, we develop low-complexity path-following algorithms. Finally, we confirm their performance by simulations.
Zhuang, Y, Leng, Y, Zhou, J, Song, R, Li, L & Su, SW 2021, 'Voluntary Control of an Ankle Joint Exoskeleton by Able-Bodied Individuals and Stroke Survivors Using EMG-Based Admittance Control Scheme', IEEE Transactions on Biomedical Engineering, vol. 68, no. 2, pp. 695-705.
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Control schemes based on electromyography (EMG) have demonstrated their superiority in human-robot cooperation due to the fact that motion intention can be well estimated by EMG signals. However, there are several limitations due to the noisy nature of EMG signals and the inaccuracy of EMG-force/torque estimation, which might deteriorate the stability of human-robot cooperation movement. To improve the movement stability, an EMG-based admittance control scheme (EACS) was proposed, comprised of an EMG-driven musculoskeletal model (EDMM), an admittance filter and an inner position controller. To investigate the performance of EACS, a series of sinusoidal tracking tasks were conducted with 12 healthy participants and 4 stroke survivors in an ankle exoskeleton in comparison with the EMG-based open-loop control scheme (EOCS). The experimental results indicated that both EACS and EOCS could improve stroke survivors' ankle range of motion (ROM). The experimental results of both healthy participants and stroke survivors showed that the assistance torque, tracking error and jerk values of EACS were lower than those of EOCS. The interaction torque of EACS decreased towards the increasing assistance ratio while that of EOCS increased. Moreover, the EMG levels of tibialis anterior (TA) decreased towards the increasing assistance ratio but were higher than those of EOCS. EACS was effective in improving movements stability, and had the potential to be applied in robot-assisted rehabilitation training to address the foot-drop problem.
Abbasi, MH & Zhang, J 1970, 'Joint Optimization of Electric Vehicle Fast Charging and DC Fast Charging Station', 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), IEEE.
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Abhijith, V, Hossain, MJ, Lei, G & A S, P 1970, 'Performance Improvement of Switched Reluctance Motor Using Hybrid Excitation Method Without Permanent Magnets', 2021 24th International Conference on Electrical Machines and Systems (ICEMS), 2021 24th International Conference on Electrical Machines and Systems (ICEMS), IEEE.
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Acharjee, J, Paul, GS, Mandal, K & Lalbakhsh, A 1970, 'Design and Analysis of Shorting Pin Loaded Triple Band Microstrip Patch Antenna with Enhanced Gain for Wireless Applications', 2021 Photonics & Electromagnetics Research Symposium (PIERS), 2021 Photonics & Electromagnetics Research Symposium (PIERS), IEEE.
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Afzal, MU, Esselle, KP, Koli, NY & Thalakotuna, DN 1970, 'Wideband Radial-Line Slot Array Antenna Technology for Near-Field Meta-Steering Systems', 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), IEEE.
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Afzal, MU, Koli, NY & Esselle, KP 1970, 'Low-Cost Radial Line Slot Array Antenna for Millimeter-Wave Backhaul Links', 2021 15th European Conference on Antennas and Propagation (EuCAP), 2021 15th European Conference on Antennas and Propagation (EuCAP), IEEE, Dusseldorf, Germany.
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The front-end antennas with a directed narrow beam are essential to establish a robust backhaul network for 5G network. The cost and profile of the antenna are imperative due to the large number of smaller cells envisaged for a millimeter-based 5G communication network. Radial-line slot array (RLSA) antennas are suitable due to their planar and thin height profile and single feed point. A low-cost RLSA is investigated for the millimeter-wave backhaul network without using dielectric materials. The lack of dielectric substantially reduces the fabrication cost of the antenna. The RLSA is made of two metal plates where radiating slots are on a thin-plate at the top. The results predicted through numerical simulations indicate antenna can create a narrow broadside beam without any excessive grating or side lobes.
Ahmed, F, Afzal, MU & Esselle, KP 1970, 'A Metal-Only Partially Reflective Surface for Metallic Resonant-Cavity Antennas', 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), IEEE.
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Ahmed, F, Afzal, MU, Hayat, T & Esselle, KP 1970, 'Low-Cost All-Metal Bandpass Frequency Selective Surface', 2021 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2021 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE.
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Ahmed, F, Shahriar, TAMR, Paul, R & Ahammad, A 1970, 'Design and Development of a Smart Surveillance System for Security of an Institution', 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), IEEE.
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Alam, MM, Lu, DD-C & Aguilera, RP 1970, 'Review of Battery Balancing Techniques based on Structure and Control Strategy', 2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021 31st Australasian Universities Power Engineering Conference (AUPEC), IEEE.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 1970, 'Multi-band SIW Cavity Based Metamaterial Perfect Absorber', 2021 IEEE Asia-Pacific Microwave Conference (APMC), 2021 IEEE Asia-Pacific Microwave Conference (APMC), IEEE, Brisbane, Australia, pp. 347-349.
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Absorbing ambient electromagnetic (EM) signals is required in some applications such as energy harvesting and sensing. Metamaterial perfect absorbers (MPAs) are a promising candidate to absorb the EM signal with near-unity efficiency. However, designing a structure with multi absorption bands is still challenging among researchers due to the requiring multi-layer and multi-resonators structures. Herein, a multi-band MPA based on SIW cavity structure is introduced. Taking advantage of various resonance modes of the cavity at higher order, a low-profile, highly efficient, and easy to implement MPA is achieved. In addition, the proposed structure is completely polarization angle insensitive, which is crucial to have a highly efficient EM waves absorber.
Ansari, M, Jones, B, Shariati, N & Jay Guo, Y 1970, '3D Luneburg Lens Antenna With Layered Structure for High-Gain Communication Systems', 2021 15th European Conference on Antennas and Propagation (EuCAP), 2021 15th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Aryal, A, Hossain, MJ & Khalilpour, K 1970, 'A Comparative study on state of charge estimation techniques for Lithium-ion Batteries.', ISGT Asia, IEEE PES Innovative Smart Grid Technologies - Asia Conference, IEEE, Brisbane, AUSTRALIA, pp. 1-5.
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State of Charge (SOC) estimation is significantly important for the optimal utilization and protection of batteries. This paper implements and compares the performance of a neural network (NN) algorithm and Coulomb Counting method for estimating state of charge (SOC) for batteries. This algorithm is applied to a battery management system (BMS) in electric vehicles. Accurate SOC information can avoid over charging and over discharging of battery, and thus increase battery life. Also, control system uses accurate SOC information to make rational decisions to save energy in electric vehicles. The advantage of NN model over Coulomb Counting method is it can be implemented in BMS Hardware where online measurements like current, voltage and temperature are available. The feature of this neural network approach is that it optimizes two important hyper-parameters to achieve a reasonable MAPE error. The performance of the proposed method is tested using two Datasets for city driving conditions. The results reveal that both methods (NN and Coulomb counting) can predict SOC with reasonable error (<6%). However, Coulomb counting outperforms Neural network MAPE for both Datasets.
Azizivahed, A, Arefi, A, Li, L & Zhang, J 1970, 'An Effective Approach for Locational Marginal Price Calculation at Distribution Level', 2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021 31st Australasian Universities Power Engineering Conference (AUPEC), IEEE, Perth, Australia, pp. 1-6.
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This paper develops an effective approach for the locational marginal price calculation for local generations in an active distribution network containing different types of distributed generators (DGs). The proposed approach is based on encouraging private units to reduce power loss and greenhouse gas (GHG) emissions. To this end, firstly, the distribution system operator (DSO) surplus profit, obtained by the reduction of power loss and GHG gas emission due to the operation of private units in the network, is considered as a financial source for encouraging private units. Then, according to the contribution of each private DG, the locational marginal price is calculated. The proposed approach is an effective and incentive-based approach for DSO to retain control over private units to reduce power loss and GHG emissions. The simulation results on a modified 118-bus standard distribution test system demonstrate the efficiency of the proposed approach compared to the previous approaches.
Bah, AO, Guo, YJ, Qin, P-Y & Bird, TS 1970, 'A Wideband Low-Profile Fabry-Perot Antenna Employing a Multi-Resonant Metasurface Based Superstrate', 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), IEEE, pp. 1705-1706.
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Barzegarkhoo, R, Farhangi, M, Aguilera, RP, Siwakoti, YP & Lee, SS 1970, 'Switched-Boost Common-Ground Five-Level (SBCG5L) Grid-Connected Inverter With Single-Stage Dynamic Voltage Boosting Concept', 2021 IEEE Energy Conversion Congress and Exposition (ECCE), 2021 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, pp. 1014-1019.
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Dynamic voltage boosting feature in photovoltaic (PV) grid-integrated application is a necessity to achieve the maximum power point of PV arrays as well as boost the input voltage to the necessary dc-link voltage requirement. Such feature is usually achieved by incorporating a front-end boost or buck-boost dc-dc converter tendem with a conventional two or three-level inverter. However, such an integration cannot efficiently meet many IEEE strict grid codes from the power quality, ground leakage current, and the overall efficiency per power density aspects. The aim of this paper is to present a new five-level (5L) inverter with an integrated single-stage dynamic voltage-boosting concept and a common-grounded (CG) feature. As a result, the concern of ground leakage current is addressed through the provided CG feature, whilst both the power quality and overall efficiency of the system are improved with a 5L single-stage boosted output voltage waveform. To confirm the effectiveness of the proposal, apart from the circuit description, some experimental results are also presented.
Barzegarkhoo, R, Siwakoti, YP & Aguilera, RP 1970, 'A New Common-Ground Switched-Boost Five-Level Inverter Suitable for both Single and Three-Phase Grid-Tied Applications', 2021 IEEE Energy Conversion Congress and Exposition (ECCE), 2021 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, Vancouver, BC, Canada, pp. 1179-1183.
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Common-Ground (CG)-type multilevel inverters exhibit some interesting features in removing the concern of ground leakage current and improving the overall efficiency of the system within a single power processing stage. In this paper, a new reduced-switch count five-level (5L)-CG-based inverter is proposed, which offers a step-up voltage boosting property and can be extended to the three-phase systems whilst maintaining its CG-feature. The circuit descriptions, comparative study and the relevant measurement results for both the single and three-phase systems are presented to verify the feasibility of the proposed topology.
Bone, D, Gay, V, Brookes, W, Trede, F & Braun, R 1970, 'Roadshow Presentations for Developing Presentation and Feedback Skills in Studio Based Learning', 2021 19th International Conference on Information Technology Based Higher Education and Training (ITHET), 2021 19th International Conference on Information Technology Based Higher Education and Training (ITHET), IEEE, Sydney.
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Abstract—Studio Based Learning (SBL) is related to the well established Problem Based Learning (PBL) approach. At theUniversity of Technology Sydney we apply SBL in the delivery of undergraduate engineering subjects. This paper updates the description of the Studio with a focus on the steps that have been taken to adapt it to online delivery in the context of the pandemic and describes the adoption of roadshow presentations in the engineering studio subjects as a method to develop students’ presentation and feedback skills. We report on the method used and feedback received from students
Candra, H, Djuana, TE, Ts, KR, Hartanti, MD & Chai, R 1970, 'Smart Container Design for Transporting Virus Sample in Remote Areas', 2021 IEEE International Conference on Health, Instrumentation & Measurement, and Natural Sciences (InHeNce), 2021 IEEE International Conference on Health, Instrumentation & Measurement, and Natural Sciences (InHeNce), IEEE.
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Cao, F, An, P, Huang, X, Yang, C & Wu, Q 1970, 'Multi-Models Fusion for Light Field Angular Super-Resolution', ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Toronto, ON, Canada, pp. 2365-2369.
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Light field (LF) imaging has received increasing attention due to its richer interpretation of the scene. However, an inherent spatial-angular trade-off exists in LF that prevents LF from practical applications. Consequently, how to break such a trade-off has become one of the main challenges in sparsely sampled LF reconstruction. LF super-resolution (SR) can provide an opportunity to solve this issue, but most methods exploit only one form of LF, thereby leading to much loss of information. We believe that different LF forms can compensate each other to obtain higher gains via fusion strategy. In this paper, therefore, we propose a multi-models fusion for LF SR in angular domain. Cascading models which are trained by different LF forms can fully exploit rich LF information. Experimental results demonstrate that our method is effective and achieves a comparable result against state-of-the-art techniques.
Chaczko, Z, Chiu, C, Borowik, G & Braun, R 1970, 'Assistive IoT-centric Robotics for Senior Living', 2021 19th International Conference on Information Technology Based Higher Education and Training (ITHET), 2021 19th International Conference on Information Technology Based Higher Education and Training (ITHET), IEEE, Sydney, Australia, pp. 1-6.
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Senior citizens consider the most significant part of aging to be the changes that occur to their bodies. For this reason, they avoid doing body exercises and therefore often prone to heart diseases, muscle disorders and many other ailments. Although weakening of bones and muscles is inevitable as we grow old, we can still maintain our health by doing regular physical exercise. Encouraging elderly people to do physical exercise can be challenging, therefore the Robot-Trainer is introduced to make their exercising journey exciting accompanied by a friendly humanoid robot. The main goal of the system is to program an IoT-Assistive Robot to interact and conduct physical exercise training sessions with elderly people and maintain their physical fitness and mental health.
Chaiwongsai, J, Boonthep, N, Miyanaga, Y, Cheosuwan, T & Innawong, B 1970, 'Agricultural Year-Round Planning Model for Market-oriented Farms', 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, IEEE.
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Cheng, T, Aguilera, RP, Lu, DD-C & Siwakoti, YP 1970, 'Evaluation of Thermal Performance of Three-Phase Systems With Zero Sequence Injection', 2021 IEEE Southern Power Electronics Conference (SPEC), 2021 IEEE Southern Power Electronics Conference (SPEC), IEEE, Kigali, Rwanda, pp. 1-6.
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Zero-sequence injection (ZSI) techniques are widely adopted in Wye- and Delta-connected cascaded H-bridge (CHB) three-phase systems to cope with the unbalanced power generation from each phase. Recent studies have shown that a ZSI can allow a $\triangle-$connected CHB converter to cope with a large range of power imbalances among phases when compared to a standard Y-connected CHB converter. The superiority in the electrical performance under unbalanced input power has been well investigated, however the comparative thermal performance for both configurations under same power imbalance condition has not been investigated. In this work, the thermal performance of a CHB converter connected in both $\triangle-$configuration and Yconfiguration operating under an unbalanced power condition is studied. For this, the enquired ZSI for both $\triangle-$ and Y-connected CHB converters is firstly analyzed. Then, the impact of ZSI on the thermal performance and lifetime expectancy of the power switches in each phase is evaluated and compared for both configurations. The thermal analysis carried out in this work shows that even though a $\triangle-$connected CHB converter offer a wider power imbalance operation than Y-connected counterpart from the electrical viewpoint, this is achieved by thermally stressing the power switches in each phase in an uneven manner, and in some case overstressing them. Consequently, there is a trade-off between electrical performance and thermal stress when dealing with power imbalances in $\triangle$-connected and Y-connected CHB converters.
Chu, NH, Hoang, DT, Nguyen, DN, Huynh, NV & Dutkiewicz, E 1970, 'Fast or Slow: An Autonomous Speed Control Approach for UAV-assisted IoT Data Collection Networks', 2021 IEEE Wireless Communications and Networking Conference (WCNC), 2021 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, China.
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Unmanned Aerial Vehicles (UAVs) have been emerging as an effective solution for IoT data collection networks thanks to their outstanding flexibility, mobility, and low operation costs. However, due to the limited energy and uncertainty from the data collection process, speed control is one of the most important factors while optimizing the energy usage efficiency and performance for UAV collectors. This work aims to develop a novel autonomous speed control approach to address this issue. To that end, we first formulate the dynamic speed control task of a UAV as a Markov decision process taking into account its energy status and location. In this way, the Q-learning algorithm can be adopted to obtain the optimal speed control policy for the UAV. To further improve the system performance, we develop a highly-effective deep dueling double Q-learning algorithm utilizing outstanding features of the deep neural networks as well as advanced dueling architecture to quickly stabilize the learning process and obtain the optimal policy. Through simulations, we show that our proposed solution can achieve up to 40% greater performance, i.e., an average throughput of the system, compared with other conventional methods. Importantly, the simulation results also reveal significant impacts of UAV’s energy and charging time on the system performance.
Cotton, D & Chaczko, Z 1970, 'GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform', 2021 IEEE Wireless Communications and Networking Conference (WCNC), 2021 IEEE Wireless Communications and Networking Conference (WCNC), IEEE.
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Deng, Y, Zou, Y, Gong, S, Lyu, B, Hoang, DT & Niyato, D 1970, 'Robust Beamforming for IRS-assisted Wireless Communications under Channel Uncertainty', 2021 IEEE Wireless Communications and Networking Conference (WCNC), 2021 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Nanjing, PEOPLES R CHINA.
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Dinh, TQ, Nguyen, DN, Hoang, DT, Vu, PT & Dutkiewicz, E 1970, 'Enabling Large-Scale Federated Learning over Wireless Edge Networks', 2021 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2021 - 2021 IEEE Global Communications Conference, IEEE, Spain.
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Ellis, MS, Arthur, P, Nourinia, J, Ghobadi, C, Lalbakhsh, A & Mohammadi, B 1970, 'Compact Low-profile Unidirectional Antenna at 2.45GHz for Body-centric Communications', 2021 Photonics & Electromagnetics Research Symposium (PIERS), 2021 Photonics & Electromagnetics Research Symposium (PIERS), IEEE.
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Emami, Y, Wei, B, Li, K, Ni, W & Tovar, E 1970, 'Deep Q-Networks for Aerial Data Collection in Multi-UAV-Assisted Wireless Sensor Networks', 2021 International Wireless Communications and Mobile Computing (IWCMC), 2021 International Wireless Communications and Mobile Computing (IWCMC), IEEE.
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Eslahi, H, Hamilton, TJ & Khandelwal, S 1970, 'Circuit Performance Analysis of Analog RF LNA Designed with Negative Capacitance FET', 2021 IEEE Asia-Pacific Microwave Conference (APMC), 2021 IEEE Asia-Pacific Microwave Conference (APMC), IEEE.
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Esselle, KP, Afzal, MU, Singh, K, Thalakotuna, DN, Ahmed, F & Sayem, ASM 1970, 'Applications of Near-Field Meta-Steering Antenna Systems', 2021 IEEE Conference on Antenna Measurements & Applications (CAMA), 2021 IEEE Conference on Antenna Measurements & Applications (CAMA), IEEE.
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Farhangi, M, Barzegarkhoo, R, Siwakoti, YP, Lu, D & Lee, SS 1970, 'A Novel Single-Source Single-Stage Switched-Boost Five-Level (S5B5L) Inverter With Dynamic Voltage Boosting Feature', 2021 IEEE Energy Conversion Congress and Exposition (ECCE), 2021 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, Vancouver, BC, Canada, pp. 2557-2562.
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Single-source inverters with integrated switched-boost (SB) module and single-stage energy power conversion architecture have become a popular solution in many power electronics applications. This paper presents a novel topology of such types of inverters, which offers a modular design with a dynamic voltage boosting feature over a wide range of input voltages. The basic configuration of the proposed topology is able to generate three output voltage levels. It is comprised of an integrated SB module and a Quasi-H-Bridge (QHB) cell. The QHB cell consists of a self-balanced floating capacitor and four power switches, and the SB cell consists of a boost inductor and a single power switch. A five-level boosted output voltage can be achieved through the differential connection of two identical QHB cells utilizing a single SB module. The circuit description of the proposed topology, a comparative study, and the simulation and measurement results are given to verify the feasibility of this proposal.
Farhangi, M, Siwakoti, YP, Barzegarkhoo, R, Hasan, SU, Lu, D & Rogers, D 1970, 'A Compact Design Using GaN Semiconductor Devices for a Flying Capacitor Five-Level Inverter', 2021 IEEE Energy Conversion Congress and Exposition (ECCE), 2021 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, Vancouver, BC, Canada, pp. 2475-2479.
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Multilevel inverters (MLIs) based on the flying capacitor (FC) concept are beneficial in many renewable energy-based applications due to their compactness, low current stress on semiconductor devices, and reasonable thermal behavior for high-power applications. However, the recently developed FC-based topologies suffer from half dc-link voltage utilization and a variable high-frequency common-mode voltage (HF-CMV). The aim of this paper is to propose an FC-based family of MLIs with a five-level (5L) output voltage, full dc-link voltage utilization, and low HF-CMV. Using redundant states and the phase-shifted sinusoidal PWM technique, the value of the flying capacitor has been reduced significantly. The performance of the converter has been verified using Gallium Nitride (GaN) power switches. Circuit description and a brief comparative study with existing MLIs are given to justify the suitability of the topology.
Fatema, I, Kong, X & Fang, G 1970, 'Analysing and Forecasting Electricity Demand and Price Using Deep Learning Model During the COVID-19 Pandemic', The 21st International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT’20), Springer Singapore, Shenzhen, China, pp. 115-127.
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The smart city integrating the smart grid as an integral part of it to guaran-tee the ever-increasing electricity demand. After the recent outbreak of the COVID-19 pandemic, the socioeconomic severances affecting total levels of electricity demand, price, and usage trends. These unanticipated changes in-troducing new uncertainties in short-term demand forecasting since its result depends on the recent usage as an input variable. Addressing this challeng-ing situation, this paper proposes an electricity demand and price forecast model based on the LSTM Deep Learning method considering the recent demand trends. Real electricity market data from the Australian Energy Market Operator (AEMO) is used to validate the effectiveness of the pro-posed model and elaborated with two scenarios to get a wider context of the pandemic impact. Exploratory data analyses results show hourly electricity demand and price reductions throughout the pandemic weeks, especially during peak hours of 8 am- 12 noon and 6 pm – 10 pm. Electricity demand and price has been dropped by 3% and 42% respectively on average. Howev-er, overall usage patterns have not changed significantly compared to the same period last year. The predictive accuracy of the proposed model is quite effective with an acceptably smaller error despite trend change phenomena triggered by the pandemic. The model performance is comprehensively com-pared with a few conventional forecast methods, Support Vector Machine (SVM) and Regression Tree (RT), and as a result, the performance indices RMSE and MAE have been improved using the proposed LSTM model.
Gao, X, Du, J, Zhang, T, Zhang, H, Huang, X & Guo, YJ 1970, 'Terahertz Communication Demonstration by using a High-Tc Superconducting Josephson Receiver Integrated with a Miniature Cryocooler', 2021 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), 2021 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), IEEE, pp. 142-144.
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Gautam, S, Hassan, W, Bhatta, A, Lu, DD-C & Xiao, W 1970, 'A Comprehensive Study of Orthogonal Signal Generation Schemes for Single Phase Systems', 2021 1st International Conference on Power Electronics and Energy (ICPEE), 2021 1st International Conference on Power Electronics and Energy (ICPEE), IEEE.
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Gong, Y, Yi, J, Chen, D-D, Zhang, J, Zhou, J & Zhou, Z 1970, 'Inferring the Importance of Product Appearance with Semi-supervised Multi-modal Enhancement', Proceedings of the 29th ACM International Conference on Multimedia, MM '21: ACM Multimedia Conference, ACM.
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Guo, K & Guo, Y 1970, 'High Precision Control of Flux Switching Linear Rotary Machine for Reelwinder', 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA), 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA), IEEE.
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Guo, S, Liu, Y, Ni, Y & Ni, W 1970, 'Lightweight SSD: Real-time Lightweight Single Shot Detector for Mobile Devices', Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 16th International Conference on Computer Vision Theory and Applications, SCITEPRESS - Science and Technology Publications.
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Guo, YJ, Chen, S-L & Liu, Y 1970, 'Reconfigurable Antenna Arrays for Integrated Space and Terrestrial Networks', 2020 International Symposium on Antennas and Propagation (ISAP), 2020 International Symposium on Antennas and Propagation (ISAP), IEEE, Osaka, Japan.
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Integrated space and terrestrial networks are expected to be a key technology for beyond 5G communications systems. One main challenge posed for the antennas is the array performance deterioration caused by orientation changes of various elements mounted to conformal platforms. To overcome this challenge, a reconfigurable antenna element that can switch among four linear polarizations is reported and applied to a conformal array. An efficient array synthesis algorithm is utilized to attain high-gain, low-sidelobes, and low-cross-polarization characteristics. A linear conformal array is presented to verify the effectiveness of the developed algorithm.
Hasanpour, S, Forouzesh, M & Siwakoti, YP 1970, 'Full Soft-Switching Ultra-High Gain DC/DC Converter Using Three-Winding Coupled-Inductor', 2021 12th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), 2021 12th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), IEEE.
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Hayat, T, Afzal, MU, Ahmed, F & Esselle, KP 1970, 'Rapid Prototyping of Ultrawideband Compact Resonant Cavity Antennas Using 3D Printing', 2021 IEEE Asia-Pacific Microwave Conference (APMC), 2021 IEEE Asia-Pacific Microwave Conference (APMC), IEEE.
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Hu, T, Yu, J, Yang, H & Ni, W 1970, 'Segmentation of Intracranial Aneurysm Based on U-Net and BiConvGRU', 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE.
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Hua, C, Gunawardane, K & Lie, TT 1970, 'Investigation of Progressing Low and Medium Voltage DC Standards to Acquire the Implementation Scenarios for Domestic/Commercial and Industrial Converters and Enabling Technologies', 2021 6th Asia Conference on Power and Electrical Engineering (ACPEE), 2021 6th Asia Conference on Power and Electrical Engineering (ACPEE), IEEE.
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Huang, H, Zhang, J, Zhang, J, Wu, Q & Xu, C 1970, 'PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning', 35th AAAI Conference on Artificial Intelligence, AAAI 2021, The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), the AAAI Press, Palo Alto, California USA,, Online, pp. 1602-1609.
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The predicament in semi-supervised few-shot learning (SSFSL) is to maximize the value of the extra unlabeled data to boost the few-shot learner. In this paper, we propose a Poisson Transfer Network (PTN) to mine the unlabeled information for SSFSL from two aspects. First, the Poisson Merriman-Bence-Osher (MBO) model builds a bridge for the communications between labeled and unlabeled examples. This model serves as a more stable and informative classifier than traditional graph-based SSFSL methods in the message-passing process of the labels. Second, the extra unlabeled samples are employed to transfer the knowledge from base classes to novel classes through contrastive learning. Specifically, we force the augmented positive pairs close while push the negative ones distant. Our contrastive transfer scheme implicitly learns the novel-class embeddings to alleviate the over-fitting problem on the few labeled data. Thus, we can mitigate the degeneration of embedding generality in novel classes. Extensive experiments indicate that PTN outperforms the state-of-the-art few-shot and SSFSL models on miniImageNet and tieredImageNet benchmark datasets.
Huang, W, Da Xu, RY, Jiang, S, Liang, X & Oppermann, I 1970, 'GAN-based Gaussian Mixture Model Responsibility Learning', 2020 25th International Conference on Pattern Recognition (ICPR), 2020 25th International Conference on Pattern Recognition (ICPR), IEEE.
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Huang, W, Du, W & Xu, RYD 1970, 'On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization', Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}, International Joint Conferences on Artificial Intelligence Organization.
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The prevailing thinking is that orthogonal weights are crucial to enforcing dynamical isometry and speeding up training. The increase in learning speed that results from orthogonal initialization in linear networks has been well-proven. However, while the same is believed to also hold for nonlinear networks when the dynamical isometry condition is satisfied, the training dynamics behind this contention have not been thoroughly explored. In this work, we study the dynamics of ultra-wide networks across a range of architectures, including Fully Connected Networks (FCNs) and Convolutional Neural Networks (CNNs) with orthogonal initialization via neural tangent kernel (NTK). Through a series of propositions and lemmas, we prove that two NTKs, one corresponding to Gaussian weights and one to orthogonal weights, are equal when the network width is infinite. Further, during training, the NTK of an orthogonally-initialized infinite-width network should theoretically remain constant. This suggests that the orthogonal initialization cannot speed up training in the NTK (lazy training) regime, contrary to the prevailing thoughts. In order to explore under what circumstances can orthogonality accelerate training, we conduct a thorough empirical investigation outside the NTK regime. We find that when the hyper-parameters are set to achieve a linear regime in nonlinear activation, orthogonal initialization can improve the learning speed with a large learning rate or large depth.
Huang, Y, Wu, Q, Xu, J, Zhong, Y & Zhang, Z 1970, 'Clothing Status Awareness for Long-Term Person Re-Identification', 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Montreal, pp. 11875-11884.
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Long-Term person re-identification (LT-reID) exposes extreme challenges because of the longer time gaps between two recording footages where a person is likely to change clothing. There are two types of approaches for LT-reID: biometrics-based approach and data adaptation based approach. The former one is to seek clothing irrelevant biometric features. However, seeking high quality biometric feature is the main concern. The latter one adopts fine-tuning strategy by using data with significant clothing change. However, the performance is compromised when it is applied to cases without clothing change. This work argues that these approaches in fact are not aware of clothing status (i.e., change or no-change) of a pedestrian. Instead, they blindly assume all footages of a pedestrian have different clothes. To tackle this issue, a Regularization via Clothing Status Awareness Network (RCSANet) is proposed to regularize descriptions of a pedestrian by embedding the clothing status awareness. Consequently, the description can be enhanced to maintain the best ID discriminative feature while improving its robustness to real-world LT-reID where both clothing-change case and no-clothing-change case exist. Experiments show that RCSANet performs reasonably well on three LT-reID datasets.
Jahan, S, Biswas, SP, Hosain, MK, Islam, MR, Rahman, MM & Guo, Y 1970, 'A New Control Scheme for Three-Phase Non-Isolated Grid Feeding PV Inverter', 2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021 31st Australasian Universities Power Engineering Conference (AUPEC), IEEE.
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Jauregi Unanue, I, Parnell, J & Piccardi, M 1970, 'BERTTune: Fine-Tuning Neural Machine Translation with BERTScore', Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Association for Computational Linguistics, pp. 915-924.
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Kamal, MS, Chowdhury, L, Dey, N, Fong, SJ & Santosh, K 1970, 'Explainable AI to Analyze Outcomes of Spike Neural Network in Covid-19 Chest X-rays', 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE.
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Karmokar, DK & Thalakotuna, DNP 1970, 'Continuous Scan of Radiation Beam from a Half-Width Microstrip Leaky-Wave Antenna at a Fixed Operating Frequency', 2021 IEEE Conference on Antenna Measurements & Applications (CAMA), 2021 IEEE Conference on Antenna Measurements & Applications (CAMA), IEEE.
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Karmokar, DK, Thalakotuna, DNP, Matekovits, L & Esselle, KP 1970, 'One Dimensional Leaky-Wave Antennas with Continuous Scan of Radiating Beam', 2021 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2021 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE.
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Khawaldeh, HA, Al-soeidat, M, Dah-Chuan Lu, D & Li, L 1970, 'Fast Photovoltaic Emulator Based on PV-cell Equivalent Circuit Model', 2021 IEEE 12th Energy Conversion Congress & Exposition - Asia (ECCE-Asia), 2021 IEEE 12th Energy Conversion Congress & Exposition - Asia (ECCE-Asia), IEEE, Singapore, Singapore, pp. 2121-2126.
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Photovoltaic (PV) emulator is a specific type of power electronic device used to simulate and produce the nonlinear characteristic curves for actual solar panel or array. It usually requires fast computing and power converters with a wide output range. However, the emulator response time is restricted by the controller bandwidth, and it must stabilize the converter for many different operating points. Hence pure power converter based solutions generally have a slower response time when compared with the real PV system. This paper presents a PV emulator based on a PV cell equivalent circuit model. It consists of a constant current source converter (CCSC) and a string of diodes to mimic the nonlinearity of any PV source. The CCSC simplifies the converter and controller designs as it operates at a fixed point for each insolation level, as compared with a converter-based solution which requires a voltage-source converter with wide output operating ranges. This study focuses on two aspects of the PV emulator design. Firstly, a detailed parametric design from model equations to the extraction of practical real PV parameters is explained to estimate the electrical performance of the PV simulator. Secondly, the CCSC and controller designs are explained. An experimental prototype is designed to validate the PV simulator. In addition to steady-state operation, the dynamic response of series connected cells is also emulated to verify the effectiveness of the proposed platform. Both simulations and experimental results are conducted. The response time of the proposed emulator system is comparable to both a benchmarked commercial product and a real PV system.
Kieu, BT, Unanue, IJ, Pham, SB, Phan, HX & Piccardi, M 1970, 'Learning Neural Textual Representations for Citation Recommendation', 2020 25th International Conference on Pattern Recognition (ICPR), 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, Online, pp. 4145-4152.
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Klempous, R, Kluwak, K, Atsushi, I, Gorski, T, Nikodem, J, Bozejko, W, Chaczko, Z, Borowik, G, Rozenblit, J & Kulbacki, M 1970, 'Neural Networks Classification for Training of Five German Longsword Mastercuts - A Novel Application of Motion Capture: Analysis of Performance of Sword Fencing in the Historical European Martial Arts (HEMA) Domain', 2021 IEEE 21st International Symposium on Computational Intelligence and Informatics (CINTI), 2021 IEEE 21st International Symposium on Computational Intelligence and Informatics (CINTI), IEEE.
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Kong, B, Liu, Y & Li, M 1970, 'Binary Coded Genetic Algorithm Application for Array Diagnosis', 2021 International Conference on Microwave and Millimeter Wave Technology (ICMMT), 2021 International Conference on Microwave and Millimeter Wave Technology (ICMMT), IEEE.
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Kumar, A, Esmaili, N & Piccardi, M 1970, 'A REINFORCEd Variational Autoencoder Topic Model', International Conference on Neural Information Processing, International Conference on Neural Information Processing, Springer International Publishing, Bali, Indonesia, pp. 360-369.
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Kurunathan, H, Li, K, Ni, W, Tovar, E & Dressler, F 1970, 'Deep Reinforcement Learning for Persistent Cruise Control in UAV-aided Data Collection', 2021 IEEE 46th Conference on Local Computer Networks (LCN), 2021 IEEE 46th Conference on Local Computer Networks (LCN), IEEE.
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Lee, SS, Gorla, NBY, Panda, SK, Lee, K-B, Siwakoti, YP & Barzegarkhoo, R 1970, 'A Common-Ground-Type Single-Stage Buck-Boost Inverter with Sinusoidal Output Voltage', 2021 IEEE 12th Energy Conversion Congress & Exposition - Asia (ECCE-Asia), 2021 IEEE 12th Energy Conversion Congress & Exposition - Asia (ECCE-Asia), IEEE.
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Li, K, Lu, N, Zheng, J, Zhang, P, Ni, W & Tovar, E 1970, 'A Practical Secret Key Management for Multihop Drone Relay Systems based on Bluetooth Low Energy', 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), IEEE.
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Li, M & Liu, Y 1970, 'Saddle-Shaped Pattern Synthesis by Element Rotation and Phase Optimization for Linear Dipole Array', 2021 International Applied Computational Electromagnetics Society (ACES-China) Symposium, 2021 International Applied Computational Electromagnetics Society (ACES-China) Symposium, IEEE.
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Li, M, Yang, Y, Iacopi, F & Nulman, J 1970, 'Additively Manufactured Multi-Layer Bandpass Filter Based on Vertically Integrated Composite Right and Left Handed Resonator', 2021 IEEE Asia-Pacific Microwave Conference (APMC), 2021 IEEE Asia-Pacific Microwave Conference (APMC), IEEE, Brisbane, Australia, pp. 175-177.
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This paper proposed a miniaturised multi-layer composite right/left-handed (CRLH) resonator. The odd- and even-mode circuit analysis method is presented to explain the resonance properties of the three-dimension (3D) CRLH resonator. Based on the CRLH resonator, a bandpass filter (BPF) with wide bandwidth, low-loss, and compact size is proposed. A fully-integrated additive manufacturing (AM) approach is introduced to fabricate the proposed BPF. Finally, the proposed compact BPF is designed at 8.3 GHz with a bandwidth of 72.3%, a low insertion loss of 0.51 dB, and a compact size of 5.8mm × 2.1 mm × 0.89 mm (0.269λg × 0.097λg× 0.041λg), which is suitable for circuit-in-package applications.
Li, M, Yang, Y, Zhang, Y, Iacopi, F, Ram, S & Nulman, J 1970, 'A Fully Integrated Conductive and Dielectric Additive Manufacturing Technology for Microwave Circuits and Antennas', 2020 50th European Microwave Conference (EuMC), 2020 50th European Microwave Conference (EuMC), IEEE, pp. 392-395.
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© 2021 EuMA. A fully-integrated additive manufacturing (AM) approach for microwave devices is presented in this work. The applied AM technology can print the circuit models simultaneously with conductive and dielectric materials. Taking advantage of this one-stop 3D manufacturing technology, multilayer circuit board with vias or holes can be prototyped rapidly and precisely. The electrical properties of the dielectric ink material are measured up to 40 GHz using a quasi-optical cavity test system. To further demonstrate the merit of this technology, an infilled-ground transmission line, as well as a microstrip patch antenna, are designed and fabricated. For proof-of-concept, the return loss, gain and radiation patterns of the antenna are measured, which demonstrate a good agreement with the simulated results.
Li, S & Piccardi, M 1970, 'Improving Adversarial Text Generation with n-Gram Matching', Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation, PACLIC 2021, pp. 647-655.
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In the past few years, generative adversarial networks (GANs) have become increasingly important in natural language generation. However, their performance seems to still have a significant margin for improvement. For this reason, in this paper we propose a new adversarial training method that tackles some of the limitations of GAN training in unconditioned generation tasks. In addition to the commonly used reward signal from the discriminator, our approach leverages another reward signal which is based on the occurrence of n-gram matches between the generated sentences and the training corpus. Thanks to the inherent correlation of this reward signal with the commonly used evaluation metrics such as BLEU, our approach implicitly bridges the gap between the objectives used during training and inference. To circumvent the non-differentiability issues associated with a discrete objective, our approach leverages the reinforcement learning policy gradient theorem. Our experimental results show that the model trained with mixed rewards from both n-gram matching and the discriminator has been able to outperform other GAN-based models in terms of BLEU score and quality-diversity trade-off at a parity of computational budget.
Li, X, Peng, Y & Xu, M 1970, 'Edge-enhanced Instance Segmentation of Wrist CT via a Semi-Automatic Annotation Database Construction Method', 2021 Digital Image Computing: Techniques and Applications (DICTA), 2021 Digital Image Computing: Techniques and Applications (DICTA), IEEE.
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Li, Y, Feng, Y, Huang, S, Ma, B, Zhu, S & Zhu, J 1970, 'An Acceleration Method for AC Steady State Performance of Dual Three-Phase Machine: Modeling and Implementation', 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA), 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA), IEEE.
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Lima, D, Li, L & Zhang, J 1970, 'Minimizing electricity costs using biogas generated from food waste', 2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021 31st Australasian Universities Power Engineering Conference (AUPEC), IEEE, Perth, Australia, pp. 1-6.
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Anaerobic digestion is a common alternative for turning food waste into biogas that can be used for energy supply. In this paper, a model that explores the use of biogas to generate electricity in a fulfilment center is conducted. Simulation results from 5 different case studies show that the fulfilment center can achieve from 9.88% to almost 16.10% in electricity cost savings by using only the food waste potential to supply a portion of its energy demand. The use of food waste for renewable energy generation in fulfilment centers has the potential to reduce electricity costs rather than disposing of them in landfills.
Lin, S, Kong, X, Wang, J, Liu, A, Fang, G & Han, Y 1970, 'Development of a UAV Path Planning Approach for Multi-building Inspection with Minimal Cost', International Conference on Parallel and Distributed Computing, Applications and Technologies, Springer International Publishing, Shenzhen, China, pp. 82-93.
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This paper presents a UAV path planning approach for multi-building inspection, which is a new application for UAV path planning. It generates helix paths for single building inspection first and defines the possible points for collecting inspection data with reasonable time slots. After inspecting one building, the UAV flies to another building with a trajectory based on a cost matrix and a visited vector defined in this algorithm. The planning of the entire inspection path is evaluated considering several factors, such as distance, time, and altitude. The proposed algorithm is applied to historical giant communal homes, Fujian Tulou, consisting of five buildings.
Lin, S, Liu, A, Kong, X & Wang, J 1970, 'Development of Swarm Intelligence Leader-Vicsek-Model for Multi-AGV Path Planning', 2021 20th International Symposium on Communications and Information Technologies (ISCIT), 2021 20th International Symposium on Communications and Information Technologies (ISCIT), IEEE, Tottori, Japan, pp. 49-54.
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Automatic guided vehicle (AGV) is the mobile robot widely used in the industry, and multiple AGVs are usually involved for industrial intelligence. Path planning for multi-AGV is essential for automatic transportation, and we propose a swarm-based approach that is inspired by biological subjects. The dynamic virtual leader is assigned for updating the locations and angles for the Leader-Vicsek-Model, and multiple agents are assigned through the centralized approach while the path planning is achieved through the decentralized method.
Lin, Y, Wang, J, Zhang, J & Li, L 1970, 'Optimal Investment Decision for Cotton Farm Microgrid Design', 2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021 31st Australasian Universities Power Engineering Conference (AUPEC), IEEE, Perth, Australia, pp. 1-6.
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The integration of renewable energy sources (RESs) into distributed microgrid systems has been widely applied in agriculture, and in particular in cotton farms. Due to the specific irrigation periods and non-irrigation periods during cotton growth, and the inherent intermittent characteristics of RESs, the design of a cotton farm microgrid system becomes challenging. Finding the optimal size of the RESs for a cotton farm microgrid needs to consider not only the energy demand for cotton irrigation but also the investment cost and the payback period. This paper presents an optimization model for cotton farm microgrid design, which explores available RESs and energy storage options to ensure reliable power supply from renewables. Furthermore, the designed microgrid utilizes solar photovoltaic (PV) units and wind turbine generator as RESs together with battery storage and demonstrates the supply and demand relationship between the microgrid and pump loads. By using RES power supply, renewable energy is optimally utilized to satisfy the seasonal loads demand, and the grid power is used as a backup power source. The objectives of optimization include investment cost, operating cost and simple payback period. In order to solve the underlying optimization problem, this paper adopts YALMIP MATLAB Toolbox. A case study is undertaken using historical energy consumption data for a cotton farm in Gunnedah, New South Wales, to verify the applicability of the proposed approach.
Lin, Y, Zhang, J & Li, L 1970, 'A Model Predictive Control for Cotton Farm Microgrid Systems in Australia', 2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021 31st Australasian Universities Power Engineering Conference (AUPEC), IEEE, Perth, Australia, pp. 1-6.
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This paper presents a model predictive control (MPC) approach to a microgrid at a cotton farm so as to minimize the water pumping operational cost while taking full advantage of renewable energy sources. The reason for using MPC is its ability in handling noise, disturbance, and real-time parameter changes. In this paper, the MPC models of grid-connected are established; moreover, the effectiveness and robustness of the MPC models are analyzed by cotton farm case studies. Simulation results show that the optimal MPC solutions for grid-connected microgrid of a farm are AU$8.4/ML less than a manual control-based strategy. In addition, the MPC solution shows outstanding robustness in controlling the water reservoir level. When the disturbance data of the rainy season in 2016 are added, the system saves 34.5% of the operating cost compared with the baseline. When the rainy season disturbance is added together, the system saves 11.74% of operating costs compared to the baseline.
Liu, A, Lin, S, Kong, X, Wang, J, Fang, G & Han, Y 1970, 'Development of Low-Cost Indoor Positioning Using Mobile-UWB-Anchor-Configuration Approach', https://www.springer.com/gp/book/9789811600098, The Joint International Conference the International Conference on Parallel and Distributed Computing, Applications and Technologies and the International Symposium on Parallel Architectures, Algorithms and Programming, Springer Singapore, Shenzhen, China, pp. 34-46.
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In recent years, with the growth of indoor positioning demand, many kinds of indoor positioning technologies have been studied. Compared with other technologies, UWB indoor positioning technology has the advantages of high positioning accuracy and strong anti-interference ability. However, the high cost of UWB hardware limits the application of this technology to practical applications. In particular, the effective communication distance of the UWB is within 10 meters, and if used in a large-area indoor environment, a plurality of anchor points is required to be installed in order to ensure the positioning accuracy. This leads to a high system hardware cost.
In this paper, we proposed a mobile-UWB-anchor-network approach. We changed the fixed anchors in UWB system into moving anchors to reduce the number of anchors in the area and reduce the cost of the system. This new approach is verified using experiments.
Liu, H, Zhu, X, Wang, Y, Men, K & Yeo, KS 1970, 'A 60 GHz Edge-Coupled 4-Way Balun Power Amplifier with 22.7 dBm Output Power and 27.7% Peak Efficiency', 2021 IEEE MTT-S International Microwave Symposium (IMS), 2021 IEEE/MTT-S International Microwave Symposium - IMS 2021, IEEE.
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Liu, L, Guo, Y, Lei, G & Zhu, J 1970, 'Design and Analysis Technologies of High Speed Permanent Magnet Machines', 2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021 31st Australasian Universities Power Engineering Conference (AUPEC), IEEE, Perth, Australia, pp. 1-6.
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Concerns about advantages such as high efficiency and power density, small size and weight as well as fast dynamic response contribute to the increasing industrial applications of high speed permanent magnet machines (HSPMMs). This paper presents a review on the design and analysis technologies for HSPMMs. Specifically, the stator/rotor structures and materials used in HSPMMs are firstly introduced. Then, the calculation models in terms of stator copper loss, stator iron loss, rotor eddy-current loss and air-friction loss are summarized. Because of the non-negligible influences of high working temperature on motor performance, different calculation approaches of HSPMM temperature distribution and rise as well as the cooling methods are compared. Moreover, the research status of HSPMM mechanical characteristics, including the rotor material strength, bearing support and dynamic behaviors, are analyzed, followed by the conclusions and future directions.
Liu, Y, Wang, M, Xu, J, Gong, S, Hoang, DT & Niyato, D 1970, 'Boosting Secret Key Generation for IRS-Assisted Symbiotic Radio Communications', 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), IEEE, ELECTR NETWORK.
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Mahmood, A, Sheng, QZ, Siddiqui, SA, Sagar, S, Zhang, WE, Suzuki, H & Ni, W 1970, 'When Trust Meets the Internet of Vehicles: Opportunities, Challenges, and Future Prospects', 2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC), 2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC), IEEE.
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Markos, C, Yu, JJQ & Xu, RYD 1970, 'Capturing Uncertainty in Unsupervised GPS Trajectory Segmentation Using Bayesian Deep Learning', 35th AAAI Conference on Artificial Intelligence, AAAI 2021, AAAI Conference on Artificial Intelligence, AAAI, Virtual, pp. 390-398.
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Intelligent transportation management requires not only statistical information on users' mobility patterns, but also knowledge of their corresponding transportation modes. While GPS trajectories can be readily obtained from GPS sensors found in modern smartphones and vehicles, these massive geospatial data are neither automatically annotated nor segmented by transportation mode, subsequently complicating transportation mode identification. In addition, predictive uncertainty caused by the learned model parameters or variable noise in GPS sensor readings typically remains unaccounted for. To jointly address the above issues, we propose a Bayesian deep learning framework for unsupervised GPS trajectory segmentation. After unlabeled GPS trajectories are preprocessed into sequences of motion features, they are used in unsupervised training of a channel-calibrated temporal convolutional neural network for timestep-level transportation mode identification. At test time, we approximate variational inference via Monte Carlo dropout sampling, leveraging the mean and variance of the predicted distributions to classify each input timestep and estimate its predictive uncertainty, respectively. The proposed approach outperforms both its non-Bayesian variant and established GPS trajectory segmentation baselines on Microsoft's Geolife dataset without using any labels.
Nabeel, MI, Afzal, MU, Thalakotuna, DN & Esselle, KP 1970, 'Investigating Square Slot Unit Cell for Low-Cost Phase-Gradient Metasurfaces', 2021 1st International Conference on Microwave, Antennas & Circuits (ICMAC), 2021 1st International Conference on Microwave, Antennas & Circuits (ICMAC), IEEE.
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Nguyen, CT, Hoang, DT, Nguyen, DN, Pham, H-A, Tuong, NH & Dutkiewicz, E 1970, 'Blockchain-based Secure Platform for Coalition Loyalty Program Management', 2021 IEEE Wireless Communications and Networking Conference (WCNC), 2021 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, China.
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In this paper, we propose a novel blockchain-based platform for the coalition loyalty program management. The platform allows the customers to freely exchange loyalty points from different existing blockchain-based loyalty programs by utilizing the sidechain technology. Moreover, by adopting the Proof-of-Stake consensus mechanism, we can further increase customer engagement by allowing the customers to participate in the consensus process to earn additional tokens. However, this might lead to situations where the customers centralize all tokens to a single chain/loyalty program if the chain offers more rewards for consensus participation. Through security and performance analyses, we show that such centralization of stakes poses a threat to the security and performance of the platform. Therefore, we develop a non-cooperative game model to analyze the rational behavior of the users. We reveal that the consensus participation rewards govern the user behavior and the decentralization of the system. Numerical experiments confirm our analytical results and show that the ratios between the consensus rewards have a significant impact on the system’s security and performance.
Nguyen, TK, Nguyen, HH & Tuan, HD 1970, 'Cell-Free Massive MIMO-NOMA with Optimal Backhaul Combining', 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE), 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE), IEEE.
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Niwa, K, Zhang, G, Kleijn, WB, Harada, N, Sawada, H & Fujino, A 1970, 'Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction', Proceedings of Machine Learning Research, virtual conference, pp. 8195-8204.
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A novel asynchronous decentralized optimization method that follows Stochastic Variance Reduction (SVR) is proposed. Average consensus algorithms, such as Decentralized Stochastic Gradient Descent (DSGD), facilitate distributed training of machine learning models. However, the gradient will drift within the local nodes due to statistical heterogeneity of the subsets of data residing on the nodes and long communication intervals. To overcome the drift problem, (i) Gradient Tracking-SVR (GT-SVR) integrates SVR into DSGD and (ii) Edge-Consensus Learning (ECL) solves a model constrained minimization problem using a primal-dual formalism. In this paper, we reformulate the update procedure of ECL such that it implicitly includes the gradient modification of SVR by optimally selecting a constraint-strength control parameter. Through convergence analysis and experiments, we confirmed that the proposed ECL with Implicit SVR (ECL-ISVR) is stable and approximately reaches the reference performance obtained with computation on a single-node using full data set.
Nurhanim, K, Elamvazuthi, I, Izhar, LI, Capi, G & Su, S 1970, 'EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm', 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), IEEE.
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Paniyil, P, Singh, R, Powar, V, Deb, N, Zhang, J, Bai, K & Dubey, A 1970, 'Batteries and Free Fuel based Photovoltaics and Complimentary Wind Energy based DC Power Networks as 100% Source of Electric Power around the Globe', 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC), 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC), IEEE.
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Parnell, J, Jauregi Unanue, I & Piccardi, M 1970, 'RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation', Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021), Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021), Association for Computational Linguistics, Online, pp. 1-11.
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Perry, S, Da Silva Cruz, LA, Dumic, E, Thi Nguyen, NH, Pinheiro, A & Alexiou, E 1970, 'Comparison of Remote Subjective Assessment Strategies in the Context of the JPEG Pleno Point Cloud Activity', 2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP), 2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP), IEEE.
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Qi, J, Li, L & Lei, G 1970, 'Economic Operation of a Workplace EV Parking Lot under Different Operation Modes', 2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021 31st Australasian Universities Power Engineering Conference (AUPEC), IEEE, Perth, Australia, pp. 1-6.
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In this paper, an electric vehicle (EV) charging station model at a workplace EV parking lot with energy storage system (ESS) and renewable energy sources (RESs) is proposed. Its economic operation under different operation modes is further explored. By comparing the paid charging mode at different prices with the free charging mode, the results show that although a sufficiently high charging price can obtain higher profit, the free charging model will bring greater profit growth with appropriate RES and ESS size as EVs will used for vehicle-to-grid (V2G) and grid-to-vehicle (G2V) transactions in return.
Qian, J, Begum, H, Yang, R & Lee, JE-Y 1970, 'Acoustically Driven Droplet Centrifugation Enabled by Frequency Operations Beyond Phononic Bandgaps', 2021 IEEE 34th International Conference on Micro Electro Mechanical Systems (MEMS), 2021 IEEE 34th International Conference on Micro Electro Mechanical Systems (MEMS), IEEE.
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Qian, J, Yang, R, Begum, H & Lee, JE-Y 1970, 'Reconfigurable Acoustofluidic Manipulation of Particles in Ring-Like Rich Patterns Enabled on a Bulk Micromachined Silicon Chip', 2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers), 2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers), IEEE.
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Quach, CH, Phung, MD, Le, HV & Perry, S 1970, 'SupSLAM: A Robust Visual Inertial SLAM System Using SuperPoint for Unmanned Aerial Vehicles', 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), IEEE.
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Rashid, FA, Sandrasegaran, K & Kong, X 1970, 'Simulation of MobiFall Dataset for Fall Detection Using MATLAB Classifier Algorithms', 2021 14th International Conference on Developments in eSystems Engineering (DeSE), 2021 14th International Conference on Developments in eSystems Engineering (DeSE), IEEE.
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Raza, A, Keshavarz, R & Shariati, N 1970, 'Miniaturized Patch Rectenna Using 3-Turn Complementary Spiral Resonator for Wireless Power Transfer', 2021 IEEE Asia-Pacific Microwave Conference (APMC), 2021 IEEE Asia-Pacific Microwave Conference (APMC), IEEE, pp. 455-457.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati Moghadam, N & Ni, W 1970, 'Statistical Learning-Based Dynamic Retransmission Mechanism for Mission Critical Communication: An Edge-Computing Approach', IEEE 45th Conference on Local Computer Networks, IEEE 45th Conference on Local Computer Networks, Sydney, NSW, Australia (Held Virtually).
Reja, VK, Bhadaniya, P, Varghese, K & Ha, QP 1970, 'Vision-Based Progress Monitoring of Building Structures Using Point-Intensity Approach', Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC), 38th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC).
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Saputra, YM, Nguyen, DN, Hoang, DT & Dutkiewicz, E 1970, 'Incentive Mechanism for AI-Based Mobile Applications with Coded Federated Learning', 2021 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2021 - 2021 IEEE Global Communications Conference, IEEE, Spain.
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Saputra, YM, Nguyen, DN, Hoang, DT & Dutkiewicz, E 1970, 'Selective Federated Learning for On-Road Services in Internet-of-Vehicles', 2021 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2021 - 2021 IEEE Global Communications Conference, IEEE, Spain.
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The Internet-of-Vehicles (IoV) can make driving safer and bring more services to smart vehicle (SV) users. Specif-ically, with IoV, the road service provider (RSP) can collaborate with SVs to provide high-accurate on-road information-based services by implementing federated learning (FL). Nonetheless, SVs' activities are very diverse in IoV networks, e.g., some SVs move frequently while other SVs are occasionally disconnected from the network. Consequently, obtaining information from all SVs for the learning process is costly and impractical. Furthermore, the quality-of-information (QoI) obtained by SVs also dramatically varies. That makes the learning process from all SVs simultaneously even worse when some SVs have low QoI. In this paper, we propose a novel selective FL approach for an IoV network to address these issues. Particularly, we first develop an SV selection method to determine a set of active SVs based on their location significance. In this case, we adopt a K-means algorithm to classify significant and insignificant areas where the SVs are located according to the areas' average annual daily flow of vehicles. From the set of SVs in the significant areas, we select the best SVs for the FL execution based on the SVs' QoI at each learning round. Through simulation results using a real-world on-road dataset, we observe that our proposed approach can converge to the FL results even with only 10% of active SVs in the network. Moreover, our results reveal that the RSP can optimize on-road services with faster convergence up to 63% compared with other baseline FL methods.
Sharma, V, Hossain, J, Yang, Y, Ali, SMN & Kashif, M 1970, 'Improved Sigma Z-source Inverter-fed Grid System for Wind Power Generation', The 9th Renewable Power Generation Conference (RPG Dublin Online 2021), The 9th Renewable Power Generation Conference (RPG Dublin Online 2021), Institution of Engineering and Technology.
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Singh, K, Afzal, MU & Esselle, KP 1970, 'An Overview On the Optimization of Beam-Steering Metasurfaces', 2021 XXXIVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), 2021 XXXIVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), IEEE.
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Singh, K, Afzal, MU, Lalbakhsh, A & Esselle, KP 1970, 'Reflecting Phase-Gradient Metasurface for Radar Cross Section Reduction', 2021 IEEE Asia-Pacific Microwave Conference (APMC), 2021 IEEE Asia-Pacific Microwave Conference (APMC), IEEE.
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Singh, K, Afzal, MU, Lalbaksh, A & Esselle, KP 1970, 'Investigating Dielectric Covers to Reduce Unwanted Lobes in Near-Field Meta-Steering Systems', 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), IEEE.
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Soltani, S, Moloudian, G, Lalbakhsh, A & Bahrami, S 1970, 'Designing of a Lowpass Filter with a Wide Stopband and High Attenuation Level', 2021 Photonics & Electromagnetics Research Symposium (PIERS), 2021 Photonics & Electromagnetics Research Symposium (PIERS), IEEE.
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Son, DH, Thi Thuy Quynh, T, Khoa, TV, Thai Hoang, D, Trung, NL, Viet Ha, N, Niyato, D, Nguyen, DN & Dutkiewicz, E 1970, 'An Effective Framework of Private Ethereum Blockchain Networks for Smart Grid', 2021 International Conference on Advanced Technologies for Communications (ATC), 2021 International Conference on Advanced Technologies for Communications (ATC), IEEE, Virtual.
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Song, L-Z, Qin, P-Y & Guo, YJ 1970, 'A Review on Conformal Transmitarrays', 2021 15th European Conference on Antennas and Propagation (EuCAP), 2021 15th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Song, L-Z, Qin, P-Y & Guo, YJ 1970, 'Millimetre-Wave Multi-Beam Shaped Transmitarray with A Wide Beam Coverage', 2021 IEEE Asia-Pacific Microwave Conference (APMC), 2021 IEEE Asia-Pacific Microwave Conference (APMC), IEEE.
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Song, L-Z, Qin, P-Y & Jay Guo, Y 1970, 'E-band Wide-Angle Multi-Beam Shaped Transmitarray', 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), IEEE.
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Song, Y, Zeng, J, Wu, T, Ni, W & Liu, RP 1970, 'Vision-Based Parking Space Detection: A Mask R-CNN Approach', 2021 IEEE/CIC International Conference on Communications in China (ICCC), 2021 IEEE/CIC International Conference on Communications in China (ICCC), IEEE, pp. 300-305.
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The rapid increment of vehicles and the inefficient management of available parking spaces lead to traffic congestion and resource waste in urban areas. Thus, there is an urgent need to develop an intelligent parking system to find out suitable parking spaces quickly. To this end, we elaborate on various object detection algorithms and parking space detection methods. Then, we propose a novel vision-based parking space detection system with a Mask R-CNN approach. It can be applied in various scenarios and infer parking spaces from the positions of the parked vehicles. Experimental results have shown that the proposed system performs well in large car parks and reduces the human effort in image processing. This study provides a successful paradigm for future intelligent parking systems, and it can also effectively promote the development of smart cities.
Soomro, WA, Guo, Y, Lu, HY, Zhu, JG, Jin, JX & Shen, B 1970, 'Numerical Investigation of AC Loss in HTS Bulks Subjected to Rotating Magnetic Fields', 2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021 31st Australasian Universities Power Engineering Conference (AUPEC), IEEE, Perth, Australia.
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High-temperature superconductor (HTS) bulks have shown very promising potential for industrial applications due to their highly attractive superconducting characteristics. The practical application, however, has been handicapped by the AC losses. In rotating electrical machines, the magnetic field is a combination of alternating and rotating fields. All the AC loss studies presented in the literature so far have only focused on the alternating AC loss due to the unavailability of experimental techniques and analytical models. This paper presents a numerical investigation of AC loss by using the H-formulation under various two-dimensional rotating magnetizations with the magnetic flux density vectors rotating in clockwise and anti-clockwise directions in the XOY, XOZ, and YOZ planes. The modeling results show that the rotational AC loss of HTS bulk material is significantly higher than the alternating AC loss.
Sun, H-H, Jones, B, Guo, YJ & Lee, YH 1970, 'Dual-Band Base Station Antenna Array with Suppressed Cross-Band Mutual Scattering', 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), IEEE.
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Sun, H-H, Zhu, H, Ding, C, Jones, B & Guo, YJ 1970, 'Spiral Choking Method for Scattering Suppression in 4G and 5G Base Station Antenna Arrays', 2021 International Symposium on Antennas and Propagation (ISAP), 2021 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 1-2.
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Sun, Z, Yao, Y, Wei, X-S, Zhang, Y, Shen, F, Wu, J, Zhang, J & Shen, HT 1970, 'Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach', 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Montreal, QC, Canada, pp. 10582-10591.
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Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets. Especially for fine-grained recognition, which targets at distinguishing subordinate categories, it will significantly reduce the labeling costs by leveraging free web data. Despite its significant practical and research value, the webly supervised fine-grained recognition problem is not extensively studied in the computer vision community, largely due to the lack of high-quality datasets. To fill this gap, in this paper we construct two new benchmark webly supervised fine-grained datasets, termed WebFG-496 and WebiNat-5089, respectively. In concretely, WebFG-496 consists of three sub-datasets containing a total of 53,339 web training images with 200 species of birds (Web-bird), 100 types of aircrafts (Web-aircraft), and 196 models of cars (Web-car). For WebiNat-5089, it contains 5089 sub-categories and more than 1.1 million web training images, which is the largest webly supervised fine-grained dataset ever. As a minor contribution, we also propose a novel webly supervised method (termed “Peer-learning”) for benchmarking these datasets. Comprehensive experimental results and analyses on two new benchmark datasets demonstrate that the proposed method achieves superior performance over the competing baseline models and states-of-the-art. Our benchmark datasets and the source codes of Peer-learning have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/weblyFG-dataset.
Tavares Vasconcelos Oliveira, F, Gay, V & Garcia Marin, J 1970, 'GAMES FOR THE COGNITIVE ASSESSMENT OF OLDER ADULTS', https://gsgs.ch/wp-content/uploads/2021/11/gsgs21-web.pdf, 6th International Conference on Gamification and Serious Games, Lausanne, Switzerland.
Teng, K-H, Raju, S, Zhu, D, Lim, JLK, Chen, DS-H, Ching, EWL, Jaibir, S, Joshua, LE-Y, Ng, EJ, Chuan, KCT & Lal, A 1970, 'An On-Chip 2-D DFT Accelerator Ultrasonic Wavefront for Convolutional Neural Networks', 2021 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), 2021 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), IEEE.
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Thalakotuna, DNP, Karmokar, DK, Hu, Z, Esselle, KP & Matekovits, L 1970, 'Improving Cross-Band Isolation in Multi-Band Antennas', 2021 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2021 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE.
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Uddin, MS & Lee, JE-Y 1970, 'A Millimeter Scale Piezoelectric Receiver with Sub-Milliwatt Output for Ultrasonic Wireless Power Transfer in Water', 2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers), 2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers), IEEE.
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Unanue, IJ & Piccardi, M 1970, 'Pretrained Language Models and Backtranslation for English-Basque Biomedical Neural Machine Translation', 5th Conference on Machine Translation, WMT 2020 - Proceedings, Fifth Conference on Machine Translation (WMT20), The Association for Computational Linguistics, Online, pp. 826-832.
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This paper describes the machine translation systems proposed by the University of Technology Sydney Natural Language Processing (UTS NLP) team for the WMT20 English-Basque biomedical translation tasks. Due to the limited parallel corpora available, we have opted to train a BERT-fused NMT model that leverages the use of pretrained language models. Furthermore, we have augmented the training corpus by backtranslating monolingual data. Our experiments show that NMT models in low-resource scenarios can benefit from combining these two training techniques, with improvements of up to 6.16 BLEU percentage points in the case of biomedical abstract translations.
Van Huynh, N, Hoang, DT, Nguyen, DN & Dutkiewicz, E 1970, 'Dynamic Optimal Coding and Scheduling for Distributed Learning over Wireless Edge Networks', 2021 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2021 - 2021 IEEE Global Communications Conference, IEEE, Spain.
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Van Huynh, N, Nguyen, DN, Hoang, DT & Dutkiewicz, E 1970, 'Defeating Reactive Jammers with Deep Dueling-based Deception Mechanism', ICC 2021 - IEEE International Conference on Communications, ICC 2021 - IEEE International Conference on Communications, IEEE, Montreal, QC, Canada.
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Conventional anti-jamming solutions like frequency hopping and rate adaptation that are more suitable for proactive jammers are not effective in dealing with reactive jammers. These advanced jammers with recent advances in signal detection can discern the activities of legitimate radios then attack them as soon as the transmission is detected. To combat this type of jammer, we develop an intelligent deception strategy in which the transmitter generates 'fake' transmissions to attract the jammer. After that, the transmitter can either harvest energy from the jamming signals or backscatter the jamming signals to transmit data. As such, we can leverage jamming signals to improve the average throughput and reduce the packet loss. To effectively learn from and adapt to the dynamic and uncertainty of jamming attacks, we develop a Markov decision process (MDP) that can dynamically construct two decision epochs in each time slot to capture the special properties of our proposed deception mechanism. The Q-learning algorithm then can be adopted to find the optimal deception strategy for the transmitter. Nevertheless, due to very-slow convergence rates, conventional Q-learning algorithms may not be effective in dealing with smart jamming attacks. We thus develop an advanced deep reinforcement learning model based on deep dueling architecture to quickly obtain the optimal defense policy. Simulation results show that the proposed framework can improve the system throughput up to 173% and reduce the packet loss by 42% compared with other anti-jamming strategies that are not equipped with the proposed deception mechanism.
Vankudoth, L, Badar, AQH, Kumar Chauhan, R & Hossain, MJ 1970, 'Economic Analysis of Energy Scheduling and Trading in Multiple-Microgrids Environment', 2021 XVIII International Scientific Technical Conference Alternating Current Electric Drives (ACED), 2021 XVIII International Scientific Technical Conference Alternating Current Electric Drives (ACED), IEEE.
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Wang, M, Li, H, Xu, X, Zhu, J & Shen, Y 1970, 'Magnetic Field Analysis of Permanent Magnet Linear Synchronous Motor Based on the Improved Complex Relative Permeance Function', 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA), 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA), IEEE.
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Wang, X, Jin, R, Qin, P-Y & Ding, C 1970, 'Low RCS Transmitarray Using Phase Controllabe Rasorber', 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), IEEE, Singapore, Singapore, pp. 161-162.
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A phase controllable rasorber, also named as phase controllable absorptive frequency-selective transmission (AFST) surface, is developed in this paper. It is composed of asymmetrical resonators with lumped resistors, achieving an absorption-transmission-absorption response. Compared with other reported rasorbers, the proposed one has an additional feature that is to realize a 1-bit phase change of its element within the transmission band by rotating the element by 90°. A low radar cross section (RCS) transmitarray is designed at 12.5 GHz with a peak realized gain of 24.4 dBi using the developed elements. The 10 dB RCS reduction bandwidths are 18% and 14% for the lower and upper absorption bands, respectively.
Wang, X, Qin, P-Y & Guo, YJ 1970, '1-Bit Reconfigurable Huygens Element for Beam-Steering Transmitarrays', 2021 International Symposium on Antennas and Propagation (ISAP), 2021 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Wang, Z, Xu, M & Xiao, F 1970, 'Recognizing 3D Orientation of a Two-RFID-Tag Labeled Object in Multipath Environments Using Deep Transfer Learning', 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS), 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS), IEEE, DC, USA, pp. 652-662.
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State-of-the-art battery-free RFID systems attach multiple RFID tags to an object and exploit their RF phase to estimate its three-dimensional (3D) orientation. However, the measured RF phase may be inaccurate because each tag's signal fingerprint (i.e., RSSI and RF Phase) is distorted by multipath interference and electromagnetic interaction between neighboring tags. In this paper, we propose RF-Orien3D that minimizes these interferences for accurate 3D orientation recognition only using two RFID tags. The electromagnet interference modifies the radiation pattern and modulation factor of each tag in the two-element tag array, which can be estimated to compensate for the distortion in RFID fingerprints. To deal with the multipath impact, we simulate multipath noise to generate huge amounts of RFID fingerprints and use them to pre-train a convolutional neural network (CNN). Then we only collect dozens of actual samples to fine-tune the CNN for multipath-tolerant orientation recognition. The experiments show RF-Orien3D recognizes a two-tag labeled object's 2D orientation with the angular error of about 16° and its 3D orientation (azimuth and elevation) with the errors of about 29° and 11° in low/rich multipath scenarios.
Wong, SYK, Chan, JSK, Azizi, L & Xu, RYD 1970, 'Supervised Temporal Autoencoder for Stock Return Time-series Forecasting', 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), IEEE.
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Wu, T, Fan, X, Zeng, J, Ni, W & Liu, RP 1970, 'Enabling URLLC under $\kappa-\mu$ Shadowed Fading', 2021 28th International Conference on Telecommunications (ICT), 2021 28th International Conference on Telecommunications (ICT), IEEE.
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Wu, T, Wang, H & Guo, Y 1970, 'Thermal Modeling of Tubular Permanent Magnet Linear Synchronous Motor Based on Random Forest', 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA), 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA), IEEE.
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Xiong, Y, Ding, C, Cheng, Z & Guo, YJ 1970, 'Cross-Band Interaction Mitigation in Dual-Band Antenna Arrays for 4G/5G and Beyond', 2021 15th European Conference on Antennas and Propagation (EuCAP), 2021 15th European Conference on Antennas and Propagation (EuCAP), IEEE, Dusseldorf, Germany.
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Collocating antennas for different purposes on a single communication platform is a big trend as it can improve space efficiency. However, strong interferences happen among the antennas working at different bands due to the close proximity, leading to deteriorated antenna performance. This paper proposes a simple yet effective method to alleviate this issue by developing low-scattering array topology without increasing array size. The proposed method is used on a typical 3G/4G dual-band dual-polarized base station antenna array as an example to illustrate its effectiveness. By relocating the positions of the antennas in such array, the antenna performance can be substantially enhanced. Although this technique cannot eliminate the interferences solely, it can be used as a supplementary with other techniques to achieve optimal performance. At last, the proposed array topology is used together with a low-scattering spiral antenna designed for cross-band scattering mitigation, which leads to an outstanding array performance with a compact size.
Xu, J, Wu, Q, Zhang, J & Tait, A 1970, 'AUTOMATIC SHEEP BEHAVIOUR ANALYSIS USING MASK R-CNN', 2021 Digital Image Computing: Techniques and Applications (DICTA), 2021 Digital Image Computing: Techniques and Applications (DICTA), IEEE, Gold Coast, Australia, pp. 01-06.
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The issue of sheep welfare during live exports has triggered a lot of public concern recently. Extensive research is being carried out to monitor and improve animal welfare. Stocking density can be a critical factor affecting sheep welfare during export and its impact can be monitored through sheep behaviour, position, group dynamics and physiology. In this paper we demonstrate the application of the instance segmentation method Mask R-CNN to support sheep behaviour recognition. As an initial step, two typical behaviours standing and lying are recognized under different group sizes in pens over time. 94%+ mAP was achieved in the validation set demonstrating the effectiveness of the method on identifying sheep behaviours. Further data analysis will provide available space requirements for additional sheep allocation and daily behaviour monitoring to detect abnormal cases which will aim to improve the health and wellbeing of sheep on ships.
Yang, R, Qian, J & Lee, JE-Y 1970, 'Boosting Q of <100> Aligned ALN-on-Silicon Laterally Vibrating Resonators by Wide Acoustic Bandgap Phononic Crystal Anchors', 2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers), 2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers), IEEE.
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Yang, S, Liu, L & Xu, M 1970, 'FREE LUNCH FOR FEW-SHOT LEARNING: DISTRIBUTION CALIBRATION', ICLR 2021 - 9th International Conference on Learning Representations.
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Learning from a limited number of samples is challenging since the learned model can easily become overfitted based on the biased distribution formed by only a few training examples. In this paper, we calibrate the distribution of these few-sample classes by transferring statistics from the classes with sufficient examples. Then an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier. We assume every dimension in the feature representation follows a Gaussian distribution so that the mean and the variance of the distribution can borrow from that of similar classes whose statistics are better estimated with an adequate number of samples. Our method can be built on top of off-the-shelf pretrained feature extractors and classification models without extra parameters. We show that a simple logistic regression classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy on three datasets (5% improvement on miniImageNet compared to the next best). The visualization of these generated features demonstrates that our calibrated distribution is an accurate estimation.
Yang, S, Xu, M, Xie, H, Perry, S & Xia, J 1970, 'Single-View 3D Object Reconstruction from Shape Priors in Memory', 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Nashville, TN, USA, pp. 3151-3160.
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Existing methods for single-view 3D object reconstruction directly learn to transform image features into 3D representations. However, these methods are vulnerable to images containing noisy backgrounds and heavy occlusions because the extracted image features do not contain enough information to reconstruct high-quality 3D shapes. Humans routinely use incomplete or noisy visual cues from an image to retrieve similar 3D shapes from their memory and reconstruct the 3D shape of an object. Inspired by this, we propose a novel method, named Mem3D, that explicitly constructs shape priors to supplement the missing information in the image. Specifically, the shape priors are in the forms of 'image-voxel' pairs in the memory network, which is stored by a well-designed writing strategy during training. We also propose a voxel triplet loss function that helps to retrieve the precise 3D shapes that are highly related to the input image from shape priors. The LSTM-based shape encoder is introduced to extract information from the retrieved 3D shapes, which are useful in recovering the 3D shape of an object that is heavily occluded or in complex environments. Experimental results demonstrate that Mem3D significantly improves reconstruction quality and performs favorably against state-of-the-art methods on the ShapeNet and Pix3D datasets.
Yang, Y, Zhang, R, Wu, W, Peng, Y & Xu, M 1970, 'Multi-camera Sports Players 3D Localization with Identification Reasoning', 2020 25th International Conference on Pattern Recognition (ICPR), 2020 25th International Conference on Pattern Recognition (ICPR), IEEE.
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Yao, L, Kusakunniran, W, Wu, Q, Zhang, J & Xu, J 1970, 'Part-based Collaborative Spatio-temporal Feature Learning for Cloth-changing Gait Recognition', 2020 25th International Conference on Pattern Recognition (ICPR), 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, pp. 2057-2064.
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In decades many gait recognition methods have been proposed using different techniques. However, due to a real-world scenario of clothing variations, a reduction of the recognition rate occurs for most of these methods. Thus in this paper, a part-based spatio-temporal feature learning method is proposed to tackle the problem of clothing variations for gait recognition. First, based on the anatomical properties, human bodies are segmented into two regions, which are affected and unaffected by clothing variations. A learning network is particularly proposed in this paper to grasp principal spatio-temporal features from those unaffected regions. Different from most part-based methods with spatial or temporal features solely being utilized, in our method these two features are associated in a more collaborative manner. Snapshots are created for each gait sequence from the H − W and T − W views. Stable spatial information is embedded in the H −W view and adequate temporal information is embedded in the T −W view. An inherent relationship exists between these two views. Thus, a collaborative spatio-temporal feature will be hybridized by concatenating these correlative spatial and temporal information. The robustness and efficiency of our proposed method are validated by experiments on CASIA Gait Dataset B and OU-ISIR Treadmill Gait Dataset B. Our proposed method can both achieve the state-of-the-art results on these two databases.
Yao, Y, Chen, T, Xie, G-S, Zhang, C, Shen, F, Wu, Q, Tang, Z & Zhang, J 1970, 'Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation', 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Nashville, TN, USA, pp. 2623-2632.
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Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels to alleviate the annotation burden of segmentation. However, existing works mainly concentrate on expanding the seed of pseudo labels within the image’s salient region. In this work, we propose a non-salient region object mining approach for weakly supervised semantic segmentation. We introduce a graph-based global reasoning unit to strengthen the classification network’s ability to capture global relations among disjoint and distant regions. This helps the network activate the object features outside the salient area. To further mine the non-salient region objects, we propose to exert the segmentation network’s self-correction ability. Specifically, a potential object mining module is proposed to reduce the false-negative rate in pseudo labels. Moreover, we propose a non-salient region masking module for complex images to generate masked pseudo labels. Our non-salient region masking module helps further discover the objects in the non-salient region. Extensive experiments on the PASCAL VOC dataset demonstrate state-of-the-art results compared to current methods. The source codes are available at https://github.com/NUST-Machine-Intelligence-Laboratory/nsrom.
Yao, Y, Sun, Z, Zhang, C, Shen, F, Wu, Q, Zhang, J & Tang, Z 1970, 'Jo-SRC: A Contrastive Approach for Combating Noisy Labels', 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Nashville, TN, USA, pp. 5188-5197.
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Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance. Existing state-of-the-art methods primarily adopt a sample selection strategy, which selects small-loss samples for subsequent training. However, prior literature tends to perform sample selection within each mini-batch, neglecting the imbalance of noise ratios in different mini-batches. Moreover, valuable knowledge within high-loss samples is wasted. To this end, we propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency). Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its 'likelihood' of being clean or out-of-distribution. Furthermore, we propose a joint loss to advance the model generalization performance by introducing consistency regularization. Extensive experiments have validated the superiority of our approach over existing state-of-the-art methods. The source code and models have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/Jo-SRC.
Ye, Y, Zhang, J & Xu, B 1970, 'A Fast Q-learning Energy Management Strategy for Battery/Supercapacitor Electric Vehicles Considering Energy Saving and Battery Aging', 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), IEEE.
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Yeganova, L, Wiemann, D, Neves, M, Vezzani, F, Siu, A, Unanue, IJ, Oronoz, M, Mah, N, Névéol, A, Martinez, D, Bawden, R, Di Nunzio, GM, Roller, R, Thomas, P, Grozea, C, de Viñaspre, OP, Navarro, MV & Yepes, AJ 1970, 'Findings of the WMT 2021 Biomedical Translation Shared Task: Summaries of Animal Experiments as New Test Set', WMT 2021 - 6th Conference on Machine Translation, Proceedings, pp. 664-683.
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In the sixth edition of the WMT Biomedical Task, we addressed a total of eight language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian, and English/Basque. Further, our tests were composed of three types of textual test sets. New to this year, we released a test set of summaries of animal experiments, in addition to the test sets of scientific abstracts and terminologies. We received a total of 107 submissions from 15 teams from 6 countries.
Yu, H, Tuan, HD, Nasir, AA, Debbah, M & Fang, Y 1970, 'Regularized Zero Forcing Beamforming for Serving More Users in Energy-Harvesting Enabled Networks', 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE), 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE), IEEE.
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Yu, R, Lu, W, Li, Y, Yu, J, Zhang, G & Zhang, X 1970, 'Sentence Semantic Matching with Hierarchical CNN Based on Dimension-augmented Representation', 2021 International Joint Conference on Neural Networks (IJCNN), 2021 International Joint Conference on Neural Networks (IJCNN), IEEE.
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Zhang, H, Huang, X & Zhang, JA 1970, 'Frequency Domain Pilot-Aided Channel Estimation for OTFS over Fast Fading Channels', 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), IEEE, pp. 1-5.
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Achieving better performance in high mobility scenarios has become an emerging topic for next generation wireless communications. Compared with traditional modulation techniques, the recently proposed orthogonal time frequency space (OTFS) shows outstanding performance over fast fading channels. In this paper, the OTFS system is first represented in the form of precoded orthogonal frequency division multiplexing (OFDM), enabling traditional estimation and equalization techniques to work under fast fading channels. Then, a novel frequency-domain pilot-aided channel estimation scheme is proposed to obtain the channel state information at the receiver. Simulation results show that the new channel estimation scheme works efficiently in different channel scenarios. Meanwhile, the overhead of the proposed scheme is also lower than those of the current popular schemes.
Zhang, L, Zhang, J, Shen, J, Xu, J, Li, Z & Yu, L 1970, 'Incorporating Multimodal Cues for Advertorial Discovery', 2021 IEEE International Conference on Multimedia and Expo (ICME), 2021 IEEE International Conference on Multimedia and Expo (ICME), IEEE.
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Zhang, M, Su, S, Pan, S, Chang, X, Abbasnejad, E & Haffari, R 1970, 'iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients', Proceedings of Machine Learning Research, International Conference on Machine Learning (ICML), JMLR-JOURNAL MACHINE LEARNING RESEARCH, ELECTR NETWORK, pp. 12557-12566.
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Differentiable ARchiTecture Search (DARTS) has recently become the mainstream of neural architecture search (NAS) due to its efficiency and simplicity. With a gradient-based bi-level optimization, DARTS alternately optimizes the inner model weights and the outer architecture parameter in a weight-sharing supernet. A key challenge to the scalability and quality of the learned architectures is the need for differentiating through the inner-loop optimisation. While much has been discussed about several potentially fatal factors in DARTS, the architecture gradient, a.k.a. hypergradient, has received less attention. In this paper, we tackle the hypergradient computation in DARTS based on the implicit function theorem, making it only depends on the obtained solution to the inner-loop optimization and agnostic to the optimization path. To further reduce the computational requirements, we formulate a stochastic hypergradient approximation for differentiable NAS, and theoretically show that the architecture optimization with the proposed method, named iDARTS, is expected to converge to a stationary point. Comprehensive experiments on two NAS benchmark search spaces and the common NAS search space verify the effectiveness of our proposed method. It leads to architectures outperforming, with large margins, those learned by the baseline methods.
Zhang, Y, Higashita, R, Fu, H, Xu, Y, Zhang, Y, Liu, H, Zhang, J & Liu, J 1970, 'A Multi-branch Hybrid Transformer Network for Corneal Endothelial Cell Segmentation', Springer International Publishing, pp. 99-108.
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Corneal endothelial cell segmentation plays a vital role in quantifying clinical indicators such as cell density, coefficient of variation, and hexagonality. However, the corneal endothelium’s uneven reflection and the subject’s tremor and movement cause blurred cell edges in the image, which is difficult to segment, and need more details and context information to release this problem. Due to the limited receptive field of local convolution and continuous downsampling, the existing deep learning segmentation methods cannot make full use of global context and miss many details. This paper proposes a Multi-Branch hybrid Transformer Network (MBT-Net) based on the transformer and body-edge branch. Firstly, we use the convolutional block to focus on local texture feature extraction and establish long-range dependencies over space, channel, and layer by the transformer and residual connection. Besides, we use the body-edge branch to promote local consistency and to provide edge position information. On the self-collected dataset TM-EM3000 and public Alisarine dataset, compared with other State-Of-The-Art (SOTA) methods, the proposed method achieves an improvement.
Zhang, Y, Zhang, L, Wang, G, Lyu, W, Ran, Y, Su, S, Xu, P & Yao, D 1970, 'Noise-assisted Multivariate Empirical Mode Decomposition based Causal Decomposition for Detecting Upper Limb Movement in EEG-EMG Hybrid Brain Computer Interface', 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE.
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Zhang, Z, Jiang, S, Huang, C & Xu, RYD 1970, 'Resolution-Invariant Person Reid Based On Feature Transformation And Self-Weighted Attention', 2021 IEEE International Conference on Image Processing (ICIP), 2021 IEEE International Conference on Image Processing (ICIP), IEEE.
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Zhao, R, Huang, Z, Liu, T, Leung, FHF, Ling, SH, Yang, D, Lee, TT-Y, Lun, DP-K, Zheng, Y-P & Lam, K-M 1970, 'Structure-Enhanced Attentive Learning For Spine Segmentation From Ultrasound Volume Projection Images.', ICASSP, IEEE, pp. 1195-1199.
