Abdollahi, M, Ashtari, S, Abolhasan, M, Shariati, N, Lipman, J, Jamalipour, A & Ni, W 2022, 'Dynamic Routing Protocol Selection in Multi-Hop Device-to-Device Wireless Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8796-8809.
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Abughalwa, M, Tuan, HD, Nguyen, DN, Poor, HV & Hanzo, L 2022, 'Finite-Blocklength RIS-Aided Transmit Beamforming', IEEE Transactions on Vehicular Technology, vol. 71, no. 11, pp. 12374-12379.
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This paper considers the downlink of an ultra-reliable low-latency communication (URLLC) system in which a base station (BS) serves multiple single-antenna users in the short (finite) blocklength (FBL) regime with the assistance of a reconfigurable intelligent surface (RIS). In the FBL regime, the users' achievable rates are complex functions of the beamforming vectors and of the RIS's programmable reflecting elements (PREs). We propose the joint design of the transmit beamformers and PREs to maximize the geometric mean (GM) of these rates (GM-rate) and show that this approach provides fair rate distribution and thus reliable links to all users. A novel computational algorithm is developed, which is based on closed forms to generate improved feasible points. Simulations show the merit of our solution.
Afroz, F, Braun, R & Chaczko, Z 2022, 'XX-MAC and EX-MAC: Two Variants of X-MAC Protocol for Low Power Wireless Sensor Networks', Ad-Hoc and Sensor Wireless Networks, vol. 51, no. 4, pp. 285-314.
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The strobed preamble approach introduced in the X-MAC protocol minimises long preamble duration, overhearing, and per-hop latency in conventional wireless sensor networks (WSNs). However, it incurs high per-packet overhead and extra delay under high traffic scenarios as it operates only in the unsynchronised state. In this paper, we model a variant of X-MAC, namely XX-MAC, which employs an adaptive dutycycling algorithm to address this issue in low data rate WSNs with short, fixed inter-packet arrival time. Furthermore, we identify the shortcoming of XX-MAC as well as propose a request-based MAC protocol, namely EX-MAC, targeting WSNs in dynamic traffic scenarios. Simulations show that at optimum slot duration, XX-MAC reduces the per-packet delay by 13.53% and 48.86% than the delay experienced by X-MAC and B-MAC, respectively. XX-MAC, on average, can deliver 92.5% of packets to the receiver, whereas X-MAC and B-MAC respectively support 91.66% and 82.91% packet delivery. XX-MAC also reduces the energy consumption per received packet by 2.61% than X-MAC, and by 65.31% than the B-MAC protocol. Experimental results also demonstrate that under variable traffic conditions, EX-MAC offers the lowest packet loss (8.55%), whilst XX-MAC and X-MAC experience 13.1% and 18.3% packet loss, respectively. EX-MAC decreases per-packet network energy consumption (3.056mJ/packet) compared with XX-MAC (3.107mJ/ packet) and X-MAC (3.424mJ/packet). Furthermore, EX-MAC minimises the mean delay per received packet by 5.758% and 10.457% (approximately) than that of XX-MAC and X-MAC, respectively.
Afzal, MU, Esselle, KP & Koli, MNY 2022, 'A Beam-Steering Solution With Highly Transmitting Hybrid Metasurfaces and Circularly Polarized High-Gain Radial-Line Slot Array Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 1, pp. 365-377.
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Ahmed, F, Afzal, MU, Hayat, T, Esselle, KP & Thalakotuna, DN 2022, 'A Near-Field Meta-Steering Antenna System With Fully Metallic Metasurfaces', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10062-10075.
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Ahmed, F, Afzal, MU, Hayat, T, Esselle, KP & Thalakotuna, DN 2022, 'Self-Sustained Rigid Fully Metallic Metasurfaces to Enhance Gain of Shortened Horn Antennas', IEEE Access, vol. 10, pp. 79644-79654.
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Ain, K, Rahma, O, Putra, A, Rahmatillah, A, Putri, YKA, Fajriaty, N & Chai, R 2022, 'Electrodermal activity for measuring cognitive and emotional stress level', Journal of Medical Signals & Sensors, vol. 12, no. 2, pp. 155-155.
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Stress can lead to harmful conditions in the body, such as anxiety disorders and depression. One of the promising noninvasive methods, which has been widely used in detecting stress and emotion, is electrodermal activity (EDA). EDA has a tonic and phasic component called skin conductance level and skin conductance response (SCR). However, the components of the EDA cannot be directly extracted and need to be deconvolved to obtain it. The EDA signals were collected from 18 healthy subjects that underwent three sessions - Stroop test with increasing stress levels. The EDA signals were then deconvoluted by using continuous deconvolution analysis (CDA) and convex optimization approach to electrodermal activity (cvxEDA). Four features from the result of the deconvolution process were collected, namely sample average, standard deviation, first absolute difference, and normalized first absolute difference. Those features were used as the input of the classification process using the extreme learning machine (ELM). The output of classification was the stress level; mild, moderate, and severe. The visual of the phasic component using cvxEDA is more precise or smoother than the CDA's result. However, both methods could separate SCR from the original skin conductivity raw and indicate the small peaks from the SCR. The classification process results showed that both CDA and cvxEDA methods with 50 hidden layers in ELM had a high accuracy in classifying the stress level, which was 95.56% and 94.45%, respectively. This study developed a stress level classification method using ELM and the statistical features of SCR. The result showed that EDA could classify the stress level with over 94% accuracy. This system could help people monitor their mental health during overworking, leading to anxiety and depression because of untreated stress.
Akter, N, Fletcher, J, Perry, S, Simunovic, MP, Briggs, N & Roy, M 2022, 'Glaucoma diagnosis using multi-feature analysis and a deep learning technique', Scientific Reports, vol. 12, no. 1.
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AbstractIn this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.
Alam, M, Lu, DD-C & Siwakoti, YP 2022, 'Time-multiplexed hysteretic control for single-inductor dual-input single-output DC-DC power converter.', Int. J. Circuit Theory Appl., vol. 50, no. 4, pp. 1235-1249.
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Single-inductor multi-input single-output (SI-MISO) switching DC-DC power converter architecture is a cost effective solution to applications where multiple input sources are required to be managed with a limited space and cost. This paper presents a new time-multiplexed hysteretic control (TMHC) scheme for SI-DISO topology to decouple the power sharing among two input sources. Unlike previously reported solutions with discontinuous conduction or pseudo-continuous conduction operation of the inductor, this paper focuses on how to keep the inductor current in a continuous conduction mode (CCM) and proposed a control scheme with considerably lower ripple current with fast transition time upon switching and higher efficiency. The mathematical proof using the expressions of inductor ripple current, comparison between efficiency and transition time from one level to other, is derived. Additionally, a low-cost analog circuitry has been implemented to incorporate the proposed control scheme. Experimental results from the hardware prototype are given to verify the proposed control scheme.
Ali, SMN, Hossain, MJ, Wang, D, Mahmud, MAP, Sharma, V, Kashif, M & Kouzani, AZ 2022, 'Thermally degraded speed estimation of traction machine drive in electric vehicle', IET Electric Power Applications, vol. 16, no. 12, pp. 1464-1475.
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The speed of an induction machine drive (IMD) in the electrified powertrain of an electric vehicle (EV) suffers from thermal degradation caused by EV loading, driving cycle schedules, EV operating conditions, traffic state and temperature. It is necessary to estimate this thermal degradation in order to design appropriate control methodologies to address this significant issue that directly affects the EV performance. This study proposes a robust linear parameter varying (LPV) observer to estimate this degradation in IMD as well as EV speed under various thermal and loading conditions in steady state and during large transients. The stability and robustness of LPV methodology is ensured by optimal gains of (Formula presented.) control and linear matrix inequalities using convex optimisation techniques. The weighting functions in LPV design are optimised by genetic algorithms. The proposed observer performance is compared with that of conventional sensorless field-oriented control and sliding mode observer. An improved speed performance during EV operation is also presented to validate the robustness of the proposed LPV observer against New European Driving Cycle. The performance analysis is conducted through NI myRIO 1900 controller-based electrical drive set-up.
Aljarajreh, H, Lu, DD-C, Siwakoti, YP & Tse, CK 2022, 'A Nonisolated Three-Port DC–DC Converter With Two Bidirectional Ports and Fewer Components', IEEE Transactions on Power Electronics, vol. 37, no. 7, pp. 8207-8216.
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This article presents a new nonisolated three-port converter with reduced component count compared with existing reported topologies. This is achieved by developing different power flow graphs and selecting the most appropriate converters arrangement. In addition, as compared to only one bidirectional port in most reported studies, this article considers two bidirectional ports to accommodate applications requiring bidirectional power flow, such as dc microgrid and regenerative braking. The proposed converter is able to work in seven different modes of operation, which cover all possible combinations of power flow among the three ports. Furthermore, seamless and smooth transition, maximum power point tracking, battery protection and output voltage regulation are achieved. Experimental waveforms, particularly for transient responses during mode transition, are reported to verify the proposed TPC.
Alsahafi, YA, Gay, V & Khwaji, AA 2022, 'Factors affecting the acceptance of integrated electronic personal health records in Saudi Arabia: The impact of e-health literacy', Health Information Management Journal, vol. 51, no. 2, pp. 98-109.
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Background: National implementation of electronic personal health record (ePHR) systems is of vital importance to governments worldwide because this type of technology promises to promote and enhance healthcare. Although there is widespread agreement as to the advantages of ePHRs, the level of awareness and acceptance of this technology among healthcare consumers has been low. Objective: The aim of this study was to identify the factors that can influence the acceptance and use of an integrated ePHR system in Saudi Arabia. Method: The unified theory of acceptance and use of technology model was extended in this study to include e-health literacy (e-HL) and tested using structural equation modelling. Data were collected via a questionnaire survey, resulting in 794 valid responses. Results: The proposed model explained 56% of the variance in behavioural intention (BI) to use the integrated ePHR system. Findings also highlighted the significance of performance expectancy, effort expectancy, social influence (SI) and e-HL as determinants of Saudi healthcare consumers’ intentions to accept and use the integrated ePHR system. Additionally, assessment of the research model moderators revealed that only gender had a moderating influence on the relationship between SI and BI. Finally, findings showed a low level of awareness among Saudi citizens about the national implementation of an integrated ePHR system, suggesting the need to promote a greater and more widespread awareness of the system and to demonstrate its usefulness. Conclusion: Findings from this study can assist governments, policymakers and developers of health information technologies and systems by identifyin...
Alsawwaf, M, Chaczko, Z, Kulbacki, M & Sarathy, N 2022, 'In Your Face: Person Identification Through Ratios and Distances Between Facial Features', Vietnam Journal of Computer Science, vol. 09, no. 02, pp. 187-202.
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These days identification of a person is an integral part of many computer-based solutions. It is a key characteristic for access control, customized services, and a proof of identity. Over the last couple of decades, many new techniques were introduced for how to identify human faces. This approach investigates the human face identification based on frontal images by producing ratios from distances between the different features and their locations. Moreover, this extended version includes an investigation of identification based on side profile by extracting and diagnosing the feature sets with geometric ratio expressions which are calculated into feature vectors. The last stage involves using weighted means to calculate the resemblance. The approach considers an explainable Artificial Intelligence (XAI) approach. Findings, based on a small dataset, achieve that the used approach offers promising results. Further research could have a great influence on how faces and face-profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate, and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89. This work is an extended version of the paper submitted in ACIIDS 2020.
Alsenwi, M, Abolhasan, M & Lipman, J 2022, 'Intelligent and Reliable Millimeter Wave Communications for RIS-Aided Vehicular Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 21582-21592.
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Utilizing the millimeter-wave (mmWave) frequency is a promising solution to meet fast-growing traffic demand over wireless networks. However, mmWave communications are sensitive to physical obstructions on signal propagation. In this paper, the reconfigurable intelligent surfaces (RISs) are investigated to overcome the limitations of mmWave communications. Particularly, an RIS is deployed to reflect the mmWave signals towards vehicular users who experience direct link blockages that may occur due to static or dynamic obstacles. To this end, a risk-averse optimization problem is designed to optimize the Base Station (BS) precoding matrix and the RIS phase shifts under stochastic link blockages. A solution approach is developed in two phases: the BS precoding optimization and the RIS phase shift control phases. In the first phase, a Decomposition and Relaxation-based Precoding Optimization (DRPO) algorithm is developed to obtain the optimal precoding matrix. In the second phase, a learning-based method is introduced to dynamically adjust the direction of reflected signals under channel uncertainty. Extensive simulations are presented to validate the efficacy of the developed algorithms. The obtained results show that the developed algorithms can ensure reliable transmissions to users in non-LoS areas and improve network performance.
Al-Zu'bi, MM, Mohan, AS, Plapper, PW & Ling, SH 2022, 'Intrabody Molecular Communication via Blood-Tissue Barrier for Internet of Bio-Nano Things.', IEEE Internet Things J., vol. 9, no. 21, pp. 21802-21810.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2022, 'Remote Water Salinity Sensor Using Metamaterial Perfect Absorber', IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6785-6794.
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Controlling water salinity plays a key role in farming efficiency. Current sensors are mostly expensive and need regular maintenance. In addition, they require electrical connections or extra power supply that leads to difficult and costly implementation in remote-sensing scenarios. In this article, an accurate and low-profile sensor is developed using a metamaterial perfect absorber (MPA) structure. The proposed sensor works based on the level and frequency of the absorbed signals. Hence, there is no need for electrical connections, which enables remote-sensing applications. Square-shaped channels have been created in a regular FR-4 substrate to facilitate sensing of water salinity levels. A 7 × 7 array with a total size of 140 mm × 160 mm has been fabricated that shows a resolution of 10 MHz per percentage of water salinity. The absorption frequency shifts from f=3.12 to 3.59 GHz for salinity level from 0% to 50%. A strong correlation between measurement and simulation results validates the design procedure.
An, Y, Lam, H-K & Ling, SH 2022, 'Auto-Denoising for EEG Signals Using Generative Adversarial Network.', Sensors, vol. 22, no. 5, pp. 1750-1750.
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The brain–computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel EEG signal automatically. A new loss function is defined to ensure that the filtered signal can retain as much effective original information and energy as possible. This model can imitate and integrate artificial denoising methods, which reduces processing time; hence it can be used for a large amount of data processing. Compared to other neural network denoising models, the proposed model has one more discriminator, which always judges whether the noise is filtered out. The generator is constantly changing the denoising way. To ensure the GAN model generates EEG signals stably, a new normalization method called sample entropy threshold and energy threshold-based (SETET) normalization is proposed to check the abnormal signals and limit the range of EEG signals. After the denoising system is established, although the denoising model uses the different subjects’ data for training, it can still apply to the new subjects’ data denoising. The experiments discussed in this paper employ the HaLT public dataset. Correlation and root mean square error (RMSE) are used as evaluation criteria. Results reveal that the proposed automatic GAN denoising network achieves the same performance as the manual hybrid artificial denoising method. Moreover, the GAN network makes the denoising process automatic, representing a significant reduction in time.
Ansari, M, Jones, B & Guo, YJ 2022, 'Spherical Luneburg Lens of Layered Structure With Low Anisotropy and Low Cost', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4307-4318.
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A spherical Luneburg lens made of parallel planar layers of lightweight foam with embedded conducting cylindrical inserts on a uniform hexagonal grid centered in each layer is presented. This work draws on the authors' previous paper (Ansari et al., 2020) describing a Luneburg lens that uses cubic conducting inserts on a uniform cubic grid. This previous lens, while being of lightweight and economical construction, suffered from anisotropy resulting in a focal length that varied with the inclination of the beam relative to the orientation of the cubic grid. The lens described here largely overcomes this problem and allows for simpler and more economical construction. A prototype lens designed for the 3.3-3.8 GHz band with a diameter of 400 mm and a beamwidth of 14° was tested. Radiation patterns at wide scanning angles were nearly identical, and cross-polarization for slant incident polarization was below -25 dB on boresight and below -18 dB for all angles. A characteristic of this lens construction is its extremely high efficiency. The measured gain at the mid-band was 21.6 dBi, agreeing with simulated gain based on lossless materials to within measurement error. It is shown that wider bandwidths are obtainable if the thickness of the layers is reduced.
Archer, NS, Bluff, A, Eddy, A, Nikhil, CK, Hazell, N, Frank, D & Johnston, A 2022, 'Odour enhances the sense of presence in a virtual reality environment', PLOS ONE, vol. 17, no. 3, pp. e0265039-e0265039.
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Virtual reality (VR) headsets provide immersive audio-visual experiences for users, but usually neglect to provide olfactory cues that can provide additional information about our environment in the real world. This paper examines whether the introduction of smells into the VR environment enhances users’ experience, including their sense of presence through collection of both psychological and physiological measures. Using precise odour administration with an olfactometer, study participants were exposed to smells while they were immersed in the popular PlayStation VR game “Resident Evil 7”. A within-subject study design was undertaken where participants (n = 22) walked-through the same VR environment twice, with or without the introduction of associated congruent odour stimuli. Directly after each gameplay, participants completed a questionnaire to determine their sense of presence from the overall gameplay and their sense of immersion in each of the virtual scenes. Additionally, physiological measurements (heart rate, body temperature and skin electrodermal activity) were collected from participants (n = 11) for each gameplay. The results showed the addition of odours significantly increased participants’ sense of spatial presence in the VR environment compared to VR with no odour. Participants also rated the realism of VR experience with odour higher compared to no odour, however odour addition did not result in change in emotional state of participants (arousal, pleasure, dominance). Further, the participants’ physiological responses were impacted by the addition of odour. Odour mediated physiological changes were dependent on whether the VR environment was novel, as the effect of odour on physiological response was lost when participants experienced the aroma on the second gameplay. Overall, the results indicate the addition of odours to a VR environment had a significant effect on both the psychological and physiological experience showing...
Arnaz, A, Lipman, J, Abolhasan, M & Hiltunen, M 2022, 'Toward Integrating Intelligence and Programmability in Open Radio Access Networks: A Comprehensive Survey', IEEE Access, vol. 10, pp. 67747-67770.
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Open RAN is an emerging vision and an advancement of the Radio Access Network (RAN). Its purpose is to implement a vendor and network-generation agnostic RAN, provide networking solutions across all service requests, and implement artificial intelligence solutions in different stages of an end-to-end communication path. The 5th Generation (5G) and beyond the 5th Generation (B5G) of networking introduce and support new use cases, such as tactile internet and autonomous driving. The complexity and innovative nature of these use cases require continuous innovation at a high pace in the RAN. The traditional approach of building end-to-end RAN solutions by only one vendor hampers the speed of innovation - furthermore, the lack of a standard approach to implementing artificial intelligence complicates the compatibility of products with the RAN ecosystem. O-RAN Alliance, a community of industry and academic experts in RAN, works on writing Open RAN specifications on top of the 3rd Generation Partnership Project (3GPP) standards. Founded on these specifications, the aim of this paper is to introduce open research topics in Open RAN that overlap the interests of both AI and telecommunication researchers. The paper provides an overview of the architecture and components of Open RAN, then explores AI use cases in Open RAN. Also, this survey includes some plausible AI deployment scenarios that the specifications have not covered. Open RAN in future cities creates opportunities for various use cases across different sectors, including engineering, operations, and research that this paper addresses.
Ashtari, S, Abdollahi, M, Abolhasan, M, Shariati, N & Lipman, J 2022, 'Performance analysis of multi-hop routing protocols in SDN-based wireless networks', Computers & Electrical Engineering, vol. 97, pp. 107393-107393.
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Wireless cellular networks have rapidly evolved to be software-defined in nature. This has created opportunities to improve their performance. One such opportunity is through enabling programming and integration of multi-hop device-to-device (MD2D) at the edge. However, efficient integration of MD2D at the edge requires a highly adaptable and scalable routing protocol, where its development is underpinned through understanding of which type of current routing characteristics and architectures are suitable over dynamic networking conditions. To develop such understanding, we conducted a detailed analysis and performance study on three routing protocols, namely virtual ad-hoc routing protocol-source based (VARP-S) Abolhasan et al. (2018), SDN-based multi-hop device-to-device routing protocol (SMDRP) Abdollahi et al. (2019) and hybrid SDN architecture for wireless distributed networks (HSAW) Abolhasan et al. (2015). Our investigations illustrate that VARP-S and SMDRP perform best in terms of energy consumption and cellular routing overhead. However, HSAW shows better performance in terms of end-to-end (E2E) delay and packet loss over lower network and traffic densities.
ashtari, S, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2022, 'Joint Mobile Node Participation and Multihop Routing for Emerging Open Radio-Based Intelligent Transportation System', IEEE Access, vol. 10, pp. 85228-85242.
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This paper proposes joint mobile node participation and routing protocol for multi-hop device-to-device (MD2D) networking in intelligent transportation systems, called fuzzy-based participation and routing protocol for MD2D (FPRM). Our proposed protocol is designed to operate over future open-radio access networks (O-RANs). We introduce a sub-layer at the network layer that can determine nodes with the highest participation probability in routing using a fuzzy logic system, thus building a framework to create more stable routes. To ensure the participating nodes are capable of handling the data traffic, two constraints are proposed, mobility and coverage constraints. The former enables the creation of sustainable communication links, and the latter enforces the communication service to the entire MD2D network. Simulation results show that our approach can increase the network lifetime, decrease the end-to-end (E2E) delay, and increase the packet delivery ratio (PDR) compared to the existing proactive routing protocol. Our protocol outperforms the benchmarked MD2D protocols and other investigated ad hoc protocols.
Ashtari, S, Zhou, I, Abolhasan, M, Shariati, N, Lipman, J & Ni, W 2022, 'Knowledge-defined networking: Applications, challenges and future work', Array, vol. 14, pp. 100136-100136.
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Future 6G wireless communication systems are expected to feature intelligence and automation. Knowledge-defined networking (KDN) is an evolutionary step toward autonomous and self-driving networks. The building blocks of the KDN paradigm in achieving self-driving networks are software-defined networking (SDN), packet-level network telemetry, and machine learning (ML). The KDN paradigm intends to integrate intelligence to manage and control networks automatically. In this study, we first introduce the disadvantages of current network technologies. Then, the KDN and associated technologies are explored with three possible KDN architectures for heterogeneous wireless networks. Furthermore, a thorough investigation of recent survey studies on different wireless network applications was conducted. The aim is to identify and review suitable ML-based studies for KDN-based wireless cellular networks. These applications are categorized as resource management, network management, mobility management, and localization. Resource management applications can be further classified as spectrum allocation, power management, quality-of-service (QoS), base station (BS) switching, cache, and backhaul management. Within network management configurations, routing strategies, clustering, user/BS association, traffic classification, and data aggregation were investigated. Applications in mobility management include user mobility prediction and handover management. To improve the accuracy of positioning in indoor environments, localization techniques were discussed. We classify existing research into the respective KDN architecture and identify how the knowledge obtained will enhance future networks; as a result, researchers can extend their work to empower intelligence and self-organization in the network using the KDN paradigm. Finally, the requirements, motivations, applications, challenges, and open issues are presented.
Babakian, A, Monclus, P, Braun, R & Lipman, J 2022, 'A Retrospective on Workload Identifiers: From Data Center to Cloud-Native Networks', IEEE Access, vol. 10, pp. 105518-105527.
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As applications move to multiple clouds, the network has become a reactive element to support cloud consumption and application needs. Through each generation of network architectures, identifiers and the use of dynamic locators evolved in different levels of the protocol stack. The identifiers and locators type is defined by the isolation boundary and how the architecture considers semantic overload in the IP address. Each solution is an outcome of incrementalism, resulting in application delivery outgrowing the underlying network. This paper contributes an industrial retrospective of how the schemes and mechanisms for identification and location of network entities have evolved in traditional data centers and how they match cloud-native application requirements. Specifically, there is an evaluation of each application artifact that forced necessary changes in the identifiers and locators. Finally, the common themes are highlighted from observations to determine the investigation areas that may play an essential role in the future of cloud-native networking.
Banerjee, S, Lyu, J, Huang, Z, Leung, FHF, Lee, T, Yang, D, Su, S, Zheng, Y & Ling, SH 2022, 'Ultrasound spine image segmentation using multi-scale feature fusion Skip-Inception U-Net (SIU-Net)', Biocybernetics and Biomedical Engineering, vol. 42, no. 1, pp. 341-361.
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Scoliosis is a 3D spinal deformation where the spine takes a lateral curvature, forming an angle in the coronal plane. Diagnosis of scoliosis requires periodic detection, and frequent exposure to radiative imaging may cause cancer. A safer and more economical alternative imaging, i.e., 3D ultrasound imaging modality, is being explored. However, unlike other radiative modalities, an ultrasound image is noisy, which often suppresses the image's useful information. Through this research, a novel hybridized CNN architecture, multi-scale feature fusion Skip-Inception U-Net (SIU-Net), is proposed for a fully automatic bony feature detection, which can be further used to assess the severity of scoliosis safely and automatically. The proposed architecture, SIU-Net, incorporates two novel features into the basic U-Net architecture: (a) an improvised Inception block and (b) newly designed decoder-side dense skip pathways. The proposed model is tested on 109 spine ultrasound image datasets. The architecture is evaluated using the popular (i) Jaccard Index (ii) Dice Coefficient and (iii) Euclidean distance, and compared with (a) the basic U-net segmentation model, (b) a more evolved UNet++ model, and (c) a newly developed MultiResUNet model. The results show that SIU-Net gives the clearest segmentation output, especially in the important regions of interest such as thoracic and lumbar bony features. The method also gives the highest average Jaccard score of 0.781 and Dice score of 0.883 and the lowest histogram Euclidean distance of 0.011 than the other three models. SIU-Net looks promising to meet the objectives of a fully automatic scoliosis detection system.
Barzegarkhoo, R, Farhangi, M, Aguilera, RP, Lee, SS, Blaabjerg, F & Siwakoti, YP 2022, 'Common-Ground Grid-Connected Five-Level Transformerless Inverter With Integrated Dynamic Voltage Boosting Feature', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 6, pp. 6661-6672.
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A single-phase common-ground five-level (5L) inverter with a dynamic voltage conversion gain and capability of operating in a wide input voltage range and a single-stage energy conversion configuration is presented in this article. The proposed topology requires nine active power switches and is comprised of an integrated switched-boost (SB) module connected in series to a switched-flying-capacitor (SFC) cell. Two self-balanced capacitors with a single boost inductor in the integrated SB module are employed to generate a 5L output voltage waveform with a dynamic voltage conversion gain. The current stress profile of all the active and passive elements is kept within a permissible input current range. By adopting an extra diode-capacitor-inductor network into the integrated SB module and with the utilization of the same SFC cell, the proposed topology is extended to achieve a quadratic voltage conversion gain while retaining the quality of ac voltage waveform. Theoretical analysis, closed-loop control/modulation principles, design guidance, comparative study, and relevant experimental results obtained from a 1.5-kW laboratory-built prototype are presented to ascertain the operation and feasibility of the proposed system.
Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Siwakoti, YP & Pou, J 2022, 'Nine-Level Nine-Switch Common-Ground Switched-Capacitor Inverter Suitable for High-Frequency AC-Microgrid Applications', IEEE Transactions on Power Electronics, vol. 37, no. 5, pp. 6132-6143.
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Voltage source multilevel inverters with reduced leakage current, single-stage voltage step-up feature, compact design, and an efficient performance are a promising technology for high-frequency ac (HFac) microgrids feeding through renewable energy sources. This article proposes a novel single-source common-grounded (CG) step-up nine-level (9L) inverter, which can be applied in HFac microgrid applications. The proposed CG-based boost inverter is comprised of only nine switches (9S) and three self-balanced capacitors. Using the switched-capacitor (SC) technique, a double voltage boosting feature within a single power processing stage is achieved, while the leakage current concern is eliminated due to a CG-based configuration between the input dc source and the null of the grid. With the help of an LC input filter, the input current profile is free from large discontinuous inrush spikes. The working principles of the proposed 9L9S-CGSC inverter are discussed in this article. The modulation and closed-loop control strategy, as well as a comparative study, are presented. Finally, the open and closed-loop grid-tied performances of the proposed topology are evaluated by both simulation and experimental results obtained from a 1.2-kW laboratory-built prototype.
Barzegarkhoo, R, Forouzesh, M, Lee, SS, Blaabjerg, F & Siwakoti, YP 2022, 'Switched-Capacitor Multilevel Inverters: A Comprehensive Review', IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 11209-11243.
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Multilevel inverters (MLIs) with switched-capacitor (SC) units have been a widely rehearsed research topic in power electronics since the last decade. Inductorless/transformerless operation with voltage-boosting feature and inherent capacitor self-voltage balancing performance with a reduced electromagnetic interference make the SC-MLI an attractive converter over the other available counterparts for various applications. There have been many developed SC-MLI structures recently put forward, where different basic switching techniques are used to generate multiple (discrete) output voltage levels. In general, the priority of the topological development is motivated by the number of output voltage levels, overall voltage gain, and full dc-link voltage utilization, while reducing the component counts and stress on devices for better efficiency and power density. To facilitate the direction of future research in SC-MLIs, this article presents a comprehensive review, critical analysis, and categorization of the existing topologies. Common fundamental units are generalized and summarized with their merits and demerits. Ultimately, major challenges and research directions are outlined leading to the future technology roadmap for more practical applications.
Barzegarkhoo, R, Khan, SA, Siwakoti, YP, Aguilera, RP, Lee, SS & Khan, MNH 2022, 'Implementation and Analysis of a Novel Switched-Boost Common-Ground Five-Level Inverter Modulated With Model Predictive Control Strategy', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 1, pp. 731-744.
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Common-ground (CG) string inverters with a transformerless (TL) circuit configuration have been broadly popular in grid-connected photovoltaic (PV) applications. The most important feature of a PV string TL inverter with a CG circuit connection is the elimination of leakage current concerns. Having the voltage boosting property within a single power processing stage can also be propitious to further facilitate the integration of low-scale PV string panels with higher efficiency. An efficient topology of such converters is presented in this study, which is able to produce a five-level stair-case output voltage waveform using an integrated switched-boost (SB) cell with only seven power switches. The proposed SB common grounded five-level (SBCG5L)-TL inverter can also be extended for the three-phase CG-based applications with the contribution of the same integrated SB cell. As for the single-phase configuration, it needs two dc-link capacitors and two power diodes along with two small inductors. A quasi-soft charging operation of the involved capacitors is also achieved. To control the injected current under the grid-connected condition, a single-step model predictive control (MPC) technique with a fixed switching frequency operation has also been presented. The proposed circuit description, the theoretical analysis of the applied MPC principles, and the comparative study with associated experimental results are also presented to ascertain the correctness and feasibility of the proposed SBCG5L-TL inverter.
Begum, H, Qian, J & Lee, JE-Y 2022, 'Effect of crystal orientation on liquid phase performance of piezoelectric-on-silicon elliptical plate resonators', Sensors and Actuators A: Physical, vol. 340, pp. 113548-113548.
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Various microelectromechanical (MEM) resonator topologies have been proposed for liquid phase sensing applications. Low liquid phase motional resistance (Rm) and moderately high liquid phase quality factor (Q) are critical to the performance of oscillators based on these resonators for real-time frequency tracking in sensing applications. We recently described a new topology we call the elliptical plate resonator EPR that delivers the lowest Rm after normalizing for area (which impacts mass sensitivity as a tradeoff for lower Rm). In this work, we show that further significant gains in performance can be made by choice of device alignment to the silicon crystal axis (< 110 > direction vs. < 100 > direction). We compare the liquid phase performance between the two orientations for a range of geometrical ratios defining the ellipse of the device. We show that the orientation makes a notable difference on trends in liquid phase Q and Rm. By aligning the EPR to the < 110 > direction, we demonstrate a liquid phase Q of 310 and Rm of 2.5 kΩ. Normalizing for area (Rm×A) to express the tradeoff between mass sensitivity and electrical performance in relation to device area, we report an Rm×A of 0.25 kΩ.mm2. We also show that these gains in liquid phase Rm and Q translate into significant lowering of the Allan deviation when these devices are embedded in close loop to track their frequency in real time with water loaded on the device as expected in liquid phase sensing applications.
Bhatnagar, P, Singh, AK, Gupta, KK & Siwakoti, YP 2022, 'A Switched-Capacitors-Based 13-Level Inverter', IEEE Transactions on Power Electronics, vol. 37, no. 1, pp. 644-658.
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Bi, S, Cui, J, Ni, W, Jiang, Y, Yu, S & Wang, X 2022, 'Three-Dimensional Cooperative Positioning for Internet of Things Provenance', IEEE Internet of Things Journal, vol. 9, no. 20, pp. 19945-19958.
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A large number of Internet of Things (IoT) devices have been interconnected for information collection and exchange. The data are only meaningful if it is captured at the expected location (i.e., the IoT devices or sensors are not removed accidentally or intentionally). This article presents a new algorithm, which cooperatively locates multiple IoT devices deployed in a 3-D space based on pairwise Euclidean distance measurements. When the distance measurement noises are negligible, a new feasibility problem of rank-3 variables is formulated. We solve the problem using the difference-of-convex (DC) programming to preserve the rank-3 constraints, rather than relaxing the constraints, using semidefinite relaxation (SDR). When the distance measurements are corrupted by additive noises and nonlight-of-sight (NLOS) propagation, a maximum-likelihood estimation (MLE) problem is formulated and transformed to a DC program solved with the rank-3 constraints preserved. Simulation results indicate that the proposed approach can achieve satisfactory accuracy results with a low complexity and strong robustness to the irregular topology, poor connectivity, and measurement errors, as compared to existing SDR-based alternatives.
Bour, H, Abolhasan, M, Jafarizadeh, S, Lipman, J & Makhdoom, I 2022, 'A multi-layered intrusion detection system for software defined networking', Computers and Electrical Engineering, vol. 101, pp. 108042-108042.
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The majority of existing DDoS defense mechanisms in SDN impose a significant computational burden on the controller and employ limited flow statistics and packet features. Tackling these issues, this paper presents a multi-layer defense mechanism that detects and mitigates three distinct types of flooding DDoS attacks. In the proposed framework, the detection process consists of flow-based and packet-based attack detection mechanisms employing Extreme Learning Machine-based Single-hidden Layer Feedforward Networks (ELM-SLFNs) and Case-based Information Entropy (C-IE), respectively. Moreover, the affected switches are avoided in the optimal path determined by the Floyd-Warshall algorithm, where the switches are classified based on the Hidden Markov Model (HMM) using the extracted packet features. Our simulation demonstrates the improved performance of our framework compared to similar schemes proposed in the literature in terms of different metrics, including attack detection rate, detection accuracy, false-positive rate, switch failure ratio, packet loss rate, response time, and CPU utilization.
Cai, Y, Lu, Z, Pan, Y, He, L, Guo, X & Zhang, J 2022, 'Optimal scheduling of a hybrid AC/DC multi-energy microgrid considering uncertainties and Stackelberg game-based integrated demand response', International Journal of Electrical Power & Energy Systems, vol. 142, pp. 108341-108341.
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Canning, J, Guo, Y & Chaczko, Z 2022, '(INVITED)Sustainability, livability and wellbeing in a bionic internet-of-things', Optical Materials: X, vol. 16, pp. 100204-100204.
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Chacon, A, Kielly, M, Rutherford, H, Franklin, DR, Caracciolo, A, Buonanno, L, D’Adda, I, Rosenfeld, A, Guatelli, S, Carminati, M, Fiorini, C & Safavi-Naeini, M 2022, 'Detection and discrimination of neutron capture events for NCEPT dose quantification', Scientific Reports, vol. 12, no. 1.
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AbstractNeutron Capture Enhanced Particle Therapy (NCEPT) boosts the effectiveness of particle therapy by capturing thermal neutrons produced by beam-target nuclear interactions in and around the treatment site, using tumour-specific $$^{10}$$ 10 B or $$^{157}$$ 157 Gd-based neutron capture agents. Neutron captures release high-LET secondary particles together with gamma photons with energies of 478 keV or one of several energies up to 7.94 MeV, for $$^{10}$$ 10 B and $$^{157}$$ 157 Gd, respectively. A key requirement for NCEPT’s translation is the development of in vivo dosimetry techniques which can measure both the direct ion dose and the ...
Chakraborty, S, Milner, LE, Zhu, X, Parker, A & Heimlich, M 2022, 'Analysis and Comparison of Marchand and Transformer Baluns Applied in GaAs', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 11, pp. 4278-4282.
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Chang, W, Shi, Y, Tuan, HD & Wang, J 2022, 'Unified Optimal Transport Framework for Universal Domain Adaptation', Advances in Neural Information Processing Systems, vol. 35.
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Universal Domain Adaptation (UniDA) aims to transfer knowledge from a source domain to a target domain without any constraints on label sets. Since both domains may hold private classes, identifying target common samples for domain alignment is an essential issue in UniDA. Most existing methods require manually specified or hand-tuned threshold values to detect common samples thus they are hard to extend to more realistic UniDA because of the diverse ratios of common classes. Moreover, they cannot recognize different categories among target-private samples as these private samples are treated as a whole. In this paper, we propose to use Optimal Transport (OT) to handle these issues under a unified framework, namely UniOT. First, an OT-based partial alignment with adaptive filling is designed to detect common classes without any predefined threshold values for realistic UniDA. It can automatically discover the intrinsic difference between common and private classes based on the statistical information of the assignment matrix obtained from OT. Second, we propose an OT-based target representation learning that encourages both global discrimination and local consistency of samples to avoid the over-reliance on the source. Notably, UniOT is the first method with the capability to automatically discover and recognize private categories in the target domain for UniDA. Accordingly, we introduce a new metric H3-score to evaluate the performance in terms of both accuracy of common samples and clustering performance of private ones. Extensive experiments clearly demonstrate the advantages of UniOT over a wide range of state-of-the-art methods in UniDA.
Chen, C & Jin, D 2022, 'Giant nonlinearity in upconversion nanoparticles', Nature Photonics, vol. 16, no. 8, pp. 553-554.
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Chen, C, Ding, L, Liu, B, Du, Z, Liu, Y, Di, X, Shan, X, Lin, C, Zhang, M, Xu, X, Zhong, X, Wang, J, Chang, L, Halkon, B, Chen, X, Cheng, F & Wang, F 2022, 'Exploiting Dynamic Nonlinearity in Upconversion Nanoparticles for Super-Resolution Imaging', Nano Letters, vol. 22, no. 17, pp. 7136-7143.
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Single-beam super-resolution microscopy, also known as superlinear microscopy, exploits the nonlinear response of fluorescent probes in confocal microscopy. The technique requires no complex purpose-built system, light field modulation, or beam shaping. Here, we present a strategy to enhance this technique's spatial resolution by modulating excitation intensity during image acquisition. This modulation induces dynamic optical nonlinearity in upconversion nanoparticles (UCNPs), resulting in variations of nonlinear fluorescence response in the obtained images. The higher orders of fluorescence response can be extracted with a proposed weighted finite difference imaging algorithm from raw fluorescence images to generate an image with higher resolution than superlinear microscopy images. We apply this approach to resolve single nanoparticles in a large area, improving the resolution to 132 nm. This work suggests a new scope for the development of dynamic nonlinear fluorescent probes in super-resolution nanoscopy.
Chen, D, Liu, Y, Li, M, Guo, P, Zeng, Z, Hu, J & Guo, YJ 2022, 'A Polarization Programmable Antenna Array', Engineering, vol. 16, pp. 100-114.
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Chen, H, Demerdash, NAO, EL-Refaie, AM, Guo, Y, Hua, W & Lee, CHT 2022, 'Investigation of a 3D-Magnetic Flux PMSM With High Torque Density for Electric Vehicles', IEEE Transactions on Energy Conversion, vol. 37, no. 2, pp. 1442-1454.
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This paper presents an investigation of a 3D-magnetic flux permanent magnet synchronous motor (3D-MF PMSM) used for electric vehicle applications. The investigated 3D-MF PMSM consists of an integrated radial-flux and axial-flux structure. It has two radial-flux air-gaps and two axial-flux air-gaps, as well as a toroidal winding wound stator. The integrated structure helps to concentrate all the flux within the motor to maximize torque production. Moreover, there are no end-windings in this motor and all the stator windings effectively are used in torque production. A comprehensive performance evaluation, in terms of the back-electromotive force, average output torque, cogging torque, torque ripple, flux-weakening capability, etc., of the investigated 3D-MF PMSM is conducted. An interior PMSM is purposely included as a benchmark for comparison. The results show that compared to the benchmark interior PMSM, the original 3D-MF PMSM exhibits significantly improved torque density, higher power factor, and higher efficiency, but suffers from serious cogging torque and torque ripple. Accordingly, an unaligned arrangement is introduced to the 3D-MF PMSM. As a result, the cogging torque and torque ripple are significantly reduced.
Chen, L, Chen, L, Ge, Z, Sun, Y, Hamilton, T & Zhu, X 2022, 'A W-Band SPDT Switch With 15-dBm P1dB in 55-nm Bulk CMOS', IEEE Microwave and Wireless Components Letters, vol. 32, no. 7, pp. 879-882.
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Power-handling capability of bulk CMOS-based single-pole double-throw (SPDT) switch operating in millimeter-wave (mm-wave) and subterahertz region is significantly limited by the reduced threshold voltage of deeply scaled transistors. A unique design technique based on impedance transformation network (ITN) is presented in this work, which improves 1-dB compression point, namely P1dB, without deteriorating other performance. To prove the presented solution is valid, a 70-100-GHz switch is designed and implemented in a 55-nm bulk CMOS technology. At 90 GHz, it achieves a measured P1dB of 15 dBm, an insertion loss (IL) of 3.5 dB, and an isolation (ISO) of 18 dB. The total area of the chip is only 0.14 mm2.
Chen, L, Liu, Y, Ren, Y, Zhu, C, Yang, S & Guo, YJ 2022, 'Synthesizing Wideband Frequency-Invariant Shaped Patterns by Linear Phase Response-Based Iterative Spatiotemporal Fourier Transform', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10378-10390.
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Chen, L, Zhu, H, Gomez-Garcia, R & Zhu, X 2022, 'Miniaturized On-Chip Notch Filter With Sharp Selectivity and >35-dB Attenuation in 0.13-μm Bulk CMOS Technology', IEEE Electron Device Letters, vol. 43, no. 8, pp. 1175-1178.
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Chen, S-L, Liu, Y, Zhu, H, Chen, D & Guo, YJ 2022, 'Millimeter-Wave Cavity-Backed Multi-Linear Polarization Reconfigurable Antenna', IEEE Transactions on Antennas and Propagation, vol. 70, no. 4, pp. 2531-2542.
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Chen, S-L, Liu, Y, Ziolkowski, RW, Li, Z & Guo, YJ 2022, 'High-Gain Single-Feed Overmoded Cavity Antenna With Closely Spaced Phased Patch Surface', IEEE Transactions on Antennas and Propagation, vol. 70, no. 1, pp. 229-239.
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Chen, S-L, Wu, G-B, Wong, H, Chen, B-J, Chan, CH & Guo, YJ 2022, 'Millimeter-Wave Slot-Based Cavity Antennas With Flexibly-Chosen Linear Polarization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6604-6616.
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Slot-based cavity antennas are hailed as promising candidates for millimeter-wave applications. Nevertheless, the linear-polarization (LP) angle of their broadside main beam is limited by the slots etched on the cavity’s top surface. In this work, an innovative technique is developed to significantly improve the selection flexibility of their LP inclination angle. It is attained by an integration of a single-layer, closely-spaced C-shaped patch surface. A TE710-mode slot-based cavity antenna is employed as the base configuration, which radiates a broadside beam with its LP along ϕ=90°. To effectively predict and monitor the polarization conversion of the surface-integrated TE710-mode cavity antenna, an analysis method using a unit cavity extracted from its original cavity antenna is presented. A subsequent surface-integrated system with the specified 45°-LP was then simulated, fabricated, and measured. The measured results validate that a 45°-LP state is achieved with an operating bandwidth from 33.3 to 36.5 GHz. Further investigation is conducted to flexibly choose the LP direction from ϕ=15° to 165°. Two more examples with the fabricated antenna prototypes successfully radiate the specified ϕ=15° and 75° LP beam, respectively. This near-field polarization conversion surface can be generalized to cavities with different resonant modes.
Chen, S-L, Ziolkowski, RW, Jones, B & Guo, YJ 2022, 'Analysis, Design, and Measurement of Directed-Beam Toroidal Waveguide-Based Leaky-Wave Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10141-10155.
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Chen, T, Xie, G-S, Yao, Y, Wang, Q, Shen, F, Tang, Z & Zhang, J 2022, 'Semantically Meaningful Class Prototype Learning for One-Shot Image Segmentation', IEEE Transactions on Multimedia, vol. 24, pp. 968-980.
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Chen, X, Wen, H, Ni, W, Zhang, S, Wang, X, Xu, S & Pei, Q 2022, 'Distributed Online Optimization of Edge Computing With Mixed Power Supply of Renewable Energy and Smart Grid', IEEE Transactions on Communications, vol. 70, no. 1, pp. 389-403.
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Cui, Q, Zhang, Z, Yanpeng, S, Ni, W, Zeng, M & Zhou, M 2022, 'Dynamic Multichannel Access Based on Deep Reinforcement Learning in Distributed Wireless Networks', IEEE Systems Journal, vol. 16, no. 4, pp. 5831-5834.
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Dang, TD, Hoang, D & Nguyen, DN 2022, 'Trust-Based Scheduling Framework for Big Data Processing with MapReduce', IEEE Transactions on Services Computing, vol. 15, no. 1, pp. 279-293.
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Security and privacy have become a great concern in cloud computing platforms in which users risk the leakage of their private data. The leakage can happen while the data is at rest (in storage), in processing, or on moving within a cloud or between different cloud infrastructures, e.g., from private to public clouds. This paper focuses on protecting data "in processing". For big data applications, the MapReduce framework has been proven as an efficient solution and has been widely deployed, e.g., in healthcare and business data analysis. In this article, we propose a trust-based framework for MapReduce in big data processing tasks. Specifically, we first quantify and propose to assign the sensitive values for data and trust values for map and reduce slots. We then compute the trust value of each resource employed in the big data processing tasks. Depending on the data's sensitivity level of a task, the task requires a given level of trust (i.e., higher sensitive data requires servers/slots with higher trust level). The MapReduce scheduling problem is then formulated as the maximum weighted matching problem of a bipartite graph that aims to maximize the total trust value over all possible assignments subject to various trust requirement of different tasks. The problem is known to be NP-hard. To tackle it, we observe that within a computing node (VM), slots share the same trust value granted from the secured transformation phase. This helps reduce the number of slot nodes of a weight bipartite graph. Leveraging this fact, we propose an efficient heuristic algorithm that achieves 94.7% of the optimal solution obtained via exhaustive search. Extensive simulations show that the trust-based scheduling scheme provides much higher protection for data sensitivity while ensuring good performance for big data applications.
Dang-Ngoc, H, Nguyen, DN, Ho-Van, K, Hoang, DT, Dutkiewicz, E, Pham, Q-V & Hwang, W-J 2022, 'Secure Swarm UAV-Assisted Communications With Cooperative Friendly Jamming', IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25596-25611.
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This article proposes a cooperative friendly jamming framework for swarm unmanned aerial vehicle (UAV)-assisted amplify-and-forward (AF) relaying networks with wireless energy harvesting. In particular, we consider a swarm of hovering UAVs that relays information from a terrestrial base station to a distant mobile user and simultaneously generates friendly jamming signals to interfere/obfuscate an eavesdropper. Due to the limited energy of the UAVs, we develop a collaborative time-switching relaying protocol that allows the UAVs to collaborate in harvesting wireless energy, relay information, and jam the eavesdropper. To evaluate the performance, we derive the secrecy outage probability (SOP) for two popular detection techniques at the eavesdropper, i.e., selection combining and maximum-ratio combining. Monte Carlo simulations are then used to validate the theoretical SOP derivation. Using the derived SOP, one can obtain engineering insights to optimize the energy harvesting time and the number of UAVs in the swarm to achieve a given secrecy protection level. Furthermore, simulations show the effectiveness of the proposed framework in terms of SOP compared to the conventional AF relaying system. The analytical SOP derived in this work can also be helpful in future UAV secure-communications optimizations (e.g., trajectory, locations of UAVs). As an example, we present a case study to find the optimal corridor to locate the swarm so as to minimize the system SOP. Our proposed framework helps secure communications for various applications that require large coverage, e.g., industrial IoT, smart city, intelligent transportation systems, and critical IoT infrastructures like energy and water.
Das, D, Hossain, MJ, Mishra, S & Singh, B 2022, 'Bidirectional Power Sharing of Modular DABs to Improve Voltage Stability in DC Microgrids', IEEE Transactions on Industry Applications, vol. 58, no. 2, pp. 2369-2377.
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Ding, L, Shan, X, Wang, D, Liu, B, Du, Z, Di, X, Chen, C, Maddahfar, M, Zhang, L, Shi, Y, Reece, P, Halkon, B, Aharonovich, I, Xu, X & Wang, F 2022, 'Lanthanide Ion Resonance‐Driven Rayleigh Scattering of Nanoparticles for Dual‐Modality Interferometric Scattering Microscopy', Advanced Science, vol. 9, no. 32, pp. e2203354-2203354.
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AbstractLight scattering from nanoparticles is significant in nanoscale imaging, photon confinement. and biosensing. However, engineering the scattering spectrum, traditionally by modifying the geometric feature of particles, requires synthesis and fabrication with nanometre accuracy. Here it is reported that doping lanthanide ions can engineer the scattering properties of low‐refractive‐index nanoparticles. When the excitation wavelength matches the ion resonance frequency of lanthanide ions, the polarizability and the resulted scattering cross‐section of nanoparticles are dramatically enhanced. It is demonstrated that these purposely engineered nanoparticles can be used for interferometric scattering (iSCAT) microscopy. Conceptually, a dual‐modality iSCAT microscopy is further developed to identify different nanoparticle types in living HeLa cells. The work provides insight into engineering the scattering features by doping elements in nanomaterials, further inspiring exploration of the geometry‐independent scattering modulation strategy.
Dinh, TH, Singh, AK, Linh Trung, N, Nguyen, DN & Lin, C-T 2022, 'EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-Based Ranking', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 1548-1556.
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Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates' magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).
Dong, W, Lu, Z, He, L, Geng, L, Guo, X & Zhang, J 2022, 'Low-carbon optimal planning of an integrated energy station considering combined power-to-gas and gas-fired units equipped with carbon capture systems', International Journal of Electrical Power & Energy Systems, vol. 138, pp. 107966-107966.
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El Hammoumi, M, Tubbal, F, El Amrani El Idrissi, N, Raad, R, Theoharis, PI, Lalbakhsh, A & Abulgasem, S 2022, 'A Wideband 5G CubeSat Patch Antenna', IEEE Journal on Miniaturization for Air and Space Systems, vol. 3, no. 2, pp. 47-52.
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Esfandiari, M, Lalbakhsh, A, Nasiri Shehni, P, Jarchi, S, Ghaffari-Miab, M, Noori Mahtaj, H, Reisenfeld, S, Alibakhshikenari, M, Koziel, S & Szczepanski, S 2022, 'Recent and emerging applications of Graphene-based metamaterials in electromagnetics', Materials & Design, vol. 221, pp. 110920-110920.
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Surface Plasmon Polaritons (SPPs) operating in mid-infrared up to terahertz (THz) frequencies have been traditionally manufactured on expensive metals such as gold, silver, etc. However, such metals have poor surface confinement that limits the optical applications of SPPs. The invention of graphene is a breakthrough in plasmon-based devices in terms of design, fabrication and applications, thanks to its plasmonic wave distribution, low-cost prototyping and its inherent reconfigurability. In addition, recent advancements in plasmon-based metamaterials and metasurfaces led to the elimination of the past constraints on regular optical devices, opening a new door in THz devices and applications. This paper provides an operational perspective of the advanced graphene-based electromagnetic devices, with a focus on graphene enabled antennas, absorbers and sensors, analyzing the strengths and limitations of various design methodologies.
Esfandiyari, M, Lalbakhsh, A, Jarchi, S, Ghaffari-Miab, M, Mahtaj, HN & Simorangkir, RBVB 2022, 'Tunable terahertz filter/antenna-sensor using graphene-based metamaterials', Materials & Design, vol. 220, pp. 110855-110855.
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In this paper, a novel tunable graphene-based bandstop filter/antenna-sensor is presented. This structure is an integrated module that can be used to combine filtering and high-gain radiation performance. The initial design of the unit cell consists of four U-shaped stubs loaded, resembling the arms of a ring and a sensing layer in the substrate. The reflection and transmission spectra are obtained for various graphene's chemical potentials and refractive index of sensing layer (Ns) of structure in the range of 1.3–1.6 THz. The proposed structure exhibits the attributes of both dual-band filter and single-band antenna-sensor. The conductivity of graphene and its structural parameters are studied to optimize the component performance. In filtering mode, the first bandstop is from 1.23 to 1.6 THz equal to 26% of fractional bandwidth (FBW) at 1.415 THz. The second stopband is centered at 3.12 THz with FBW of 14% for Ns = 1.6 and 0.6 eV chemical potential. In the antenna mode, a single band of the antenna-sensor is centered at 1.95 THz for the same Ns and same chemical potential. It is shown that a sensitivity of 0.145 THz/RIU is achieved at Ns = 1.5 and chemical potential of 0.6 eV. Additionally, the performance of the proposed filter/antenna-sensor module is investigated for different wave polarizations and oblique angles.
Eslahi, H, Hamilton, TJ & Khandelwal, S 2022, 'Compact and Energy Efficient Neuron With Tunable Spiking Frequency in 22-nm FDSOI', IEEE Transactions on Nanotechnology, vol. 21, pp. 189-195.
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Esselle, K, Matekovits, L, Yang, Y, Thalakotuna, D, Afzal, M, Kovaleva, M & Singh, K 2022, 'Guest Editorial Disruptive Beam-Steering Antenna Technologies for Emerging and Future Satellite Services', IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 11, pp. 2211-2218.
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Faisal, SN & Iacopi, F 2022, 'Thin-Film Electrodes Based on Two-Dimensional Nanomaterials for Neural Interfaces', ACS Applied Nano Materials, vol. 5, no. 8, pp. 10137-10150.
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Fan, S, Ni, W, Tian, H, Huang, Z & Zeng, R 2022, 'Carrier Phase-Based Synchronization and High-Accuracy Positioning in 5G New Radio Cellular Networks', IEEE Transactions on Communications, vol. 70, no. 1, pp. 564-577.
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Inspired by excellent precision of carrier phase positioning, this paper presents a new carrier phase positioning technique for 5G new radio cellular networks with a focus on clock synchronization and integer ambiguity resolution. A carrier-phase based clock offset estimation method is first proposed to achieve precise clock synchronization among base stations, and proved to achieve the Cramér-Rao Lower Bound (CRLB) asymptotically. A fusion method is developed to fuse the estimated positions of a mobile station (MS) based on time-difference-of-arrival, with the estimated position changes based on the temporal changes of carrier phase measurements. While circumventing the integer ambiguities of the carrier phase measurements, the fusion method provides quality interim estimates of the MS positions, at which the measurements can be linearized to resolve the integer ambiguities. As a result, precise MS positions can be obtained based on the disambiguated carrier phase measurements. Numerical simulations show that the proposed carrier phase positioning can achieve a centimeter-level accuracy in wireless cellular networks.
Farah, N, Lei, G, Zhu, J & Guo, Y 2022, 'Two-vector Dimensionless Model Predictive Control of PMSM Drives Based on Fuzzy Decision Making', CES Transactions on Electrical Machines and Systems, vol. 6, no. 4, pp. 393-403.
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Farasat, M, Thalakotuna, D, Hu, Z & Yang, Y 2022, 'A Simple and Effective Approach for Scattering Suppression in Multiband Base Station Antennas', Electronics, vol. 11, no. 21, pp. 3423-3423.
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The high band pattern distortions in an 1810–2690 MHz frequency band, introduced due to low band radiators working in 690–960 MHz, are mitigated by a simple yet effective change to the low band-radiating elements. A novel horizontal and vertical radiating element is designed instead of a conventional slant polarized low band-radiating element to reduce the scattering. The slant polarization is achieved from the horizontal and vertical dipoles, using a 180° hybrid coupler. The vertical dipole length is optimized to improve the high band patterns. The experimental results verified that the proposed horizontal and vertical low band dipole result in the reduction of high band pattern distortions. The low band-radiating elements provide >12 dB return loss over the entire frequency band 690–960 MHz and provide comparable pattern performance to a conventional slant low band dipole.
Farhangi, M, Barzegarkhoo, R, Aguilera, RP, Lee, SS, Lu, DD-C & Siwakoti, YP 2022, 'A Single-Source Single-Stage Switched-Boost Multilevel Inverter: Operation, Topological Extensions, and Experimental Validation', IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 11258-11271.
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In this article, we present a family of multilevel converters with the single-stage dynamic voltage-boosting feature, reduced number of circuit components, modular structure, bidirectional operation, continuous input current, and acceptable overall efficiency. The proposed structure is based on a three-level single-stage boost integrated inverter with an embedded quasi-H-bridge (QHB) cell. It is comprised of five unidirectional power switches and a floating capacitor. By differential connection of two or three QHB cells and with the aim of a single inductor/input dc source, several derived topologies for both the single and three-phase applications with different multilevel output voltage performances have been achieved. The aforementioned advantages make this converter a suitable candidate for renewable energy applications. Theoretical analysis, design consideration, comparative study, and several experimental results for a 3-kW laboratory-built system are presented to validate the effectiveness and feasibility of this proposal.
Fazeli, A, Nguyen, HH, Tuan, HD & Poor, HV 2022, 'Non-Coherent Multi-Level Index Modulation', IEEE Transactions on Communications, vol. 70, no. 4, pp. 2240-2255.
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Gao, S, Guo, YJ, Safavi-Naeini, SA, Hong, W & Yang, X-X 2022, 'Guest Editorial Low-Cost Wide-Angle Beam-Scanning Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7378-7383.
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Gautam, S, Xiao, W, Ahmed, H & Lu, DD-C 2022, 'Enhanced Single-Phase Phase Locked Loop Based on Complex-Coefficient Filter', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-8.
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Gautam, S, Xiao, W, Lu, DD-C, Ahmed, H & Guerrero, JM 2022, 'Development of Frequency-Fixed All-Pass Filter-Based Single-Phase Phase-Locked Loop', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 1, pp. 506-517.
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Phase-locked loops (PLL) are widely used in the synchronization of grid interfaced power converters. One solution is based on orthogonal signal generation (OSG), which requires the grid frequency information for their appropriate operation. This article developed a new solution to achieve the PLL function for single-phase grid interconnection but eradicate additional frequency feedback loops in the traditional architecture of all-pass filter PLL (APF-PLL). Four new topologies are developed along with their small-signal modeling and dynamic analysis. A thorough comparison among them on their dynamic response, steady-state accuracy, implementation, and disturbance rejection capability is carried out. Finally, the best approach of frequency-fixed (FF) APF-PLL is experimentally evaluated with frequency adaptive APF-PLL and FF PLLs belonging to time delay (TD) and second-order generalized integrator (SOGI) families.
Gholami, K, Azizivahed, A, Li, L & Zhang, J 2022, 'Accuracy enhancement of second-order cone relaxation for AC optimal power flow via linear mapping', Electric Power Systems Research, vol. 212, pp. 108646-108646.
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Optimal power flow (OPF) has always been one of the most crucial tools for power system operations. OPF problem formulation involves non-linear alternative current (AC) power flow equations, and a wide range of challenges occur as a result. This is because the resulting non-convex optimization problems are not only complex and time-consuming, but also difficult to find a global optimum as many local optimums are present. So far, different relaxations have been provided to address these issues. One of the most effective strategies for convexifying such formulations is second-order cone programming (SOCP). Although SOCP is an efficient instrument for convexifying AC OPF equations, it is unable to reach the global optimal solution compared to other methods. The aim of this paper is therefore to provide a new method to approach the global optimum of AC OPF relaxed by SOCP. This method is obtained with the aid of a new linrear tranfsormation called semi-Lorentz transformation as it similar to the Lorentz transformation in the special relativity theory. In this method second-order cone AC OPF equations are mapped to a new model via semi-Lorentz transformation. In addition, an approximation approach is also presented to reach the best semi-Lorentz factor, the main driver in semi-Lorentz transformation, for each particular problem based on the network parameters. From the comparative analysis in case studies, the proposed OPF solution method has robust precision and higher efficiency while consuming less computing time.
Golestanifar, A, Karimi, G & Lalbakhsh, A 2022, 'Varactor-tuned wideband band-pass filter for 5G NR frequency bands n77, n79 and 5G Wi-Fi', Scientific Reports, vol. 12, no. 1, p. 16330.
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AbstractA wide-band band-pass filter (BPF) using coupled lines, rectangular stubs and Stepped-Impedance Resonators (SIRs) is presented in this paper. The proposed BPF operates over a large pass-band from 3.15 to 6.05 GHz covering 5G New Radio (NR) frequency Bands n77, n79 and 5G Wi-Fi, which includes the G band of US (3.3 to 4.2 GHz), 5G band of Japan (4.4 to 5 GHz) and 5G Wi-Fi (5.15 to 5.85 GHz). The presented filter has a maximum pass-band Insertion-Loss (IL) of 2 dB, a sharp roll-off rate and suppresses all the unwanted harmonics from 4.2 GHz up to 12 GHz with a 15 dB attenuation level. The performance of each section can be analyzed based on lumped-element circuit models. The electrical size of the BPF is 0.258 λg × 0.255 λg, where λg is the guided wavelength at the central frequency. The design accuracy is verified through implementing and testing the final BPF. The pass-band band-width can be controlled by adding the varactor diodes. A good relationship between the band-width and the varactor diodes are extracted by the curve fitting technique.
Gong, S, Zou, Y, Xu, J, Hoang, DT, Lyu, B & Niyato, D 2022, 'Optimization-Driven Hierarchical Learning Framework for Wireless Powered Backscatter-Aided Relay Communications', IEEE Transactions on Wireless Communications, vol. 21, no. 2, pp. 1378-1391.
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In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. The wireless relays can operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall throughput by jointly optimizing the transmit beamforming and the relays' radio modes and operating parameters. Due to the non-convex and combinatorial problem structure, we develop a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach to adapt the beamforming and relay strategies. The optimization-driven H-DDPG algorithm firstly decomposes the binary relay mode selection into the outer-loop deep $Q$ -network (DQN) algorithm and then optimizes the continuous beamforming and relaying strategies by using the inner-loop DDPG algorithm. Secondly, to improve the learning efficiency, we integrate the model-based optimization into the inner-loop DDPG framework by providing a better-informed target estimation for DNN training. Simulation results reveal that these two special designs ensure a more stable learning performance and achieve a higher reward, up to 20%, compared to the conventional model-free DDPG approach.
Gong, Y, Li, Z, Zhang, J, Liu, W & Zheng, Y 2022, 'Online Spatio-Temporal Crowd Flow Distribution Prediction for Complex Metro System', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 2, pp. 865-880.
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Goudarzi, S, Ahmad Soleymani, S, Hossein Anisi, M, Ciuonzo, D, Kama, N, Abdullah, S, Abdollahi Azgomi, M, Chaczko, Z & Azmi, A 2022, 'Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network', Computers, Materials & Continua, vol. 70, no. 1, pp. 715-738.
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Guo, CA & Guo, YJ 2022, 'A General Approach for Synthesizing Multibeam Antenna Arrays Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7556-7564.
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Guo, K & Guo, Y 2022, 'Design and Analysis of an Outer Mover Linear-Rotary Vernier Machine', Journal of Electrical Engineering & Technology, vol. 17, no. 2, pp. 1087-1095.
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Guo, K, Guo, Y & Fang, S 2022, 'Flux Leakage Analytical Calculation in the E-Shape Stator of Linear Rotary Motor With Interlaced Permanent Magnet Poles', IEEE Transactions on Magnetics, vol. 58, no. 8, pp. 1-6.
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Guo, X, Zhang, H, Tang, W, Lu, Z, Hua, C, Siwakoti, YP, Malinowski, M & Blaabjerg, F 2022, 'Overview of Recent Advanced Topologies for Transformerless Dual-Grounded Inverters', IEEE Transactions on Power Electronics, vol. 37, no. 10, pp. 12679-12704.
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Transformerless inverters, most of which are H-bridge inverters, have been widely used and studied in grid-connected power systems in the last decades. However, the H-bridge inverter is affected by the low- and high-frequency common-mode voltage between the input and output terminals, resulting in a large common-mode leakage current. An alternative solution is to connect the ground of the input terminal to the output load or grid, that is, the dual-grounded inverter. In this case, the low- and high-frequency common-mode voltages can be mitigated or eliminated. As a matter of fact, scholars have made several research results on dual-grounded inverters. However, as of now, there is still no literature that comprehensively and systematically summarizes these research results. To fill this gap, this article classifies different types of dual-grounded inverters from the perspective of topology for the first time, and compares and summarizes their advantages and disadvantages. More than 60 works of literature have been reviewed to identify the practical implementation challenges and research opportunities in the application of dual-grounded inverters.
Guo, Y, Liu, L, Ba, X, Lu, H, Lei, G, Sarker, P & Zhu, J 2022, 'Characterization of Rotational Magnetic Properties of Amorphous Metal Materials for Advanced Electrical Machine Design and Analysis', Energies, vol. 15, no. 20, pp. 7798-7798.
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Amorphous metal (AM), specifically amorphous ferromagnetic metal, is considered as a satisfactory magnetic material for exploring electromagnetic devices with high-efficiency and high-power density, such as electrical machines and transformers, benefits from its various advantages, such as reasonably low power loss and very high permeability in medium to high frequency. However, the characteristics of these materials have not been investigated comprehensively, which limits its application prospects to good-performance electrical machines that have the magnetic flux density with generally rotational and non-sinusoidal features. The appropriate characterization of AMs under different magnetizations is among the fundamentals for utilizing these materials in electrical machines. This paper aims to extensively overview AM property measurement techniques in the presence of various magnetization patterns, particularly rotational magnetizations, and AM property modeling methods for advanced electrical machine design and analysis. Possible future research tasks are also discussed for further improving AM applications.
Haakenstad, A, Yearwood, JA, Fullman, N, Bintz, C, Bienhoff, K, Weaver, MR, Nandakumar, V, LeGrand, KE, Knight, M, Abbafati, C, Abbasi-Kangevari, M, Abdoli, A, Abeldaño Zuñiga, RA, Adedeji, IA, Adekanmbi, V, Adetokunboh, OO, Afzal, MS, Afzal, S, Agudelo-Botero, M, Ahinkorah, BO, Ahmad, S, Ahmadi, A, Ahmadi, S, Ahmed, A, Ahmed Rashid, T, Aji, B, Akande-Sholabi, W, Alam, K, Al Hamad, H, Alhassan, RK, Ali, L, Alipour, V, Aljunid, SM, Ameyaw, EK, Amin, TT, Amu, H, Amugsi, DA, Ancuceanu, R, Andrade, PP, Anjum, A, Arabloo, J, Arab-Zozani, M, Ariffin, H, Arulappan, J, Aryan, Z, Ashraf, T, Atnafu, DD, Atreya, A, Ausloos, M, Avila-Burgos, L, Ayano, G, Ayanore, MA, Azari, S, Badiye, AD, Baig, AA, Bairwa, M, Bakkannavar, SM, Baliga, S, Banik, PC, Bärnighausen, TW, Barra, F, Barrow, A, Basu, S, Bayati, M, Belete, R, Bell, AW, Bhagat, DS, Bhagavathula, AS, Bhardwaj, P, Bhardwaj, N, Bhaskar, S, Bhattacharyya, K, Bhurtyal, A, Bhutta, ZA, Bibi, S, Bijani, A, Bikbov, B, Biondi, A, Bolarinwa, OA, Bonny, A, Brenner, H, Buonsenso, D, Burkart, K, Busse, R, Butt, ZA, Butt, NS, Caetano dos Santos, FL, Cahuana-Hurtado, L, Cámera, LA, Cárdenas, R, Carneiro, VLA, Catalá-López, F, Chandan, JS, Charan, J, Chavan, PP, Chen, S, Chen, S, Choudhari, SG, Chowdhury, EK, Chowdhury, MAK, Cirillo, M, Corso, B, Dadras, O, Dahlawi, SMA, Dai, X, Dandona, L, Dandona, R, Dangel, WJ, Dávila-Cervantes, CA, Davletov, K, Deuba, K, Dhimal, M, Dhimal, ML, Djalalinia, S, Do, HP, Doshmangir, L, Duncan, BB, Effiong, A, Ehsani-Chimeh, E, Elgendy, IY, Elhadi, M, El Sayed, I, El Tantawi, M, Erku, DA, Eskandarieh, S, Fares, J, Farzadfar, F, Ferrero, S, Ferro Desideri, L, Fischer, F, Foigt, NA, Foroutan, M, Fukumoto, T, Gaal, PA, Gaihre, S, Gardner, WM, Garg, T, Getachew Obsa, A, Ghafourifard, M, Ghashghaee, A, Ghith, N, Gilani, SA, Gill, PS, Goharinezhad, S, Golechha, M, Guadamuz, JS, Guo, Y, Gupta, RD, Gupta, R, Gupta, VK, Gupta, VB, Hamiduzzaman, M, Hanif, A, Haro, JM, Hasaballah, AI, Hasan, MM, Hasan, MT, Hashi, A, Hay, SI, Hayat, K, Heidari, M, Heidari, G, Henry, NJ, Herteliu, C, Holla, R, Hossain, S, Hossain, SJ, Hossain, MBH, Hosseinzadeh, M, Hostiuc, S, Hoveidamanesh, S, Hsieh, VC-R, Hu, G, Huang, J, Huda, MM, Ifeagwu, SC, Ikuta, KS, Ilesanmi, OS, Irvani, SSN, Islam, RM, Islam, SMS, Ismail, NE, Iso, H, Isola, G, Itumalla, R, Iwagami, M, Jahani, MA, Jahanmehr, N & et al. 2022, 'Assessing performance of the Healthcare Access and Quality Index, overall and by select age groups, for 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019', The Lancet Global Health, vol. 10, no. 12, pp. e1715-e1743.
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BACKGROUND: Health-care needs change throughout the life course. It is thus crucial to assess whether health systems provide access to quality health care for all ages. Drawing from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019), we measured the Healthcare Access and Quality (HAQ) Index overall and for select age groups in 204 locations from 1990 to 2019. METHODS: We distinguished the overall HAQ Index (ages 0-74 years) from scores for select age groups: the young (ages 0-14 years), working (ages 15-64 years), and post-working (ages 65-74 years) groups. For GBD 2019, HAQ Index construction methods were updated to use the arithmetic mean of scaled mortality-to-incidence ratios (MIRs) and risk-standardised death rates (RSDRs) for 32 causes of death that should not occur in the presence of timely, quality health care. Across locations and years, MIRs and RSDRs were scaled from 0 (worst) to 100 (best) separately, putting the HAQ Index on a different relative scale for each age group. We estimated absolute convergence for each group on the basis of whether the HAQ Index grew faster in absolute terms between 1990 and 2019 in countries with lower 1990 HAQ Index scores than countries with higher 1990 HAQ Index scores and by Socio-demographic Index (SDI) quintile. SDI is a summary metric of overall development. FINDINGS: Between 1990 and 2019, the HAQ Index increased overall (by 19·6 points, 95% uncertainty interval 17·9-21·3), as well as among the young (22·5, 19·9-24·7), working (17·2, 15·2-19·1), and post-working (15·1, 13·2-17·0) age groups. Large differences in HAQ Index scores were present across SDI levels in 2019, with the overall index ranging from 30·7 (28·6-33·0) on average in low-SDI countries to 83·4 (82·4-84·3) on average in high-SDI countries. Similarly large ranges between low-SDI and high-SDI countries, respectively, were estimated in the HAQ Index for the young (40·4-89·0), working (33·8-82·8), and post-working ...
Hadei, M, Dadashzadeh, G, Torabi, Y & Lalbakhsh, A 2022, 'Terahertz beamforming network with a nonuniform contour', Applied Optics, vol. 61, no. 4, pp. 1087-1087.
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This paper presents a terahertz beamforming network based on a nonlocal lens with a 2D beam-scanning demonstration through leaky-wave antennas. The proposed design methodology is novel, to the best of our knowledge, in the aspect of using unconventional optimization parameters to significantly reduce the phase error associated with this class of beamformers. In this approach, a nonuniform contour defined by Fourier series expansion is used as a new optimization parameter to significantly decrease the phase error over a larger scan-angle than that in the previous works. The proposed system is a good candidate for industrial and security applications such as automotive radar sensors and electromagnetic THz imaging, thanks to its extensive 2D scanning range: − 68 ∘ to 0° in the elevation plane and − 45 ∘ to + 45
Hannan, MA, Abd Rahman, MS, Al-Shetwi, AQ, Begum, RA, Ker, PJ, Mansor, M, Mia, MS, Hossain, MJ, Dong, ZY & Mahlia, TMI 2022, 'Impact Assessment of COVID-19 Severity on Environment, Economy and Society towards Affecting Sustainable Development Goals', Sustainability, vol. 14, no. 23, pp. 15576-15576.
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The COVID-19 pandemic has affected every sector in the world, ranging from the education sector to the health sector, administration sector, economic sector and others in different ways. Multiple kinds of research have been performed by research centres, education institutions and research groups to determine the extent of how huge of a threat the COVID-19 pandemic poses to each sector. However, detailed analysis and assessment of its impact on every single target within the 17 Sustainable Development Goals (SDGs) have not been discussed so far. We report an assessment of the impact of COVID-19 effect towards achieving the United Nations SDGs. In assessing the pandemic effects, an expert elicitation model is used to show how the COVID-19 severity affects the positive and negative impact on the 169 targets of 17 SDGs under environment, society and economy groups. We found that the COVID-19 pandemic has a low positive impact in achieving only 34 (20.12%) targets across the available SDGs and a high negative impact of 54 targets (31.95%) in which the most affected group is the economy and society. The environmental group is affected less; rather it helps to achieve a few targets within this group. Our elicitation model indicates that the assessment process effectively measures the mapping of the COVID-19 pandemic impact on achieving the SDGs. This assessment identifies that the COVID-19 pandemic acts mostly as a threat in enabling the targets of the SDGs.
Hassan, M, Hossain, J & Shah, R 2022, 'Threshold-free localized scheme for DC fault identification in multiterminal HVDC systems', Electric Power Systems Research, vol. 210, pp. 108081-108081.
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He, F, Mahmud, MAP, Kouzani, AZ, Anwar, A, Jiang, F & Ling, SH 2022, 'An Improved SLIC Algorithm for Segmentation of Microscopic Cell Images.', Biomed. Signal Process. Control., vol. 73, pp. 103464-103464.
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He, T, Wu, M, Lu, DD-C, Song, K & Zhu, J 2022, 'Model Predictive Sliding Control for Cascaded H-Bridge Multilevel Converters With Dynamic Current Reference Tracking', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 2, pp. 1409-1421.
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Heidari, A, Bansal, RC, Hossain, J & Zhu, J 2022, 'Strategic risk aversion of smart energy hubs in the joined energy markets applying a stochastic game approach', Journal of Cleaner Production, vol. 349, pp. 131386-131386.
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Hieu, NQ, Hoang, DT, Niyato, D, Wang, P, Kim, DI & Yuen, C 2022, 'Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles With Joint Radar-Data Communications', IEEE Transactions on Communications, vol. 70, no. 8, pp. 5164-5180.
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Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments. For this, the AVs equipped with Joint Radar-Communications (JRC) functions can enhance the driving safety by utilizing both radar detection and data communication functions. However, optimizing the performance of the AV system with two different functions under uncertainty and dynamic of surrounding environments is very challenging. In this work, we first propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions in selecting JRC operation functions under the dynamic and uncertainty of the surrounding environment. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV without requiring any prior information about surrounding environment. Furthermore, to make our proposed framework more scalable, we develop a Transfer Learning (TL) mechanism that enables the AV to leverage valuable experiences for accelerating the training process when it moves to a new environment. Extensive simulations show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches. With the deep reinforcement learning and transfer learning approaches, our proposed solution can find its applications in a wide range of autonomous driving scenarios from driver assistance to full automation transportation.
Hoang, D & Hoang, S 2022, 'Deep learning - cancer genetics and application of deep learning to cancer oncology', Vietnam Journal of Science and Technology, vol. 60, no. 6, pp. 885-928.
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Arguably the human body has been one of the most sophisticated systems we encounter but until now we are still far from understanding its complexity. We have been trying to replicate human intelligence by way of artificial intelligence but with limited success. We have discovered the molecular structure in terms of genetics, performed gene editing to change an organism’s DNA and much more, but their translatability into the field of oncology has remained limited. Conventional machine learning methods achieved some degree of success in solving problems that we do not have an explicit algorithm. However, they are basically shallow learning methods, not rich enough to discover and extract intricate features that represent patterns in the real environment. Deep learning has exceeded human performance in pattern recognition as well as strategic games and are powerful for dealing with many complex problems. High-throughput sequencing and microarray techniques have generated vast amounts of data and allowed the comprehensive study of gene expression in tumor cells. The application of deep learning with molecular data enables applications in oncology with information not available from clinical diagnosis. This paper provides fundamental concepts of deep learning, an essential knowledge of cancer genetics, and a review of applications of deep learning to cancer oncology. Importantly, it provides an insightful knowledge of deep learning and an extensive discussion on its challenges. The ultimate purpose is to germinate ideas and facilitate collaborations between cancer biologists and deep learning researchers to address challenging oncological problems using advanced deep learning technologies.
Hoang, LM, Andrew Zhang, J, Nguyen, DN & Thai Hoang, D 2022, 'Frequency Hopping Joint Radar-Communications With Hybrid Sub-Pulse Frequency and Duration Modulation', IEEE Wireless Communications Letters, vol. 11, no. 11, pp. 2300-2304.
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Frequency-hopping (FH) joint radar-communications (JRC) can offer excellent security for integrated sensing and communication systems. However, existing JRC schemes mainly embed information using only the sub-pulse frequencies and hence the data rate is limited. In this letter, we propose to use both sub-pulse frequencies and durations for information modulation, leading to higher communication data rates. For information demodulation, we propose a novel scheme by using the time-frequency analysis (TFA) technique and a 'you only look once' (YOLO)-based detection system. As such, our system does not require channel estimation, simplifying the transmission signal frame design. Simulation results demonstrate the effectiveness of our scheme, and show that it is robust against the Doppler shift and timing offset between the transceiver and the communication receiver.
Hoang, PM, Tuan, HD, Son, TT, Poor, HV & Hanzo, L 2022, 'Learning Unbalanced and Sparse Low-Order Tensors', IEEE Transactions on Signal Processing, vol. 70, pp. 5624-5638.
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Efficient techniques are developed for completing unbalanced and sparse low-order tensors, which cannot be effectively completed by popular matrix-rank optimization based techniques such as compressed sensing and/or the ℓq-matrix-metric. We use our previously developed 2D-index encoding technique for tensor augmentation in order to represent these incomplete low-order tensors by high-order but low-dimensional tensors with their modes building up a coarse-grained hierachy of correlations among the incomplete tensor entries. The concept of tensor-trains is then exploited for decomposing these augmented tensors into trains of balanced and sparse matrices for efficient completion. More explicitly, we develop powerful algorithms exhibiting an excellent performance vs. complexity trade-off, which are supported by numerical examples by relying on matrix data and third-order tensor data derived from color image pixels.
Hu, S, Ni, W, Wang, X & Jamalipour, A 2022, 'Disguised Tailing and Video Surveillance With Solar-Powered Fixed-Wing Unmanned Aerial Vehicle', IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5507-5518.
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Disguised tailing and visual monitoring of suspicious mobile targets is a promising application of security unmanned aerial vehicles (UAVs). But trajectory planning is non-trivial, especially for fixed-wing UAVs with more constrained maneuverability and dynamic models. This paper proposes a new framework to optimize collectively the propulsion power and the three-dimensional (3D) trajectory of a solar-powered, fixed-wing UAV on a disguised tailing and video surveillance mission. The multi-objective optimization strikes a balance between distance keeping, elevation variance, and power efficiency. A key aspect is that we develop a new propulsion power model of the fixed-wing UAV by analyzing the forces undergone while the UAV is ascending or descending. Another important aspect is a series of non-trivial reformulations, which convexify the multi-objective problem progressively with increasingly tightening linear approximation and solve the problem with a polynomial time-complexity. Our algorithm can control the trajectory of the UAV on-the-fly. Simulations confirm that the algorithm outperforms existing schemes in terms of visual disguise and power efficiency. The fixed-wing UAV also demonstrates its advantage of energy efficiency and sustainability to elongate the surveillance mission, over its rotary-wing counterpart.
Huang, H, Savkin, AV & Ni, W 2022, 'Decentralized Navigation of a UAV Team for Collaborative Covert Eavesdropping on a Group of Mobile Ground Nodes', IEEE Transactions on Automation Science and Engineering, vol. 19, no. 4, pp. 3932-3941.
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Huang, H, Zhang, J, Yu, L, Zhang, J, Wu, Q & Xu, C 2022, 'TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization With Few Labeled Samples', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 2, pp. 853-866.
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Huang, T, Ben, X, Gong, C, Zhang, B, Yan, R & Wu, Q 2022, 'Enhanced Spatial-Temporal Salience for Cross-View Gait Recognition', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 10, pp. 6967-6980.
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Gait recognition can be used in person identification and re-identification by itself or in conjunction with other biometrics. Although gait has both spatial and temporal attributes, and it has been observed that decoupling spatial feature and temporal feature can better exploit the gait feature on the fine-grained level. However, the spatial-temporal correlations of gait video signals are also lost in the decoupling process. Direct 3D convolution approaches can retain such correlations, but they also introduce unnecessary interferences. Instead of common 3D convolution solutions, this paper proposes an integration of decoupling process into a 3D convolution framework for cross-view gait recognition. In particular, a novel block consisting of a Parallel-insight Convolution layer integrated with a Spatial-Temporal Dual-Attention (STDA) unit is proposed as the basic block for global spatial-temporal information extraction. Under the guidance of the STDA unit, this block can well integrate spatial-temporal information extracted by two decoupled models and at the same time retain the spatial-temporal correlations. In addition, a Multi-Scale Salient Feature Extractor is proposed to further exploit the fine-grained features through context awareness extension of part-based features and adaptively aggregating the spatial features. Extensive experiments on three popular gait datasets, namely CASIA-B, OULP and OUMVLP, demonstrate that the proposed method outperforms state-of-the-art methods.
Huang, X, Li, S, Zuo, Y, Fang, Y, Zhang, J & Zhao, X 2022, 'Unsupervised Point Cloud Registration by Learning Unified Gaussian Mixture Models', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7028-7035.
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Huang, X, Nan, Y & Guo, YJ 2022, 'Radio Frequency Camera: A Noncoherent Circular Array SAR With Uncoordinated Illuminations', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14.
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A novel noncoherent microwave imaging principle with periodical or random radio frequency (RF) illumination is proposed in this article. Implemented with circular array synthetic aperture radar (SAR) frontend and low-complexity signal processing algorithms, the imaging device, called RF camera, achieves some desired properties similar to an optical camera, such as the capability to operate with multiple uncoordinated illuminators. Different from conventional multistatic imaging, the RF camera does not require any knowledge about an illuminator's location or signal waveform. A static illumination sensor (IS) can be used to provide a reference signal for image reconstruction. With periodical illumination, the RF camera can even operate without IS, but the imaging performance can be improved with IS. With random illumination, the IS is necessary for the RF camera operation, and the imaging distortion can be described by a point blur function. Theoretical analyses on the imaging signal-to-noise ratios are performed under different RF camera operation modes. Simulation and experimental tests are conducted using 77-GHz millimeter wave frequency to verify the noncoherent imaging principle and its performance.
Huang, X, Wang, Y, Li, S, Mei, G, Xu, Z, Wang, Y, Zhang, J & Bennamoun, M 2022, 'Robust real-world point cloud registration by inlier detection', Computer Vision and Image Understanding, vol. 224, pp. 103556-103556.
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Real-world point cloud registration is challenging because of large outliers in correspondence search. The mixture variations, such as partial overlap, noise and cross sources, are the root cause of these large outliers. Existing methods face challenges in effectively removing the large outliers. We propose a novel coarse-to-fine framework to remove the outliers by detecting the accurate inlier correspondences. Specifically, our coarse module predicts the top-K accurate correspondences. The coarse module is trained by jointly leveraging global and local structured information. Then, our refinement module checks the correspondences further using our proposed novel higher-order filter, which enables the structure conformity of correspondences to improve the quality of inlier correspondences. The final transformation matrix is calculated by using the refined inlier correspondences. Furthermore, a new cross-source point cloud dataset is proposed to further demonstrate the robustness in real-world point clouds. Experimental results demonstrate that our algorithm achieves the state-of-the-art accuracy on both indoor and outdoor, same-source and newly proposed cross-source real-world point clouds.
Huang, Y, Li, Y, Heyes, T, Jourjon, G, Cheng, A, Seneviratne, S, Thilakarathna, K, Webb, D & Xu, RYD 2022, 'Task adaptive siamese neural networks for open-set recognition of encrypted network traffic with bidirectional dropout', Pattern Recognition Letters, vol. 159, pp. 132-139.
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Existing deep learning approaches have achieved high performance in encrypted network traffic analysis tasks. However, practical requirements such as open-set recognition on dynamically changing tasks (e.g., changes in the target website list), challenge existing methods. While few-shot learning and open-set recognition methods have been proposed for domains such as computer vision, few-shot open-set recognition for encrypted network traffic remains an unexplored area. This paper proposes a task adaptive siamese neural network for open-set recognition of encrypted network traffic with bidirectional dropout data augmentation. Our contributions are three-fold: First, we introduce generated positive and negative pairs into the siamese neural network training process to shape a more precise similarity boundary through bidirectional dropout data augmentation. Second, we utilize Dirichlet Process Gaussian Mixture Model (DPGMM) distribution to fit the similarity scores of the negative pairs constructed by the support set of each query task, and create a new open-set recognition metric. Third, by leveraging the extracted features at coarse and fine granular levels, we construct a hierarchical cross entropy loss to improve the confidence of the similarity score. Extensive experiments on a network traffic dataset and the Omniglot dataset demonstrate the superiority and generalizability of our proposed approach.
Huang, Y, Wu, Q, Xu, J, Zhong, Y, Zhang, P & Zhang, Z 2022, 'Alleviating Modality Bias Training for Infrared-Visible Person Re-Identification', IEEE Transactions on Multimedia, vol. 24, pp. 1570-1582.
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The task of infrared-visible person re-identification (IV-reID) is to recognize people across two modalities (i.e., RGB and IR). Existing cutting-edge approaches normally use a pair of images that have the same IDs (i.e., ID-tied cross-modality image pairs) and input them into an ImageNet-trained ResNet50. The ResNet50 backbone model can learn shared features across modalities to tolerate modality discrepancies between RGB and IR. This work will unveil a Modality Bias Training (MBT) problem that is less discussed in IV-reID, which will demonstrate that MBT significantly compromises the performance of IVreID. Due to MBT, IR information can be overwhelmed by RGB information during training when the ResNet50 model is pretrained based on a large amount of RGB images from ImageNet. Thus, the trained models are more inclined to RGB information. Accordingly, the cross-modality generalization ability of the model is also compromised. To tackle this issue, we present a Dual-level Learning Strategy (DLS) that 1) enforces the focus of the network on ID-exclusive (rather than ID-tied) labels of cross-modality image pairs to mitigate the problem of MBT and 2) introduces third modality data that contain both RGB and IR information to further prevent the information from the IR modality from being overwhelmed during training. Our third modality images are generated by a generative adversarial network. A dynamic ID-exclusive Smooth (dIDeS) label is proposed for the generated third modality data. In experiments, without adopting a fancy network architecture, the effectiveness of the proposed DLS is verified by using the classic ID-discriminative Embedding (IDE) model. Comprehensive experiments are carried out to demonstrate the success of DLS in tackling the MBT issue exposed in IV-reID.
Huang, Z, Zhao, R, Leung, FHF, Banerjee, S, Lee, TT-Y, Yang, D, Lun, DP-K, Lam, K-M, Zheng, Y-P & Ling, SH 2022, 'Joint Spine Segmentation and Noise Removal From Ultrasound Volume Projection Images With Selective Feature Sharing.', IEEE Trans. Medical Imaging, vol. 41, no. 7, pp. 1610-1624.
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Volume Projection Imaging from ultrasound data is a promising technique to visualize spine features and diagnose Adolescent Idiopathic Scoliosis. In this paper, we present a novel multi-task framework to reduce the scan noise in volume projection images and to segment different spine features simultaneously, which provides an appealing alternative for intelligent scoliosis assessment in clinical applications. Our proposed framework consists of two streams: 1) A noise removal stream based on generative adversarial networks, which aims to achieve effective scan noise removal in a weakly-supervised manner, i.e., without paired noisy-clean samples for learning; 2) A spine segmentation stream, which aims to predict accurate bone masks. To establish the interaction between these two tasks, we propose a selective feature-sharing strategy to transfer only the beneficial features, while filtering out the useless or harmful information. We evaluate our proposed framework on both scan noise removal and spine segmentation tasks. The experimental results demonstrate that our proposed method achieves promising performance on both tasks, which provides an appealing approach to facilitating clinical diagnosis.
Huo, C, Wang, Y, Liu, C & Lei, G 2022, 'Study on the residual flux density measurement method for power transformer cores based on magnetising inductance', IET Electric Power Applications, vol. 16, no. 2, pp. 224-235.
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When a power transformer is reconnected to a power grid, if the residual flux in its iron core is large, significant inrush current may be generated and result in closing failure. Therefore, accurate residual flux measurement is necessary to avoid the harmful effects of inrush current. This work proposes a residual flux density measurement method for the power transformer core based on magnetising inductance. Firstly, when positive and negative DC voltages are applied along or opposite to the direction of the initial residual flux density, the measured positive magnetising inductance is smaller than the negative so that the direction of residual flux density can be determined by comparing their values. Secondly, the magnitude of residual flux density can calculated by analysing the empirical formula between residual flux density and positive magnetising inductance using the finite element method. Finally, this work takes the square iron core as the research object, establishes the corresponding empirical formula, and verifies its accuracy through experiments. The experimental results show that the proposed method has higher accuracy compared with the voltage integration method widely used in this field.
Iacopi, F & Lin, C-T 2022, 'A perspective on electroencephalography sensors for brain-computer interfaces', Progress in Biomedical Engineering, vol. 4, no. 4, pp. 043002-043002.
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Abstract This Perspective offers a concise overview of the current, state-of-the-art, neural sensors for brain-machine interfaces, with particular attention towards brain-controlled robotics. We first describe current approaches, decoding models and associated choice of common paradigms, and their relation to the position and requirements of the neural sensors. While implanted intracortical sensors offer unparalleled spatial, temporal and frequency resolution, the risks related to surgery and post-surgery complications pose a significant barrier to deployment beyond severely disabled individuals. For less critical and larger scale applications, we emphasize the need to further develop dry scalp electroencephalography (EEG) sensors as non-invasive probes with high sensitivity, accuracy, comfort and robustness for prolonged and repeated use. In particular, as many of the employed paradigms require placing EEG sensors in hairy areas of the scalp, ensuring the aforementioned requirements becomes particularly challenging. Nevertheless, neural sensing technologies in this area are accelerating thanks to the advancement of miniaturised technologies and the engineering of novel biocompatible nanomaterials. The development of novel multifunctional nanomaterials is also expected to enable the integration of redundancy by probing the same type of information through different mechanisms for increased accuracy, as well as the integration of complementary and synergetic functions that could range from the monitoring of physiological states to incorporating optical imaging.
Ibrahim, IA & Hossain, MJ 2022, 'A benchmark model for low voltage distribution networks with PV systems and smart inverter control techniques', Renewable and Sustainable Energy Reviews, vol. 166, pp. 112571-112571.
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Unbalanced three-phase low-voltage distribution networks (LVDNs) modeling, optimization, and control are essential for enabling high photovoltaic (PV) penetration levels. Accordingly, a new case study is developed to show the gaps and challenges at different PV penetration levels in LVDNs. In this case study, the aim is to provide a better understanding of LVDNs’ behavior in order to support the development and validation of the models and tools. Therefore, a reduction model is proposed to decrease the simulation time by lowering the number of buses in the IEEE European LV Test Feeder, with a negligible error. In addition, an OpenDSS-Julia interface is developed to demonstrate the effects of different PV penetration levels on the inverters’ behavior, active power curtailment, and voltage level in LVDNs. Results are demonstrated concerning several limitations and challenges in using existing smart inverter control techniques, in terms of the inverters’ behavior, active power curtailment, and the voltage level. These limitations and challenges include over-voltage issues using the constant power factor technique, high active power curtailment using the volt–watt technique, and high current flows in the network assets and poor power factors using the volt–var technique. In addition, state-of-the-art system models have not taken-into-account the modeling of uncertainty effects on the performance of PV modules. Similarly, such models have largely ignored the internal and standby losses in the inverter models. These neglected issues may lead to under- or over-estimation of the impacts of PV systems on LVDNs and inaccurate estimations of the network's ability to accommodate high PV penetration levels.
Ibrahim, IA, Hossain, MJ & Duck, BC 2022, 'A hybrid wind driven-based fruit fly optimization algorithm for identifying the parameters of a double-diode photovoltaic cell model considering degradation effects', Sustainable Energy Technologies and Assessments, vol. 50, pp. 101685-101685.
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The identification of unknown parameters of photovoltaic modules is the keystone to model their performance accurately. This paper introduces a novel hybrid wind driven-based fruit fly optimization algorithm to determine a double-diode photovoltaic cell model's seven unknown parameters. Due to the limitations of reaching a matured convergence of the classical wind driven optimization for complex multi-modal optimization problems, this paper presents a hybrid algorithm by integrating the wind driven optimization algorithm's exploitation and fruit fly optimization algorithm's exploration capacities. The effectiveness of the proposed model is validated using real data from three photovoltaic technologies: mono-crystalline, poly-crystalline, and thin-film. Besides, its computational efficiency and precision are compared with those of various models: deterministic- and metaheuristic-based models. The average values of the standard deviation, normalized-root-mean-square error, mean absolute percentage error, coefficient of determination, and convergence speed of the proposed model were 8.1101 × 10-9, 0.0911%, 2.5661%, 99.0115%, and 10.0112 s. for mono-crystalline PV module, 7.1129 × 10-9, 0.1029%, 2.6334%, 98.9331%, and 8.1201 s. for poly-crystalline PV module, and 6.2212 × 10-9, 0.0871%, 2.3129%, 99.1256% and 9.3211 s. for thin-film PV module. Findings indicate that the proposed model outperforms the aforementioned models in accuracy, convergence speed and feasibility. In addition, it can work blindly with any current-voltage characteristic curve on a 15-min. basis under any weather condition without the need for any initial guess or previous information about any parameter.
Ilahi, I, Usama, M, Qadir, J, Janjua, MU, Al-Fuqaha, A, Hoang, DT & Niyato, D 2022, 'Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning', IEEE Transactions on Artificial Intelligence, vol. 3, no. 2, pp. 90-109.
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Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to its ability to achieve high performance in a range of environments with little manual oversight. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. To address this problem, we provide a comprehensive survey that discusses emerging attacks on DRL-based systems and the potential countermeasures to defend against these attacks. We first review the fundamental background on DRL and present emerging adversarial attacks on machine learning techniques. We then investigate the vulnerabilities that an adversary can exploit to attack DRL along with state-of-the-art countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks on DRL-based intelligent systems.
Inwumoh, J, Baguley, C & Gunawardane, K 2022, 'A Dynamic Control Methodology for DC Fault Ride Through of Modular Multilevel Converter based High Voltage Direct Current Systems', Computers and Electrical Engineering, vol. 100, pp. 107940-107940.
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Inwumoh, J, Baguley, CA & Gunawardane, K 2022, 'A Fast and Accurate Fault Location Technique for High Voltage Direct Current (HVDC) Systems Une technique rapide et précise de localisation des défauts pour les systèmes de courant continu à haute tension (CCHT)', IEEE Canadian Journal of Electrical and Computer Engineering, vol. 45, no. 4, pp. 383-393.
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Irmawati, Chai, R, Basari & Gunawan, D 2022, 'Optimizing CNN Hyperparameters for Blastocyst Quality Assessment in Small Datasets', IEEE Access, vol. 10, pp. 88621-88631.
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Islam, MA, Paul, AK, Hossain, B, Sarkar, AK, Rahman, MM, Sayem, ASM, Simorangkir, RBVB, Shobug, MA, Buckley, JL, Chakrabarti, K & Lalbakhsh, A 2022, 'Design and Analysis of GO Coated High Sensitive Tunable SPR Sensor for OATR Spectroscopic Biosensing Applications', IEEE Access, vol. 10, pp. 103496-103508.
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Islam, MR, Lu, H, Hossain, MJ & Li, L 2022, 'Coordinating Electric Vehicles and Distributed Energy Sources Constrained by User’s Travel Commitment', IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5307-5317.
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Jayawickrama, BA & He, Y 2022, 'Improved Layered Normalized Min-Sum Algorithm for 5G NR LDPC', IEEE Wireless Communications Letters, vol. 11, no. 9, pp. 2015-2018.
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Jiang, M, Wu, T, Wang, Z, Gong, Y, Zhang, L & Liu, RP 2022, 'A Multi-Intersection Vehicular Cooperative Control Based on End-Edge-Cloud Computing', IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 2459-2471.
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Jiang, S, Li, K & Da Xu, RY 2022, 'Magnitude Bounded Matrix Factorisation for Recommender Systems', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 4, pp. 1856-1869.
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Jin, JX, Zhou, Q, Yang, RH, Li, YJ, Li, H, Guo, YG & Zhu, JG 2022, 'A superconducting magnetic energy storage based current-type interline dynamic voltage restorer for transient power quality enhancement of composited data center and renewable energy source power system', Journal of Energy Storage, vol. 52, pp. 105003-105003.
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Jin, Z, Sun, X, Lei, G, Guo, Y & Zhu, J 2022, 'Sliding Mode Direct Torque Control of SPMSMs Based on a Hybrid Wolf Optimization Algorithm', IEEE Transactions on Industrial Electronics, vol. 69, no. 5, pp. 4534-4544.
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Direct torque control has been widely used to control surface-mounted permanent magnet synchronous motors (SPMSMs). To reduce the torque ripple and improve the flux tracking accuracy of SPMSM drives, sliding mode direct torque control (SMDTC) was developed. However, its optimal performance is hardly obtained by trial and error tuning of the control parameters. Hence, a hybrid wolf optimization algorithm (HWOA) is proposed to automatically adjust the controller's parameters of SMDTC for SPMSMs in this article. This algorithm combines the grey wolf optimization algorithm and coyote optimization algorithm. A conversion probability is designed to use them simultaneously. The proposed HWOA holds the advantages of the two algorithms. It converges very fast and can avoid local optimums effectively. Furthermore, a special fitness index with penalty terms is designed to enhance flux tracking accuracy and reduce the torque ripple of SPMSM drives. The superiority of the proposed control method is verified by an experiment.
Jung, MC, Chai, R, Zheng, J & Nguyen, H 2022, 'Enhanced myoelectric control against arm position change with weighted recursive Gaussian process', Neural Computing and Applications, vol. 34, no. 7, pp. 5015-5028.
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Kamal, MS, Dey, N, Chowdhury, L, Hasan, SI & Santosh, KC 2022, 'Explainable AI for Glaucoma Prediction Analysis to Understand Risk Factors in Treatment Planning', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-9.
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Karambasti, BM, Naghashzadegan, M, Ghodrat, M, Ghorbani, G, Simorangkir, RBVB & Lalbakhsh, A 2022, 'Optimal Solar Greenhouses Design Using Multiobjective Genetic Algorithm', IEEE Access, vol. 10, pp. 73728-73742.
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Katzmarek, DA, Pradeepkumar, A, Ziolkowski, RW & Iacopi, F 2022, 'Review of graphene for the generation, manipulation, and detection of electromagnetic fields from microwave to terahertz', 2D Materials, vol. 9, no. 2, pp. 022002-022002.
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AbstractGraphene has attracted considerable attention ever since the discovery of its unprecedented properties, including its extraordinary and tunable electronic and optical properties. In particular, applications within the microwave to terahertz frequency spectrum can benefit from graphene’s high electrical conductivity, mechanical flexibility and robustness, transparency, support of surface-plasmon-polaritons, and the possibility of dynamic tunability with direct current to light sources. This review aims to provide an in-depth analysis of current trends, challenges, and prospects within the research areas of generating, manipulating, and detecting electromagnetic fields using graphene-based devices that operate from microwave to terahertz frequencies. The properties of and models describing graphene are reviewed first, notably those of importance to electromagnetic applications. State-of-the-art graphene-based antennas, such as resonant and leaky-wave antennas, are discussed next. A critical evaluation of the performance and limitations within each particular technology is given. Graphene-based metasurfaces and devices used to manipulate electromagnetic fields, e.g. wavefront engineering, are then examined. Lastly, the state-of-the-art of detecting electromagnetic fields using graphene-based devices is discussed.
Keshavarz, R & Shariati, N 2022, 'Highly Sensitive and Compact Quad-Band Ambient RF Energy Harvester', IEEE Transactions on Industrial Electronics, vol. 69, no. 4, pp. 3609-3621.
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Keshavarz, R & Shariati, N 2022, 'High-Sensitivity and Compact Time Domain Soil Moisture Sensor Using Dispersive Phase Shifter for Complex Permittivity Measurement', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10.
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This article presents a time domain transmissometry soil moisture sensor (TDT-SMS) using a dispersive phase shifter (DPS), consisting of an interdigital capacitor that is loaded with a stacked four-turn complementary spiral resonator (S4-CSR). Soil moisture measurement technique of the proposed sensor is based on the complex permittivity sensing property of a DPS in time domain. Soil relative permittivity which varies with its moisture content is measured by burying the DPS under a soil mass and changing its phase difference while excited with a 114-MHz sine wave (single tone). DPS output phase and magnitude are compared with the reference signal and measured with a phase/loss detector. The proposed sensor exhibits accuracy better than ±1.2% at the highest volumetric water content (VWC = 30%) for sandy-type soil. Precise design guide is developed and simulations are performed to achieve a highly sensitive sensor. The measurement results validate the accuracy of theoretical analysis and design procedure. Owning the advantages of low profile, low power consumption, and high sensitivity makes the proposed TDT-SMS a good candidate for precision farming and internet of things (IoT) systems.
Khaliliboroujeni, S, He, X, Jia, W & Amirgholipour, S 2022, 'End-to-end metastasis detection of breast cancer from histopathology whole slide images', Computerized Medical Imaging and Graphics, vol. 102, pp. 102136-102136.
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Worldwide breast cancer is one of the most frequent and mortal diseases across women. Early, accurate metastasis cancer detection is a significant factor in raising the survival rate among patients. Diverse Computer-Aided Diagnostic (CAD) systems applying medical imaging modalities, have been designed for breast cancer detection. The impact of deep learning in improving CAD systems' performance is undeniable. Among all of the medical image modalities, histopathology (HP) images consist of richer phenotypic details and help keep track of cancer metastasis. Nonetheless, metastasis detection in whole slide images (WSIs) is still problematic because of the enormous size of these images and the massive cost of labelling them. In this paper, we develop a reliable, fast and accurate CAD system for metastasis detection in breast cancer while applying only a small amount of annotated data with lower resolution. This saves considerable time and cost. Unlike other works which apply patch classification for tumor detection, we employ the benefits of attention modules adding to regression and classification, to extract tumor parts simultaneously. Then, we use dense prediction for mask generation and identify individual metastases in WSIs. Experimental outcomes demonstrate the efficiency of our method. It provides more accurate results than other methods that apply the total dataset. The proposed method is about seven times faster than an expert pathologist, while producing even more accurate results than an expert pathologist in tumor detection.
Khan, MNH, Barzegarkhoo, R, Siwakoti, YP, Khan, SA, Li, L & Blaabjerg, F 2022, 'A new switched-capacitor multilevel inverter with soft start and quasi resonant charging capabilities', International Journal of Electrical Power & Energy Systems, vol. 135, pp. 107412-107412.
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Switched-capacitor multilevel inverters (SCMLIs) are gaining widespread attention in recent decades due to their simple design, voltage boosting capability, and inherent capacitor voltage balancing feature. However, the advantages offered by SCMLIs come at the cost of employing a higher number of active and passive components and capacitor voltage balancing issues with an inrush current profile. This paper introduces a novel configuration of SCMLIs with a lower number of power components with inherent voltage boost. The basic 5-level topology consists of a single capacitor, an inductor, a diode, and seven active switching elements. To improve the transient response and the inrush current profile of the converter, a soft start and quasi-resonant charging capability has been explored and implemented. Considering the inherent capacitor voltage balancing of the proposed SCMLI, a new finite control set-model predictive control (FCS-MPC) method with a single objective and a less computational burden is also developed, which contributes to injecting a fully controlled current for the grid-connected applications. The proposed topology is compared with other existing five-level inverter topologies to show its superior capabilities/advantages. And finally, the performance of the proposed topology and its associated FCS-MPC mechanism are validated by the measurement results.
Khodasevych, I, Rufangura, P & Iacopi, F 2022, 'Designing concentric nanoparticles for surface-enhanced light-matter interaction in the mid-infrared', Optics Express, vol. 30, no. 13, pp. 24118-24118.
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Nanosized particles with high responsivity in the infrared spectrum are of great interest for biomedical applications. We derive a closed-form expression for the polarizability of nanoparticles made of up to three concentric nanolayers consisting of a frequency dependent polar dielectric core, low permittivity dielectric spacer shell and conductive graphene outer shell, using the electrostatic Mie theory in combination with conductive layer in a dipole approximation. We use the obtained formula to investigate SiC, GaN and hBN as core materials, and graphene as conductive shell, separated by a low-permittivity dielectric spacer. Three-layer nanoparticles demonstrate up to a 12-fold increased mid-infrared (MIR) absorption as compared to their monolithic polar dielectrics, and up to 1.7 as compared to two-layer (no spacer) counterparts. They also show orders of magnitude enhancement of the nanoparticle scattering efficiency. The enhancement originates from the phonon-plasmon hybridization thanks to the graphene and polar dielectric combination, assisted by coupling via the low permittivity spacer, resulting in the splitting of the dielectric resonance into two modes. Those modes extend beyond the dielectric’s Reststrahlen band and can be tuned by tailoring the nanoparticles characteristics as they can be easily calculated through the closed-form expression. Nanoparticles with dual band resonances and enhanced absorption and scattering efficiencies in the MIR are of high technological interest for biomedical applications, such as surface -enhanced vibrational spectroscopies allowing simultaneous imaging and spectroscopy of samples, as well as assisting guided drug delivery.
Kiyani, A, Nasimuddin, N, Hashmi, RM, Baba, AA, Abbas, SM, Esselle, KP & Mahmoud, A 2022, 'A Single-Feed Wideband Circularly Polarized Dielectric Resonator Antenna Using Hybrid Technique With a Thin Metasurface', IEEE Access, vol. 10, pp. 90244-90253.
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A compact metasurface-based circularly polarized (CP) dielectric resonator antenna (DRA) is proposed with wideband characteristics. The antenna forms a very simple structure, composed of a rectangular DR, a single coaxial probe, and plus-shaped unit cells-based metasurface. The metasurface is realized on a grounded FR-4 substrate. Next, a rectangular DR is loaded centrally over the metasurface. The DR is fed with a perturbed probe feed at an appropriate angle of ( θ =29°), along the diagonal line. Thus, a novel hybrid technique involving the angle of feed location from the center of DR, and the N × N unit cells-based metasurface is utilized for generating a wideband CP radiation. The resonance from the rectangular DR and surface waves along the 7× 7 plus-shaped unit cells-based metasurface is exploited to achieve a wide 3-dB axial ratio (AR) and impedance matching bandwidth. The fabricated antenna prototype used for the validation of predicted results confirms the successful implementation of the proposed technique. Measured results demonstrate a wide impedance bandwidth of 32% (3.6 GHz - 7.0 GHz) and an overlapping 3-dB AR bandwidth of 20.4% (4.2 GHz - 5.2 GHz). Moreover, the antenna adopts a left-hand circular polarization (LHCP) with 6-7 dBic measured gain within the operational frequency range. Overall, the proposed antenna offers low-profile, simplicity, ease of design, and high performance.
Koli, MNY, Afzal, MU & Esselle, KP 2022, 'Increasing the Gain of Beam-Tilted Circularly Polarized Radial Line Slot Array Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4392-4403.
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Kumar, A, Esmaili, N & Piccardi, M 2022, 'Neural Topic Model Training with the REBAR Gradient Estimator', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 21, no. 5, pp. 1-18.
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Topic modelling is an important approach of unsupervised machine learning that allows automatically extracting the main “topics” from large collections of documents. In addition, topic modelling is able to identify the topic proportions of each individual document, which can be helpful for organizing the collections. Many topic modelling algorithms have been proposed to date, including several that leverage advanced techniques such as variational inference and deep autoencoders. However, to date topic modelling has made limited use of reinforcement learning, a framework that has obtained vast success in many other unsupervised learning tasks. For this reason, in this article we propose training a neural topic model using a reinforcement learning objective and minimizing the objective with the recently-proposed REBAR gradient estimator. Experiments performed over two probing datasets have shown that the proposed model has achieved improvements over all the compared models in terms of both model perplexity and topic coherence, and produced topics that appear qualitatively informative and consistent.
Kurdkandi, NV, Marangalu, MG, Mohammadsalehian, S, Tarzamni, H, Siwakoti, YP, Islam, MR & Muttaqi, KM 2022, 'A New Six-Level Transformer-Less Grid-Connected Solar Photovoltaic Inverter With Less Leakage Current.', IEEE Access, vol. 10, pp. 63736-63753.
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This paper presents a novel structure of the transformer-less grid-connected inverters. The proposed inverter is combined with six power switches and two power diodes which can generate six voltage levels at the output. Furthermore, the proposed inverter can overcome the leakage current issue in the photovoltaic (PV) system, which is the major problem in grid-tied PV applications. Additional significant features include- reduced filter size, lower total harmonic distortion (THD) of the injected current to the grid, and voltage boosting ability. Moreover, the proposed topology provides full reactive power support to the grid. A control strategy is designed and implemented to provide a voltage boost ability without using any additional dc-dc boost converter. Finally, the performance of the proposed inverter is validated by the 770 W laboratory prototype.
Lalbakhsh, A, Afzal, MU, Esselle, KP & Smith, SL 2022, 'All-Metal Wideband Frequency-Selective Surface Bandpass Filter for TE and TM Polarizations', IEEE Transactions on Antennas and Propagation, vol. 70, no. 4, pp. 2790-2800.
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A novel technique to design a low-cost frequency-selective surface (FSS) bandpass filter is presented in this article. Wideband polarization-independent FSS bandpass filters are predominantly made of multiple microwave dielectric substrates or noncommercially available composite materials with or without active components, contributing to a very high manufacturing cost. The presented FSS filter has neither microwave substrates, nor any active devices, while it has a large controllable operational frequency band, which can support all polarizations, due to its symmetrical configuration. To the best of our knowledge, such a polarization-independent wideband bandpass response has never been achieved by any low-cost fully metallic FSS filter. The proposed FSS filter is made of three thin metal sheets composed of an engineered metallic substrate (EMS) and a metallic orthogonal dipole resonator (ODR). The EMS is responsible for ensuring the mechanical integrity of the filter without imposing electromagnetic (EM) restrictions throughout the desired frequency band. The integration of EMS and ODRs realizes a fully controllable wideband bandpass verified thorough circuital and modal analyses. According to the predicted and measured results, the FSS filter has a large bandwidth of around 31%, extending from 8.76 to 11.96 GHz with sharp roll-offs for the normal incidence. Simulated and measured results show a low sensitivity of the FSS filter response to oblique angles of incidence for both TM and TE polarizations.
Lalbakhsh, A, Pitcairn, A, Mandal, K, Alibakhshikenari, M, Esselle, KP & Reisenfeld, S 2022, 'Darkening Low-Earth Orbit Satellite Constellations: A Review', IEEE Access, vol. 10, pp. 24383-24394.
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Lau, CW, Qu, Z, Draper, D, Quan, R, Braytee, A, Bluff, A, Zhang, D, Johnston, A, Kennedy, PJ, Simoff, S, Nguyen, QV & Catchpoole, D 2022, 'Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts', Scientific Reports, vol. 12, no. 1, p. 11337.
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AbstractThe significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine.
Le, AT, Huang, X & Guo, YJ 2022, 'A Two-Stage Analog Self-Interference Cancelation Structure for High Transmit Power In-Band Full-Duplex Radios', IEEE Wireless Communications Letters, vol. 11, no. 11, pp. 2425-2429.
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Le, AT, Huang, X, Tran, LC & Guo, YJ 2022, 'On the Impacts of I/Q Imbalance in Analog Least Mean Square Adaptive Filter for Self-Interference Cancellation in Full-Duplex Radios', IEEE Transactions on Vehicular Technology, vol. 71, no. 10, pp. 10683-10693.
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Lee, SS, Siwakoti, YP, Barzegarkhoo, R & Blaabjerg, F 2022, 'A Novel Common-Ground-Type Nine-Level Dynamic Boost Inverter', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 4, pp. 4435-4442.
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Li, H, Huang, X, Zhang, JA, Zhang, H & Cheng, Z 2022, 'Dual pulse shaping transmission with sinc‐function based complementary Nyquist pulses', IET Communications, vol. 16, no. 17, pp. 2091-2104.
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Due to difficulties in manufacturing, data conversion devices with extremely high sampling rate are becoming the bottleneck in realising high-speed communication systems with a large bandwidth. Dual pulse shaping (DPS) transmission allows half-symbol-rate conversion devices to be used for two parallel data streams to achieve full-rate transmission, and is proved to be an effective solution. Here, two sets of ideal sinc-function based complementary Nyquist pulses for DPS transmission are proposed. Theoretically, it is shown that the proposed pulses satisfy the inter-symbol and cross-symbol interference-free conditions, and can achieve full-Nyquist-rate transmission with half of the sampling rate. With reference to commercially available D/As, two sets of practical dual spectral shaping pulses are further proposed, and the close relationship between the ideal and practical pulses are disclosed. Performance analysis for linear equalisation is provided in the presence of both timing offset between dual shaping pulses and carrier-frequency offset. Two approaches are then proposed to improve the system robustness by adjusting the clock phase of the D/As and A/Ds. Simulation results are presented to provide a comparison between the proposed DPS transmission schemes and the state of the art, in terms of the performance metrics of peak-to-average power ratio and bit error rate.
Li, K, Ni, W & Dressler, F 2022, 'Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks', IEEE Transactions on Mobile Computing, vol. 21, no. 8, pp. 2732-2744.
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Li, K, Ni, W & Dressler, F 2022, 'LSTM-Characterized Deep Reinforcement Learning for Continuous Flight Control and Resource Allocation in UAV-Assisted Sensor Network', IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4179-4189.
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Unmanned aerial vehicles (UAVs) can be employed to collect sensory data in remote wireless sensor networks (WSNs). Due to UAV's maneuvering, scheduling a sensor device to transmit data can overflow data buffers of the unscheduled ground devices. Moreover, lossy airborne channels can result in packet reception errors at the scheduled sensor. This article proposes a new deep reinforcement learning-based flight resource allocation framework (DeFRA) to minimize the overall data packet loss in a continuous action space. DeFRA is based on deep deterministic policy gradient (DDPG), optimally controls instantaneous headings and speeds of the UAV, and selects the ground device for data collection. Furthermore, a state characterization layer, leveraging long short-term memory (LSTM), is developed to predict network dynamics, resulting from time-varying airborne channels and energy arrivals at the ground devices. To validate the effectiveness of DeFRA, experimental data collected from a real-world UAV testbed and energy harvesting WSN are utilized to train the actions of the UAV. Numerical results demonstrate that the proposed DeFRA achieves a fast convergence while reducing the packet loss by over 15%, as compared to the existing deep reinforcement learning solutions.
Li, K, Ni, W, Emami, Y & Dressler, F 2022, 'Data-Driven Flight Control of Internet-of-Drones for Sensor Data Aggregation Using Multi-Agent Deep Reinforcement Learning', IEEE Wireless Communications, vol. 29, no. 4, pp. 18-23.
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Li, M, Liu, Y, Chen, S-L, Hu, J & Guo, YJ 2022, 'Synthesizing Shaped-Beam Cylindrical Conformal Array Considering Mutual Coupling Using Refined Rotation/Phase Optimization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10543-10553.
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Li, T, Sun, X, Lei, G, Guo, Y, Yang, Z & Zhu, J 2022, 'Finite-Control-Set Model Predictive Control of Permanent Magnet Synchronous Motor Drive Systems—An Overview', IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 12, pp. 2087-2105.
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Permanent magnet synchronous motors (PMSMs) have been widely employed in the industry. Finite-control-set model predictive control (FCS-MPC), as an advanced control scheme, has been developed and applied to improve the performance and efficiency of the holistic PMSM drive systems. Based on the three elements of model predictive control, this paper provides an overview of the superiority of the FCS-MPC control scheme and its shortcomings in current applications. The problems of parameter mismatch, computational burden, and unfixed switching frequency are summarized. Moreover, other performance improvement schemes, such as the multi-vector application strategy, delay compensation scheme, and weight factor adjustment, are reviewed. Finally, future trends in this field is discussed, and several promising research topics are highlighted.
Li, X, Leung, FHF, Su, SW & Ling, SH 2022, 'Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals.', Sensors, vol. 22, no. 15, pp. 5560-5560.
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Introduction: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. Methods: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO2), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. Results: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60 s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (ac...
Li, X, Zhang, JA, Wu, K, Cui, Y & Jing, X 2022, 'CSI-Ratio-Based Doppler Frequency Estimation in Integrated Sensing and Communications', IEEE Sensors Journal, vol. 22, no. 21, pp. 20886-20895.
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Estimating the Doppler frequency is an important part of sensing moving targets in integrated sensing and communications (ISAC) systems, such as human tracking and activity recognition. However, it can be highly challenging when there is clock asynchronism between the transmitter (Tx) and the receiver (Rx), in bistatic setups that are common nowadays. In this article, we propose three algorithms for Doppler frequency estimation based on the ratio of channel state information (CSI). These algorithms explore different properties of the CSI ratio, including the circle-preserving property of the Mobius transform, the periodicity of the CSI ratio, and the difference (or correlation) between segments of CSI-ratio signals. Experimental results demonstrate that the proposed algorithms can estimate Doppler frequency accurately, outperforming the commonly used approach based on cross-antenna cross correlation (CACC).
Li, XL, Tse, CK & Lu, DD-C 2022, 'Synthesis of Reconfigurable and Scalable Single-Inductor Multiport Converters With No Cross Regulation', IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 10889-10902.
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Li, Y, Huang, Y, Seneviratne, S, Thilakarathna, K, Cheng, A, Jourjon, G, Webb, D, Smith, DB & Xu, RYD 2022, 'From traffic classes to content: A hierarchical approach for encrypted traffic classification', Computer Networks, vol. 212, pp. 109017-109017.
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The vast majority of Internet traffic is now end-to-end encrypted, and while encryption provides user privacy and security, it has made network surveillance an impossible task. Various parties are using this limitation to distribute problematic content such as fake news, copy-righted material, and propaganda videos. Recent advances in machine learning techniques have shown great promise in extracting content fingerprints from encrypted traffic captured at the various points in IP core networks. Nonetheless, content fingerprinting from listening to encrypted wireless traffic remains a challenging task due to the difficulty in distinguishing re-transmissions and multiple flows on the same link. In this paper, we show the potential of fingerprinting internet traffic by passively sniffing WiFi frames in air, without connecting to the WiFi network by leveraging deep learning methods. First, we show the possibility of building a generic traffic classifier using a hierarchical approach that is able to identity most common traffic types in the Internet and reveal fine-granular details such as identifying the exact content of the traffic. Second, we demonstrate the possibility of using Multi-Layer Perceptron (MLP) and Recurrent Neural Networks (RNNs) to identify streaming traffic, such as video and music, from a closed set, by sniffing WiFi traffic that is encrypted at both Media Access Control (MAC) and Transport layers. Overall, our results demonstrate that we can achieve over 95% accuracy in identifying traffic types such as web, video streaming, and audio streaming as well as identifying the exact content consumed by the user.
Liao, Q, Wang, D & Xu, M 2022, 'Category attention transfer for efficient fine-grained visual categorization', Pattern Recognition Letters, vol. 153, pp. 10-15.
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Fine-Grained Visual Categorization (FGVC) aims at distinguishing subordinate-level categories with subtle interclass differences. Although previous research shows the impressive effectiveness of the recurrent multi-attention models and the second-order feature encoding, they often require an enormous amount of both computation and memory space, making them inadequate for mobile applications. This paper proposed a Category Attention Transfer CNN (CAT-CNN) to address the efficiency issue in solving FGVC problems. We transfer part attention knowledge from a very large-scale FGVC network to a small but efficient network to significantly improve its presentation ability. Using the proposed CAT-CNN, the accuracy of the efficient networks, such as ShuffleNet, MobilieNet, and EfficientNet, can be improved by up to 5.7% on the CUB-2011-200 dataset without increasing computation complexity or memory cost. Our experiments show that the proposed CAT-CNN can be applied to multiple structures to enhance their performance. With a single efficient network structure and single inference, the proposed CAT-MobileNet-large-1.0 and the CAT-EfficientNet-b0 can achieve accuracies of 86.5% and 86.7%, respectively, on the CUB-2011-200 dataset, which is close to or better than the results from state-of-the-art methods using large scale networks and multiple inferences, and make FGVC feasible on mobile devices.
Lionnie, R, Apriono, C, Chai, R & Gunawan, D 2022, 'Curvature Best Basis: A Novel Criterion to Dynamically Select a Single Best Basis as the Extracted Feature for Periocular Recognition', IEEE Access, vol. 10, pp. 113523-113542.
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Litov, N, Falkner, B, Zhou, H, Mehta, A, Gondwe, W, Thalakotuna, DN, Mirshekar-Syahkal, D, Esselle, K & Nakano, H 2022, 'Radar Cross Section Analysis of Two Wind Turbines via a Novel Millimeter-Wave Technique and Scale Model Measurements', IEEE Access, vol. 10, pp. 17897-17907.
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Liu, B, Li, L, Xiao, Q, Ni, W & Yang, Z 2022, 'Remote Sensing Fine-Grained Ship Data Augmentation Pipeline With Local-Aware Progressive Image-to-Image Translation', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16.
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Liu, B, Liao, J, Song, Y, Chen, C, Ding, L, Lu, J, Zhou, J & Wang, F 2022, 'Multiplexed structured illumination super-resolution imaging with lifetime-engineered upconversion nanoparticles', Nanoscale Advances, vol. 4, no. 1, pp. 30-38.
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We report a tailor-made multiplexed super-resolution imaging method using the lifetime fingerprints from luminescent nanoparticles, which can resolve the particles within the diffraction-limited spots and enable higher multiplexing capacity in space.
Liu, B, Ni, W, Liu, RP, Zhu, Q, Guo, YJ & Zhu, H 2022, 'Novel Integrated Framework of Unmanned Aerial Vehicle and Road Traffic for Energy-Efficient Delay-Sensitive Delivery', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 10692-10707.
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Unmanned aerial vehicle (UAV) has demonstrated its usefulness in goods delivery. However, the delivery distances are often restrained by the battery capacity of UAVs. This paper integrates UAVs into intelligent transportation systems for energy-efficient, delay-sensitive goods delivery. Dynamic programming (DP) is first applied to minimize the energy consumption of a UAV and ensure its timely arrival at its destination, by optimizing the control policy of the UAV. The control policy involves decisions including flight speed, hitchhiking (on collaborative ground vehicles), or recharging at roadside charging stations. Another key aspect is that we reveal the conditions of the remaining flight distance or the elapsed time, only under which the optimal action of the UAV changes. Accordingly, thresholds are derived, and the optimal control policy can be instantly made by comparing the remaining flight distance and the elapsed time with the thresholds. Simulations show that the proposed algorithms can improve the flight distance by 48%, as compared with existing alternatives. The proposed threshold-based technique can achieve the same performance as the DP-based solution, while significantly reducing the computational complexity.
Liu, C, Wang, X, Wang, S, Wang, Y, Lei, G & Zhu, J 2022, 'Magnetothermal Coupling Analysis of Permanent Magnet Claw Pole Machine Using Combined 3D Magnetic and Thermal Network Method', IEEE Transactions on Applied Superconductivity, vol. 32, no. 6, pp. 1-5.
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The permanent magnet claw pole machine (PMCPM) is a special kind of transverse flux machine, different from conventional electrical machines the main magnetic flux path of PMCPM is 3D. Therefore, for accurate performance analysis,the 3D finite element method (FEM) is required to calculate both the electromagnetic characteristics and thermal distribution. However, it is time consuming especially when the coupling effect needs to be considered. In this paper, a combined 3D magnetic and thermal network method is proposed to obtain the performance of PMCPM. As the developed thermal network shares the same structure as the magnetic network, the calculated core loss can be regarded as the heat source in the thermal analysis easily, in which 3D rotational core loss is calculated as the 3D network is adopted. The proposed method holds the advantages of close magnetothermal coupling and fast calculating speed.For the calculation results verification, 3D FEM is used.
Liu, C, Wang, X, Wang, Y, Lei, G, Guo, Y & Zhu, J 2022, 'Comparative study of rotor PM transverse flux machine and stator PM transverse flux machine with SMC cores', Electrical Engineering, vol. 104, no. 3, pp. 1153-1161.
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Liu, H, Zhang, C, Yao, Y, Wei, X-S, Shen, F, Tang, Z & Zhang, J 2022, 'Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Open-Set Noise and Utilizing Hard Examples', IEEE Transactions on Multimedia, vol. 24, no. 99, pp. 546-557.
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Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images for fine-grained recognition has attracted broad attention. However, the presence of label noise and hard examples in web images are two obstacles for training robust fine-grained recognition models. To this end, in this paper, we propose a novel approach to remove irrelevant samples from real-world web images during training, while employing useful hard examples to update the network. Thus, our approach can alleviate the harmful effects of irrelevant noisy web images and hard examples to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods.
Liu, L, Guo, Y, Yin, W, Lei, G & Zhu, J 2022, 'Design and Optimization Technologies of Permanent Magnet Machines and Drive Systems Based on Digital Twin Model', Energies, vol. 15, no. 17, pp. 6186-6186.
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One of the keys to the success of the fourth industrial revolution (Industry 4.0) is to empower machinery with cyber–physical systems connectivity. The digital twin (DT) offers a promising solution to tackle the challenges for realizing digital and smart manufacturing which has been successfully projected in many scenes. Electrical machines and drive systems, as the core power providers in many appliances and industrial equipment, are supposed to be reinforced on the verge of Industry 4.0 in the fields of design optimization, fault prognostic and coordinated control. Therefore, this paper aims to investigate the DT modelling method and the applications in electrical drive systems. Firstly, taking the high-speed permanent-magnet machine drive system as an example, multi-disciplinary design fundamentals and technologies, aiming at building initial mechanism and simulation models, are reviewed. The state-of-the-art of DT technologies is figured out to serve for high-precision and multi-scale dynamic modelling, by which a framework for DT models of electrical drive systems is presented. More importantly, fault diagnosis and optimization strategies of electrical drive systems in the decision and application layer are also discussed for the DT models, followed by the conclusions presenting open questions and possible directions.
Liu, T, Zhang, W, Li, J, Ueland, M, Forbes, SL, Zheng, WX & Su, SW 2022, 'A Multiscale Wavelet Kernel Regularization-Based Feature Extraction Method for Electronic Nose', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 11, pp. 7078-7089.
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Lu, X, Cong Luong, N, Hoang, DT, Niyato, D, Xiao, Y & Wang, P 2022, 'Secure Wirelessly Powered Networks at the Physical Layer: Challenges, Countermeasures, and Road Ahead', Proceedings of the IEEE, vol. 110, no. 1, pp. 193-209.
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Harvesting wireless power to energize miniature devices has been envisioned as a promising solution to sustain future-generation energy-sensitive networks, e.g., Internet-of-Things systems. However, due to the limited computing and communication capabilities, wirelessly powered networks (WPNs) may be incapable of employing complex security practices, e.g., encryption, which may incur considerable computation and communication overheads. This challenge makes securing energy harvesting communications an arduous task and, thus, limits the use of WPNs in many high-security applications. In this context, security at the physical layer (PHY) that exploits the intrinsic properties of the wireless medium to achieve secure communication has emerged as an alternative paradigm. This article first introduces the fundamental principles of primary PHY attacks, covering jamming, eavesdropping, and detection of covert, and then presents an overview of the prevalent countermeasures to secure both active and passive communications in WPNs. Furthermore, a number of open research issues are identified to inspire possible future research.
Lu, X, Qiu, J, Lei, G & Zhu, J 2022, 'Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia', Applied Energy, vol. 308, pp. 118296-118296.
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Lu, Y, Xiao, W & Lu, DD-C 2022, 'Optimal Dynamic and Steady-State Performance of PV-Interfaced Converters Using Adaptive Observers', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 12, pp. 4909-4913.
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Lu, Z, Qi, S, Zhang, J, Cai, Y, Guo, X & Luo, S 2022, 'An improved multi-objective bacterial colony chemotaxis algorithm based on Pareto dominance', Soft Computing, vol. 26, no. 1, pp. 69-87.
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Luu, HM, Walsum, TV, Mai, HS, Franklin, DR, Nguyen, TTT, Le, TM, Moelker, A, Le, VK, Vu, DL, Le, NH, Long, TQ, Duc, TC & Trung, NL 2022, 'Automatic scan range for dose-reduced multiphase CT imaging of the liver utilizing CNNs and Gaussian models.', Medical Image Anal., vol. 78, pp. 102422-102422.
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Multiphase CT scanning of the liver is performed for several clinical applications; however, radiation exposure from CT scanning poses a nontrivial cancer risk to the patients. The radiation dose may be reduced by determining the scan range of the subsequent scans by the location of the target of interest in the first scan phase. The purpose of this study is to present and assess an automatic method for determining the scan range for multiphase CT scans. Our strategy is to first apply a CNN-based method for detecting the liver in 2D slices, and to use a liver range search algorithm for detecting the liver range in the scout volume. The target liver scan range for subsequent scans can be obtained by adding safety margins achieved from Gaussian liver motion models to the scan range determined from the scout. Experiments were performed on 657 multiphase CT volumes obtained from multiple hospitals. The experiment shows that the proposed liver detection method can detect the liver in 223 out of a total of 224 3D volumes on average within one second, with mean intersection of union, wall distance and centroid distance of 85.5%, 5.7 mm and 9.7 mm, respectively. In addition, the performance of the proposed liver detection method is comparable to the best of the state-of-the-art 3D liver detectors in the liver detection accuracy while it requires less processing time. Furthermore, we apply the liver scan range generation method on the liver CT images acquired from radiofrequency ablation and Y-90 transarterial radioembolization (selective internal radiation therapy) interventions of 46 patients from two hospitals. The result shows that the automatic scan range generation can significantly reduce the effective radiation dose by an average of 14.5% (2.56 mSv) compared to manual performance by the radiographer from Y-90 transarterial radioembolization, while no statistically significant difference in performance was found with the CT images from intra RFA intervent...
Lyu, B, Ramezani, P, Hoang, DT & Jamalipour, A 2022, 'IRS-Assisted Downlink and Uplink NOMA in Wireless Powered Communication Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 1083-1088.
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This paper studies the integration of the newly-emerged intelligent reflecting surface (IRS) technology into non-orthogonal multiple access (NOMA)-based wireless powered communication networks (WPCNs). We consider two WPCNs which communicate with a common hybrid access point (HAP), where there exists two types of devices in each WPCN, namely information receiving device (IRD) and harvest-then-transmit device (HTTD). Downlink communication from the HAP to IRDs, downlink energy transfer (ET) from the HAP to HTTDs, and uplink information transmission (IT) from the HTTDs to the HAP are assisted by two IRSs, one in each WPCN. Under this setup, we propose efficient algorithms to optimize reflection coefficients, beamforming vectors, and resource allocation for the sake of uplink sum-rate maximization, taking into account the minimum rate requirement at the IRDs. Numerical results show the considerable performance gain of the proposed NOMA-based scheme as compared to the conventional orthogonal multiple access (OMA)-based counterpart.
Ma, B, Wang, X, Ni, W & Liu, RP 2022, 'Personalized Location Privacy With Road Network-Indistinguishability', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 20860-20872.
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The proliferation of location-based services (LBS) leads to increasing concern about location privacy. Location obfuscation is a promising privacy-preserving technique but yet to be adequately tailored for vehicles in road networks. Existing obfuscation schemes are based primarily on the Euclidean distances and can lead to infeasible results, e.g., off-road locations. In this paper, we define Road Network-Indistinguishability (RN-I) to evaluate obfuscation-based location privacy-preserving schemes in road networks. To protect drivers' location privacy in road networks, we propose a Personalized Location Privacy-Preserving (PLPP) scheme and prove it achieves RN-I. The PLPP scheme employs a dual-obfuscation algorithm, consisting of a connection perturbation and an interval perturbation, to obfuscate on-road locations. An efficient personalization algorithm is designed for the PLPP scheme to fine-tune location privacy budgets for capturing drivers' sensitive locations and privacy requirements. Experiments upon two real-world datasets confirm the location privacy-preserving capability, data utility, and efficiency of the proposed PLPP scheme.
Ma, H, Li, L, Fan, Y, Guo, Y, Jin, Z & Luo, J 2022, 'A Discrete Current Controller for High Power-Density Synchronous Machines', Energies, vol. 15, no. 17, pp. 6396-6396.
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This paper proposes a complex vector discrete current controller based on the flux-linkage data to solve the current loop oscillation problem of high power-density synchronous machines. An offline flux-linkage table measurement method considering cross saturation is introduced, and the data are used to deduce the symmetrical complex vector model. The influence of latch and delay of inverters on the line voltage of machines at high speed is analyzed and compensated during the controller design process. The proposed controller, which only needs to tune one parameter, can deal with the inductance mismatch issues caused by iron core saturation. The controller can be adopted in the current loop of saturated salient or nonsalient synchronous machines. Simulations and experiments have verified the effectiveness of the proposed method.
Ma, Y, Wu, N, Zhang, JA, Li, B & Hanzo, L 2022, 'Generalized Approximate Message Passing Equalization for Multi-Carrier Faster-Than-Nyquist Signaling', IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 3309-3314.
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Multi-carrier faster-than-Nyquist (MFTN) signaling constitutes a promising spectrally efficient non-orthogonal physical layer waveform. In this correspondence, we propose a pair of low-complexity generalized approximate message passing (GAMP)-based frequency-domain equalization (FDE) algorithms for MFTN systems operating in multipath channels. To mitigate the ill-condition of the resultant equivalent channel matrix, we construct block circulant interference matrices by inserting a few cyclic postfixes, followed by truncating the duration of the inherent two-dimensional interferences. Based on the decomposition of the block circulant matrices, we develop a novel frequency-domain received signal model using the two-dimensional fast Fourier transform for mitigating the colored noise imposed by the non-orthogonal matched filter. Moreover, we derive a GAMP-based FDE algorithm and its refined version, where the latter relies on approximations for circumventing the emergence of the ill-conditioned matrices. Our simulation results demonstrate that, for a fixed spectral efficiency, MFTN signaling can significantly improve the bit error rate (BER) performance by jointly optimizing the time- and frequency-domain packing factors. Compared to its Nyquist-signaling counterpart, our proposed MFTN systems employing the refined GAMP equalizer can achieve about 39% higher transmission rates at a negligible BER performance degradation.
Makhdoom, I, Abolhasan, M & Lipman, J 2022, 'A comprehensive survey of covert communication techniques, limitations and future challenges', Computers & Security, vol. 120, pp. 102784-102784.
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Data encryption aims to protect the confidentiality of data at storage, during transmission, or while in processing. However, it is not always the optimum choice as attackers know the existence of the ciphertext. Hence, they can exploit various weaknesses in the implementation of encryption algorithms and can thus decrypt or guess the related cryptographic primitives. Moreover, in the case of proprietary applications such as online social networks, users are at the mercy of the vendor's security measures. Therefore, users are vulnerable to various security and privacy threats. Contrary to this, covert communication techniques hide the existence of communication and thus achieve security through obscurity and hidden communication channels. Over the period, there has been a significant advancement in this field. However, existing literature fails to encompass all the aspects of covert communications in a single document. This survey thus endeavors to highlight the latest trends in covert communication techniques, related challenges, and future directions.
Makhdoom, I, Lipman, J, Abolhasan, M & Challen, D 2022, 'Science and Technology Parks: A Futuristic Approach', IEEE Access, vol. 10, pp. 31981-32021.
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Most of the existing science and technology parks resort to various conventional ways to attract different stakeholders to the park. Some of these traditional measures include business support, workspaces, laboratories, networking events, accommodation, and essential commodities. Besides, with rampantly changing multidisciplinary technologies and increased data-oriented business models, the classic science and technology park value-creation strategies may not be instrumental in the near future. Hence, we foresee that future science and a technology parks should be fully integrated, sustainable, and innovative living science cities. Where park tenants can actively interact and contribute to emerging technologies. Therefore, this paper carries out an in-depth study of world s best practices in smart cities and science and technology parks, their characteristics, and value-added contributions that excite the prospective tenants. Developing on the detailed survey, we propose a unique feature of Autonomous Systems as a Service to bestow a futuristic look to the science and technology parks. It is envisaged that autonomous systems will not only provide value-added services to the park tenants but will also provide an infrastructure for testing new technologies within park premises. Furthermore, this study evaluates security and privacy challenges associated with autonomous systems and data-oriented services and recommends appropriate security measures. The role of universities in the success of a science and technology park is also delineated. Finally, the components deemed essential for the attainment of science and technology parks objectives are highlighted.
Masangkay, J, Munasinghe, N, Watterson, P & Paul, G 2022, 'Simulation and experimental characterisation of a 3D-printed electromagnetic vibration sensor', Sensors and Actuators A: Physical, vol. 338, pp. 113470-113470.
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Additive manufacturing, also known as 3D printing has already transformed from a rapid prototyping tool to a final end-product manufacturing technique. 3D printing can be used to develop various types of sensors. This paper investigates the ability to use the electromagnetic induction properties of 3D printed carbon-based filament for developing sensors. The paper presents a novel prototype vibration sensor which is 3D-printable, except for an included NdFeB magnet. Motion is detected from the voltage induced by the relative motion of the magnet. The devised vibration sensor is simulated using ANSYS, and a novel prototype is 3D-printed for physical testing to characterise and understand its electromagnetic properties. Simulation helped establish constraints for the design. Two types of experimental setups were physically tested, one setup with a magnet freely sliding inside a cylindrical cavity within an oscillating coil, and the other setup with a stationary coil and oscillating magnet. At a frequency of 10 Hz and a motion travel of about 12 mm, the induced voltage for the moving coil case varied from 5.4 mV RMS for pure sliding motion of the internal magnet to 22.1 mV RMS. The findings of this paper suggest that future sensors can be developed using the electromagnetic induction properties of the carbon-based filament.
Masrur, H, Shafie-Khah, M, Hossain, MJ & Senjyu, T 2022, 'Multi-Energy Microgrids Incorporating EV Integration: Optimal Design and Resilient Operation', IEEE Transactions on Smart Grid, vol. 13, no. 5, pp. 3508-3518.
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Mishra, DK, Ghadi, MJ, Li, L, Zhang, J & Hossain, MJ 2022, 'Active distribution system resilience quantification and enhancement through multi-microgrid and mobile energy storage', Applied Energy, vol. 311, pp. 118665-118665.
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The functional capability of the active distribution network is continually challenged by extreme weather and unforeseen events. A complete resilience quantification framework is required to assess the resilience of a distribution system. With this objective, a framework for demonstrating resilience enhancement through the utilization of multi-microgrids (MMGs) and mobile energy storage in extreme operating conditions is developed in this paper. In the proposed framework, four resilience indices, that is, withstand, recovery, adapt, and prevent (WRAP), are introduced. Withstand index signifies the coping capability after the event, where the MG plays a vital role. The recovery index measures the restoration after the event ends through the system reconfiguration using MGs, tie-lines, and mobile energy storage. The adapt index shows the stability of the system before and during the events. Finally, the prevent index suggests how different resources are important and responsible for fast recovery and minimizing consequences. WRAP, as a resilience quantification framework, is formulated in this study, and indices are quantified and enhanced through the MMG and mobile energy storages. The IEEE 33-bus system is considered for this study, and simulation is performed with different scenarios and measured resilience indices. It is found that appropriate reconfiguration through the use of MMG, tie-lines, and mobile storages can remarkably enhance the resilience of a distribution system.
Mishra, DK, Ray, PK, Li, L, Zhang, J, Hossain, MJ & Mohanty, A 2022, 'Resilient control based frequency regulation scheme of isolated microgrids considering cyber attack and parameter uncertainties', Applied Energy, vol. 306, pp. 118054-118054.
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Cyber-physical attacks and parameter uncertainties are becoming a compelling issue on load frequency control, directly affecting the resilience (i.e., reliability plus security) of the microgrid and multi-microgrid systems enabled by internet of things and the fifth generation communication system. A resilient system aims to endure and quickly restore a system's transients during extreme events. Therefore, it is critically important to have a resilient system to evade the total system failure or blackout in order to make them attack-resilient. With this objective, this paper presents a resilience-based frequency regulation scheme in a microgrid under different operating conditions, such as, step and random change in load and different wind speed patterns. Furthermore, a cyber-attack model is considered in the problem formulation to make the system robust against external attacks. To protect against the cyber-attack and parameter uncertainties in the system, different control schemes are employed, and their robustness characteristics are compared through various performance indices. Besides, the proposed control schemes are validated through a real-time software synchronisation environment, i.e., OPAL-RT. As noted, the proposed type-2 fuzzy proportional-integral-derivative based controller provides the most significant improvement in the dynamic performance for frequency regulation compared to that of the others under the cyber-attack and uncertainties.
Mishra, DK, Złotecka, D & Li, L 2022, 'Significance of SMES Devices for Power System Frequency Regulation Scheme considering Distributed Energy Resources in a Deregulated Environment', Energies, vol. 15, no. 5, pp. 1766-1766.
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Nowadays, the restructuring of power systems is extremely urgent due to the depletion of fossil fuels on the one hand and the environmental impact on the other. In the restructured environment, the incorporation of renewable energy sources and storage devices is key as they have helped achieve a milestone in the form of microgrid technology. As the restructuring of the power system increases, there are several types of generation sources, and distribution companies express their interest in trading in a deregulated environment to operate economically. When considering the power system deregulation, the contract value deviates in some situations, resulting in an imbalance between the generation and the energy consumption, which can bring the system into a power outage condition. In particular, load frequency control has been a great challenge over the past few decades to ensure the stable operation of power systems. This study considers two generation sources: mini-hydro in GENCO-1 and 3 and microgrid (combination of wind, fuel cell, battery storage, and diesel engine) in GENCO-2 and 4. It is two equal-area networks; in area-1, GENCO-1 and 2, and in area-2, GENCO-3 and 4 are considered, respectively. In addition, a FOPID controller and two ancillary devices, such as a unified power flow controller and a superconducting magnetic energy storage system, have been incorporated. Three different test networks have been formed according to the contract value, such as unilateral, bilateral, and agreement violations. The simulation results show that ancillary devices and controller participation significantly enhance the system response by reducing the frequency and tie-line power fluctuation. To validate the efficacy of the proposed method, respective performance indices and percentages of improvement have been obtained. Finally, this study demonstrated the effectiveness of the proposed restructured power system in a deregulated environment.
Mishra, M, Chaudhuri, S, Kshetrimayum, RS, Alphones, A & Esselle, KP 2022, 'Space Efficient Meta-Grid Lines for Mutual Coupling Reduction in Two-Port Planar Monopole and DRA Array', IEEE Access, vol. 10, pp. 49829-49838.
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Moridian, P, Ghassemi, N, Jafari, M, Salloum-Asfar, S, Sadeghi, D, Khodatars, M, Shoeibi, A, Khosravi, A, Ling, SH, Subasi, A, Abdulla, SA, Alizadehsani, R, Górriz, JM & Acharya, UR 2022, 'Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review.', CoRR, vol. abs/2206.11233, pp. 1-32.
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Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
Nan, Y, Huang, X & Guo, YJ 2022, '3-D Millimeter-Wave Helical Imaging', IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 4, pp. 2499-2511.
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This article proposes a low-cost three-dimensional (3-D) millimeter-wave (MMW) holographic imaging system using helical scanning with multiple receivers to achieve a fast continuous scanning over a large two-dimensional (2-D) cylindrical surface. First, the system geometry and its imaging process based on the back-projection algorithm (BPA) are presented. The corresponding imaging point spread function (PSF) and resolutions are analyzed accordingly. To reduce the computational cost significantly, a novel 3-D helical imaging algorithm is then proposed based on the piecewise constant Doppler (PCD) principle. The slant range difference resulting from the helical scanning can be compensated jointly along angular and vertical directions. The proposed imaging is prototyped using the AWR1843 radar sensor from Texas Instruments (TIs) and a moving platform composed of step motors and a micro-controller unit (MCU). The digital imaging process and the number of the required complex multiplications are also discussed in detail. Finally, simulation and experimental results are provided to validate the accuracy and efficiency of the proposed imaging system.
Nan, Y, Huang, X & Guo, YJ 2022, 'A Panoramic Synthetic Aperture Radar', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13.
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This article proposes a new synthetic aperture radar (SAR), named as panoramic SAR, based on a combination of linear and rotational SARs, by which a large 360° panoramic view of the observed scene can be reconstructed. First, the system geometry and its imaging process based on the back-projection algorithm (BPA) are presented. The combined movement constitutes a 2-D synthetic aperture, and thus higher imaging resolutions can be obtained. The corresponding resolution analysis and the sampling criteria are discussed accordingly. Then, a novel dynamic piecewise compensation (DPC) algorithm, a recursive imaging process, is proposed to reduce the processing complexity significantly. The imaging implementation and the complexity are also studied respectively. Finally, a prototype of panoramic SAR is built based on an frequency-modulated continuous wave (FMCW) radar and a moving platform, and the simulation and experimental results are provided to validate the proposed panoramic SAR principle and the DPC algorithm.
Nan, Y, Huang, X & Guo, YJ 2022, 'An Universal Circular Synthetic Aperture Radar', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15.
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This article presents an universal circular synthetic aperture radar (SAR) (UCSAR) by which the targets to be observed at any radial distance can be imaged, thus making SAR imaging possible in a more general scenario with a circular movement of the radar platform. The UCSAR point spread function (PSF) is firstly analyzed based on the time-domain correlation imaging approach, and thus a three-dimension (3-D) spatial variant PSF of the target can be formulated. The closed-form PSF expressions with single-frequency and frequency-modulated continuous wave (FMCW) transmitted signals are derived respectively to quantify the imaging resolutions, showing that the PSF is a product of a sinc function and a zeroth-order Bessel function when using a wideband FMCW signal. Secondly, a fast UCSAR imaging algorithm and its further simplified version are proposed to reduce the computational cost significantly based on the piecewise constant Doppler (PCD) principle. To quantify the imaging performance, we derive an error function of the slant range approximation for the proposed algorithm, serving as a practical guideline for the UCSAR parameter selection. Finally, the simulation and experimental results are provided to validate the PSF analysis, the fast imaging algorithm, and the implementation of the proposed UCSAR.
Nasir, AA, Tuan, HD, Dutkiewicz, E & Hanzo, L 2022, 'Finite-Resolution Digital Beamforming for Multi-User Millimeter-Wave Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9647-9662.
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Recent studies have shown that low-resolution analog-to-digital-converters and digital-to-analog-converters (ADCs and DACs) can make fully-digital beamforming more power efficient than its analog or hybrid beamforming counterpart over wide-band millimeter-wave (mmWave) channels. Inspired by this, we propose a computationally efficient fully-digital beamformer relying on low-resolution ADCs/DACs for multi-user mmWave communication networks. Both a generalized (unstructured) beamformer (GB) and a structured zero-forcing beamformer (ZFB) are proposed. For maintaining fairness among all users in the network, specifically tailored objective functions are considered under sum-power constraints, namely that of maximizing the geometric mean (GM) of users' rate and their max-min rate. These computationally challenging beamforming design problems are tackled by developing computationally efficient steep ascent algorithms, which have the radical benefit of relying on a closed-form solution at each iteration. Moreover, to facilitate the employment of low-cost amplifiers at each antenna, the GB design problem subject to the equal-gain transmission constraint is considered, which assigns equal transmit power to each transmit antenna. The proposed algorithms promise a user-rate distribution having a reduced deviation among the user-rates, i.e., improved rate-fairness. Our extensive simulation results show an approximately upto 45% reduction for the GM-rate of a 2-bit ADC (4-bin quantization) compared to the $\infty$-resolution ADC.
Nasir, AA, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Low-Resolution RIS-Aided Multiuser MIMO Signaling', IEEE Transactions on Communications, vol. 70, no. 10, pp. 6517-6531.
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A multi-antenna aided base station (BS) supporting several multi-antenna downlink users with the aid of a reconfigurable intelligent surface (RIS) of programmable reflecting elements (PREs) is considered. Low-resolution PREs constrained by a set of sparse discrete values are used for reasons of cost-efficiency. Our challenging objective is to jointly design the beamformers at the BS and the RIS's PREs for improving the throughput of all users by maximizing their geometric-mean, under a variety of different access schemes. This constitutes a computationally challenging problem of mixed continuous-discrete optimization, because each user's throughput is a complicated function of both the continuous-valued beamformer weights and of the discrete-valued PREs. We develop low-complexity algorithms, which iterate by directly evaluating low-complexity closed-form expressions. Our simulation results show the advantages of non-orthogonal multiple access-aided signaling, which allows the users to decode a part of the multi-user interference for enhancing their throughput.
Nasir, AA, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Relay-Aided Multi-User OFDM Relying on Joint Wireless Power Transfer and Self-Interference Recycling', IEEE Transactions on Communications, vol. 70, no. 1, pp. 291-305.
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Relay-aided multi-user OFDM is investigated under which multiple sources transmit their signals to a multi-antenna relay during the first relaying stage and then the relay amplifies and forwards the composite signal to all destinations during the second stage. The signal transmission of both stages experience frequency selectivity. The relay is powered both by an energy source through the wireless power transfer as well as by the energy recycled from its own self-interference during the second stage. Accordingly, we jointly design the power allocations both at the multiple source nodes and at a common relay node for maximizing the network's sum-throughput, which poses a large-scale nonconvex problem, regardless whether proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS) is used for signal transmission to the relay. We develop new alternating descent procedures for solving our joint optimization problems, which are based on closed-forms and thus are of very low computational complexity even for large numbers of subcarriers. The results show the superiority of IGS over PGS in terms of both its sum-rate and individual user-rate. Another benefit of IGS over PGS is that the former promises fairer rate distribution across the subcarriers. Moreover, the recycled self-interference also provides a beneficial complementary energy source.
Ngo, QT, Phan, KT, Xiang, W, Mahmood, A & Slay, J 2022, 'Two-Tier Cache-Aided Full-Duplex Hybrid Satellite–Terrestrial Communication Networks', IEEE Transactions on Aerospace and Electronic Systems, vol. 58, no. 3, pp. 1753-1765.
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Nguyen, CT, Van Huynh, N, Chu, NH, Saputra, YM, Hoang, DT, Nguyen, DN, Pham, Q-V, Niyato, D, Dutkiewicz, E & Hwang, W-J 2022, 'Transfer Learning for Wireless Networks: A Comprehensive Survey', Proceedings of the IEEE, vol. 110, no. 8, pp. 1073-1115.
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With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, can impede the effectiveness and applicability of ML in wireless networks. To address these problems, transfer learning (TL) has recently emerged to be a promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks and valuable experiences accumulated from the past to facilitate the learning of new problems. By doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods' robustness to different wireless environments. This article aims to provide a comprehensive survey on the applications of TL in wireless networks. Particularly, we first provide an overview of TL, including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, signal recognition, security, caching, localization, and human activity recognition, which are all important to next-generation networks, such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.
Nguyen, D-A, Tran, X-T, Dang, KN & Iacopi, F 2022, 'A low-power, high-accuracy with fully on-chip ternary weight hardware architecture for Deep Spiking Neural Networks', Microprocessors and Microsystems, vol. 90, pp. 104458-104458.
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Recently, Deep Spiking Neural Network (DSNN) has emerged as a promising neuromorphic approach for various AI-based applications, such as image classification, speech recognition, robotic control etc. on edge computing platforms. However, the state-of-the-art offline training algorithms for DSNNs are facing two major challenges. Firstly, many timesteps are required to reach comparable accuracy with traditional frame-based DNNs algorithms. Secondly, extensive memory requirements for weight storage make it impossible to store all the weights on-chip for DSNNs with many layers. Thus the inference process requires continue access to expensive off-chip memory, ultimately leading to performance degradation in terms of throughput and power consumption. In this work, we propose a hardware-friendly training approach for DSNN that allows the weights to be constrained to ternary format, hence reducing the memory footprints and the energy consumption. Software simulations on MNIST and CIFAR10 datasets have shown that our training approach could reach an accuracy of 97% for MNIST (3-layer fully connected networks) and 89.71% for CIFAR10 (VGG16). To demonstrate the energy efficiency of our approach, we have proposed a neural processing module to implement our trained DSNN. When implemented as a fixed, 3-layers fully-connected system, the system has reached at energy efficiency of 74nJ/image with a classification accuracy of 97% for MNIST dataset. We have also considered a scalable design to support more complex network topologies when we integrate the neural processing module with a 3D Network-on-Chip.
Nguyen, HAD & Ha, QP 2022, 'Wireless Sensor Network Dependable Monitoring for Urban Air Quality', IEEE Access, vol. 10, no. 99, pp. 40051-40062.
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Nguyen, M-D, Lee, S-M, Pham, Q-V, Hoang, DT, Nguyen, DN & Hwang, W-J 2022, 'HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks', IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-13.
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Nguyen, NHT, Perry, S, Bone, D, Le Thanh, H, Xu, M & Nguyen, TT 2022, 'Combination of Images and Point Clouds in a Generative Adversarial Network for Upsampling Crack Point Clouds', IEEE Access, vol. 10, pp. 67198-67209.
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Nguyen, TG, Phan, TV, Hoang, DT, Nguyen, HH & Le, DT 2022, 'DeepPlace: Deep reinforcement learning for adaptive flow rule placement in Software-Defined IoT Networks', Computer Communications, vol. 181, pp. 156-163.
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In this paper, we propose a novel and adaptive flow rule placement system based on deep reinforcement learning, namely DeepPlace, in Software-Defined Internet of Things (SDIoT) networks. DeepPlace can provide a fine-grained traffic analysis capability while assuring QoS of traffic flows and proactively avoiding the flow-table overflow issue in the data plane. Specifically, we first investigate the traffic forwarding process in an SDIoT network, i.e., routing and flow rule placement tasks. We design a cost function for the routing to set up traffic flow paths in the data plane. Next, we propose an adaptive flow rule placement approach to maximize the number of match-fields in a flow rule at SDN switches. To deal with the dynamics of IoT traffic flows, we model the system operation by using the Markov decision process (MDP) with a continuous action space and formulate its optimization problem. Subsequently, we develop a deep deterministic policy gradient-based algorithm to help the system obtain the optimal policy. The evaluation results demonstrate that DeepPlace can efficiently maintain a significant number of match-fields in a flow rule, i.e., approximately 86% of the maximum level, while minimizing the QoS violation ratio of traffic flows, i.e., 6.7%, in a highly dynamic traffic scenario, which outperforms three other existing solutions, i.e., FlowMan, FlowStat, and DeepMatch.
Nguyen, TK, Nguyen, HH, Tuan, HD & Ngo, HQ 2022, 'Improved Pilot Designs for Enhancing Connectivity in Multicarrier Massive MIMO Systems', IEEE Wireless Communications Letters, vol. 11, no. 5, pp. 1057-1061.
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Ni, W, Liu, W, Zhao, Z, Yuan, X, Sun, Y, Zhang, H, Wang, L, Zhou, M, Yin, P & Xu, J 2022, 'Body Mass Index and Mortality in Chinese Older Adults —New Evidence from a Large Prospective Cohort in China', The Journal of nutrition, health and aging, vol. 26, no. 6, pp. 628-636.
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Ni, Z, Zhang, JA, Yang, K, Huang, X & Tsiftsis, TA 2022, 'Multi-Metric Waveform Optimization for Multiple-Input Single-Output Joint Communication and Radar Sensing', IEEE Transactions on Communications, vol. 70, no. 2, pp. 1276-1289.
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Nizami, S, Tushar, W, Hossain, MJ, Yuen, C, Saha, T & Poor, HV 2022, 'Transactive energy for low voltage residential networks: A review', Applied Energy, vol. 323, pp. 119556-119556.
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Transactive Energy (TE) is envisaged as an advanced demand response (DR) variant to leverage the flexibility of distributed energy resources (DERs) for enhancing energy balance and network management in modern power systems. However, there have been limited implementations of TE frameworks for low voltage (LV) residential networks to capture the underutilised flexibility potential of DER-equipped residential prosumers. The main purpose of this paper is to identify the rationale behind this gap in light of recent advances in TE-based energy management for residential networks. As such, first, we identify the motivation and significance of the evolution of TE framework from traditional DR schemes by reviewing their relative efficacies in utilising demand-side flexibility of DER-rich residential networks for enhancing energy balance and local network management. Second, we provide an overview of the key components of the TE framework that are essential to facilitate active negotiation and trading of demand-side flexibility in residential networks. Third, we review the state-of-the-art TE methodologies and industry projects that have utilised demand-side flexibility of residential prosumers. Finally, several challenges relevant to TE frameworks in LV residential networks are identified followed by some concluding remarks at the end of the paper.
Ong, YR, Cao, S, Lee, SS, Lim, CS, Chen, MM, Kurdkandi, NV, Barzegarkhoo, R & Siwakoti, YP 2022, 'A Dual-Buck-Boost DC–DC/AC Universal Converter', Electronics, vol. 11, no. 13, pp. 1973-1973.
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This paper proposes a universal converter that is capable of operating in three modes for generating positive dc voltage, negative dc voltage, and sinusoidal ac voltage. By controlling the duty-cycle of two half-bridges, an inductor is operated at a high frequency to control the voltage across two film capacitors that constitute a dual-buck-boost converter. Two additional half-bridges operating at a fixed state or line frequency are used to select the mode of operation. Compared to the latest universal converter in the recent literature, the proposed topology has the same switch count while reducing the number of conducting switches for inductor current and reducing the number of switches operating at high frequency. The operation of the proposed dual-buck-boost dc–dc/ac universal converter is analyzed. Experimental results are presented for validation. The power conversion efficiency of the 100 W experimental prototype modeled in PLECS is approximately 98%.
Pal, PK, Jana, KC, Siwakoti, YP, Majumdar, S & Blaabjerg, F 2022, 'An Active-Neutral-Point-Clamped Switched-Capacitor Multilevel Inverter With Quasi-Resonant Capacitor Charging', IEEE Transactions on Power Electronics, vol. 37, no. 12, pp. 14888-14901.
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Pearce, A, Zhang, JA & Xu, R 2022, 'A Combined mmWave Tracking and Classification Framework Using a Camera for Labeling and Supervised Learning', Sensors, vol. 22, no. 22, pp. 8859-8859.
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Millimeter wave (mmWave) radar poses prosperous opportunities surrounding multiple-object tracking and sensing as a unified system. One of the most challenging aspects of exploiting sensing opportunities with mmWave radar is the labeling of mmWave data so that, in turn, a respective model can be designed to achieve the desired tracking and sensing goals. The labeling of mmWave datasets usually involves a domain expert manually associating radar frames with key events of interest. This is a laborious means of labeling mmWave data. This paper presents a framework for training a mmWave radar with a camera as a means of labeling the data and supervising the radar model. The methodology presented in this paper is compared and assessed against existing frameworks that aim to achieve a similar goal. The practicality of the proposed framework is demonstrated through experimentation in varying environmental conditions. The proposed framework is applied to design a mmWave multi-object tracking system that is additionally capable of classifying individual human motion patterns, such as running, walking, and falling. The experimental findings demonstrate a reliably trained radar model that uses a camera for labeling and supervision that can consistently produce high classification accuracy across environments beyond those in which the model was trained against. The research presented in this paper provides a foundation for future research in unified tracking and sensing systems by alleviating the labeling and training challenges associated with designing a mmWave classification model.
Peng, Y, Liu, Y, Li, M, Liu, H & Guo, YJ 2022, 'Synthesizing Circularly Polarized Multi-Beam Planar Dipole Arrays With Sidelobe and Cross-Polarization Control by Two-Step Element Rotation and Phase Optimization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4379-4391.
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Perera, D, Wang, Y-K, Lin, C-T, Nguyen, H & Chai, R 2022, 'Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators', Sensors, vol. 22, no. 16, pp. 6230-6230.
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This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.
Phan, TC, Pranata, A, Farragher, J, Bryant, A, Nguyen, HT & Chai, R 2022, 'Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain', Sensors, vol. 22, no. 17, pp. 6694-6694.
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This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward’s method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward’s method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy.
Poostchi, H & Piccardi, M 2022, 'BiLSTM-SSVM: Training the BiLSTM with a Structured Hinge Loss for Named-Entity Recognition', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 203-212.
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Building on the achievements of the BiLSTM-CRF in named-entity recognition (NER), this paper introduces the BiLSTM-SSVM, an equivalent neural model where training is performed using a structured hinge loss. The typical loss functions used for evaluating NER are entity-level variants of the F1 score such as the CoNLL and MUC losses. Unfortunately, the common loss function used for training NER - the cross entropy - is only loosely related to the evaluation losses. For this reason, in this paper we propose a training approach for the BiLSTM-CRF that leverages a hinge loss bounding the CoNLL loss from above. In addition, we present a mixed hinge loss that bounds either the CoNLL loss or the Hamming loss based on the density of entity tokens in each sentence. The experimental results over four benchmark languages (English, German, Spanish and Dutch) show that training with the mixed hinge loss has led to small but consistent improvements over the cross entropy across all languages and four different evaluation measures
Poursafar, N, Hossain, MJ & Taghizadeh, S 2022, 'Distributed DC-Bus Signaling Control of Photovoltaic Systems in Islanded DC Microgrid', CSEE Journal of Power and Energy Systems, vol. 8, no. 6, pp. 1741-1750.
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The stability of an islanded DC microgrid (DCMG) is highly dependent on the presence and performance of the backup energy storage system (BESS), due to the lack of main grid support. This condition makes the DCMG vulnerable to the critical situation of absence of the BESS, which could be caused by a fault or being fully charged or flat. This paper presents an enhanced distributed DC-bus signaling control strategy for converters of photovoltaic systems (PVs) to make the islanded DCMG less dependent on the BESS. Unlike a conventional control approach that utilizes PVs to operate in maximum power point tracking (MPPT) mode and the BESS solely regulating DC-bus voltage, the proposed control method maintains DC-bus voltage via intelligently managing output powers of the PVs. The proposed control method continuously monitors DC-bus voltage and regulates the output powers of all the PVs via switching between MPPT mode and voltage regulating mode. Accordingly, if the DC-bus voltage level is less than a predefined maximum level, the PVs work in MPPT mode; otherwise, the PVs work in voltage regulating mode to maintain DC-bus voltage at an acceptable range. Such switching between MPPT and voltage regulating control operations results in protecting the DCMG from unavoidable shutdowns conventionally necessary during the absence of the BESS unit. Moreover, the proposed control method reduces oscillations on the DC-bus voltage during existence of the BESS. The performance and effectiveness of the proposed control strategy are validated through different case studies in MATLAB/Simulink.
Poursafar, N, Taghizadeh, S, J. Hossain, M & M. Guerrero, J 2022, 'An Optimized Distributed Cooperative Control to Improve the Charging Performance of Battery Energy Storage in a Multiphotovoltaic Islanded DC Microgrid', IEEE Systems Journal, vol. 16, no. 1, pp. 1170-1181.
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Qian, J, Begum, H & Lee, JE-Y 2022, 'Acoustic Centrifugation Facilitating Particle Sensing in Liquid on a Piezoelectric Resonator', IEEE Electron Device Letters, vol. 43, no. 5, pp. 801-804.
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Qin, P-Y, Song, L-Z & Guo, YJ 2022, 'Conformal Transmitarrays for Unmanned Aerial Vehicles Aided 6G Networks', IEEE Communications Magazine, vol. 60, no. 1, pp. 14-20.
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Rafique, S, Nizami, MSH, Irshad, UB, Hossain, MJ & Mukhopadhyay, SC 2022, 'A two-stage multi-objective stochastic optimization strategy to minimize cost for electric bus depot operators', Journal of Cleaner Production, vol. 332, pp. 129856-129856.
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Rafique, S, Nizami, MSH, Irshad, UB, Hossain, MJ & Mukhopadhyay, SC 2022, 'EV Scheduling Framework for Peak Demand Management in LV Residential Networks', IEEE Systems Journal, vol. 16, no. 1, pp. 1520-1528.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2022, 'Statistical Learning-Based Grant-Free Access for Delay-Sensitive Internet of Things Applications', IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5492-5506.
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Mission-critical Internet-of-Things (IoT) applications require communication interfaces that provide ultra-reliability and low latency. Acquiring knowledge regarding the number of active devices and their latency-reliability requirements becomes essential to optimize resource allocation in heterogeneous networks. Due to the inherent heavy computation overheads, the conventional centralized decision-making approaches result in large latency. The distributed computing and device-level prediction of network parameters can play a significant role in designing mission-critical IoT applications operating in dynamic environments. This paper considers the medium access control (MAC) layer of heterogeneous networks employing a framed-ALOHA-based restricted transmission strategy to enhance reliability. We present a statistical learning-based device-level network exploration mechanism in which end-devices use their transmission history to predict different network parameters. The IoT devices share the learned parameters with the base station (BS) to identify different groups presented in the network. The simulation results show that the mean square error (MSE) in predicting different network parameters can be reduced by increasing the history window size. In this regard, the optimal size of the history window under the given accuracy constraints is also determined. We demonstrate that the proposed device-level network load prediction mechanism is more robust as compared to the BS-centered approach.
Reja, VK, Varghese, K & Ha, QP 2022, 'Computer vision-based construction progress monitoring', Automation in Construction, vol. 138, pp. 104245-104245.
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Automating the process of construction progress monitoring through computer vision can enable effective control of projects. Systematic classification of available methods and technologies is necessary to structure this complex, multi-stage process. Using the PRISMA framework, relevant studies in the area were identified. The various concepts, tools, technologies, and algorithms reported by these studies were iteratively categorised, developing an integrated process framework for Computer-Vision-Based Construction Progress Monitoring (CV-CPM). This framework comprises: data acquisition and 3D-reconstruction, as-built modelling, and progress assessment. Each stage is discussed in detail, positioning key studies, and concurrently comparing the methods used therein. The four levels of progress monitoring are defined and found to strongly influence all stages of the framework. The need for benchmarking CV-CPM pipelines and components are discussed, and potential research questions within each stage are identified. The relevance of CV-CPM to support emerging areas such as Digital Twin is also discussed.
Rodriguez, J, Garcia, C, Mora, A, Flores-Bahamonde, F, Acuna, P, Novak, M, Zhang, Y, Tarisciotti, L, Davari, SA, Zhang, Z, Wang, F, Norambuena, M, Dragicevic, T, Blaabjerg, F, Geyer, T, Kennel, R, Khaburi, DA, Abdelrahem, M, Zhang, Z, Mijatovic, N & Aguilera, RP 2022, 'Latest Advances of Model Predictive Control in Electrical Drives—Part I: Basic Concepts and Advanced Strategies', IEEE Transactions on Power Electronics, vol. 37, no. 4, pp. 3927-3942.
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The application of model predictive control in electrical drives has been studied extensively in the past decade. This article presents what the authors consider the most relevant contributions published in the last years, mainly focusing on three relevant issues: weighting factor calculation when multiple objectives are utilized in the cost function, current/torque harmonic distortion optimization when the power converter switching frequency is reduced, and robustness improvement under parameters uncertainties. Therefore, this article aims to enable readers to have a more precise overview while facilitating their future research work in this exciting area.
Rutherford, H, Saha Turai, R, Chacon, A, Franklin, DR, Mohammadi, A, Tashima, H, Yamaya, T, Parodi, K, Rosenfeld, AB, Guatelli, S & Safavi-Naeini, M 2022, 'An inception network for positron emission tomography based dose estimation in carbon ion therapy', Physics in Medicine & Biology, vol. 67, no. 19, pp. 194001-194001.
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Abstract Objective. We aim to evaluate a method for estimating 1D physical dose deposition profiles in carbon ion therapy via analysis of dynamic PET images using a deep residual learning convolutional neural network (CNN). The method is validated using Monte Carlo simulations of 12C ion spread-out Bragg peak (SOBP) profiles, and demonstrated with an experimental PET image. Approach. A set of dose deposition and positron annihilation profiles for monoenergetic 12C ion pencil beams in PMMA are first generated using Monte Carlo simulations. From these, a set of random polyenergetic dose and positron annihilation profiles are synthesised and used to train the CNN. Performance is evaluated by generating a second set of simulated 12C ion SOBP profiles (one 116 mm SOBP profile and ten 60 mm SOBP profiles), and using the trained neural network to estimate the dose profile deposited by each beam and the position of the distal edge of the SOBP. Next, the same methods are used to evaluate the network using an experimental PET image, obtained after irradiating a PMMA phantom with a 12C ion beam at QST’s Heavy Ion Medical Accelerator in Chiba facility in Chiba, Japan. The performance of the CNN is compared to that of a recently published iterative technique using the same simulated and experimental 12C SOBP profiles. Main results. The CNN estimated the simulated dose profiles with a mean relative error (MRE) of 0.7% ± 1.0% and the distal edge position with an accuracy of 0.1 mm ± 0.2 mm, and estimate the dose delivered by the experimental 12C ion beam with a MRE of 3.7%, and the distal edge with an accuracy of 1.7 mm. Significance. The CNN was able to produce estimates ...
Saki, M, Abolhasan, M, Lipman, J & Jamalipour, A 2022, 'Mobility Model for Contact-Aware Data Offloading Through Train-to-Train Communications in Rail Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 597-609.
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In this paper, we propose a novel mobility model providing train traffic traces essential for train-to-train communication models. As the proposed mobility model works only based on trip timetables and train timetables are currently available in real-time, the produced mobility traces will be also in real-time. Additionally, as no GPS module is used in this method, our proposed model can provide a practical solution when signal from GPS or Assisted GPS is poor or unavailable such as in urban area or inside tunnels. Furthermore, as we used an energy optimization function, the proposed mobility model will provide a guidance trajectory for trains to have an energy-optimized operation. We also develop an algorithm that can determine the specifications of contacts between trains based on the traffic traces obtained from the mobility model. Such specifications includes duration, rate and location of train contacts used for estimation of data exchange capacity between trains through train-to-train communications. We validate our proposed model using data collected from Sydney Trains of Australia. The results obtained from our proposed model show over 98 percent accuracy in comparison with the real data collected via a GPS module from Sydney Trains.
Saputra, YM, Nguyen, D, Dinh, HT, Pham, Q-V, Dutkiewicz, E & Hwang, W-J 2022, 'Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services', IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-1.
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This work proposes a novel framework to address straggling and privacy issues for federated learning (FL)-based mobile application services, considering limited computing/communications resources at mobile users (MUs)/mobile application provider (MAP), privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP. Particularly, the MAP first determines a set of the best MUs for the FL process based on MUs' provided information/features. Then, each selected MU can encrypt part of local data and upload the encrypted data to the MAP for an encrypted training process, in addition to the local training process. For that, the selected MU can propose a contract to the MAP according to its expected local and encrypted data. To find optimal contracts that can maximize utilities while maintaining high learning quality of the system, we develop a multi-principal one-agent contract-based problem considering the MUs' privacy cost, the MAP's limited computing resources, and asymmetric information between the MAP and MUs. Experiments with a real-world dataset show that our framework can speed up training time up to 49% and improve prediction accuracy up to 4.6 times while enhancing network's social welfare up to 114% under the privacy cost consideration compared with those of baseline methods.
Sarker, PC, Guo, Y, Lu, H & Zhu, JG 2022, 'Improvement on parameter identification of modified Jiles-Atherton model for iron loss calculation', Journal of Magnetism and Magnetic Materials, vol. 542, pp. 168602-168602.
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The physical behaviour of a magnetic material can be characterized by Jiles-Atherton (J-A) model where some model parameters are generally identified by optimization techniques. For identification of model parameters using optimization techniques, an error criterion based on the error between the measured and calculated magnetic flux density (B) or magnetic field strength (H) is commonly considered where the relative error in the calculation of iron loss is ignored. Consequently, the calculated iron loss from B-H loop sometimes highly differs from its experimental value. In this paper, the error criteria for J-A model's parameter identification are designed as the combination of the relative iron loss error criterion and the general existing error criterion. Furthermore, a modified J-A model is proposed to improve the agreement between experimental and calculated results especially at the low magnetic induction levels by introducing a scaling factor in the anhysteretic magnetization. The proposed modified J-A model and the effectiveness of the error criteria for its parameter identification are tested by comparing calculated results with the experimental results as well as recently works in the literature.
Sayem, ASM, Lalbakhsh, A, Esselle, KP, Buckley, JL, O'Flynn, B & Simorangkir, RBVB 2022, 'Flexible Transparent Antennas: Advancements, Challenges, and Prospects', IEEE Open Journal of Antennas and Propagation, vol. 3, pp. 1109-1133.
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Sharma, V, Hossain, MJ & Mukhopadhyay, S 2022, 'Fault-Tolerant Operation of Bidirectional ZSI-Fed Induction Motor Drive for Vehicular Applications', Energies, vol. 15, no. 19, pp. 6976-6976.
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This paper presents an efficient and fast fault-tolerant control scheme for a bidirectional Z-source inverter (BiZSI)-fed induction-motor drive system for vehicular applications. The proposed strategy aims for the fault detection, localization and diagnosis of the proposed system during switch failures in the inverter module. Generally, power–semiconductor switch failures in inverter modules occur due to open- and short-circuit faults. An efficient modulation scheme is proposed and design specifications are thoroughly derived to obtain high voltage gains across the BiZSI network. A suitably fast detection and diagnosis scheme to isolate the faulty leg and resume the normal operation is discussed in this paper. The control scheme is provided such that the faulty leg is isolated and the motor phase is fed from a redundant leg to resume the operation. A feasible localization algorithm based on experimentally derived values and switching vectors is implemented. In addition, a fast fault diagnosis method based on current estimation and motor speed variation is designed and implemented. Moreover, the most important advantages of the proposed strategy include lower hardware requirements and less harmonic distortion in the output currents. Finally, the simulation and experimental results are presented to validate the feasibility of the theoretical analysis. An extensive performance evaluation of the proposed system with fault ride-through capabilities is performed to prove its suitability for vehicular applications. To validate its merits, the proposed strategy is compared with similar fault-tolerant schemes currently used in the industry.
Shi, Q, Wu, N, Nguyen, DN, Huang, X, Wang, H & Hanzo, L 2022, 'Low-Complexity Iterative Detection for Dual-Mode Index Modulation in Dispersive Nonlinear Satellite Channels', IEEE Transactions on Communications, vol. 70, no. 2, pp. 1261-1275.
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Shi, Z, Cheng, Q, Zhang, JA & Yi Da Xu, R 2022, 'Environment-Robust WiFi-Based Human Activity Recognition Using Enhanced CSI and Deep Learning', IEEE Internet of Things Journal, vol. 9, no. 24, pp. 24643-24654.
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Deep learning has demonstrated its great potential in channel state information (CSI)-based human activity recognition (HAR), and hence has attracted increasing attention in both the industry and academic communities. While promising, most existing high-accuracy methodologies require to retrain their models when applying the previous-trained ones to a new/unseen environment. This issue has limited their practical usabilities. In order to overcome this challenge, this article proposes an innovative scheme, which combines an activity-related feature extraction and enhancement (AFEE) method and matching network (AFEE-MatNet). The proposed scheme is 'one-fits-all,' meaning that the trained model can be directly applied in new/unseen environments without any retraining. We introduce the AFEE method to enhance CSI quality by eliminating noise. Specifically, the approach mitigates environmental noises unrelated to activity while better compressing and preserving the behavior-related information. Moreover, the size of feature signals generated by AFEE are reduced, which in turn significantly shortens the training time. For effective feature extraction, we propose to use the MatNet architecture to learn transferable features shared among source environments. To further improve the recognition performance, we introduce a prediction checking and correction scheme to rectify some classification errors that do not abide by the state transition of human behaviors. Extensive experimental results demonstrate that our proposed AFEE-MatNet significantly outperforms existing state-of-the-art HAR methods, in terms of both recognition accuracy and training time.
Shi, Z, Sun, X, Lei, G, Tian, X, Guo, Y & Zhu, J 2022, 'Multiobjective Optimization of a Five-Phase Bearingless Permanent Magnet Motor Considering Winding Area', IEEE/ASME Transactions on Mechatronics, vol. 27, no. 5, pp. 2657-2666.
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Shi, Z, Zhang, JA, Xu, RY & Cheng, Q 2022, 'Environment-Robust Device-Free Human Activity Recognition With Channel-State-Information Enhancement and One-Shot Learning', IEEE Transactions on Mobile Computing, vol. 21, no. 2, pp. 540-554.
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Shrestha, S, Abbas, SM, Asadnia, M & Esselle, KP 2022, 'Realization of Three Dimensional Printed Multi Layer Wide Band Prototype', IEEE Access, vol. 10, pp. 130944-130954.
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Singh, A 2022, 'High Voltage Gain Bidirectional DC-DC Converters for Supercapacitor Assisted Electric Vehicles: A Review', CPSS Transactions on Power Electronics and Applications, vol. 7, no. 4, pp. 386-398.
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Integration of supercapacitor alongwith battery in electric vehicles (EVs) improves the life cycle of the battery. Additionally, supercapacitor supplies or absorbs a large amount of instantaneous power during sudden demand such as acceleration or regenerative braking operation, hence also improve the dynamics of the internal power system. However, a major challenge with supercapacitor is that its terminal voltage is low and varies in a wide range during charging and discharging operation. Thus, a high voltage conversion ratio based bidirectional DC-DC converters are required to connect the lower supercapacitor voltage to higher DC-link voltage. The steep voltage conversion ratio with continuous gain based bidirectional DC-DC converters are an integral part of such applications. Various high gain converters exist in the literature such as isolated, non-isolated, cascaded, switched, flying capacitor, and coupled inductor based DC-DC converters, however all these converters have some limitations in context to high gain/high power applications. Therefore, to understand the development of next-generation high voltage gain bidirectional DC-DC converter, this paper focus to comprehensively review and classify various bidirectional step-up DC-DC converters based on their characteristics and voltage-boosting techniques. Further, practical suitability of the reviewed converters and future research directions in switched capacitor converters in context to EVs are also discussed in detail.
Singh, K, Afzal, MU & Esselle, KP 2022, 'Accurate optimization technique for phase-gradient metasurfaces used in compact near-field meta-steering systems', Scientific Reports, vol. 12, no. 1.
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AbstractNear-Field Meta-Steering (NFMS) is a constantly evolving and progressively emerging novel antenna beam-steering technology that involves an elegant assembly of a base antenna and a pair of Phase-Gradient Metasurfaces (PGMs) placed in the near-field region of the antenna aperture. The upper PGM in an NFMS system receives an oblique incidence from the lower PGM at all times, a fact that is ignored in the traditional design process of upper metasurfaces. This work proposes an accurate optimization method for metasurfaces in NFMS systems to reduce signal leakage by suppressing the grating lobes and side lobes that are innate artifacts of beam-steering. We detail the design and optimization approach for both upper and lower metasurface. Compared to the conventionally optimized compact 2D steering system, the proposed system exhibits higher directivity and lower side-lobe and grating lobe levels within the entire scanning range. The broadside directivity is 1.4 dB higher, and the side-lobe level is 4 dB lower in comparison. The beam-steering patterns for the proposed 2D compact design are experimentally validated, and the measured and predicted results are in excellent concurrence. The versatile compatibility of truncated PGMs with a low gain antenna makes it a compelling technology for wireless backhaul mesh networks and future antenna hardware.
Song, L-Z, Qin, P-Y, Zhu, H & Du, J 2022, 'Wideband Conformal Transmitarrays for E-Band Multi-Beam Applications', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10417-10425.
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Wideband conformal transmitarrays at E-band are developed for multi-beam applications in this paper. A triple-layer element with double split rings is presented for wideband transmissions, achieving a 360° continuous phase variation range at 74 GHz with less than 2.3-dB transmission loss. A comprehensive design methodology of multi-beam conformal transmitarrays is demonstrated for various platforms with different curvatures. To validate the theoretical analysis, conformal transmitarrays with two different curvatures are designed, fabricated, and measured. Multiple radiation beams are realized between ±30° and ±45° for the two prototypes, respectively. Good agreement is obtained between simulation and measurement. The 3-dB gain bandwidths are 30% from 66.5 GHz to 90 GHz, and 27.8% from 68 GHz to 90 GHz for the two designs, respectively, covering the entire E-band.
Song, L-Z, Wang, X & Qin, P-Y 2022, 'Single-Feed Multibeam Conformal Transmitarrays With Phase and Amplitude Modulations', IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 8, pp. 1669-1673.
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Single-feed multibeam conformal transmitarrays using a superposition method are presented in this letter. The arrays consist of ultrathin Huygens elements with independent amplitude and phase manipulations. Three cylindrical conformal transmitarrays with dual-beam radiation patterns are designed at 10 GHz, producing dual beams at ±30°, +30°, and -20°, +30° and -10°, respectively. As an experimental validation, the prototype with symmetrical dual beams is fabricated and measured. Two beams at +29° and -28° along the H-plane are achieved with a measured 18.29 dBi peak gain. The gain difference between the two beams is 0.13 dB. Good agreement between simulation and measurement is observed.
Song, Z, Lu, J, Yao, Y & Zhang, J 2022, 'Self-Supervised Depth Completion From Direct Visual-LiDAR Odometry in Autonomous Driving', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 11654-11665.
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Soomro, WA, Guo, Y, Lu, H, Jin, J, Shen, B & Zhu, J 2022, 'Experimental Setup for Measurement of AC Loss in HTS under Rotating Magnetic Field', Energies, vol. 15, no. 21, pp. 7857-7857.
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High-temperature superconducting materials have shown great potential for the design of large-scale industry applications. However, they are complicated under AC conditions, resulting in penalties such as power loss or AC loss. This loss has to be considered in order to design reliable and efficient superconducting devices. Furthermore, when superconductors are used in rotating machines, they may be exposed to rotating magnetic fields, which is critical for the design of such machines. Existing AC loss measuring techniques are limited to measuring under one-dimensional AC magnetic fields or transport currents. Therefore, it is essential to develop and investigate robust experimental techniques to investigate the loss mechanism in HTS machines. In this paper, a new and novel experimental technique has been presented to measure AC loss in rotating magnetic field conditions. The loss under rotating magnetic fields is measured and compared by numerical modeling methods, and the results show a strong correlation with the numerical modeling and show the effectiveness of the experimental setup.
Soomro, WA, Guo, Y, Lu, H, Zhu, J, Jin, J & Shen, B 2022, 'Three-Dimensional Numerical Characterization of High-Temperature Superconductor Bulks Subjected to Rotating Magnetic Fields', Energies, vol. 15, no. 9, pp. 3186-3186.
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High-temperature superconductor (HTS) bulks have shown very promising potential for industrial applications due to the ability to trap much higher magnetic fields compared to traditional permanent magnets. In rotating electrical machines, the magnetic field is a combination of alternating and rotating fields. On the contrary, all studies on electromagnetic characterization of HTS presented in the literature so far have only focused on alternating AC magnetic fields and alternating AC loss due to the unavailability of robust experimental techniques and analytical models. This paper presents a numerical investigation on the characterization of HTS bulks subjected to rotating magnetic fields showing AC loss, current density distribution in three-dimensional axes, and trapped field analysis. A three-dimensional numerical model has been developed using H-formulation based on finite element analysis. An HTS cubic sample is magnetized and demagnetized with two-dimensional magnetic flux density vectors rotating in circular orientation around the XOY, XOZ, and YOZ planes.
Sun, X, Feng, L, Zhu, Z, Lei, G, Diao, K, Guo, Y & Zhu, J 2022, 'Optimal Design of Terminal Sliding Mode Controller for Direct Torque Control of SRMs', IEEE Transactions on Transportation Electrification, vol. 8, no. 1, pp. 1445-1453.
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A nonsingular terminal sliding mode controller (NTSMC) based on a direct torque control is presented for a switched reluctance motor (SRM) in this paper. To guarantee dynamic stability, the nonsingular terminal sliding mode based on an improved reaching law is employed to design the speed controller. The torque ripple of the system can be suppressed, and the disturbance caused by uncertainties like load disturbance and parameter perturbation can be suppressed by the proposed NTSMC. Moreover, the gray wolf optimization algorithm is applied to automatically adjust the parameters of the controllers and the value of given flux, thereby acquiring a satisfactory result. The NTSMC is validated by both simulation and experimental results with a six-phase 12/10 SRM. Compared with PI and conventional sliding mode control, NTSMC improves the convergence rate of state and exhibits better performance in torque ripple reduction and anti-disturbance ability. The robustness and dynamic performance of the system can be ensured.
Sun, X, Tang, X, Tian, X, Lei, G, Guo, Y & Zhu, J 2022, 'Sensorless Control With Fault-Tolerant Ability for Switched Reluctance Motors', IEEE Transactions on Energy Conversion, vol. 37, no. 2, pp. 1272-1281.
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Sun, X, Zhang, Y, Lei, G, Guo, Y & Zhu, J 2022, 'An Improved Deadbeat Predictive Stator Flux Control With Reduced-Order Disturbance Observer for In-Wheel PMSMs', IEEE/ASME Transactions on Mechatronics, vol. 27, no. 2, pp. 690-700.
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In this paper, an improved deadbeat predictive stator flux control (DPSFC) based on disturbance observer is proposed to improve the control performance of in-wheel permanent magnet synchronous motors (PMSMs) with parameter mismatch and disturbance. First, the sensitivity of conventional deadbeat predictive current control to the parameter variation, including flux linkage, stator resistance and stator inductance, is analyzed. Then, a reduced-order observer based on additional disturbance state variables is designed to predict the future stator flux and observe the system disturbance caused by parameter mismatch. The proposed DPSFC method is able to enhance the robustness of the drive performance effectively via the compensations of one-step delay and stator voltage. Finally, the performance of the proposed control method is validated by simulations and experiments on a prototype of an in-wheel PMSM drive.
Tabandeh, A & Hossain, MJ 2022, 'Hybrid Scenario-IGDT-Based Congestion Management Considering Uncertain Demand Response Firms and Wind Farms', IEEE Systems Journal, vol. 16, no. 2, pp. 3108-3119.
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Demand response resources (DRRs) have been recently introduced as one of the most economic tools of congestion alleviation in power systems. Nevertheless, the severe uncertainty of multiple DRRs constituting a demand response firm (DRF) is an indispensable issue to be considered for the resilient operation of future power systems. Consequently, the information gap decision theory (IGDT) technique is utilized for addressing the uncertainty of consumers’ participation in demand response programs. This article presents a novel hybrid scenario-IGDT-based framework, designated as SIGDT, for corrective transmission congestion management (CM) in the presence of large-scale uncertain wind farms and DRFs as well as the uncertainty of conventional generating units. A reliability network modeling of a repairable N-component wind turbine (WT) is presented, considering failure and repair rates of turbines’ components, and then the uncertainty of wind farms’ generation is handled using the scenario-based approach. The proposed framework is applied to the IEEE-reliability test system (RTS) system to demonstrate its accuracy and capability. The results discuss the impact of failure and repair rates of WTs components on the proposed SIGDT-based CM and emphasize the application of the proposed framework for decision-makers to ensure the optimal operation of power systems under uncertainties of DRFs, wind farms, and conventional units.
Tabandeh, A, Hossain, MJ & Li, L 2022, 'Integrated multi-stage and multi-zone distribution network expansion planning with renewable energy sources and hydrogen refuelling stations for fuel cell vehicles', Applied Energy, vol. 319, pp. 119242-119242.
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In line with the growing pressures on implementing zero-carbon emission policies and the implementation of hydrogen in the transport sector, energy markets are experiencing inevitable transformation and interactions. Since fuel cell electric vehicles have been attracting considerable attention, the production and supply of renewable hydrogen through hydrogen refuelling stations (HRSs) are of great importance. The growing energy demand, inappropriate siting and sizing of HRSs, and high penetration of distributed renewable energy sources (RESs) make power distribution network planning very challenging. This paper proposes an integrated multi-stage and multi-zone expansion planning framework to coordinate the investment and scheduling of HRSs, wind and solar energy sources, and the distribution network. To model the green HRSs, water electrolysers powered by renewable electricity and storage tanks to locally produce and store hydrogen are suggested. The objective of the proposed problem is to minimise the investment, operation, emissions, and maintenance costs of network's assets, RESs, and HRSs. The RESs expansion scheme comprises the installation of two different groups; the former pertains to distributed renewable sources installed over the network while the latter is integrated into HRSs. Case studies are conducted on 6-node and real Australian 100-node distribution networks. The results show the effectiveness of the proposed model in terms of optimal timing, sizing, location, and operational schedules of HRSs, RESs, and distribution network's assets.
Tashima, T, Takashima, H, Schell, AW, Tran, TT, Aharonovich, I & Takeuchi, S 2022, 'Hybrid device of hexagonal boron nitride nanoflakes with defect centres and a nano-fibre Bragg cavity', Scientific Reports, vol. 12, no. 1, pp. 1-7.
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AbstractSolid-state quantum emitters coupled with a single mode fibre are of interest for photonic and quantum applications. In this context, nanofibre Bragg cavities (NFBCs), which are microcavities fabricated in an optical nanofibre, are promising devices because they can efficiently couple photons emitted from the quantum emitters to the single mode fibre. Recently, we have realized a hybrid device of an NFBC and a single colloidal CdSe/ZnS quantum dot. However, colloidal quantum dots exhibit inherent photo-bleaching. Thus, it is desired to couple an NFBC with hexagonal boron nitride (hBN) as stable quantum emitters. In this work, we realize a hybrid system of an NFBC and ensemble defect centres in hBN nanoflakes. In this experiment, we fabricate NFBCs with a quality factor of 807 and a resonant wavelength at around 573 nm, which matches well with the fluorescent wavelength of the hBN, using helium-focused ion beam (FIB) system. We also develop a manipulation system to place hBN nanoflakes on a cavity region of the NFBCs and realize a hybrid device with an NFBC. By exciting the nanoflakes via an objective lens and collecting the fluorescence through the NFBC, we observe a sharp emission peak at the resonant wavelength of the NFBC.
Torabi, Y, Dadashzadeh, G, Lalbakhsh, A & Oraizi, H 2022, 'High-gain and low-profile dielectric-image-line leaky-wave-antenna for wide-angle beam scanning at sub-THz frequencies', Optics & Laser Technology, vol. 150, pp. 107968-107968.
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Tuan, HD, Nasir, AA, Ngo, HQ, Dutkiewicz, E & Poor, HV 2022, 'Scalable User Rate and Energy-Efficiency Optimization in Cell-Free Massive MIMO', IEEE Transactions on Communications, vol. 70, no. 9, pp. 6050-6065.
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This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users' rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs.
Uddin, MB, Chow, CM, Ling, SH & Su, SW 2022, 'A generalized algorithm for the automatic diagnosis of sleep apnea from per-sample encoding of airflow and oximetry', Physiological Measurement, vol. 43, no. 6, pp. 065004-065004.
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Abstract Objective. Sleep apnea is a common sleep breathing disorder that can significantly decrease sleep quality and have major health consequences. It is diagnosed based on the apnea hypopnea index (AHI). This study explored a novel, generalized algorithm for the automatic diagnosis of sleep apnea employing airflow (AF) and oximetry (SpO2) signals. Approach. Of the 988 polysomnography records, 45 were randomly selected for developing the automatic algorithm and the remainder 943 for validating purposes. The algorithm detects apnea events by a per-sample encoding process applied to the peak excursion of AF signal. Hypopnea events were detected from the per-sample encoding of AF and SpO2 with an adjustment to time lag in SpO2. Total recording time was automatically processed and optimized for computation of total sleep time (TST). Total number of detected events and computed TST were used to estimate AHI. The estimated AHI was validated against the scored data from the Sleep Heart Health Study. Main results. Intraclass correlation coefficient of 0.94 was obtained between estimated and scored AHIs. The diagnostic accuracies were 93.5%, 92.4%, and 96.6% for AHI cut-off values of ≥5, ≥15, and ≥30 respectively. The overall accuracy for the combined severity categories (normal, mild, moderate, and severe) and kappa were 83.4% and 0.77 respectively. Significance. This new automatic technique was found to be superior to the other existing methods and can be applied to any portable sleep devices especially for home sleep apnea tests.
Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2022, 'Low-profile dual-band pixelated defected ground antenna for multistandard IoT devices', Scientific Reports, vol. 12, no. 1, p. 11479.
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AbstractA low-profile dual-band pixelated defected ground antenna has been proposed at 3.5 GHz and 5.8 GHz bands. This work presents a flexible design guide for achieving single-band and dual-band antenna using pixelated defected ground (PDG). The unique pixelated defected ground has been designed using the binary particle swarm optimization (BPSO) algorithm. Computer Simulation Technology Microwave Studio incorporated with Matlab has been utilized in the antenna design process. The PDG configuration provides freedom of exploration to achieve the desired antenna performance. Compact antenna design can be achieved by making the best use of designated design space on the defected ground (DG) plane. Further, a V-shaped transfer function based on BPSO with fast convergence allows us to efficiently implement the PDG technique. In the design procedure, pixelization is applied to a small rectangular region of the ground plane. The square pixels on the designated defected ground area of the antenna have been formed using a binary bit string, consisting of 512 bits taken during each iteration of the algorithm. The PDG method is concerned with the shape of the DG and does not rely on the geometrical dimension analysis used in traditional defected ground antennas. Initially, three single band antennas have been designed at 3.5 GHz, 5.2 GHz and 5.8 GHz using PDG technique. Finally, same PDG area has been used to design a dual-band antenna at 3.5 GHz and 5.8 GHz. The proposed antenna exhibits almost omnidirectional radiation performance with nearly 90% efficiency. It also shows dual radiation pattern property with similar patterns having different polarizations at each operational band. The antenna is fabricated on a ROGERS RO4003 substrate with 1.52 mm thickness. Reflection coefficient and radiation patterns are measured to validate its performance. The simulated and measured results of the antenna are closely correlated. The propos...
Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J, Esselle, KP & Shariati, N 2022, 'A Review on Antenna Technologies for Ambient RF Energy Harvesting and Wireless Power Transfer: Designs, Challenges and Applications', IEEE Access, vol. 10, pp. 17231-17267.
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Radio frequency energy harvesting (RFEH) and wireless power transmission (WPT) are two emerging alternative energy technologies that have the potential to offer wireless energy delivery in the future. One of the key components of RFEH or WPT system is the receiving antenna. The receiving antenna's performance has a considerable impact on the power delivery capability of an RFEH or WPT system. This paper provides a well-rounded review of recent advancements of receiving antennas for RFEH and WPT. Antennas discussed in this paper are categorized as low-profile antennas, multi-band antennas, circularly polarized antennas, and array antennas. A number of contemporary antennas from each category are presented, compared, and discussed with particular emphasis on design approach and performance. Current design and fabrication challenges, future development, open research issues of the antennas and visions for RFEH and WPT are also discussed in this review.
Uzair, M, Eskandari, M, Li, L & Zhu, J 2022, 'Machine Learning Based Protection Scheme for Low Voltage AC Microgrids', Energies, vol. 15, no. 24, pp. 9397-9397.
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The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional methods inefficient, and a new paradigm in protection schemes is needed for IIDGs-dominated MGs. In this paper, a machine-learning (ML)-based protection technique is developed for IIDG-based AC MGs by extracting unique and novel features for detecting and classifying symmetrical and unsymmetrical faults. Different signals, namely, 400 samples, for wide variations in operating conditions of an MG are obtained through electromagnetic transient simulations in DIgSILENT PowerFactory. After retrieving and pre-processing the signals, 10 different feature extraction techniques, including new peaks metric and max factor, are applied to obtain 100 features. They are ranked using the Kruskal–Wallis H-Test to identify the best performing features, apart from estimating predictor importance for ensemble ML classification. The top 18 features are used as input to train 35 classification learners. Random Forest (RF) outperformed all other ML classifiers for fault detection and fault type classification with faulted phase identification. Compared to previous methods, the results show better performance of the proposed method.
Van Huynh, N, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2022, 'Joint Coding and Scheduling Optimization for Distributed Learning Over Wireless Edge Networks', IEEE Journal on Selected Areas in Communications, vol. 40, no. 2, pp. 484-498.
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Unlike theoretical analysis of distributed learning (DL) in the literature, DL over wireless edge networks faces the inherent dynamics/uncertainty of wireless connections and edge nodes, making DL less efficient or even inapplicable under the highly dynamic wireless edge networks. This article addresses these problems by leveraging recent advances in coded computing and the deep dueling neural network architecture. By introducing coded structures/redundancy, a distributed learning task can be completed without waiting for straggling nodes. Unlike conventional coded computing that only optimizes the code structure, coded distributed learning over the wireless edge also requires to optimize the selection/scheduling of wireless edge nodes with heterogeneous connections, computing capability, and straggling effects. However, even neglecting the aforementioned dynamics/uncertainty, the resulting joint optimization of coding and scheduling to minimize the distributed learning time turns out to be NP-hard. To tackle this and to account for the dynamics and uncertainty of wireless connections and edge nodes, we reformulate the problem as a Markov Decision Process and design a novel deep reinforcement learning algorithm that employs the deep dueling neural network architecture to find the jointly optimal coding scheme and the best set of edge nodes for different learning tasks without explicit information about the wireless environment and edge nodes’ straggling parameters. Simulations show that the proposed framework reduces the average learning delay in wireless edge computing up to 66% compared with other DL approaches. The jointly optimal framework in this article is also applicable to any distributed learning scheme with heterogeneous and uncertain computing nodes.
Van Huynh, N, Nguyen, DN, Hoang, DT, Vu, TX, Dutkiewicz, E & Chatzinotas, S 2022, 'Defeating Super-Reactive Jammers With Deception Strategy: Modeling, Signal Detection, and Performance Analysis', IEEE Transactions on Wireless Communications, vol. 21, no. 9, pp. 7374-7390.
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This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and is equipped with the self-interference suppression capability to simultaneously attack and listen to the transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus, we introduce a smart deception mechanism to attract the jammer to continuously attack the channel and then leverage jamming signals to transmit data based on the ambient backscatter communication technology. To detect the backscattered signals, the maximum likelihood detector can be adopted. However, this method is notorious for its high computational complexity and requires the model of the current propagation environment as well as channel state information. Hence, we propose a deep learning-based detector that can dynamically adapt to any channels and noise distributions. With a Long Short-Term Memory network, our detector can learn the received signals’ dependencies to achieve a performance close to that of the optimal maximum likelihood detector. Through simulation and theoretical results, we demonstrate that with our approaches, the more power the jammer uses to attack the channel, the better bit error rate performance the transmitter can achieve.
Van Nguyen, L, Phung, MD & Ha, QP 2022, 'Game Theory-Based Optimal Cooperative Path Planning for Multiple UAVs', IEEE Access, vol. 10, pp. 108034-108045.
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Veitch, D, Mani, SK, Cao, Y & Barford, P 2022, 'iHorology: Lowering the Barrier to Microsecond-Level Internet Time', IEEE/ACM Transactions on Networking, vol. 30, no. 6, pp. 2544-2558.
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High accuracy, synchronized clocks are essential to a growing number of Internet applications. Standard protocols and their associated server infrastructure typically enable client clocks to synchronize to the order of tens of milliseconds. We address one of the key challenges to high precision Internet timekeeping - the intrinsic contribution to clock error of underlying path asymmetry between client and time server, a fundamental barrier to microsecond level accuracy. We first exploit results of a unique measurement study to reliably quantify asymmetry by taking routing changes into account for the first time, and then to infer the impacts on timing. We then describe three approaches to addressing the path asymmetry problem: LBBE, SBBE and K-SBBE, each based on timestamp exchange with multiple servers, with the goal of tightening bounds on asymmetry for each client. We explore their capabilities and limitations through simulation and model-based argument. We show that substantial improvements are possible, and discuss whether, and how, the goal of microsecond accuracy might be attained.
Vosoughi Kurdkandi, N, Husev, O, Matiushkin, O, Vinnikov, D, Siwakoti, YP & Lee, SS 2022, 'Novel Family of Flying Inductor-Based Single-Stage Buck–Boost Inverters', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 5, pp. 6020-6032.
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Single-phase buck-boost inverters are very popular nowadays due to the wide input voltage range regulation capability. This feature is mostly demanded by photovoltaic (PV), fuel cell, or battery storage applications. In this study, four new structures from the family of flying inductor (FI)-based inverters are presented. Performance in a wide range of input dc voltages and the ability to increase the voltage in single-power processing stage is one of the features of the proposed structures. Three of the four structures are common ground and completely bypass the parasitic capacitors which make them attractive for PV application. Nonuse of electrolytic capacitors in the proposed structures helps to increase the life of the converter and also there is no unpleasant inrush current of charging electrolytic capacitors in these structures. The operation modes are fully explained, as well as the theoretical analysis and design of passive elements have been done. Finally, the simulation and 2 kW laboratory circuit for the proposed Type-III structure are performed and the results are investigated.
Vu, L, Cao, VL, Nguyen, QU, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2022, 'Learning Latent Representation for IoT Anomaly Detection', IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3769-3782.
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Wang, L, Huang, W, Zhang, M, Pan, S, Chang, X & Su, SW 2022, 'Pruning graph neural networks by evaluating edge properties', Knowledge-Based Systems, vol. 256, pp. 109847-109847.
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The emergence of larger and deeper graph neural networks (GNNs) makes their training and inference increasingly expensive. Existing GNN pruning methods simultaneously prune the graph adjacency matrix and the model weights on a pretrained neural network by directly leveraging the lottery-ticket hypothesis, but the benefits of such methods are mainly via weight pruning, and methods based on saliency metrics struggle to outperform random pruning when pruning only the graph adjacency matrix. This motivates us to use different scoring standards for graph edges and network weights during GNN pruning. Thus, rather than measuring the importance of graph edges based on saliency metrics, we formulate the performance of GNNs mathematically with respect to the properties of their edges, elucidating how the performance drop can be avoided by pruning negative edges and nonbridges. This leads to our simple but effective two-step method for GNN pruning, leveraging the saliency metrics for the network pruning while sparsifying the graph with preservation of the loss performance. Experimental results show the effectiveness and efficiency of the proposed method on both small-scale graph datasets (Cora, Citeseer, and PubMed) and a large-scale dataset (Ogbn-ArXiv), where our method saves up to 98% of floating-point operations per second (FLOPs) on the small graphs and 94% of FLOPs on the large one, with no significant drop in accuracy.
Wang, L, Yang, Y, Gao, F, Teng, S, Tan, Z-G, Zhang, X, Lou, J & Deng, L 2022, 'Terahertz reconfigurable dielectric metasurface hybridized with vanadium dioxide for two-dimensional multichannel multiplexing', Frontiers in Physics, vol. 10.
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The metasurface hybridized with vanadium dioxide (VO2) can be dynamically tuned, which has attracted enormous attention in recent years and orbital angular momentum (OAM) multiplexing based on metasurfaces has shown promising prospects in terahertz communications. However, existing research on VO2 metasurface focuses on the metallic metasurface. The dielectric VO2 metasurface used for OAM multiplexing is rarely reported to the present. This paper proposed a terahertz reconfigurable dielectric metasurface hybridized with VO2 for two-dimensional multichannel multiplexing combing with spatial and frequency domains. The metasurface works in both reflection and transmission modes and simultaneously the polarization control and operating frequency band regulation can be realized by switching the VO2 from the metallic state to the insulator state. For the reflective or transmissive metasurface, when 4×M-channel (M is a positive integer) off-axis plane waves are incident on the metasurface, the co-polarization reflected or cross-polarization transmitted waves are transformed into 4×M-channel orthogonal on-axis beams with topological or frequency orthogonality. A metasurface composed of 14 × 14 unit cells is designed for verification. The simulated result shows that two-dimensional 12-channel multiplexing combing with OAM and frequency by the designed metasurface can be realized on the reflection and transmission modes in two different frequency bands. The proposed metasurface has great potential in terahertz communications.
Wang, N, Liu, ZX, Ding, C, Zhang, J-N, Sui, G-R, Jia, H-Z & Gao, X-M 2022, 'High Efficiency Thermoelectric Temperature Control System With Improved Proportional Integral Differential Algorithm Using Energy Feedback Technique', IEEE Transactions on Industrial Electronics, vol. 69, no. 5, pp. 5225-5234.
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This paper proposes an efficient thermoelectric temperature control system based on an improved proportional integral differential algorithm in which energy feedback technology is used to enhance thermoelectric cooling. In the proposed power management system, two groups of batteries are efficiently and alternatingly charged and discharged such that the information of the circuit can be monitored in real time. The PID algorithm is improved by using the idea of a state machine to control the thermoelectric coolers through an H-bridge circuit with pulse-width modulation. Finally, the energy feedback circuit combined with improved synchronous switching technology is designed to recycle the energy to drive the sensor. By inputting current of 3.1A, a wide range of temperature control from 1.437 to 60.187 was implemented. While targeting a temperature of 10 at an ambient temperature of 22, the proposed temperature control system had a control time of 30.5s, compared with 287s when using the conventional method, with an accuracy of 0.1, and an error of only 0.35. The results confirm that electric energy at a peak voltage of 1.2V and current of 24A can be recovered. The proposed energy feedback system can thus improve the efficiency of energy utilization of TEC from peripheral circuits.
Wang, N, Zhang, J-N, Ni, H, Jia, H-Z & Ding, C 2022, 'Improved MPPT System Based on FTSMC for Thermoelectric Generator Array Under Dynamic Temperature and Impedance', IEEE Transactions on Industrial Electronics, vol. 69, no. 10, pp. 10715-10723.
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The thermoelectric generator (TEG) is typically used as a clean power supply to harvest waste heat energy in applications involving a large thermal gradient, such as industrial heat removal and power electronic equipment systems. However, it is often difficult to achieve the optimal output power in the loop of the array system of the TEG owing to different output loads. This study proposes an improved fast terminal sliding-mode variable-structure control algorithm (FTSMC) to maximize power point tracking. The variable-structure sliding-mode control function used in the nonlinear sliding-mode surface of the algorithm allows us to obtain the characteristics of global stability that can enable it to converge to the sliding-mode surface at any position to reduce chatter. Digital modeling and simulation as well as experimental developmental Field Programmable Gate Array (FPGA) platforms were built to verify the effectiveness of the proposed FTSMC. It can attain the nonlinear sliding mode more quickly than the traditional sliding-mode algorithm. The results of experiments show that it can reach a tracking response speed of 0.08 s and a maximum conversion efficiency of 99.91%. The work here provides a new way for the efficient use of the TEG array for waste heat recovery.
Wang, S, Lu, J, Li, B, Liu, C, Wang, Y, Lei, G, Guo, Y & Zhu, J 2022, 'Design and analysis of mechanical flux-weakening device of axial flux permanent magnet machines', Journal of Power Electronics, vol. 22, no. 4, pp. 653-663.
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Due to the low inductance of an axial flux permanent magnet machine (AFPMM), the constant power speed regulation range is small. A new mechanical flux-weakening method for single-rotor single-stator AFPMMs is proposed in this paper. By installing a mechanical flux-weakening device on one side of the stator and rotating it certain angle, the speed regulation of the flux-weakening can be realized. The device is simple in structure, easy to operate, and can be operated in the process of machine operation. The validity of the device is verified by applying it to a machine. Finite-element software is used to calculate and analyze the performances of two machines with the device.
Wang, S, Tao, J, Qiu, X & Burnett, IS 2022, 'A natural ventilation window for transformer noise control based on coiled-up silencers consisting of coupled tubes', Applied Acoustics, vol. 192, pp. 108744-108744.
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For transformers located inside rooms, openings in the walls are often required for ventilation and heat dissipation with the result that transformer noise radiates to the outside. A soundproof window for indoor transformers is proposed in this paper, which provides both air circulation and noise reduction simultaneously. A silencer consisting of two coupled tubes with different cross sections is designed and coiled up in space to minimize the thickness of the structure. With carefully chosen parameters, just one such silencer can achieve sound attenuation at up to 4 frequencies. With a combination of a staggered window and specially designed silencers, effective noise reduction is obtained at 100 Hz, 200 Hz, 300 Hz, 400 Hz and 500 Hz, where harmonic components contribute the most to the transformer noise. The experimental results with a 1:4 scale down model show the feasibility of the proposed design.
Wang, S, Tao, J, Qiu, X & Burnett, IS 2022, 'Improving the performance of an active staggered window with multiple resonant absorbers', The Journal of the Acoustical Society of America, vol. 151, no. 3, pp. 1661-1671.
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The active noise control (ANC) technique has been applied in staggered windows to improve the noise reduction at low frequencies. The control performance of such a system deteriorates significantly at some frequencies where the secondary source cannot radiate effectively due to the reflection at the boundaries of the staggered window. A resonant absorber consisting of a perforated panel and coiled up tubes is proposed to solve the problem. By designing a combination of different absorbers, a proper sound absorption coefficient is achieved around the ineffective frequency. Numerical simulations show that the active sound power reduction increases by 13.5 dB at the frequency with the absorbers attached on one end of the staggered window, and the overall sound power reduction between 100 and 500 Hz increases from 25.9 to 31.2 dB. Attaching the sound absorbers elsewhere in the upstream of the secondary source, for example, on the side walls of the duct also works. The active sound power reduction at 435 Hz increases by 6.3 dB after attaching the absorbers in the experiments, and the noise reduction increment at the evaluation point is 13.6 dB, which agrees with simulation results and demonstrates the feasibility of the proposed sound absorbers.
Wang, X, Fei, Z, Zhang, JA & Huang, J 2022, 'Sensing-Assisted Secure Uplink Communications With Full-Duplex Base Station', IEEE Communications Letters, vol. 26, no. 2, pp. 249-253.
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This letter proposes a sensing-assisted uplink communications framework between a single-antenna user and a full-duplex (FD) base station (BS) against an aerial eavesdropper (AE). To protect the information from being overheard, the BS transmits radar signals to localize and jam AE while receiving uplink signals. The radar signal transmission is divided into detection phase and tracking phase. In detection phase, the BS synthesizes a wide beam to localize the AE under the secrecy rate constraint; while in tracking phase, the BS maximizes the signal-to-interference-plus-noise ratio (SINR) of its received signals under the AE’s SINR constraint while guaranteeing a predefined radar echo signal signal-to-noise ratio (SNR) level. To deal with the self interference, we jointly optimize the radar waveform and receive beamforming vector. An alternating optimization algorithm and a successive convex approximation (SCA) based algorithm are proposed to solve the two formulated problems, respectively. Simulation results verify the effectiveness of the proposed algorithms. They also show that the secrecy rate can be significantly improved with the assistance of BS sensing.
Wang, X, Fei, Z, Zhang, JA & Xu, J 2022, 'Partially-Connected Hybrid Beamforming Design for Integrated Sensing and Communication Systems', IEEE Transactions on Communications, vol. 70, no. 10, pp. 6648-6660.
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Beamforming design is an important technique for enhancing the performance of integrated sensing and communication (ISAC) systems. However, related research based on the hybrid analog-digital (HAD) architecture is still limited. In this paper, we investigate the partially-connected hybrid beamforming design for multi-user ISAC systems. Instead of the commonly used beampattern related metric, the Cramér-Rao bound (CRB) is employed as the sensing performance metric for direction of arrival (DOA) estimation. We aim to minimize the CRB while satisfying the signal-to-interference-plus-noise ratio (SINR) constraints for individual communication users by jointly optimizing the digital and analog beamformers. Subsequently, we propose an alternating optimization based framework, which is significantly different from the conventional methods based on the approximation of the optimal fully-digital beamformer with a hybrid one. We also consider an alternative formulation of optimizing the SINR of radar echo signals. Based on optimal receive beamformer design, we transform the SINR based joint transmitter and receiver optimization problem to a series of problems sharing a similar form with the CRB based transmitter optimization problem, which can be efficiently solved via the proposed algorithm. Simulation results show that the proposed designs provide significant performance gains in DOA estimation over the existing beampattern approximation based design.
Wang, X, Qin, P-Y, Tuyen Le, A, Zhang, H, Jin, R & Guo, YJ 2022, 'Beam Scanning Transmitarray Employing Reconfigurable Dual-Layer Huygens Element', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7491-7500.
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A Ku-band electronic 2-dimensional (2-D) beam-scanning transmitarray employing a new reconfigurable dual-layer Huygens element is developed in this article. The Huygens element consists of two metallic crosses printed on two layers of a dielectric substrate, which enables a near nonreflection Huygens resonance. A 1 bit phase compensation with low transmission loss is realized by controlling two p-i-n diodes on the element. Compared with many other reconfigurable transmitarray elements using multilayer structures with metallic vias, the proposed reconfigurable Huygens element has a much simpler configuration with a simpler biasing network, and it is not affected by multilayer alignment errors. This particularly facilitates large aperture array development at higher frequencies. To validate the design concept, an electronically reconfigurable transmitarray with the proposed element is fabricated at 13 GHz. Good agreement between the measured and simulated results is found, showing 2-D scanning beams within ±50° in the E-plane and ±40° in the H-plane with a maximum realized gain of 18.4 dBi.
Wang, X, Yu, G, Liu, RP, Zhang, J, Wu, Q, Su, SW, He, Y, Zhang, Z, Yu, L, Liu, T, Zhang, W, Loneragan, P, Dutkiewicz, E, Poole, E & Paton, N 2022, 'Blockchain-Enabled Fish Provenance and Quality Tracking System', IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8130-8142.
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Wang, Y, Zhao, M, Li, S, Yuan, X & Ni, W 2022, 'Dispersed Pixel Perturbation-Based Imperceptible Backdoor Trigger for Image Classifier Models', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 3091-3106.
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Typical deep neural network (DNN) backdoor attacks are based on triggers embedded in inputs. Existing imperceptible triggers are computationally expensive or low in attack success. In this paper, we propose a new backdoor trigger, which is easy to generate, imperceptible, and highly effective. The new trigger is a uniformly randomly generated three-dimensional (3D) binary pattern that can be horizontally and/or vertically repeated and mirrored and superposed onto three-channel images for training a backdoored DNN model. Dispersed throughout an image, the new trigger produces weak perturbation to individual pixels, but collectively holds a strong recognizable pattern to train and activate the backdoor of the DNN. We also analytically reveal that the trigger is increasingly effective with the improving resolution of the images. Experiments are conducted using the ResNet-18 and MLP models on the MNIST, CIFAR-10, and BTSR datasets. In terms of imperceptibility, the new trigger outperforms existing triggers, such as BadNets, Trojaned NN, and Hidden Backdoor, by over an order of magnitude. The new trigger achieves an almost 100% attack success rate, only reduces the classification accuracy by less than 0.7%-2.4%, and invalidates the state-of-the-art defense techniques.
Wang, Z, Lv, T, Zeng, J & Ni, W 2022, 'Placement and Resource Allocation of Wireless-Powered Multiantenna UAV for Energy-Efficient Multiuser NOMA', IEEE Transactions on Wireless Communications, vol. 21, no. 10, pp. 8757-8771.
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Wang, Z, Zhang, JA, Xiao, F & Xu, M 2022, 'Accurate AoA Estimation for RFID Tag Array With Mutual Coupling', IEEE Internet of Things Journal, vol. 9, no. 15, pp. 12954-12972.
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Angle-of-Arrival (AoA) estimation is an important problem in passive radio-frequency identification (RFID) systems. Affixing an RFID tag array to an object enables to acquire its orientation information. However, the electromagnetic interaction between the tags can induce mutual coupling interference, distorting the RFID fingerprint measurements used for AoA estimation. Moreover, RFID reader modes with radio-frequency (RF) noise-tolerant Miller encoding can induce π-radians phase jump. In this article, we propose a scheme called RF-Mirror that can resolve the mutual coupling and phase jump problems and achieve accurate AoA estimation for an array with two or more tags. First, we characterize the impact of mutual coupling on a tag's signal fingerprint and develop novel RSSI/phase-distance models. We then develop new experimental methods and signal processing techniques to verify the effectiveness of the proposed models. Based on the validated models, we develop new AoA estimation algorithms for tag arrays that deal with the mutual coupling effect explicitly. We provide extensive experimental results, which demonstrate that RF-Mirror can achieve significantly improved performance compared to baseline schemes, with median AoA estimation errors of 11.65° and 6.29° for two- and four-tag arrays, respectively.
Wen, S, Li, D, Liu, Y, Chen, C, Wang, F, Zhou, J, Bao, G, Zhang, L & Jin, D 2022, 'Power-Dependent Optimal Concentrations of Tm3+ and Yb3+ in Upconversion Nanoparticles', The Journal of Physical Chemistry Letters, vol. 13, no. 23, pp. 5316-5323.
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Lanthanide-doped upconversion nanoparticles (UCNPs) have enabled a broad range of emerging nanophotonics and biophotonics applications. Here, we provide a quantitative guide to the optimum concentrations of Yb3+ sensitizer and Tm3+ emitter ions, highly dependent on the excitation power densities. To achieve this, we fabricate the inert-core@active-shell@inert-shell architecture to sandwich the same volume of the optically active section. Our results show that highly doped UCNPs enable an approximately 18-fold enhancement in brightness over that of conventional ones. Increasing the Tm3+ concentration improves the brightness by 6 times and increases the NIR/blue ratio by 11 times, while the increase of Yb3+ concentration enhances the brightness by 3 times and only slightly affects the NIR/blue ratio. Moreover, the optimal doping concentration of Tm3+ varies from 2% to 16%, which is highly dependent on the excitation power density ranging from 102 to 107 W/cm2. This work provides a guideline for designing bright UCNPs under different excitation conditions.
Wen, Y, Qin, P-Y, Wei, G-M & Ziolkowski, RW 2022, 'Circular Array of Endfire Yagi-Uda Monopoles With a Full 360° Azimuthal Beam Scanning', IEEE Transactions on Antennas and Propagation, vol. 70, no. 7, pp. 6042-6047.
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Wisanmongkol, J, Taparugssanagorn, A, Tran, LC, Le, AT, Huang, X, Ritz, C, Dutkiewicz, E & Phung, SL 2022, 'An ensemble approach to deep‐learning‐based wireless indoor localization', IET Wireless Sensor Systems, vol. 12, no. 2, pp. 33-55.
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The authors investigate the use of deep learning in wireless indoor localization to address the shortcomings of the existing range-based (e.g. trilateration and triangulation) and range-free (e.g. fingerprinting) localization. Instead of relying on geometric models and hand-picked features, deep learning can automatically extract the relationship between the observed data and the target's location. Nevertheless, a deep neural network (DNN) model providing a satisfactory accuracy might perform differently when it is retrained in the deployment. To mitigate this issue, the authors propose an ensemble method where DNN models obtained from multiple training sessions are combined to locate the target. In the authors' evaluation, several DNN models are trained on the data, which consists of the received signal strength (RSS), angle of arrival (AOA), and channel state information (CSI), used in the existing hybrid RSS/AOA and RSS/CSI fingerprinting, and their root-mean-square error (RMSE) values are compared accordingly. The results show that the proposed method achieves the lower RMSE than the existing methods, and the RMSE can be lowered by up to 1.47 m compared with the ones obtained from a single model. Moreover, for some DNN models, the RMSE values are even lower than the minimum RMSE obtained by their single-model counterparts.
Wong, HX & Lee, JE-Y 2022, 'A Silicon Migration Model Incorporating Anisotropic Surface Energy and Non-Uniform Diffusivity', Journal of Microelectromechanical Systems, vol. 31, no. 6, pp. 943-950.
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Wong, SYK, Chan, JSK, Azizi, L & Xu, RYD 2022, 'Time‐varying neural network for stock return prediction', Intelligent Systems in Accounting, Finance and Management, vol. 29, no. 1, pp. 3-18.
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AbstractWe consider the problem of neural network training in a time‐varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often time varying. We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics. We compare the proposed algorithm to current approaches on predicting monthly US stock returns and show its superiority. We also show that prominent factors (such as the size and momentum effects) and industry indicators exhibit time‐varying predictive power on stock returns. We find that during market distress, industry indicators experience an increase in importance at the expense of firm level features. This indicates that industries play a role in explaining stock returns during periods of heightened risk.
Wu, K, Zhang, JA & Guo, YJ 2022, 'Fast and Accurate Linear Fitting for an Incompletely Sampled Gaussian Function With a Long Tail [Tips & Tricks]', IEEE Signal Processing Magazine, vol. 39, no. 6, pp. 76-84.
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Fitting experiment data onto a curve is a common signal processing technique to extract data features and establish the relationship between variables. Often, we expect the curve to comply with some analytical function and then turn data fitting into estimating the unknown parameters of a function. Among analytical functions for data fitting, the Gaussian function is the most widely used one due to its extensive applications in numerous science and engineering fields. To name just a few, the Gaussian function is highly popular in statistical signal processing and analysis, thanks to the central limit theorem [1], and the Gaussian function frequently appears in the quantum harmonic oscillator, quantum field theory, optics, lasers, and many other theories and models in physics [2]; moreover, the Gaussian function is widely applied in chemistry for depicting molecular orbitals, in computer science for imaging processing, and in artificial intelligence for defining neural networks.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Frequency-Hopping MIMO Radar-Based Communications: An Overview', IEEE Aerospace and Electronic Systems Magazine, vol. 37, no. 4, pp. 42-54.
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Abstract—Enabled by the advancement in radio frequency technologies, the convergence of radar and communication systems becomes increasingly promising and is envisioned as a key feature of future sixth-generation networks. Recently, the frequency-hopping (FH) MIMO radar is introduced to underlay dual-function radar-communication (DFRC) systems. Superior to many previous radar-centric DFRC designs, the symbol rate of FH-MIMO radar-based DFRC (FH-MIMO DFRC) can exceed the radar pulse repetition frequency. However, many practical issues, particularly those crucial to achieving effective data communications, are unexplored or unsolved. To promote the awareness and general understanding of the novel DFRC, this article is devoted to providing a timely introduction of FH-MIMO DFRC. We comprehensively review many essential aspects of the novel DFRC: channel/signal models, signaling strategies, modulation/demodulation processing and channel estimation methods, to name a few. We also highlight major remaining issues in FHMIMO DFRC and suggest potential solutions to shed light on future research directions.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Integrating Low-Complexity and Flexible Sensing Into Communication Systems', IEEE Journal on Selected Areas in Communications, vol. 40, no. 6, pp. 1873-1889.
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Integrating sensing into standardized communication systems can potentially benefit many consumer applications that require both radio frequency functions. However, without an effective sensing method, such integration may not achieve the expected gains of cost and energy efficiency. Existing sensing methods, which use communication payload signals, either have limited sensing performance or suffer from high complexity. In this paper, we develop a novel and flexible sensing framework which has a complexity only dominated by a Fourier transform and also provides the flexibility in adapting to different sensing needs. We propose to segment a whole block of echo signal evenly into sub-blocks; adjacent ones are allowed to overlap. We design a virtual cyclic prefix (VCP) for each sub-block that allows us to employ two common ways of removing communication data symbols and generate two types of range-Doppler maps (RDMs) for sensing. We perform a comprehensive analysis of the signal components in the RDMs, proving that their interference-plus-noise (IN) terms are approximately Gaussian distributed. The statistical properties of the distributions are derived, which leads to the analytical comparisons between the two RDMs as well as between the prior and our sensing methods. Moreover, the impact of the lengths of sub-block, VCP and overlapping signal on sensing performance is analyzed. Criteria for designing these lengths for better sensing performance are also provided. Extensive simulations validate the superiority of the proposed sensing framework over prior methods in terms of signal-to-IN ratios in RDMs, detecting performance and flexibility.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Integrating Secure Communications Into Frequency Hopping MIMO Radar With Improved Data Rate', IEEE Transactions on Wireless Communications, vol. 21, no. 7, pp. 5392-5405.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Joint Communications and Sensing Employing Multi- or Single-Carrier OFDM Communication Signals: A Tutorial on Sensing Methods, Recent Progress and a Novel Design', Sensors, vol. 22, no. 4, pp. 1613-1613.
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Joint communications and sensing (JCAS) has recently attracted extensive attention due to its potential in substantially improving the cost, energy and spectral efficiency of Internet of Things (IoT) systems that need both radio frequency functions. Given the wide applicability of orthogonal frequency division multiplexing (OFDM) in modern communications, OFDM sensing has become one of the major research topics of JCAS. To raise the awareness of some critical yet long-overlooked issues that restrict the OFDM sensing capability, a comprehensive overview of OFDM sensing is provided first in this paper, and then a tutorial on the issues is presented. Moreover, some recent research efforts for addressing the issues are reviewed, with interesting designs and results highlighted. In addition, the redundancy in OFDM sensing signals is unveiled, on which, a novel method is based and developed in order to remove the redundancy by introducing efficient signal decimation. Corroborated by analysis and simulation results, the new method further reduces the sensing complexity over one of the most efficient methods to date, with a minimal impact on the sensing performance.
Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Removing False Targets for Cyclic Prefixed OFDM Sensing with Extended Ranging', Sensors, vol. 22, no. 22, pp. 9015-9015.
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Employing a cyclic prefixed OFDM (CP-OFDM) communication waveform for sensing has attracted extensive attention in vehicular integrated sensing and communications (ISAC). A unified sensing framework was developed recently, enabling CP-OFDM sensing to surpass the conventional limits imposed by underlying communications. However, a false target issue still remains unsolved. In this paper, we investigate and solve this issue. Specifically, we unveil that false targets are caused by periodic cyclic prefixes (CPs) in CP-OFDM waveforms. We also derive the relation between the locations of false and true targets, and other features, e.g., strength, of false targets. Moreover, we develop an effective solution to remove false targets. Simulations are provided to confirm the validity of our analysis and the effectiveness of the proposed solution. In particular, our design can reduce the false alarm rate caused by false targets by over 50% compared with the prior art.
Wu, K, Zhang, JA, Huang, X, Guo, YJ, Nguyen, DN, Kekirigoda, A & Hui, K-P 2022, 'Analog-Domain Suppression of Strong Interference Using Hybrid Antenna Array', Sensors, vol. 22, no. 6, pp. 2417-2417.
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The proliferation of wireless applications, the ever-increasing spectrum crowdedness, as well as cell densification makes the issue of interference increasingly severe in many emerging wireless applications. Most interference management/mitigation methods in the literature are problem-specific and require some cooperation/coordination between different radio frequency systems. Aiming to seek a more versatile solution to counteracting strong interference, we resort to the hybrid array of analog subarrays and suppress interference in the analog domain so as to greatly reduce the required quantization bits of the analog-to-digital converters and their power consumption. To this end, we design a real-time algorithm to steer nulls towards the interference directions and maintain flat in non-interference directions, solely using constant-modulus phase shifters. To ensure sufficient null depth for interference suppression, we also develop a two-stage method for accurately estimating interference directions. The proposed solution can be applicable to most (if not all) wireless systems as neither training/reference signal nor cooperation/coordination is required. Extensive simulations show that more than 65 dB of suppression can be achieved for 3 spatially resolvable interference signals yet with random directions.
Wu, Z, Khalilpour, K & Hämäläinen, RP 2022, 'A decision support tool for multi-attribute evaluation of demand-side commercial battery storage products', Sustainable Energy Technologies and Assessments, vol. 50, pp. 101723-101723.
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With the diversification of commercial energy storage technologies, choosing a suitable technology is becoming a complex decision-making process. The complexity is rooted in the many decision criteria such as technology, brand reputation, energy capacity, volume, weight, aging, and warranty among many others. As such, for non-expert users, particularly small households or enterprises, the act of energy storage adoption is becoming growingly cumbersome. To address this problem, this paper introduces a decision support tool for the evaluation of commercial (small-scale) energy storage products. It then identifies the most suitable option(s) based on the users' preferences. For the reasons elaborated in the paper, nine multi-criteria decision-making (MCDM) methodologies have been employed. Altogether, 19 attributes are identified for the evaluation of (battery) energy storage technologies. The decision support tool is developed in the Matlab environment and includes a graphical user interface for easier interaction of non-expert users. For the demonstration, three scenario cases have been studied for users with different preferences. The ranking results clearly show the marked impact of users preferences on the recommended energy storage technologies. This implies that a tool like this can help small users in the selection of their right technology and avoid resource loss due to inappropriate technology selection, which can be neither economical nor sustainable.
Xi, Y, Jia, W, Miao, Q, Liu, X, Fan, X & Li, H 2022, 'FiFoNet: Fine-Grained Target Focusing Network for Object Detection in UAV Images', Remote Sensing, vol. 14, no. 16, pp. 3919-3919.
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Detecting objects from images captured by Unmanned Aerial Vehicles (UAVs) is a highly demanding task. It is also considered a very challenging task due to the typically cluttered background and diverse dimensions of the foreground targets, especially small object areas that contain only very limited information. Multi-scale representation learning presents a remarkable approach to recognizing small objects. However, this strategy ignores the combination of the sub-parts in an object and also suffers from the background interference in the feature fusion process. To this end, we propose a Fine-grained Target Focusing Network (FiFoNet) which can effectively select a combination of multi-scale features for an object and block background interference, which further revitalizes the differentiability of the multi-scale feature representation. Furthermore, we propose a Global–Local Context Collector (GLCC) to extract global and local contextual information and enhance low-quality representations of small objects. We evaluate the performance of the proposed FiFoNet on the challenging task of object detection in UAV images. A comparison of the experiment results on three datasets, namely VisDrone2019, UAVDT, and our VisDrone_Foggy, demonstrates the effectiveness of FiFoNet, which outperforms the ten baseline and state-of-the-art models with remarkable performance improvements. When deployed on an edge device NVIDIA JETSON XAVIER NX, our FiFoNet only takes about 80 milliseconds to process an drone-captured image.
Xi, Y, Jia, W, Miao, Q, Liu, X, Fan, X & Lou, J 2022, 'DyCC-Net: Dynamic Context Collection Network for Input-Aware Drone-View Object Detection', Remote Sensing, vol. 14, no. 24, pp. 6313-6313.
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Benefiting from the advancement of deep neural networks (DNNs), detecting objects from drone-view images has achieved great success in recent years. It is a very challenging task to deploy such DNN-based detectors on drones in real-life applications due to their excessive computational costs and limited onboard computational resources. Large redundant computation exists because existing drone-view detectors infer all inputs with nearly identical computation. Detectors with less complexity can be sufficient for a large portion of inputs, which contain a small number of sparse distributed large-size objects. Therefore, a drone-view detector supporting input-aware inference, i.e., capable of dynamically adapting its architecture to different inputs, is highly desirable. In this work, we present a Dynamic Context Collection Network (DyCC-Net), which can perform input-aware inference by dynamically adapting its structure to inputs of different levels of complexities. DyCC-Net can significantly improve inference efficiency by skipping or executing a context collector conditioned on the complexity of the input images. Furthermore, since the weakly supervised learning strategy for computational resource allocation lacks of supervision, models may execute the computationally-expensive context collector even for easy images to minimize the detection loss. We present a Pseudo-label-based semi-supervised Learning strategy (Pseudo Learning), which uses automatically generated pseudo labels as supervision signals, to determine whether to perform context collector according to the input. Extensive experiment results on VisDrone2021 and UAVDT, show that our DyCC-Net can detect objects in drone-captured images efficiently. The proposed DyCC-Net reduces the inference time of state-of-the-art (SOTA) drone-view detectors by over 30 percent, and DyCC-Net outperforms them by 1.94% in AP75.
Xia, J, Zhang, H, Wen, S, Yang, S & Xu, M 2022, 'An efficient multitask neural network for face alignment, head pose estimation and face tracking', Expert Systems with Applications, vol. 205, pp. 117368-117368.
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Xiao, D, Chen, S, Ni, W, Zhang, J, Zhang, A & Liu, R 2022, 'A sub-action aided deep reinforcement learning framework for latency-sensitive network slicing', Computer Networks, vol. 217, pp. 109279-109279.
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Network slicing is a core technique of fifth-generation (5G) systems and beyond. To maximize the number of accepted network slices with limited hardware resources, service providers must avoid over-provisioning of quality-of-service (QoS), which could prevent them from lowering capital expenditures (CAPEX)/operating expenses (OPEX) for 5G infrastructure. In this paper, we propose a sub-action aided double deep Q-network (SADDQN)-based network slicing algorithm for latency-aware services. Specifically, we model network slicing as a Markov decision process (MDP), where we consider virtual network function (VNF) placements to be the actions of the MDP, and define a reward function based on cost and service priority. Furthermore, we adopt the Dijkstra algorithm to determine the forwarding graph (FG) embedding for a given VNF placement and design a resource allocation algorithm – binary search assisted gradient descent (BSAGD) – to allocate resources to VNFs given the VNF-FG placement. For every service request, we first use the DDQN to choose an MDP action to determine the VNF placement (main action). Next, we employ the Dijkstra algorithm (first-phase sub-action) to find the shortest path for each pair of adjacent VNFs in the given VNF chain. Finally, we implement the BSAGD (second-phase sub-action) to realize this service with the minimum cost. The joint action results in an MDP reward that can be utilized to train the DDQN. Numerical evaluations show that, compared to state-of-the-art algorithms, the proposed algorithm can improve the cost-efficiency while giving priority to higher-priority services and maximizing the acceptance ratio.
Xie, H, Mengersen, K, Di, C, Zhang, Y, Lipman, J & Van Huffel, S 2022, 'A Variational Bayesian Gaussian Mixture-Nonnegative Matrix Factorization Model to Extract Movement Primitives for Robust Control', IEEE Transactions on Human-Machine Systems, vol. 52, no. 6, pp. 1258-1270.
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Nonnegative matrix factorization (NMF) is a powerful tool for parameter estimation applied in numerous robotics applications, such as path planning, motion trajectory prediction, and motion intention detection. In particular, NMF has been successfully used to extract simplified and organized movement primitives from myoelectric signal (MES) for robust control of multi-degree of freedom humanoid robots. However, MES is typically contaminated by complex noise sources. The system performance often degrades due to the simplified Gaussian assumption of the noise distribution in existing NMF methods. Furthermore, most existing NMF models are unable to automatically determine the rank of the latent matrices. To address these issues, this article presents a hybrid variational Bayesian Gaussian mixture and NMF (GMNMF) model with a finite Gaussian mixture model adopted to fit the mixed noise density function of MES. In addition, the automatic relevant determination criterion is applied to automatically infer the number of movement primitives. The coordinate descent update rules for the proposed model are formulated by mean-field variational Bayesian inference. We assess the model performance on five synthetic noise distribution functions and an experimental MES dataset to perform six wrist movements. The results demonstrate that GMNMF yields low error and high robustness in extracting the movement primitives over four competitive methods for robust cybernetic control.
Xie, H, Zheng, J, Sun, Z, Wang, H & Chai, R 2022, 'Finite-time tracking control for nonholonomic wheeled mobile robot using adaptive fast nonsingular terminal sliding mode', Nonlinear Dynamics, vol. 110, no. 2, pp. 1437-1453.
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AbstractSystem uncertainties and external disturbances are the major causes of the trajectory tracking performance degradation in nonholonomic wheeled mobile robots (NWMRs). In this article, an adaptive fast nonsingular terminal sliding mode dynamic control (AFNTSMDC) method is proposed to provide enhanced robust and finite-time tracking performance for the NWMR. The proposed AFNTSMDC is a systematic design method based upon both the kinematic and dynamic model of the NWMR. The proposed controller has a simple form without singularity issue in the control input, which makes it practically implementable. The finite-time stability of the proposed tracking-error function is also proved using the Lyapunov function. Finally, circular trajectory tracking experiments are conducted to validate the robustness and convergence rate of the proposed AFNTSMDC scheme in comparison with the existing methods including classic kinematic control, robust sliding mode kinematic control, and conventional sliding mode dynamic control in the presence of uncertainties and external disturbances.
Xu, M, Hoang, DT, Kang, J, Niyato, D, Yan, Q & Kim, DI 2022, 'Secure and Reliable Transfer Learning Framework for 6G-Enabled Internet of Vehicles', IEEE Wireless Communications, vol. 29, no. 4, pp. 132-139.
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In the coming 6G era, Internet of Vehicles (IoV) has been evolving towards 6G-enabled IoV with super-high data rate, seamless networking coverage, and ubiquitous intelligence by Artificial Intelligence (AI). Transfer Learning (TL) has great potential to empower promising 6G-enabled IoV, such as smart driving assistance, with its outstanding features including enhancing the quality and quantity of training data, speeding up learning processes, and reducing computing demands. Although TL had been widely adopted in wireless applications (e.g., spectrum management and caching), its reliability and security in 6G-enabled IoV were still not well investigated. For instance, malicious vehicles in source domains may transfer and share untrustworthy models (i.e., knowledge) about connection availability to target domains, thus adversely affecting the performance of learning processes. Therefore, it is important to select and also incentivize trustworthy vehicles to participate in TL. In this article, we first introduce the integration of TL and 6G-enabled IoV and provide TL applications for 6G-enabled IoV. We then design a secure and reliable transfer learning framework by using reputation to evaluate the reliability of pre-trained models and utilizing the consortium blockchain to achieve secure and efficient decentralized reputation management. Moreover, a deep learning-based auction scheme for the TL model market is designed to motivate high-reputation vehicles to participate in model sharing. Finally, the simulation results demonstrate that the proposed framework is secure and reliable with well-designed incentives for TL in 6G-enabled IoV.
Yan, B, Zhao, Q, Li, M, Zhang, J, Zhang, JA & Yao, X 2022, 'Fitness landscape analysis and niching genetic approach for hybrid beamforming in RIS-aided communications', Applied Soft Computing, vol. 131, pp. 109725-109725.
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Reconfigurable intelligent surface (RIS) is a revolutionizing technology to achieve cost-effective communications. The active beamforming at the base station (BS) and the discrete phase shifts at RIS should be jointly designed to customize the propagation environment. However, current phase-shift setting methods ignore the non-separable property of phase shifts, degrading the performance, especially in cases with a large-sized RIS. To understand the problem characteristics related to the phase shifts and further tailor an eligible method with such characteristics, this paper, for the first time, analyzes the fitness landscape of the sum-rate maximization problem (maximizing the sum rate of users in a downlink multi-user multiple-input single-output system assisted by a RIS). Results show that the problem has a severe unstructured and rugged landscape, especially in cases with a large-sized RIS. This observation answers why current methods are ineligible and provides insightful guidance for designing a more intelligent method. With the landscape findings in mind, this paper introduces a niching genetic algorithm to solve the problem. In particular, the niching idea is employed to locate multiple local optima. These local optima act as stepping stones to facilitate approaching the global optima. Simulation results demonstrate that the proposed niching genetic algorithm obtains significant capacity gains over current methods in cases with large-sized RIS.
Yang, R, Zhang, Y, Qian, J & Lee, JE-Y 2022, 'Effect of Phononic Crystal Orientation on AlN-on-Silicon Lamb Wave Micromechanical Resonators', IEEE Sensors Journal, vol. 22, no. 17, pp. 16811-16819.
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Phononic crystals (PnCs) have been used to boost the quality factor (Q) of AlN-on-Silicon Lamb Wave Resonators (LWRs). But most reports on applying PnCs to resonators have focused on the common <110> orientation within (100) silicon. Little is known on the applicability of other crystal orientations. In this work, we explore the effect of orientation on the acoustic band gap (ABG) of two PnC designs and their effect on boosting Q: a disk PnC and a ring PnC. From Finite Element simulation, we show that the disk PnC’s ABG is insensitive to orientation while adding a hole into the disk to form a ring changes its ABG to be much more sensitive to orientation. Leveraging the PnCs as anchoring boundary of LWRs, the disk PnC exhibits comparable effectiveness to boost Q > 11,000 in the <110> and <100> directions while the ring PnC is effective only in the <110> direction. We further corroborate these trends by incorporating the disk PnC into delay lines in either crystal axis.
Yang, S, Wu, S, Liu, T & Xu, M 2022, 'Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 9830-9843.
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A major gap between few-shot and many-shot learning is the data distribution empirically observed by the model during training. In few-shot learning, the learned model can easily become over-fitted based on the biased distribution formed by only a few training examples, while the ground-truth data distribution is more accurately uncovered in many-shot learning to learn a well-generalized model. In this paper, we propose to calibrate the distribution of these few-sample classes to be more unbiased to alleviate such an over-fitting problem. The distribution calibration is achieved by transferring statistics from the classes with sufficient examples to those few-sample classes. After calibration, an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier. Extensive experiments on three datasets, miniImageNet, tieredImageNet, and CUB, show that a simple linear classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy by a large margin. We also establish a generalization error bound for the proposed distribution-calibration-based few-shot learning, which consists of the distribution assumption error, the distribution approximation error, and the estimation error. This generalization error bound theoretically justifies the effectiveness of the proposed method.
Yang, W, Wang, S, Yin, X, Wang, X & Hu, J 2022, 'A Review on Security Issues and Solutions of the Internet of Drones', IEEE Open Journal of the Computer Society, vol. 3, pp. 96-110.
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The Internet of Drones (IoD) has attracted increasing attention in recent years because of its portability and automation, and is being deployed in a wide range of fields (e.g., military, rescue and entertainment). Nevertheless, as a result of the inherently open nature of radio transmission paths in the IoD, data collected, generated or handled by drones is plagued by many security concerns. Since security and privacy are among the foremost challenges for the IoD, in this paper we conduct a comprehensive review on security issues and solutions for IoD security, discussing IoD-related security requirements and identifying the latest advancement in IoD security research. This review analyzes a host of important security technologies with emphases on authentication techniques and blockchain-powered schemes. Based on a detailed analysis, we present the challenges faced by current methodologies and recommend future IoD security research directions. This review shows that appropriate security measures are needed to address IoD security issues, and that newly designed security solutions should particularly consider the balance between the level of security and cost efficiency.
Yang, X, Wang, S, Xing, Y, Li, L, Xu, RYD, Friston, KJ & Guo, Y 2022, 'Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19', PLOS Computational Biology, vol. 18, no. 2, pp. e1009807-e1009807.
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Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art ‘DARt’ system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.
Yang, Y, Wang, L, Su, S, Watsford, M, Wood, LM & Duffield, R 2022, 'Inertial Sensor Estimation of Initial and Terminal Contact during In-Field Running', Sensors, vol. 22, no. 13, pp. 4812-4812.
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Given the popularity of running-based sports and the rapid development of Micro-electromechanical systems (MEMS), portable wireless sensors can provide in-field monitoring and analysis of running gait parameters during exercise. This paper proposed an intelligent analysis system from wireless micro–Inertial Measurement Unit (IMU) data to estimate contact time (CT) and flight time (FT) during running based on gyroscope and accelerometer sensors in a single location (ankle). Furthermore, a pre-processing system that detected the running period was introduced to analyse and enhance CT and FT detection accuracy and reduce noise. Results showed pre-processing successfully detected the designated running periods to remove noise of non-running periods. Furthermore, accelerometer and gyroscope algorithms showed good consistency within 95% confidence interval, and average absolute error of 31.53 ms and 24.77 ms, respectively. In turn, the combined system obtained a consistency of 84–100% agreement within tolerance values of 50 ms and 30 ms, respectively. Interestingly, both accuracy and consistency showed a decreasing trend as speed increased (36% at high-speed fore-foot strike). Successful CT and FT detection and output validation with consistency checking algorithms make in-field measurement of running gait possible using ankle-worn IMU sensors. Accordingly, accurate IMU-based gait analysis from gyroscope and accelerometer information can inform future research on in-field gait analysis.
Yang, Z, Tran, LC, Safaei, F, Le, AT & Taparugssanagorn, A 2022, 'Real-Time Step Length Estimation in Indoor and Outdoor Scenarios', Sensors, vol. 22, no. 21, pp. 8472-8472.
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In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our developed wearable hardware in indoor and outdoor ambulation scenarios. The human walking step length is estimated by a reliable range of RSSI values. The upper threshold and the lower threshold of this range are determined experimentally. This paper advances our previous step length measurement technique by proposing a novel exponential weighted moving average (EWMA) algorithm to update the upper and lower thresholds, and thus the step length estimation, recursively. The EWMA algorithm allows our measurement technique to process each shorter subset of the dataset, called a time window, and estimate the step length, rather than having to process the whole dataset at a time. The step length is periodically updated on the fly when the time window is “sliding” forwards. Thus, the EWMA algorithm facilitates the step length estimation in real-time. The impact of the EWMA parameter is analysed, and the optimal parameter is discovered for different experimental scenarios. Our experiments show that the EWMA algorithm could achieve comparable accuracy as our previously proposed technique with errors as small as 3.02% and 0.30% for the indoor and outdoor scenarios, respectively, while the processing time required to output an estimation of the step length could be significantly shortened by 53.96% and 60% for the indoor walking and outdoor walking, respectively.
Yao, L, Kusakunniran, W, Wu, Q, Xu, J & Zhang, J 2022, 'Collaborative Feature Learning for Gait Recognition Under Cloth Changes', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 6, pp. 3615-3629.
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Since gait can be utilized to identify individuals from a far distance without their interaction and coordination, recently many gait recognition methods have been proposed. However, due to a real-world scenario of clothing changes, a degradation occurs for most of these methods. Thus in this paper, a more efficient gait recognition method is proposed to address the problem of clothing variances. First, part-based gait features are formulated from two different perspectives, i.e., the separated body parts that are more robust to clothing changes and the estimated human skeleton key-point regions. It is reasonable to formulate such features for cloth-changing gait recognition, because these two perspectives are both less vulnerable to clothing changes. Given that each feature has its own advantages and disadvantages, a more efficient gait feature is generated in this paper by assembling these two features together. Moreover, since local features are more discriminative than global features, in this paper more attention is focused on the local short-range features. Also, unlike most methods, in our method we treat the estimated key-point features as a set of word embeddings, and a transformer encoder is specifically used to learn the dependence of each correlative key-points. The robustness and effectiveness of our proposed method are certified by experiments on CASIA Gait Dataset B, and it has achieved the state-of-the-art performance on this dataset.
Yao, L, Kusakunniran, W, Wu, Q, Xu, J & Zhang, J 2022, 'Recognizing Gaits Across Walking and Running Speeds', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 18, no. 3, pp. 1-22.
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For decades, very few methods were proposed for cross-mode (i.e., walking vs. running) gait recognition. Thus, it remains largely unexplored regarding how to recognize persons by the way they walk and run. Existing cross-mode methods handle the walking-versus-running problem in two ways, either by exploring the generic mapping relation between walking and running modes or by extracting gait features which are non-/less vulnerable to the changes across these two modes. However, for the first approach, a mapping relation fit for one person may not be applicable to another person. There is no generic mapping relation given that walking and running are two highly self-related motions. The second approach does not give more attention to the disparity between walking and running modes, since mode labels are not involved in their feature learning processes. Distinct from these existing cross-mode methods, in our method, mode labels are used in the feature learning process, and a mode-invariant gait descriptor is hybridized for cross-mode gait recognition to handle this walking-versus-running problem. Further research is organized in this article to investigate the disparity between walking and running. Running is different from walking not only in the speed variances but also, more significantly, in prominent gesture/motion changes. According to these rationales, in our proposed method, we give more attention to the differences between walking and running modes, and a robust gait descriptor is developed to hybridize the mode-invariant spatial and temporal features. Two multi-task learning-based networks are proposed in this method to explore these mode-invariant features. Spatial features describe the body parts non-/less affected by mode changes, and temporal features depict the instinct motion relation of each person. Mode labels are also adopted in the training phase to guide the network to give more attention to the disparity across walking and run...
You, F, Ni, W, Li, J & Jamalipour, A 2022, 'New Three-Tier Game-Theoretic Approach for Computation Offloading in Multi-Access Edge Computing', IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9817-9829.
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Yu, H, Guo, Y, Ye, L & Su, SW 2022, 'Statistical Analysis of In-Field Magnetometer Calibration for Two Representative Methods', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-8.
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Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Maximizing the Geometric Mean of User-Rates to Improve Rate-Fairness: Proper vs. Improper Gaussian Signaling', IEEE Transactions on Wireless Communications, vol. 21, no. 1, pp. 295-309.
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This paper considers a reconfigurable intelligent surface (RIS)-aided network, which relies on a multiple antenna array aided base station (BS) and an RIS for serving multiple single antenna downlink users. To provide reliable links to all users over the same bandwidth and same time-slot, the paper proposes the joint design of linear transmit beamformers and the programmable reflecting coefficients of an RIS to maximize the geometric mean (GM) of the users' rates. A new computationally efficient alternating descent algorithm is developed, which is based on closed-forms only for generating improved feasible points of this nonconvex problem. We also consider the joint design of widely linear transmit beamformers and the programmable reflecting coefficients to further improve the GM of the users' rates. Hence another alternating descent algorithm is developed for its solution, which is also based on closed forms only for generating improved feasible points. Numerical examples are provided to demonstrate the efficiency of the proposed approach.
Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'RIS-Aided Zero-Forcing and Regularized Zero-Forcing Beamforming in Integrated Information and Energy Delivery', IEEE Transactions on Wireless Communications, vol. 21, no. 7, pp. 5500-5513.
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This paper considers a network of a multi-antenna array base station (BS) and a reconfigurable intelligent surface (RIS) to deliver both information to information users (IUs) and power to energy users (EUs). The RIS links the connection between the IUs and the BS as there is no direct path between the former and the latter. The EUs are located nearby the BS in order to effectively harvest energy from the high-power signal from the BS, while the much weaker signal reflected from the RIS hardly contributes to the EUs' harvested energy. To provide reliable links for all users over the same time-slot, we adopt the transmit time-switching (transmit-TS) approach, under which information and energy are delivered over different time-slot fractions. This allows us to rely on conjugate beamforming for energy links and zero-forcing/regularized zero-forcing beamforming (ZFB/RZFB) and on the programmable reflecting coefficients (PRCs) of the RIS for information links. We show that ZFB/RZFB and PRCs can be still separately optimized in their joint design, where PRC optimization is based on iterative closed-form expressions. We then develop a path-following algorithm for solving the max-min IU throughput optimization problem subject to a realistic constraint on the quality-of-energy-service in terms of the EUs' harvested energy thresholds. We also propose a new RZFB for substantially improving the IUs' throughput.
Yu, H, Tuan, HD, Nasir, AA, Debbah, M & Fang, Y 2022, 'New generalized zero forcing beamforming for serving more users in energy-harvesting enabled networks', Physical Communication, vol. 50, pp. 101500-101500.
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Yu, L, Li, Z, Xu, M, Gao, Y, Luo, J & Zhang, J 2022, 'Distribution-Aware Margin Calibration for Semantic Segmentation in Images', International Journal of Computer Vision, vol. 130, no. 1, pp. 95-110.
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Yu, L, Zhang, J & Wu, Q 2022, 'Dual Attention on Pyramid Feature Maps for Image Captioning', IEEE Transactions on Multimedia, vol. 24, no. 99, pp. 1775-1786.
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Yu, P, Ni, W, Liu, RP, Zhang, Z, Zhang, H & Wen, Q 2022, 'Efficient Encrypted Range Query on Cloud Platforms', ACM Transactions on Cyber-Physical Systems, vol. 6, no. 3, pp. 1-23.
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In the Internet of Things (IoT) era, various IoT devices are equipped with sensing capabilities and employed to support clinical applications. The massive electronic health records (EHRs) are expected to be stored in the cloud, where the data are usually encrypted, and the encrypted data can be used for disease diagnosis. There exist some numeric health indicators, such as blood pressure and heart rate. These numeric indicators can be classified into multiple ranges, and each range may represent an indication of normality or abnormity. Once receiving encrypted IoT data, the CS maps it to one of the ranges, achieving timely monitoring and diagnosis of health indicators. This article presents a new approach to identify the range that an encrypted numeric value corresponds to without exposing the explicit value. We establish the sufficient and necessary condition to convert a range query to matchings of encrypted binary sequences with the minimum number of matching operations. We further apply the minimization of range queries to design and implement a secure range query system, where numeric health indicators encrypted independently by multiple IoT devices can be cohesively stored and efficiently queried by using Lagrange polynomial interpolation. Comprehensive performance studies show that the proposed approach can protect both the health records and range query against untrusted cloud platforms and requires less computational and communication cost than existing techniques.
Yu, P, Ni, W, Zhang, H, Ping Liu, R, Wen, Q, Li, W & Gao, F 2022, 'Secure and Differentiated Fog-Assisted Data Access for Internet of Things', The Computer Journal, vol. 65, no. 8, pp. 1948-1963.
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Abstract The ability of Fog computing to admit and process huge volumes of heterogeneous data is the catalyst for the fast expansion of Internet of things (IoT). The critical challenge is secure and differentiated access to the data, given limited computation capability and trustworthiness in typical IoT devices and Fog servers, respectively. This paper designs and develops a new approach for secure, efficient and differentiated data access. Secret sharing is decoupled to allow the Fog servers to assist the IoT devices with attribute-based encryption of data while preventing the Fog servers from tampering with the data and the access structure. The proposed encryption supports direct revocation and can be decoupled among multiple Fog servers for acceleration. Based on the decisional $q$-parallel bilinear Diffie–Hellman exponent assumption, we propose a new extended $q$-parallel bilinear Diffie–Hellman exponent (E$q$-PBDHE) assumption and prove that the proposed approach provides ‘indistinguishably chosen-plaintext attacks secure’ data access for legitimate data subscribers. As numerically and experimentally verified, the proposed approach is able to reduce the encryption time by 20% at the IoT devices and by 50% at the Fog network using parallel computing as compared to the state of the art .
Yu, X, Li, H, Zhang, JA, Huang, X & Cheng, Z 2022, 'Enhanced Angle-of-Arrival and Polarization Parameter Estimation Using Localized Hybrid Dual-Polarized Arrays', Sensors, vol. 22, no. 14, pp. 5207-5207.
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The millimeter wave (mmWave) channel is dominated by line-of-sight propagation. Therefore, the acquisition of angle-of-arrival (AoA) and polarization state of the wave is of great significance to the receiver. In this paper, we investigate AoA and polarization estimation in a mmWave system employing dual-polarized antenna arrays. We propose an enhanced AoA estimation method using a localized hybrid dual-polarized array for a polarized mmWave signal. The use of dual-polarized arrays greatly improves the calibration of differential signals and the signal-to-noise ratio (SNR) of the phase offset estimation between adjacent subarrays. Given the estimated phase offset, an initial AoA estimate can be obtained, and is then used to update the phase offset estimation. This leads to a recursive estimation with improved accuracy. We further propose an enhanced polarization estimation method, which uses the power of total received signals at dual-polarized antennas to compute the cross-correlation-to-power ratio instead of using only one axis dipole. Thus the accuracy of polarization parameter estimation is improved. We also derive a closed-form expression for mean square error lower bounds of AoA estimation and present an average SNR analysis for polarization estimation performance. Simulation results demonstrate the superiority of the enhanced AoA and polarization parameter estimation methods compared to the state of the art.
Zeng, J, Xu, Q, Fan, X, Ye, N, Ni, W & Guo, YJ 2022, 'Achieving URLLC by MU-MIMO With Imperfect CSI: Under κ–μ Shadowed Fading', IEEE Wireless Communications Letters, vol. 11, no. 12, pp. 2560-2564.
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Zhang, C, Meng, G, Xu, RYD, Xiang, S & Pan, C 2022, 'Learning adversarial point-wise domain alignment for stereo matching', Neurocomputing, vol. 491, pp. 564-574.
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Zhang, G, Niwa, K & Kleijn, WB 2022, 'Revisiting the Primal-Dual Method of Multipliers for Optimisation Over Centralised Networks', IEEE Transactions on Signal and Information Processing over Networks, vol. 8, no. 99, pp. 228-243.
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Zhang, J, Cui, Q, Zhang, X, Ni, W, Lyu, X, Pan, M & Tao, X 2022, 'Online Optimization of Energy-Efficient User Association and Workload Offloading for Mobile Edge Computing', IEEE Transactions on Vehicular Technology, vol. 71, no. 2, pp. 1974-1988.
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Zhang, J, Hanjalic, A, Jain, R, Hua, X, Satoh, S, Yao, Y & Zeng, D 2022, 'Guest Editorial: Learning From Noisy Multimedia Data', IEEE Transactions on Multimedia, vol. 24, pp. 1247-1252.
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Zhang, JA, Rahman, ML, Wu, K, Huang, X, Guo, YJ, Chen, S & Yuan, J 2022, 'Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey', IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 306-345.
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Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). Radio sensing here refers to information retrieval from received mobile signals for objects of interest in the environment surrounding the radio transceivers, and it may go beyond the functions of localization, tracking, and object recognition of traditional radar. In PMNs, JCAS integrates sensing into communications, sharing a majority of system modules and the same transmitted signals. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications, whilst providing non-compromised communications. In this paper, we present a broad picture of the motivation, methodologies, challenges, and research opportunities of realizing PMN, by providing a comprehensive survey for systems and technologies developed mainly in the last ten years. Beginning by reviewing the work on coexisting communication and radar systems, we highlight their limits on addressing the interference problem, and then introduce the JCAS technology. We then set up JCAS in the mobile network context and envisage its potential applications. We continue to provide a brief review of three types of JCAS systems, with particular attention to their differences in design philosophy. We then introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing. Subsequently, we discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, organized under nine topics: performance bounds, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, resolution of sensing ambiguity, pattern analysis, networked sensing unde...
Zhang, JA, Wu, K, Huang, X, Guo, YJ, Zhang, D & Heath, RW 2022, 'Integration of Radar Sensing into Communications with Asynchronous Transceivers', IEEE Communications Magazine, vol. 60, no. 11, pp. 106-112.
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Clock asynchronism is a critical issue in integrating radar sensing into communication networks. It can cause ranging ambiguity and prevent coherent processing of discontinuous measurements in integration with asynchronous transceivers. Should it be resolved, sensing can be efficiently realized in communication networks, requiring few network infrastructure and hardware changes. This article provides a systematic overview of existing and potential new techniques for tackling this fundamental problem. We first review existing solutions, including using a finetuned global reference clock, and single-node-based and network-based techniques. We then examine open problems and research opportunities, offering insights into what may be better realized in each of the three solution areas.
Zhang, L, Shen, J, Zhang, J, Xu, J, Li, Z, Yao, Y & Yu, L 2022, 'Multimodal Marketing Intent Analysis for Effective Targeted Advertising', IEEE Transactions on Multimedia, vol. 24, pp. 1830-1843.
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Zhang, L, Xu, J, Gong, Y, Yu, L, Zhang, J & Shen, J 2022, 'Unsupervised Image and Text Fusion for Travel Information Enhancement', IEEE Transactions on Multimedia, vol. 24, pp. 1415-1425.
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Zhang, R, Xu, L, Yu, Z, Shi, Y, Mu, C & Xu, M 2022, 'Deep-IRTarget: An Automatic Target Detector in Infrared Imagery Using Dual-Domain Feature Extraction and Allocation', IEEE Transactions on Multimedia, vol. 24, pp. 1735-1749.
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Recently, convolutional neural networks (CNNs) have brought impressive improvements for object detection. However, detecting targets in infrared images still remains challenging, because the poor texture information, low resolution and high noise levels of the thermal imagery restrict the feature extraction ability of CNNs. In order to deal with these difficulties in the feature extraction, we propose a novel backbone network named Deep-IRTarget, composing of a frequency feature extractor, a spatial feature extractor and a dual-domain feature resource allocation model. Hypercomplex Infrared Fourier Transform is developed to calculate the infrared intensity saliency by designing hypercomplex representations in the frequency domain, while a convolutional neural network is invoked to extract feature maps in the spatial domain. Features from the frequency domain and spatial domain are stacked to construct Dual-domain features. To efficiently integrate and recalibrate them, we propose a Resource Allocation model for Features (RAF). The well-designed channel attention block and position attention block are used in RAF to respectively extract interdependent relationships among channel and position dimensions, and capture channel-wise and position-wise contextual information. Extensive experiments are conducted on three challenging infrared imagery databases. We achieve 10.14%, 9.1% and 8.05% improvement on mAP scores, compared to the current state of the art method on MWIR, BITIR and WCIR respectively.
Zhang, T, Du, J & Guo, YJ 2022, 'High-Tc Superconducting Microwave and Millimeter Devices and Circuits—An Overview', IEEE Journal of Microwaves, vol. 2, no. 3, pp. 374-388.
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Zhang, T, Jin, B & Jia, W 2022, 'An anchor-free object detector based on soften optimized bi-directional FPN', Computer Vision and Image Understanding, vol. 218, pp. 103410-103410.
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We propose an anchor-free object detector that combines a weighted bi-directional Feature Pyramid Network (BiFPN) and Soft Anchor Point Detector to address the object detection problem in a pixel-wise paradigm. The current mainstream object detection methods are anchor-based, which require to set hyper parameters such as scale and aspect ratio. This requires strong prior knowledge and can be difficult to design. Therefore, we propose an anchor-free detector that completely avoids the complex calculations and all the hyper parameters related to the anchor box by eliminating the predefined set of anchor boxes in an anchor-free way. Anchor-free detectors are essentially dense prediction methods. Although the huge solution space can yield high recall, simple anchor-free methods tend to return too many false positives, which leads to the problem of semantic ambiguity caused by the high overlap of object centers. Therefore, we propose BiFPN to alleviate the impact of high overlap which also effectively addresses the problems related to multi-scale features. Moreover, in order to utilize the power of feature pyramid better, we tackle the issues with a novel training strategy that involves two soften optimization techniques, i.e., soft-weighted anchor points and soft-selected pyramid levels. This training strategy further re-weights the quality of the detection results to make our detection results more stable.
Zhang, T, Zhang, H, Huang, X, Suzuki, H, Pathikulangara, J, Smart, K, Du, J & Guo, J 2022, 'A 245 GHz Real-Time Wideband Wireless Communication Link with 30 Gbps Data Rate', Photonics, vol. 9, no. 10, pp. 683-683.
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This paper presents a 245 GHz wireless communications system with a data rate of 30 Giga bits per second (Gbps) at a 1.2 m distance, which proves the potential for future high-speed communications beyond 5G technology. The system consists of low-complexity and real-time base-band modules to provide the high-speed wideband signal processing capability. Multi-channel base-band signals are combined and converted to 15.65 ± 6.25 GHz wideband intermediate frequency (IF) signals. A novel 245 GHz waveguide bandpass filter (BPF) with low loss and high selectivity is designed and applied to a terahertz (THz) front-end for image rejection and noise suppression. Configuration of the base-band, IF, and THz front-end modules is also given in detail. The 245 GHz wireless communication link is demonstrated over a distance of 1.2 m.
Zhang, T, Zhu, T, Liu, R & Zhou, W 2022, 'Correlated data in differential privacy: Definition and analysis', Concurrency and Computation: Practice and Experience, vol. 34, no. 16.
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SummaryDifferential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a dataset are independent when differential privacy is applied. However, in real‐world datasets, records are likely to be correlated, which may lead to unexpected data leakage. In this survey, we investigate the issue of privacy loss due to data correlation under differential privacy models. Roughly, we classify existing literature into three lines: (1) using parameters to describe data correlation in differential privacy, (2) using models to describe data correlation in differential privacy, and (3) describing data correlation based on the framework of Pufferfish. First, a detailed example is given to illustrate the issue of privacy leakage on correlated data in real scenes. Then our main work is to analyze and compare these methods, and evaluate situations that these diverse studies are applied. Finally, we propose some future challenges on correlated differential privacy.
Zhang, W, Liu, T, Brown, A, Ueland, M, Forbes, SL & Su, SW 2022, 'The Use of Electronic Nose for the Classification of Blended and Single Malt Scotch Whisky', IEEE Sensors Journal, vol. 22, no. 7, pp. 7015-7021.
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As with any profitable industry, the whisky market is subject to fraudulent activity, including adulteration. An expert can identify the differences between whiskies, but it is difficult for the majority of consumers to differentiate fraudulent beverages. Complex chemical and analytical analyses have been able to detect the differences between whiskies; however, this type of analysis is time-consuming, complex, requires trained professionals, and can only be conducted in the laboratory. A rapid and real-time assessment of whisky quality could prove beneficial to wholesalers and consumers. The odour of whiskies can be used to identify their brands, regions and styles, as thus has the potential for quality assessment and fraudulent detection. One type of technology used for real-time odour analysis is an electronic nose (e-nose). This study investigates the capability of a new e-nose prototype (called NOS.E) developed by our team to identify the differences between six whiskies with respect to their brand names, regions, and styles. This study investigates the capability of a new e-nose prototype (called NOS.E) developed by our team in identifying the differences among whiskies. Ensemble of several classifiers is adopted to improve the classification accuracy of the system. The proposed e-nose solution was verified by a field testing displayed at the CEBIT Australia 2019 trade show, by reaching an accuracy of 96.15%, 100%, and 92.31% in brand name, region, and style classification, respectively. Confirmation of the NOS.E findings was further carried out using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC $\times $ GC-TOFMS).
Zhang, X, Liu, J, Li, Y, Cui, Q, Tao, X, Liu, RP & Li, W 2022, 'Vehicle-oriented ridesharing package delivery in blockchain system', Digital Communications and Networks.
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Zhang, X, Xia, W, Wang, X, Liu, J, Cui, Q, Tao, X & Liu, RP 2022, 'The Block Propagation in Blockchain-Based Vehicular Networks', IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8001-8011.
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Zhang, Y-F, Zheng, J, Jia, W, Huang, W, Li, L, Liu, N, Li, F & He, X 2022, 'Deep RGB-D Saliency Detection Without Depth', IEEE Transactions on Multimedia, vol. 24, no. 99, pp. 755-767.
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The existing saliency detection models based on RGB colors only leverage appearance cues to detect salient objects. Depth information also plays a very important role in visual saliency detection and can supply complementary cues for saliency detection. Although many RGB-D saliency models have been proposed, they require to acquire depth data, which is expensive and not easy to get. In this paper, we propose to estimate depth information from monocular RGB images and leverage the intermediate depth features to enhance the saliency detection performance in a deep neural network framework. Specically, we rst use an encoder network to extract common features from each RGB image and then build two decoder networks for depth estimation and saliency detection, respectively. The depth decoder features can be fused with the RGB saliency features to enhance their capability. Furthermore, we also propose a novel dense multiscale fusion model to densely fuse multiscale depth and RGB features based on the dense ASPP model. A new global context branch is also added to boost the multiscale features. Experimental results demonstrate that the added depth cues and the proposed fusion model can both improve the saliency detection performance. Finally, our model not only outperforms state-of-the-art RGB saliency models, but also achieves comparable results compared with state-of-the-art RGB-D saliency models.
Zhang, Z, Jiang, S, Huang, C & Da Xu, RY 2022, 'Unsupervised Clothing Change Adaptive Person ReID', IEEE Signal Processing Letters, vol. 29, pp. 304-308.
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Zhang, Z, Wu, Q, Wang, Y & Chen, F 2022, 'Exploring Pairwise Relationships Adaptively From Linguistic Context in Image Captioning', IEEE Transactions on Multimedia, vol. 24, pp. 3101-3113.
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For image captioning, recent works start to focus on exploring visual relationships for generating high-quality interactive words (i.e. verbs and prepositions). However, many existing works only focus on semantic level by analysing the feature similarity between objects in the visual domain but ignore the linguistic context included in the caption decoder. When captioning is being carried out, the entity words can be inferred based on visual information of objects. The interactive words representing the relationships between entity words can only be inferred based on high-level language meaning generated in the process of captioning decoding. Such high-level language meaning is called linguistic context, which refers to the relational context between words or phrases in the caption sentences. The linguistic context can be used as strong guidance to explore related visual relationships between different objects effectively. To achieve this, we propose a novel context-adaptive attention module that is strongly driven by the linguistic context from the caption decoder. In this module, a novel design of visual relationship attention is proposed based on a bilinear self-attention model to explore related visual relationships and encode more discriminative features under the linguistic context. It works parallelly with visual region attention. To achieve the adaptive process of attending to related visual relationships for generating interactive words or related visual objects for entity words, an attention modulator is integrated as an attention channel controller responding to the changing linguistic context of the caption decoder dynamically. To take full advantage of the linguistic context in the caption, an additional interaction dataset is extracted from the COCO caption datasets and COCO Entities dataset to supervise the training of the proposed context-adaptive attention module explicitly. Demonstrated by experiments on MSCOCO caption dataset, it is e...
Zhao, J, Zhang, JA, Li, Q, Zhang, H & Wang, X 2022, 'Recursive constrained generalized maximum correntropy algorithms for adaptive filtering', Signal Processing, vol. 199, pp. 108611-108611.
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Thanks to the ability of preventing the accumulation of errors, constrained adaptive filtering (CAF) algorithms have been widely applied. However, in practice, non-Gaussian noise may significantly degrade the filtering performance of CAFs derived from the second-order signal statistics. In this paper, we propose several constrained generalized maximum correntropy (CGMC) algorithms to overcome this problem, inspired by the robustness and flexibility of GMC to non-Gaussian noises. We first introduce a CGMC algorithm based on the gradient method. To improve its convergence rate with correlated inputs, we further propose a recursive CGMC (RCGMC) algorithm. For RCGMC, we conduct the convergence analysis, and characterize the theoretical transient mean square deviation (MSD) performance. Furthermore, we derive a low-complexity version of RCGMC by using the weighting method and the leading dichotomous coordinate descent (DCD) algorithm. Simulation results demonstrate the effectiveness of our proposed algorithms in non-Gaussian noise environment, and the consistency between the analytical and simulation results.
Zhao, J, Zhang, JA, Li, Q, Zhang, H & Wang, X 2022, 'Recursive Maximum Correntropy Algorithms for Second-Order Volterra Filtering', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 4, pp. 2336-2340.
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As a special case of the Volterra system, the second-order Volterra (SOV) filter is very efficient for nonlinear system identification. The improved correntorpy based on the generalized Gaussian density function has been proven robust against impulsive noise. In this brief, we propose several SOV filters based on a recursive maximum correntropy (RMC) algorithm for nonlinear system identification. We first introduce a basic RMC algorithm, which faces a trade-off between filtering accuracy and tracking capability due to the use of a fixed forgetting factor (FFF). Two RMCs with variable FF (VFF) are further proposed to enhance the tracking ability. Simulation results demonstrate that our proposed algorithms outperform existing ones in impulsive noise environments and/or in time-varying systems.
Zhao, L, Chen, Z, Wang, H, Li, L, Mao, X, Li, Z, Zhang, J & Wu, D 2022, 'An Improved Deadbeat Current Controller of PMSM Based on Bilinear Discretization', Machines, vol. 10, no. 2, pp. 79-79.
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Based on the bilinear discretization mathematical model of permanent magnet synchronous motor (PMSM), an improved incremental deadbeat current prediction control algorithm is proposed. Aiming at the system instability caused by the forward Euler discretization method, this paper combines the deadbeat current prediction control and the improved bilinear discretization method to improve the system stability. Further, the proposed controller considers the two-beat delay of a digital system to make the mathematical model more accurate. Moreover, the proposed bilinear discretization predictive current controller is not affected by the permanent magnet flux of the motor. Then, the system stability conditions of the proposed controller are analyzed. The simulation and experimental results verify the feasibility and effectiveness of the proposed method.
Zhao, L-H, Wen, S, Xu, M, Shi, K, Zhu, S & Huang, T 2022, 'PID Control for Output Synchronization of Multiple Output Coupled Complex Networks', IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1553-1566.
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This article attempts to address output synchronization and $\mathcal {H}_{\infty }$ output synchronization problems for multiple output coupled complex networks (MOCCNs) under proportional-derivative (PD) and proportional-integral (PI) controllers. Firstly, two classes of MOCCNs without and with external disturbances are separately put forward. Secondly, based on the PD and PI control schemes, several output synchronization criteria for MOCCNs are formulated by using the Lyapunov functional method and inequality techniques. Thirdly, $\mathcal {H}_{\infty }$ output synchronization for MOCCNs is also studied with the help of the PD and PI controllers. Finally, two numerical examples are separately presented to demonstrate the validity of acquired theoretical results.
Zhao, S & Burnett, IS 2022, 'Evolutionary array optimization for multizone sound field reproduction', The Journal of the Acoustical Society of America, vol. 151, no. 4, pp. 2791-2801.
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Multizone sound field reproduction aims to generate personal sound zones in a shared space with multiple loudspeakers. Traditional multizone sound field reproduction methods have focused on optimizing the source strengths given a preset array configuration. Recently, however, various methods have explored optimization of the loudspeaker locations. These can be categorized into sparse regularization and iterative methods with existing studies based on numerical simulations and mostly aiming at single-zone sound field reproduction. In this paper, unique experiments compare the state-of-the-art loudspeaker placement optimization methods by selecting a smaller number of loudspeakers from the candidates uniformly placed along a circle. An evolutionary array optimization scheme is proposed and shown to outperform the best existing methods in terms of mean square error in the bright zone and acoustic contrast between the bright and dark zones at frequencies below 1 kHz. The proposed evolutionary optimization scheme is simple, flexible, and can be extended to broadband optimization and other cost functions.
Zhao, S, Zhu, Q, Cheng, E & Burnett, IS 2022, 'A room impulse response database for multizone sound field reproduction (L)', The Journal of the Acoustical Society of America, vol. 152, no. 4, pp. 2505-2512.
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This letter introduces a database of Room Impulse Responses (RIRs) measured in seven different rooms for multizone sound field reproduction research in various acoustic environments. A circular array of 60 loudspeakers was installed in each room, with two microphone arrays placed sequentially in five different zones inside the loudspeaker array. A total of 260 400 RIRs were measured to establish the database. As a demonstration application of the database for multizone sound field reproduction, simulations were performed on the pressure matching and acoustic contrast control methods to investigate how a system optimized with the RIRs measured in one room would perform in other rooms.
Zheng, J, Li, K, Mhaisen, N, Ni, W, Tovar, E & Guizani, M 2022, 'Exploring Deep-Reinforcement-Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoT', IEEE Internet of Things Journal, vol. 9, no. 21, pp. 21099-21110.
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Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large data sets for training, which gives rise to a higher energy consumption. On the other hand, the energy consumption can be reduced by selecting the IoT devices with small data sets for FL, resulting in a falling learning accuracy. In this article, we formulate a new resource allocation problem for privacy-preserving EdgeIoT to balance the learning accuracy of FL and the energy consumption of the IoT device. We propose a new FL-enabled twin-delayed deep deterministic policy gradient (FL-DLT3) framework to achieve the optimal accuracy and energy balance in a continuous domain. Furthermore, long short-term memory (LSTM) is leveraged in FL-DLT3 to predict the time-varying network state while FL-DLT3 is trained to select the IoT devices and allocate the transmit power. Numerical results demonstrate that the proposed FL-DLT3 achieves fast convergence (less than 100 iterations) while the FL accuracy-to-energy consumption ratio is improved by 51.8% compared to the existing state-of-the-art benchmark.
Zhong, Y, Bi, T, Wang, J, Zeng, J, Huang, Y, Jiang, T, Wu, Q & Wu, S 2022, 'Empowering the V2X Network by Integrated Sensing and Communications: Background, Design, Advances, and Opportunities', IEEE Network, vol. 36, no. 4, pp. 54-60.
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To enable next-generation connected autonomous vehicles (CAVs), the future Vehicle-to-everything (V2X) network is expected to provide centimeter-accurate localization service while attaining low-latency transmissions in high-mobility environments. Nevertheless, these unprecedented requirements are far beyond the capabilities of 5G vehicular networks. Given the above evolution trend, a natural idea is thus to design a joint system architecture that combines both communications and sensing subsystems. To this end, research efforts toward integrated sensing and communications (ISAC) for the V2X network are well underway. It is our belief that ISAC should facilitate both sensing and communication via a single system in a spectrum-/energy-/cost-efficient way. Moreover, it can also improve the performance of both functionalities with mutual assistance, which is also essential to enable CAV's mission-critical services for 6G and beyond V2X. In this article, we first provide a brief historical overview of V2X and ISAC. In particular, we analyze the forces driving the usage of ISAC in V2X. Then we introduce three ISAC design schemes based on their underlying systems. We also survey state-of-the-art enabling technologies by reviewing recent developments of ISAC-assisted beamforming technologies in vehicular networks. Finally, we shed light on some potential challenges and research directions.
Zhou, I, Lipman, J, Abolhasan, M & Shariati, N 2022, 'Minute-wise frost prediction: An approach of recurrent neural networks', Array, vol. 14, pp. 100158-100158.
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Frost events incur substantial economic losses to farmers. These events could induce damage to plants and crops by damaging the cells. In this article, a recurrent neural network-based method, automating the frost prediction process, is proposed. The recurrent neural network-based models leveraged in this article include the standard recurrent neural network, long short-term memory, and gated recurrent unit. The proposed method aims to increase the prediction frequency from once per 12–24 h for the next day or night events to minute-wise predictions for the next hour events. To achieve this goal, datasets from NSW and ACT of Australia are obtained. The experiments are designed considering the scene of deploying the model to the Internet of Things systems. Factors such as model processing speed, long-term error and data availability are reviewed. After model construction, there are three experiments. The first experiment tests the errors between different model types. The second and third experiments test the effect of sequence length on error and performance for recurrent neural network-based models. All tests introduce artificial neural network models as the baseline. Also, all tests for model error are conducted in two rounds with testing datasets from the current year (2016) and next year (2017). As a result, recurrent neural network-based models are more suitable for short-term deployment with a smaller sequence length. In contrast, artificial neural network models demonstrate a lower error over the long term with faster processing time. With the results presented, the limitations of the proposed method are discussed.
Zhu, H, Ansari, M & Guo, YJ 2022, 'Wideband Beam-Forming Networks Utilizing Planar Hybrid Couplers and Phase Shifters', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7592-7602.
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Zhu, H, Zhang, T & Guo, YJ 2022, 'Wideband Hybrid Couplers With Unequal Power Division/Arbitrary Output Phases and Applications to Miniaturized Nolen Matrices', IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 6, pp. 3040-3053.
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Zhu, J, Yang, Y, Liao, S, Li, S & Xue, Q 2022, 'Dual-Band Aperture-Shared Fabry–Perot Cavity-Integrated Patch Antenna for Millimeter-Wave/Sub-6 GHz Communication Applications', IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 5, pp. 868-872.
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This letter presents a dual-band antenna with a large frequency ratio of 11.7 (2.4 GHz/28 GHz) by integrating an mm-wave Fabry-Perot cavity (FPC) antenna into a sub-6 GHz patch antenna. The patch with periodic slots functions as both the 2.4 GHz radiator and the partially reflective surface (PRS) of the 28 GHz FPC antenna. By properly tuning the length of the periodic slot, the PRS's reflection can be easily adjusted. As the periodic slot's length and width operating at 28 GHz are much smaller than the wavelength at 2.4 GHz, periodic slot operating at 28 GHz have little impact on the radiation of the patch. Furthermore, because of the Fabry-Perot resonance, the antenna can have a peak gain reaching 15 dBi at 28 GHz band with an easy feeding structure. For demonstration, a prototype is fabricated and experimentally verified. Note that the frequency ratio is not limited to the proposed design (11.7 for demonstration). It can be easily adjusted based on the same principle.
Zhu, W, Tuan, HD, Dutkiewicz, E & Hanzo, L 2022, 'Collaborative Beamforming Aided Fog Radio Access Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 7, pp. 7805-7820.
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The success of fog radio access networks (F-RANs) is critically dependent on the potential quality of service (QoS) that they can offer to users in the face of capacity-constrained fronthaul links and limited caches at their remote radio heads (RRHs). In this context, the collaborative beamforming design is very challenging, since it constitutes a large-dimensional nonlinearly constrained optimization problem. The paper develops a new technique for tackling these critical challenges in fog computing. We show that all the associated constraints can be efficiently dealt with maximizing the geometric mean (GM) of the user throughputs (GM-throughput) subject to the affordable total transmit power constraints. To elaborate, the GM-throughput maximization judiciously exploits the fronthaul links and the RRHs' caches by relying on our novel algorithm, which evaluates low-complexity closed-form expressions in each of its iterations. The problem of F-RAN energy-efficiency is also addressed while maintaining the target throughput. Numerical examples are provided for quantifying the efficiency of the proposed algorithms.
Zou, Y, Long, Y, Gong, S, Hoang, DT, Liu, W, Cheng, W & Niyato, D 2022, 'Robust Beamforming Optimization for Self-Sustainable Intelligent Reflecting Surface Assisted Wireless Networks', IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 856-870.
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We focus on an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) system where the IRS sustains its operations by harvesting energy from the access point (AP) in the power splitting (PS) protocol. We aim to minimize the AP's transmit power subject to the receivers' signal-to-noise ratio (SNR) and the IRS's energy budget constraints. A two-stage optimization framework is proposed to jointly optimize the AP's active beamforming, the IRS's passive beamforming, and the reflection amplitude. Given the reflection amplitude, we employ alternating optimization to update the beamforming strategies. Then, we determine the lower and upper bounds of the reflection amplitude in closed-form expressions, which help to update the reflection amplitude in a bisection method. We further extend our study to the robust case with uncertain channels. Our analysis reveals that the robust counterpart can be solved by the same optimization framework. Extensive simulations reveal that our algorithm is efficacy to balance the IRS's energy budget and the receiver's SNR performance. With uncertain channel information, a larger size of the IRS does not always ensure a higher performance improvement to information transmissions.
Zuo, Y, Wang, H, Fang, Y, Huang, X, Shang, X & Wu, Q 2022, 'MIG-Net: Multi-Scale Network Alternatively Guided by Intensity and Gradient Features for Depth Map Super-Resolution', IEEE Transactions on Multimedia, vol. 24, pp. 3506-3519.
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The studies of previous decades have shown that the quality of depth maps can be significantly lifted by introducing the guidance from intensity images describing the same scenes. With the rising of deep convolutional neural network, the performance of guided depth map super-resolution is further improved. The variants always consider deep structure, optimized gradient flow and feature reusing. Nevertheless, it is difficult to obtain sufficient and appropriate guidance from intensity features without any prior. In fact, the features in gradient domain, e.g., edges, present strong correlations between the intensity image and the corresponding depth map. Therefore, the guidance in gradient domain can be more efficiently explored. In this paper, the depth features are iteratively upsampled by 2$\times$. In each upsampling stage, the low-quality depth features and the corresponding gradient features are iteratively refined by the guidance from the intensity features via two parallel streams. Then, to make full use of depth features in pixel and gradient domains, the depth features and gradient features are alternatively complemented with each other. Compared with state-of-the-art counterparts, the sufficient experimental results show improvements according to the objective and subjective assessments.
Abbasi, MH, Zhang, J & Krovi, V 1970, 'A Lyapunov Optimization Approach to the Quality of Service for Electric Vehicle Fast Charging Stations', 2022 IEEE Vehicle Power and Propulsion Conference (VPPC), 2022 IEEE Vehicle Power and Propulsion Conference (VPPC), IEEE.
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Abhijith, V, Hossain, MJ, Lei, G & Sreelekha, PA 1970, 'A Hybrid Excited Switched Reluctance Motor for Torque Enhancement Without Permanent Magnet Behavior in Electric Vehicle Applications', 2022 IEEE 10th Power India International Conference (PIICON), 2022 IEEE 10th Power India International Conference (PIICON), IEEE.
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Ahmed, F, Afzal, MU, Hayat, T, Thalakotuna, D & Esselle, KP 1970, 'Near-Field Phase Transforming Structures for High-Performance Antenna Systems', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE.
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Ahmed, F, Afzal, MU, Singh, K, Hayat, T & Esselle, KP 1970, 'Highly Transparent Fully Metallic 1-Bit Coding Metasurfaces for Near-Field Transformation', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Ahmed, F, Hayat, T, Afzal, MU & Esselle, KP 1970, 'All-Dielectric Phase Correcting Surface Using Fused Deposition Modeling Technique', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Ahmed, F, Singh, K, Esselle, KP & Thalakotuna, D 1970, 'Metasurface-Driven Beam Steering Antenna for Satellite Communications', 2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA), IEEE, pp. 1-5.
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Ahmed, F, Singh, K, Hayat, T, Afzal, MU & Esselle, KP 1970, 'Ku-band Metallic Metasurfaces for High-Power Microwave Applications', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 1-2.
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Metallic metasurfaces operating at Ku-band and suitable for high-power microwave applications are presented. They are made of cheap off-the-shelf metal sheets and synthesized based on the near-field phase transformation principle. Two pairs of non-uniform slots are etched in the center of the thin metal sheet, and such identical layers are stacked to form the phase-shifting cell. Slots' lengths can control the full 360° phase range with high transmission efficiency. Cells are strategically arranged to form the near-field phase transforming metasurfaces (NF-PTMs). They are innovatively applied to enhance the antenna gain by two-fold and steer the antenna beam within the 104° large conical space. In addition, the proposed NF-PTMs have a power handling capability of 1.9 GW level.
Alam, M, Lu, D & Siwakoti, YP 1970, 'A Novel Non-Isolated Three-Port Converter for Battery Management Systems', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE.
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Alam, MM, Aljarajreh, H, Farhangi, M, Dylan D.-C., L & Siwakoti, YP 1970, 'A Novel DC/DC Three Port Converter with Fault-Tolerant Ability', 2022 5th International Conference on Power Electronics and their Applications (ICPEA), 2022 5th International Conference on Power Electronics and their Applications (ICPEA), IEEE, Hail, Saudi Arabia, pp. 1-7.
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This article proposes a novel three-port converter with a fault-tolerant (FT) capability to reconFigure automatically under different switching fault conditions, i.e., open circuit fault (OCF) or short circuit fault (SCF) of the power transistors, and to continue achieving different control objectives such as effective battery charging, maximum power point tracking (MPPT) and output voltage regulation. The proposed fault-tolerant design with a one-level redundancy structure enhances the reliability of traditional three port converters (TPCs), but it also addresses the component failure while different modes of operations using fewer components by incorporating dual-input single-inductor structure into the converter design. Simulation results are presented to explain the performance of the proposed converter during different fault conditions and the reconfiguration mechanism during these conditions.
Alanazi, F & Gay, V 1970, 'e-Health Care Development in Saudi Arabia: Challenges and Problems in e-Health Systems', Proceedings of the Information Systems Education Conference, ISECON, pp. 154-165.
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This systematic review aimed to identify the challenges and problems facing e-health in Saudi Arabia. This information is essential for subsequent identification of e-health modelling requirements and e-health opportunities in the country. A search in Google Scholar using the topic as the search term generated 19 papers for review. The results are presented as abstracted findings of each paper (Supplementary Material) and categorisation by topic, type, and research methods. Analysis of the tabulated data showed that 10 papers dealt explicitly with the topic of this review, that is, problems, challenges and barriers in e-health. The remaining 9 addressed other topics, but included discussion of barriers, problems or challenges. There were 8 conference papers and 11 journal articles. Surveys (10) were the most frequently used (10) research method. Some studies used more than one method. In relation to specific problems, barriers or challenges, 29 papers discussed technological issues, 20 were related to ICT infrastructure and 13 identified organisational and psychosocial factors. This report discusses these results and makes three recommendations.
Alanazi, F, Gay, V & Alturki, R 1970, 'A Model for a Mobile-enabled e-Health System in Saudi Arabia for the Self-management of Diabetes', Proceedings of the Information Systems Education Conference, ISECON, pp. 137-153.
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This paper prescribes the design requirements for a mobile-enabled e-health system for the self-management of diabetes by Saudi diabetes patients. The findings from a survey and a focus group were integrated to achieve this. The requirements, challenges and problems were identified and were supported by published works on the topic. The findings showed that since a variety of stakeholders are involved in such an ecosystem, it is imperative to ensure smooth coordination and an improvement in the outreach of public health campaigns. The findings thus far have highlighted the demographic groups to be targeted for designing and implementing targeted interventions to tackle diabetes in Saudi Arabia. Doing this would require interventions in the healthcare system, hospital and home-based management, and targeted patient interventions. The finer aspects of the system design need to be determined based on similar successful models and expert opinions. Some comments on the boundaries of this research are also provided.
Ali, H, Afzal, MU, Mukhopadhyay, S & Esselle, KP 1970, 'Polarization Diversity via Aperture Sharing Between Orthogonal Sub-arrays', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE.
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Ali, H, Afzal, MU, Shrestha, S, Mukhopadhyay, S & Esselle, KP 1970, 'Enhancement of Near-Field Beam Steering with a Flanched-Cross Cell', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE.
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An, Y, Han, SH & Ling, SH 1970, 'Multi-classification for EEG Motor Imagery Signals using Auto-selected Filter Bank Regularized Common Spatial Pattern.', ISMICT, IEEE International Symposium on Medical Information and Communication Technology, IEEE, Lincoln, NE, USA, pp. 1-6.
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Motor Imagery MI is a critical topic in Brain Computer Interface BCI Due to the low signal to noise ratio it is not easy to accurately classify motor imagery signals especially for multiple classification tasks Common Spatial Pattern CSP is a spatial transformation method that can effectively extract spatial features of EEG signals However the covariance matrix is inaccurate due to the small training data size Thus in this paper a regularization parameter auto selection algorithm is proposed to automatically adjust the ratio of the covariance matrix calculated by other subjects data based on the mutual information It can be used to tackle the problem of an inaccurate mixed covariance matrix caused by fixed regularization parameters To illustrate the merits of the proposed Auto selected Filter Bank Regularized Common Spatial Pattern AFBRCSP we used the ten folds cross validation accuracy and Kappa as the evaluation metrics to evaluate two data sets BCI4 2a and BCI3a data set Both data set include four mental classes By using BCI4 2a data set we found that the mean accuracy of AFBRSP is 77 31 and the Kappa is 0 6975 which is higher than Filter Bank Regularized Common Spatial Pattern FBRCSP by 5 67 and 0 0756 respectively By using BCI3a data set the proposed AFBRCSP improved the accuracy by 8 34 and the Kappa by 0 1111 compared with FBRCSP where the mean accuracy of AFBRCSP is 80 56 and the kappa is 0 7407 The overall Kappa obtained by the proposed method is also higher than some state of the art methods implying that the proposed method is more reliable
An, Y, Zhao, S & Zhang, G 1970, 'A Stacking Ensemble Approach for Supervised Video Summarization', Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing, VSIP 2022: 2022 4th International Conference on Video, Signal and Image Processing, ACM.
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Ang, JD & Zhu, X 1970, 'Recent Advances in On-Chip Silicon-based Passive Components for RF and Millimeter-Wave Applications', 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), IEEE.
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Ang, JD, Hora, JA & Zhu, X 1970, 'Design of Millimetre-Wave Low-Noise Amplifier in 130-nm SiGe HBT Technology', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Ang, JD, Hora, JA & Zhu, X 1970, 'Design of Millimetre-Wave Passive Mixer in 45-nm SOI CMOS Technology', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Ansari, M, Jones, B & Jay Guo, Y 1970, 'A Wide Angle Scanning Spherical Luneburg Lens Antenna Employing Metamaterial', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE.
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Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP & Siwakoti, YP 1970, 'A Novel Seven-Level Switched-Boost Common-Ground Inverter With Single-Stage Dynamic Voltage Boosting Gain', 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), IEEE, Himeji, Japan, pp. 873-877.
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Single-stage grid-connected PV inverters with a transformerless (TL) concept have been a hot spot research topic in both the academia and industry in the latest years. Dynamic voltage boosting feature through the adjustment of de duty cycle, reduced value of the current and voltage stress across the switches, common-ground (CG)-based circuit architecture and capability of larger number of output voltage levels generation can make these types of inverters an attractive option for an efficient and compact design. In this paper, a novel seven-level (7L)-CG-based TL inverter is proposed, which possesses all the above-mentioned features for a compact design. The working principle and modulation strategy are discussed. Some simulation results are presented to attest a proper performance of the converter.
Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Siwakoti, YP & Liserre, M 1970, 'Active Neutral Point-Clamped Five-Level Inverter With Single-Stage Dynamic Voltage Boosting Capability', 2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), IEEE, pp. 1-6.
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The circuit performance of conventional active neutral point-clamped (ANPC) inverter is widely accepted in many renewable energy-based applications like photovoltaic (PV) or electric vehicle grid-connected systems. This is mainly because of its excellent characteristics in terms of voltage/current stress profile of the switches, bidirectional power flow capability, and efficient operation. Nonetheless, due to its half-dc link voltage utilization in the ac output voltage, another power processing stage with additional active and passive elements is required to make its output voltage compatible with the grid when low and wide varying input dc source is available. In this paper, a novel ANPC-based five-level (ANPC5L) inverter with a single-stage boost-integrated circuit design is presented. The proposed topology is able to make the peak output voltage of the conventional ANPC5L inverter followed by a front-end bidirectional boost converter double using the same number of power switches but with less total standing voltage across semiconductors. The working principles of the proposed topology is discussed. Experimental results obtained from 1.3 kW laboratory-built prototype under the grid-connected condition are also given to support the discussion.
Bau', M, Zini, M, Nastro, A, Ferrari, M, Ferrari, V & Lee, JE-Y 1970, 'Electronic technique and system for non-contact reading of temperature sensors based on piezoelectric MEMS resonators', 2022 IEEE International Symposium on Circuits and Systems (ISCAS), 2022 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, Austin, TX, USA, pp. 2409-2413.
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This work investigates an electronic technique and system for non contact reading of the temperature dependent resonant frequency of piezoelectric MEMS resonators The proposed approach exploits magnetic coupling between an interrogation unit and a sensor unit to achieve non contact operation A dedicated electronic circuit in the interrogation unit alternatively switches the system between the excitation and detection phases thus implementing a time gated technique The MEMS resonator in the sensor unit is driven into resonance during the excitation phase while its damped response is sensed in the detection phase An electronic circuit down mixes the damped response of the resonator and the frequency of the resulting signal is measured through a post processing technique based on autocorrelation The system has been applied to the reading of a temperature sensor based on a MEMS aluminum nitride thin film piezoelectric on silicon disk resonator vibrating in radial contour mode The experimental characterization of the non contact system determined the temperature coefficient of frequency of the MEMS resonator to be 47 4 ppm C in good agreement with the measurements taken by directly probing the resonator
Beck, BRG, Tipper, J & Su, S 1970, 'Comparison of Constant PID Controller and Adaptive PID Controller via Reinforcement Learning for a Rehabilitation Robot', 2022 Australian & New Zealand Control Conference (ANZCC), 2022 Australian & New Zealand Control Conference (ANZCC), IEEE.
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Cedieu, S, Grigoletto, FB, Lee, SS, Barzegarkhoo, R & Siwakoti, YP 1970, 'Four-Switch Five-Level Common-Ground Transformerless Inverter', 2022 14th Seminar on Power Electronics and Control (SEPOC), 2022 14th Seminar on Power Electronics and Control (SEPOC), IEEE.
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Chemalamarri, VD, Abolhasan, M & Braun, R 1970, 'An agent-based approach to disintegrate and modularise Software Defined Networks controller', 2022 IEEE 47th Conference on Local Computer Networks (LCN), 2022 IEEE 47th Conference on Local Computer Networks (LCN), IEEE, Edmonton, CANADA, pp. 407-413.
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The Software Defined Network paradigm deviates from traditional networks by logically centralising and physically separating the control plane from the data plane. In this work, we present the idea of a modular, agent-based SDN controller. We first highlight issues with current SDN controller designs, followed by a description of the proposed framework. We present a prototype for our design to demonstrate the controller in action using a few common use-cases. We continue the discussion by highlighting areas that require further research.
Chen, C, Chen, Z, Zhao, J, Guo, Y & Liao, X 1970, 'An Impulse Modulation Strategy for the M-Phase Permanent Magnet Synchronous Motor with the Current Source Inverter', 2022 International Conference on Power Energy Systems and Applications (ICoPESA), 2022 International Conference on Power Energy Systems and Applications (ICoPESA), IEEE.
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Chen, C, Liu, B, Liao, J, Ding, L, Shan, X & Wang, F 1970, 'Lanthanide ions in nanocrystals for biophotonics application', 2021 International Conference on Optical Instruments and Technology: Optical Systems, Optoelectronic Instruments, Novel Display, and Imaging Technology, 2021 International Conference on Optical Instruments and Technology: Optical Systems, Optoelectronic Instruments, Novel Display and Imaging Technology, SPIE, pp. 122770f-122770f.
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Upconversion nanoparticles (UCNPs) is a series of lanthanoid ions doped nanocrystals that are of great interest for biomedical applications, including nanoscale optical sensing and imaging, benefiting from its bright, stable, multicolour emission. Each of the nanoparticles contains thousands of Lanthanide ions, which works as both sensitizers and activators to absorb the near-infrared photons and transfer the energy from sensitizers to activators through nonlinear energy transferring process for an upconverting emission. A few new super-resolution imaging methods have been developed recently based on UCNPs’ unique nonlinear energy transferring process. Most recently, upon these advances, we have found that the thousands of Lanthanide ions provide a strong dielectric resonance effect in a single UCNP. In this work, we will review using the nonlinear response of lanthanoid ions to improve super-resolution nanoscopy. We will also report the ion resonance effect in UCNPs could substantially increase the permittivity and polarizability of nanocrystals, leading to an enhanced optical force on a single 23.3 nm radius UCNP, more than 30 times stronger than the reported value for gold nanoparticles with the same size. The enhanced optical force also provides a way to bypass the optical trapping requirement of “refractive index mismatch”. We further report that the resonance effect could engineer the Rayleigh scattering of UCNPs. These applications suggest a new potential of UCNPs as force probe, scattering probe and fluorescence probe simultaneously for multiplexed imaging.
Chen, D, Li, M, Guo, P & Liu, Y 1970, 'A Novel Multi-Linear Polarization Reconfigurable Antenna Array', 2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP), 2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP), IEEE.
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Chen, S-L, Liu, Y, Chen, D & Guo, YJ 1970, 'High-Gain Multi-Linear Polarization Reconfigurable Antenna in the Millimeter-Wave Band', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Chen, S-L, Ziolkowski, RW, Jones, B & Guo, YJ 1970, 'Closed-Path Toroidal-Waveguide Leaky-Wave Antenna with Directive Beam', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Chen, X, Dai, W, Ni, W, Wang, X, Zhang, S, Xu, S & Sun, Y 1970, 'New Two-Stage Deep Reinforcement Learning for Task Admission and Channel Allocation of Wireless-Powered Mobile Edge Computing', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE.
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Chen, Y, Ding, C, Zhu, H & Guo, YJ 1970, 'A Dual-Slant-Polarized Differentially-Fed In-band Full-duplex (IBFD) Antenna', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 221-222.
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In this paper, a dual-polarized antenna is developed with high isolation between its transmitting (TX) and receiving (RX) ports for in-band full-duplex (IBFD) applications. A square patch antenna with horizontal and vertical polarizations is adopted as the antenna element. A new self-interference cancellation (SIC) feed network is proposed to differentially feed the antenna and combine the horizontal/vertical polarizations into ±45° polarizations. By making use of the symmetry of the antenna configuration and differential feeding, the proposed network can cancel out the coupled and reflected signals, leading to high isolation between the TX and RX ports. A high isolation of 46 dB is realized within the working band from 3.31 to 4 GHz (18.5%) and the gain is above 7.5 dBi. In addition, across the operation band, the radiation patterns show a good stability with the frequency variation.
Choong, DSW, Goh, DJ, Liu, J, Merugu, S, Zhang, QX, Lee, HK, Chang, P, Leotti, A, Tan, H-S, Magbujos, V, Hur, YJ, Lin, H, Chadnra Rao, BSS, Ghosh, S, Ramegowda, PC, Chen, DS-H, Giusti, D, Quaglia, F, Ng, EJ & Lee, JE-Y 1970, 'Correlation of Wafer-scale Film Stress Effects on ScAlN pMUT Parameters', 2022 IEEE International Ultrasonics Symposium (IUS), 2022 IEEE International Ultrasonics Symposium (IUS), IEEE.
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Cui, L, Long, Y, Hoang, DT & Gong, S 1970, 'Hierarchical Learning Approach for Age-of-Information Minimization in Wireless Sensor Networks', 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), IEEE, pp. 130-136.
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In this paper, we focus on a multi-user wireless network coordinated by a multi-antenna access point (AP). Each user can generate the sensing information randomly and report it to the AP. The freshness of information is measured by the age of information (AoI). We formulate the AoI minimization problem by jointly optimizing the users' scheduling and transmission control strategies. Moreover, we employ the intelligent reflecting surface (IRS) to enhance the channel conditions and thus reduce the transmission delay by controlling the AP's beamforming vector and the IRS's phase shifting matrices. The resulting AoI minimization becomes a mixed-integer program and difficult to solve due to uncertain information of the sensing data arrivals at individual users. By exploiting the problem structure, we devised a hierarchical deep reinforcement learning (DRL) framework to search for optimal solution in two iterative steps. Specifically, the users' scheduling strategy is firstly determined by the outer-loop DRL approach, and then the inner-loop optimization adapts either the uplink information transmission or downlink energy transfer to all users. Our numerical results verify that the proposed algorithm can outperform typical baselines in terms of the average AoI performance.
Dang-Ngoc, H, Nguyen, DN, Hoang, DT, Ho-Van, K & Dutkiewicz, E 1970, 'Cooperative Friendly Jamming in Swarm UAV-assisted Communications with Wireless Energy Harvesting', 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), IEEE, Helsinki, Finland.
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This article proposes a cooperative friendly jamming framework for swarm unmanned aerial vehicle (UAV)-assisted amplify-and-forward (AF) relaying networks with wireless energy harvesting. We consider a swarm of hovering UAVs that relays information from a terrestrial source to a distant mobile user and simultaneously generates jamming signals to obfuscate an eavesdropper. Due to the limited energy of the UAVs, we develop a collaborative time-switching relaying protocol that allows the UAVs to collaborate to harvest wireless energy, relay information, and jam the eavesdropper. To evaluate the secrecy rate, we derive the expressions of the secrecy outage probability (SOP) in the integral form for two popular detection techniques used by the eavesdropper, i.e., selection combining and maximum-ratio combining in high signal-to-noise ratio regime. Monte Carlo simulations validate the derived SOP and show that the proposed framework outperforms the conventional AF relaying system, in terms of SOP. The insights from SOP and analysis in this work sheds light on optimizing the energy harvesting time, the number of UAVs in the swarm as well as their placements, to achieve the required secrecy protection level.
Dinh, PV, Nguyen, DN, Hoang, DT, Uy, NQ, Bao, SP & Dutkiewicz, E 1970, 'Balanced Twin Auto-Encoder for IoT Intrusion Detection', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, pp. 3387-3392.
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Intrusion detection systems (IDSs) provide an ef-fective solution for protecting loT systems. However, due to the massive number of loT devices (in billions) and their heterogeneity, IDSs face challenges posed by the complexity of loT data such as correlation-based features, high dimensions, and imbalance. To address these problems, this paper proposes a novel neural network architecture, called Balanced Twin Auto-Encoder (BTAE) which consists of three components, i.e., an encoder, a hermaphrodite, and a decoder. The encoder of BTAE first aims to transfer the input data into the latent space before data samples (pre-images) are translated into this space by different translation vectors. In addition, the data of the skewed labels are also generated in the latent space to address the problem of imbalanced data in which the number of attack samples is often significantly lower than those of the benign samples. Second, the hermaphrodite component serves as a bridge to move the data from the encoder to the decoder. Third, the decoder tries to copy the distribution of the samples in the latent space. BTAE is trained by a supervised learning technique, and its data representation extracted from the decoder can well distinguish the attack from the normal data. The experiments on five loT botnet datasets show that BTAE outperforms three existing groups of methods, e.g., the typical supervised learning, the well-known sampling, and the state-of-the-art representation learning. In addition, the false alarm rate (FAR) of BTAE applied for loT intrusion detection is less than equal to 1.2%.
Dinh, PV, Quang Uy, N, Nguyen, DN, Thai Hoang, D, Bao, SP & Dutkiewicz, E 1970, 'Twin Variational Auto-Encoder for Representation Learning in IoT Intrusion Detection', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 848-853.
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Intrusion detection systems (IDSs) play a pivotal role in defending IoT systems. However, developing a robust and efficient IDS is challenging due to the rapid and continuing evolving of various forms of cyber-attacks as well as a massive number of low-end IoT devices. In this paper, we introduce a novel deep learning architecture based on auto-encoders that allows to develop a robust intrusion detection system. Specifically, we propose a novel neural network architecture called Twin Variational Auto-Encoder (TVAE) for representation learning. TVAE includes a variational Auto-Encoder (VAE) and an Auto-Encoder (AE) that share a common stage where the decoder of the VAE is used as the encoder of the AE. The TVAE is trained in an unsupervised manner to effectively transform the original representation of data at the input of the VAE into a new representation at the output of the AE. In the new representation space, the difference between normal and attack data is more distinguishable. A variant of TVAE, namely Twin Sparse Variational Auto-Encoder (TSVAE) is also introduced by imposing a sparsity constraint on the representation units. The effectiveness of TVAE and TSVAE is evaluated using popular IDS and IoT botnet datasets. The simulation results show that the accuracy of TVAE and TSVAE can achieve the best results on six datasets, which is higher than those of state-of-the-art AE and VAE variants. We also investigate various characteristics of TVAE in the latent space as well as in the data extraction process. Besides applications on the IoT IDS, TVAE can also be applicable to all conventional network IDSs.
Dinh, TH, Doan, QM, Trung, NL, Nguyen, DN & Lin, C-T 1970, 'Masked Face Detection with Illumination Awareness', 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT), 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT), IEEE, pp. 1-6.
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Mask mandate has been applied in many countries in the last two years as a simple but effective way to limit the Covid-19 transmission. Besides the guidance from authorities regarding mask use in public, numerous vision-based approaches have been developed to aid with the monitoring of face mask wearing. Despite promising results have been obtained, several challenges in vision-based masked face detection still remain, primarily due to the insufficient of a quality dataset covering adequate variations in lighting conditions, object scales, mask types, or occlusion levels. In this paper, we investigate the effectiveness of a lightweight masked face detection system under different lighting conditions and the possibility of enhancing its performance with the employment of an image enhancement algorithm and an illumination awareness classifier. A dataset of human subjects with and without face masks in different lighting conditions is first introduced. An illumination awareness classifier is then trained on the collected dataset, the labeling of which is processed automatically based on the difference in detection accuracy when an image enhancement algorithm is taken into account. Experimental results have shown that the combination of the masked face detection system with the illumination awareness and an image enhancement algorithm can boost the system performance to up to 8.6%, 7.4%, and 8.5% in terms of Accuracy, F1-score, and AP-M, respectively.
Eskandari, M, Huang, H, Savkin, AV & Ni, W 1970, 'Autonomous Guidance of an Aerial Drone for Maintaining an Effective Wireless Communication Link with a Moving Node Using an Intelligent Reflecting Surface', 2022 14th International Conference on Computer and Automation Engineering (ICCAE), 2022 14th International Conference on Computer and Automation Engineering (ICCAE), IEEE.
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Eslahi, H, Hamilton, TJ & Khandelwal, S 1970, 'Ultra Compact and Linear 4-bit Digital-to-Analog Converter in 22nm FDSOI Technology', 2022 IEEE International Symposium on Circuits and Systems (ISCAS), 2022 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE.
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Farhangi, M, Brazegarkhoo, R, Lee, SS, Lu, D & Siwakoti, Y 1970, 'An Interleaved Switched-Boost Common-Ground Five-Level Inverter', 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), IEEE, pp. 867-872.
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Transfomerless grid-connected inverters with common-grounded circuit architectures and single-stage dy-namic voltage boosting gain are promising candidates for PV energy conversion applications. In this work, a new five-level (5L) variant of this category of inverters is introduced. Key features of the presented inverter are the reduced current stress profile, modularity, uniform peak voltage stress across the switches, higher power handling capability, and bi-directional power flow operation. The proposed topology is comprised of two input inductors, two capacitors, and ten power switches. Through a modular design with a phase-shifted SPWM technique, the injected grid current can be shared among the modules, while the size of the grid-interface filters can be reduced. The working principle of the converter is discussed, and some simulation and experimental results are presented to validate its feasibility.
Fattoruso, V, Sepehrirahnama, S, Tofigh, F, Lai, JCS, Nowotny, M & Oberst, S 1970, 'CONSIDERATION ON HOW TO IMPROVE GROUND REACTION FORCE MEASUREMENTS IN SMALL WALKING INSECTS', Proceedings of the International Congress on Sound and Vibration, 28th International Congress on Sound and Vibration, Singapore.
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Micro-vibrations caused by the motion of insects, provide a content-rich signal that may be perceived by nestmates, competitors or predators. Knowing the ground reaction forces of a single leg impacting the surface can provide quantitative information about the interaction with the substrate, the substrate itself, physiological and behavioural state of an individual, through mechanistic constraints and the diversity of the gait. Micro-force plates have been used for measuring the ground reaction forces in the order of micro-Newton, using highly sensitive strain gauges attached to compliant load-bearing parts of an underlying mechanical structure. However, their calibration and signal-to-noise-ratio are some of the main challenges of designing these highly sensitive systems. For fine movement analysis, the micro-force plates need to be coupled to high speed video recording systems; the synchronisation of the camera and force plate represents another challenge. For an existing micro-force plate designed for ant measurements, which showed linear signal response in the calibrated force with a lower limit of 120 μN, the linearity of force measurement and sensitivity of the device are investigated in a lower force range, extending the opportunity to study also insects with a lighter footfall. We take into account the difficulties of adapting such devices to the insects' needs related to the environment (i.e. temperature, light...) and morphology (i.e. dimension, weight...). Based on the experiments of the force plate, we consider how to design an experimental setup that overcomes many of the behavioural and technical challenges, to enable more efficient and accurate measurements for insects with body weights less than 5 mg.
Ghosh, S, Ramegowda, P, Ng, E, Ali, Z, Goh, DJ, Sharma, J, Wong, HX & Lee, J 1970, 'Parameter Extraction of Thin-Film Scandium-Doped Aluminum Nitride in Piezoelectric Over Silicon-On-Nothing Platform', 2022 IEEE International Ultrasonics Symposium (IUS), 2022 IEEE International Ultrasonics Symposium (IUS), IEEE, Venice, Italy, pp. 1-4.
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We present a method to extract transverse elastic properties Young s modulus shear modulus and Poisson s ratio and relative permittivity of 15 scandium Sc doped aluminum nitride AIN film from electrical measurements of resonators and parallel plate capacitors The resonators comprise a vertical stack of boldsymbol 0 3 mu mathbf m thick mathbf Sc boldsymbol 0 15 mathbf Al boldsymbol 0 85 mathbf N being sandwiched between boldsymbol 0 2 mu mathbf m thick molybdenum Mo and boldsymbol 2 mu mathbf m thick degenerately doped pre released silicon Si membrane on cavity as fabricated using our piezoelectric over silicon on nothing platform Parallel plate capacitors followed the same vertical stack except that these were fabricated on unreleased but degenerately doped Si layers Despite the large thickness ratio between pre released degenerately doped Si membrane to mathbf Sc boldsymbol 0 15 mathbf Al boldsymbol 0 85 mathbf N film we have successfully extracted 2 elements mathbf S 11 and mathbf S 12 from the compliance matrix using an iterative gradient descent method and relative permittivity of mathbf Sc boldsymbol 0 15 mathbf Al boldsymbol 0 85 mathbf N film
Grigorev, A, Mihaita, A-S, Saleh, K & Piccardi, M 1970, 'Traffic incident duration prediction via a deep learning framework for text description encoding', 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, pp. 1770-1777.
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Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents. This paper proposes a new fusion framework for predicting the incident duration from limited information by using an integration of machine learning with traffic flow/speed and incident description as features, encoded via several Deep Learning methods (ANN autoencoder and character-level LSTM-ANN sentiment classifier). The paper constructs a cross-disciplinary modelling approach in transport and data science. The approach improves the incident duration prediction accuracy over the top-performing ML models applied to baseline incident reports. Results show that our proposed method can improve the accuracy by 60% when compared to standard linear or support vector regression models, and a further 7% improvement with respect to the hybrid deep learning auto-encoded GBDT model which seems to outperform all other models. The application area is the city of San Francisco, rich in both traffic incident logs (Countrywide Traffic Accident Data set) and past historical traffic congestion information (5-minute precision measurements from Caltrans Performance Measurement System).
Gu, Z, Yang, X, Jia, W, Xu, C, Yu, P, He, X, Chen, H & Lin, Y 1970, 'StrokePEO: Construction of a Clinical Ontology for Physical Examination of Stroke', 2022 9th International Conference on Digital Home (ICDH), 2022 9th International Conference on Digital Home (ICDH), IEEE, pp. 218-223.
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Clinical ontology is a standardized medical knowledge representation model that facilitates the integration and analysis of a large amount of heterogeneous electronic health record (EHR) data. Using ontologies to represent clinical terms can improve data integration to build robust and interoperable medical information systems. To date, there is no ontology existing to represent the medical knowledge for physical examination of stroke, which has inhibited the stroke physicians to make full use of clinical information captured in EHR data to understand stroke patient's health status and plan effective medication and rehabilitation treatment. In this research, we co-design with two stroke clinical specialists a stroke clinical ontology 'StrokePEO'using advanced natural language processing and deep learning techniques to extract terms and their relationships from real clinical case records provided by a tertiary hospital in China. We apply the W3C Resource Description Framework (RDF) data model to represent these clinical terms and relationships, and successfully store all case data in a graph database with StrokePEO. Our experiment results suggest that our methods and the output of StrokePEO can be applied in various medical contexts that require extraction of medical knowledge from free text for decision making. These include, but not limited to, physical assessment, drug and rehabilitation treatment outcome evaluation, medication effect analysis, and patient risk prediction.
Guo, Y, Ba, X, Liu, L, Hou, L, Lei, G & Zhu, J 1970, 'Performance Enhancement of Permanent Magnet Synchronous Motors Based on Improved Circuit Models', 2022 25th International Conference on Electrical Machines and Systems (ICEMS), 2022 25th International Conference on Electrical Machines and Systems (ICEMS), IEEE, pp. 1-6.
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With the merits of low power loss, high torque density and high power density, permanent magnet synchronous motors (PMSMs) have been widely applied in various areas. In many applications, the PMSMs are requested to operate with high efficiency over wide speed and load ranges, so the study on proper core loss prediction has attracted much attention for the modeling, design, optimization and control of PMSMs. However, the equivalent circuit model, which is usually applied for motor performance analysis with fast calculation, rarely considers the core loss. This paper presents improved performance analysis of a permanent magnet transverse flux motor with soft magnetic composite stator, based on a modified equivalent circuit model considering core loss. Compared with the experimental measurements on the motor prototype, the performance prediction accuracy based on this new circuit model is much higher than that based on conventional circuit model without core loss.
Hanna, B, Xu, G, Wang, X & Hossain, J 1970, 'Blockchain-based solutions for humanitarian supply chain management', AMCIS 2022 Proceedings, Americas Conference on Information Systems, AMCIS, Minneapolis, USA, pp. 195-218.
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The outbreak of the novel COVID-19 demonstrates how pandemics disturb supply chains (SC) all across the world. Policymakers and private-sector partners are increasingly acknowledging that we cannot tackle today's issues without leveraging the promise of new technology. Blockchain technology is increasingly being adopted to help humanitarian efforts in various fields. This paper presents conceptual research designed to assess how Blockchain distributed ledger technology can be leveraged to enhance humanitarian supply chain management (HSCM). This paper fills the present research gap on the Blockchain's potential implications for HSCM by proposing a framework built on the foundations of five prominent institutional economic theories: social exchange theory, principal-agent theory, transaction cost theory, resource-based view, and network theory. These theories could be utilized to generate research topics that are theory-based and industry-relevant. This conceptual framework assists institutions in making decisions about how to recover and rebuild their SC during disasters.
Hanna, B, Xu, G, Wang, X & Hossain, J 1970, 'Data-Driven Computational Algorithms for Predicting Electricity Consumption Missing Values: A Comparative Study', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE, Adelaide, Australia.
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Hasan, SU, Siwakoti, YP & Lu, D 1970, 'Electromagnetic Compatibility Issues with Off-the-Shelf Power Converters: A Case Study', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE.
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Hayat, T, Afzal, MU, Ahmed, F & Esselle, KP 1970, 'Design and Performance Comparison of Compact Resonant Cavity Antennas Using Customized 3D Printing Techniques', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE.
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He, Y, Ding, C, Wei, G & Guo, YJ 1970, 'An Embedded Dual-Band Base Station Antenna Array Employing Choked Bowl-Shaped Antenna for Cross-Band Scattering Mitigation', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE, Madrid, SPAIN.
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An embedded dual-band dual-polarized base station antenna (BSA) array is proposed in this paper. The array consists of two low-scattering bowl-shaped antenna elements working at the lower band (LB) and five cross-dipoles operating at the higher band (HB). Such an array configuration is intended to mitigate the negative effect on the HB antennas' radiation pattern caused by the presence of adjacent LB antennas. In this paper, a new LB antenna loaded with metal chokes is proposed to further reduce its scattering to the HB radiation. The results obtained with conventional bowl-shaped LB antenna and with choked LB antenna are compared to demonstrate the superiority of this de-scattering method. The simulation results show that the HB performance is significantly improved with the help of metal chokes while the LB performance remains nearly unchanged.
Herrera, IT, Nguyen, LV, Le, T, Aguilera, RP & Ha, Q 1970, 'UAV Target Tracking using Nonlinear Model Predictive Control', 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), IEEE, Prague, Czech Republic, pp. 1-7.
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This paper presents a Nonlinear Model Predictive Control (NMPC) formulation for the attitude control of a fixed-wing Unmanned Aerial Vehicle (UAV) tracking a ground target. The vehicle is required to orbit around the target and as such, the tracking system can be modeled in two dimensions, namely the range and bearing angle. The system constraints are considered to account for real-world limitations. Subject to these constraints, the optimal input is obtained from solving a quadratic cost function. Extensive simulation was conducted for several case studies with various trajectories of the target, given position measurements of the UAV. The control development is then applied to track an estimated path taken from a mining truck during operation. The proposed control formulation is compared with a standard linear Model Predictive Control (MPC). The numerical results show that NMPC can cope with both constraints and nonlinearities, resulting in highly accurate tracking even when the UAV initial position is far away from the target, and overcoming poor tracking performance when using linear MPC. Using the current hardware standards, a quantitative analysis is also provided based on the required execution time for solving the constrained quadratic optimization problem at each sampling instant.
Hoang, LM, Nguyen, D, Zhang, JA & Thai Hoang, D 1970, 'Multiple Correlated Jammers Suppression: A Deep Dueling Q-Learning Approach', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 998-1003.
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For wireless networks under jamming attacks, suppressing the jammer is essential to guarantee a rehable communication link. However, it can be problematic to nullify the jamming signal when the correlations between transmitted jamming signals are deliberately varied over tone. Specifically recent studies reveal that the time-varying correlations create a 'virtual change'm the jamming channel and thus their nullspace, even when the physical channels remain unchanged Unlike existing studies that only consider unchanged correlations or merely propose a heuristic solution to the 'virtual change'problem by continuously monitoring the residual jamming signal then updating the beam-forming matrix, we develop a deep dueling Q-learning technique to minimize the magnitude of the 'virtual change'by choosing a suitable allocated time for different phases of each communication frame. Extensive simulations show that the proposed techniques can suppress the jamming signal, even when the correlations vary over time, and the correlations' trajectory is unrevealed. Moreover, our techniques do not require monitoring the residual jamming signals then updating the beam-forming matrix. Therefore, our technique can improve the system's spectral efficiency and reduce the outage probability.
Huang, J, Zhang, L, Gong, Y, Zhang, J, Nie, X & Yin, Y 1970, 'Series Photo Selection via Multi-View Graph Learning', 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022 IEEE International Conference on Multimedia and Expo (ICME), IEEE.
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Huang, S, Li, Y, Ma, B, Feng, Y, Lei, G & Zhu, J 1970, 'A Mortar Method Based Domain Decomposition Approach for Winding Loss Computation of Electrical Machines', 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), IEEE.
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Huang, Y, Kang, D, Chen, L, Zhe, X, Jia, W, Bao, L & He, X 1970, 'CAR: Class-Aware Regularizations for Semantic Segmentation', Computer Vision – ECCV 2022, Springer Nature Switzerland, pp. 518-534.
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Huang, Y, Kang, D, Jia, W, Liu, L & He, X 1970, 'Channelized Axial Attention - Considering Channel Relation within Spatial Attention for Semantic Segmentation', THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 36th AAAI Conference on Artificial Intelligence / 34th Conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence, ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, ELECTR NETWORK, pp. 1016-1025.
Huang, Y, Kang, D, Jia, W, Liu, L & He, X 1970, 'Channelized Axial Attention – considering Channel Relation within Spatial Attention for Semantic Segmentation', Proceedings of the AAAI Conference on Artificial Intelligence, The Thirty-Sixth AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Online virtual conference, pp. 1016-1025.
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Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. However, computing spatial and channel attentions separately sometimes causes errors, especially for those difficult cases. In this paper, we propose Channelized Axial Attention (CAA) to seamlessly integrate channel attention and spatial attention into a single operation with negligible computation overhead. Specifically, we break down the dot-product operation of the spatial attention into two parts and insert channel relation in between, allowing for independently optimized channel attention on each spatial location. We further develop grouped vectorization, which allows our model to run with very little memory consumption without slowing down the running speed. Comparative experiments conducted on multiple benchmark datasets, including Cityscapes, PASCAL Context, and COCO-Stuff, demonstrate that our CAA outperforms many state-of-the-art segmentation models (including dual attention) on all tested datasets.
Iqbal, H, Zheng, J, Chai, R & Chandrasekaran, S 1970, 'Regression Based Real Time Hand Gesture Recognition and Control for Electric Powered Wheelchair', Australasian Conference on Robotics and Automation, ACRA.
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Steering an electric-powered wheelchair is an onerous task for a paralyzed person. Hence, there is a need for either designing a new one or modifying the existing electric-powered wheelchair that is intelligent enough and provides easy daily use for a person who is not capable of handling the manual steering process. Our proposed system is designed to receive, process and classify the surface electromyography (sEMG) signals and gesture recognition techniques before controlling the wheelchair. This paper is based on an analysis of sEMG signals and gesture recognition techniques of a user's dominant limb, and its deployment through Artificial Intelligence based machine learning algorithms. In myoelectric control, classification has been showing promising results with high accuracy but is well known for non-intuitive control. The regression model, on the other hand, allows human-like natural movements, producing proportional and simultaneous control. We are using hand gesture control of an unidirectional wheelchair using sEMG as wearable sensors. Five basic gestures are recognized and classified using feature extraction and a machine learning algorithm. These gestures are mapped to the unidirectional motion commands to steer the wheelchair. The classified algorithm and realtime navigation of the smart wheelchair using the proposed algorithm have been tested by 6 healthy subjects. The results demonstrate performance improvement and gesture recognition accuracy of 95.50% and reduced training time (< 2 mins), compared to state-of-art regression models. In addition, this algorithm has been applied to proportional and simultaneous myoelectric control in real-time.
Khan, AF & Nanda, P 1970, 'Hybrid blockchain-based Authentication Handover and Flow Rule Validation for Secure Software Defined 5G HetNets', 2022 International Wireless Communications and Mobile Computing (IWCMC), 2022 International Wireless Communications and Mobile Computing (IWCMC), IEEE.
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Khawaldeh, HA, Al-soeidat, M, Lu, DD-C & Li, L 1970, 'Power Loss Reduction for PV Emulator Using Transistor-based PV Model', 2022 IEEE Energy Conversion Congress and Exposition (ECCE), 2022 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, pp. 1-8.
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Recently, a photovoltaic (PV) emulator based on a combination of a constant current source and a semiconductor string, i.e., transistors or diodes, has demonstrated much faster dynamics than switching mode power supply (SMPS) based solution and shown also compatible performance with that of a real PV system. While it has high power efficiency at the maximum power point (MPP), the power loss of the emulator increases beyond the MPP and is at the highest at the open-circuit voltage (OCV) operation condition. This paper presents a hybrid solution where the semiconductor string works in the current-source region of the I-V curve and a new switching circuit, which sits in parallel with the semiconductor string, activates in the voltage-source region. Experimental results show that the efficiency and temperature of the PV emulator based on transistor string alone configuration reach 4.8% and 93.5°C, respectively, in the worst-case scenario, i.e., OCV condition, compared to 88.3% and 26.3°C, respectively, for the proposed solution. The switching circuit handles only a fraction of the rated emulator power and has much narrower control bandwidth requirement than pure switching converter based solution. A new control algorithm is proposed to manage the transition between the two regions seamlessly.
Khoi Tran, N, Sabir, B, Babar, MA, Cui, N, Abolhasan, M & Lipman, J 1970, 'ProML: A Decentralised Platform for Provenance Management of Machine Learning Software Systems', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 16th European Conference on Software Architecture (ECSA), Springer International Publishing, Prague, CZECH REPUBLIC, pp. 49-65.
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Large-scale Machine Learning (ML) based Software Systems are increasingly developed by distributed teams situated in different trust domains. Insider threats can launch attacks from any domain to compromise ML assets (models and datasets). Therefore, practitioners require information about how and by whom ML assets were developed to assess their quality attributes such as security, safety, and fairness. Unfortunately, it is challenging for ML teams to access and reconstruct such historical information of ML assets (ML provenance) because it is generally fragmented across distributed ML teams and threatened by the same adversaries that attack ML assets. This paper proposes ProML, a decentralised platform that leverages blockchain and smart contracts to empower distributed ML teams to jointly manage a single source of truth about circulated ML assets’ provenance without relying on a third party, which is vulnerable to insider threats and presents a single point of failure. We propose a novel architectural approach called Artefact-as-a-State-Machine to leverage blockchain transactions and smart contracts for managing ML provenance information and introduce a user-driven provenance capturing mechanism to integrate existing scripts and tools to ProML without compromising participants’ control over their assets and toolchains. We evaluate the performance and overheads of ProML by benchmarking a proof-of-concept system on a global blockchain. Furthermore, we assessed ProML’s security against a threat model of a distributed ML workflow.
Kiyani, A, Nasimuddin, N, Abbas, SM, Asadnia, M & Esselle, KP 1970, 'A Hybrid Design Technique for Realizing Metasurface based Wideband and Wide Dual-Band Circularly Polarized Dielectric Resonator Antennas', 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), IEEE.
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Kluwak, K, Klempous, R, Ito, A, Górski, T, Nikodem, J, Wojciechowski, K, Rozenblit, J, Borowik, G, Chaczko, Z, Bożejko, W & Kulbacki, M 1970, 'Reference Datasets for Analysis of Traditional Japanese and German Martial Arts', Springer Nature Switzerland, pp. 504-511.
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Koh, Y, Goh, DJ, Choong, DSW, Chen, W, Chen, DS-H, Ng, EJ & Lee, JE-Y 1970, 'Trapping of Microbead Spheroids by pMUTs in Microfluidic Channels Embedded with an Acoustic Reflector', 2022 IEEE International Ultrasonics Symposium (IUS), 2022 IEEE International Ultrasonics Symposium (IUS), IEEE.
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Koh, Y, Goh, DJ, Ghosh, S, Wong, HX, Sharma, J, Lal, A, Ng, EJ & Lee, JE-Y 1970, 'Nano-Gap Contact MEMS Torsional Mode Acceleration Switch Wake-up Sensor', 2022 IEEE Sensors, 2022 IEEE Sensors, IEEE.
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Koli, MNY, Esselle, KP, Thalakotuna, DN, Afzal, MU & Islam, MZ 1970, 'Highly Efficient and Wideband Millimeter-Wave Slotted-Array Antenna Technology for 5G Communications', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Koli, NY, Esselle, KP, Mukhopadhyay, S & Islam, MZ 1970, 'Design and Performance Evaluation of a Compact Beam-Tilted Circularly Polarized Slotted Waveguide Antenna', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE.
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Kumar, A, Esmaili, N & Piccardi, M 1970, 'A Temperature-Modified Dynamic Embedded Topic Model', Communications in Computer and Information Science, Springer Nature Singapore, pp. 15-27.
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Topic models are natural language processing models that can parse large collections of documents and automatically discover their main topics. However, conventional topic models fail to capture how such topics change as the collections evolve. To amend this, various researchers have proposed dynamic versions which are able to extract sequences of topics from timestamped document collections. Moreover, a recently-proposed model, the dynamic embedded topic model (DETM), joins such a dynamic analysis with the representational power of word and topic embeddings. In this paper, we propose modifying its word probabilities with a temperature parameter that controls the smoothness/sharpness trade-off of the distributions in an attempt to increase the coherence of the extracted topics. Experimental results over a selection of the COVID-19 Open Research Dataset (CORD-19), the United Nations General Debate Corpus, and the ACL Title and Abstract dataset show that the proposed model – nicknamed DETM-tau after the temperature parameter – has been able to improve the model’s perplexity and topic coherence for all datasets.
Larpruenrudee, P, Bennett, NS, Hossain, J, Fitch, R & Islam, MS 1970, 'Hydrogen Energy Storage System: How does the semi-cylindrical helical coil heat exchanger affect metal hydride beds' thermal conductivity?', Australasian Fluid Mechanics Conference, Australasian Fluid Mechanics Conference, Sydney, Australia.
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Metal hydride (MH) is classified as one of the solid material storage technologies for hydrogen storage. This material has been recently used worldwide because of its ability to provide a large hydrogen storage capacity, low operating pressure and high safety. However, the disadvantage of this material is having low thermal conductivity, which leads to it having a slow hydrogen absorption time. For the absorption process, faster heat removal from the MH storage will result in faster absorption. Therefore, enhancing heat transfer performance is one of the most effective ways to improve storage performance. This paper aims to improve the heat transfer performance by employing a semi-cylindrical coil as a heat exchanger embedded inside the storage material. Air is used as the heat transfer fluid (HTF). A comparison of the hydrogen absorption duration and the bed temperature between the semi-cylindrical coil heat exchanger (SCHE) and the traditional helical coil heat exchanger (HCHE) has been made to investigate the effect of heat exchanger configuration designs. These two configurations are designed based on the constant volume of the heat exchanger tube and metal hydride. The numerical simulations are performed by using ANSYS Fluent 2020 R2. The results from this study indicate that the average bed temperature inside the storage by using SCHE is reduced faster than using HCHE, which leads to having a faster hydrogen absorption, approximately 59% time reduction. The key finding from this study could be an important enabler for industrial applications.
Lee, SS, Siwakoti, YP, Barzegarkhoo, R & Lee, K-B 1970, 'A Five-Level Unity-Gain Active Neutral-Point-Clamped Inverter Designed Using Half-Bridges', 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), IEEE, pp. 859-866.
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This paper proposes a novel 5-level active neutral-point-clamped (ANPC) inverter that doubles the voltage gain of the conventional topology from half to unity. Each phase of the proposed topology is constituted by three half-bridges that control a flying capacitor to generate 5 symmetrical ac voltage levels. Natural voltage balancing of dc-link and flying capacitors in the proposed topology implies that the sensors and voltage balancing controller commonly used in the conventional ANPC inverter is no longer necessary. In addition to switch count reduction, the most noteworthy merit of the proposed topology is its ease of implementation with commercial half-bridge modules, where the design of dedicated circuit is not needed. The operation of the proposed 5-level unity-gain ANPC (5L-VG-ANPC) inverter is analyzed and validated through simulation and experimental tests.
Li, K, Cui, Q, Zhu, Z, Ni, W & Tao, X 1970, 'Lightweight, Privacy-Preserving Handover Authentication for Integrated Terrestrial-Satellite Networks', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE.
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Li, K, Ni, W, Kurunathan, H & Dressler, F 1970, 'Data-driven Deep Reinforcement Learning for Online Flight Resource Allocation in UAV-aided Wireless Powered Sensor Networks', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE.
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Li, M, Liu, J, Hu, Z & Yang, Y 1970, '3D Broadband FSS with Through Holes and Low Profile for UHF and SHF Applications', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE, pp. 443-444.
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Modern communication systems need low-frequency devices with high gain and wide operational bands. This paper proposes a frequency selective surface (FSS) with a wide passband and low profile, which can be additively manufactured. Each unit of the FSS consists of a centre cube and four surrounding walls with two metal layers covering the top and bottom sides. Through drills are introduced in the design to improve the return loss and the insertion loss in the operational band. The proposed FSS prototype is designed and can be fabricated in a single substrate with a multi-material additively manufacturing technology, and its performance is verified in simulation. It resonates at 3.75 GHz with a fractional bandwidth of 29.3%. Good out-of-band suppression is obtained as well
Li, M, Yang, Y, Nulman, J, Yamada, M & Iacopi, F 1970, 'Unique multi -level metal layer electronics solutions offered by advanced 3D printing', 2022 6th IEEE Electron Devices Technology & Manufacturing Conference (EDTM), 2022 6th IEEE Electron Devices Technology & Manufacturing Conference (EDTM), IEEE, Oita, Japan, pp. 144-144.
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Integrated additively manufactured electronics (AME) is a novel manufacturing technique enabled by the recent progress in simultaneous 3D printing of dielectric materials and conductive inks with flexible interlayer distance. This capability allows for the realization of complex passive electronics and antenna elements as a one-stop-shop for circuit-in-package applications. Specifically, we will address the capabilities of the DragonFlyTM LDM system to realize high-frequency applications such as multilayer bandpass filters [1], and multi-layer stacked antennas for millimeter-wave with superior integration capabilities to achieve smaller form factors [2], but also to enable the realization of vertically stacked metamaterial approaches [3]. In addition, the fabrication can take place at low temperature.
Li, Q, Tian, P, Shi, Y, Shi, Y & Tuan, HD 1970, 'Distributionally Robust Optimization for Vehicle-to-grid with Uncertain Renewable Energy', 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), IEEE.
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Li, X, Cui, Q, Xue, Q, Ni, W, Guo, J & Tao, X 1970, 'A New Batch Access Scheme with Global QoS Optimization for Satellite-Terrestrial Networks', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE.
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Li, Z, Zeng, J, Zhang, W, Zhou, S & Liu, RP 1970, '6G mURLLC over Cell-Free Massive MIMO Systems in the Finite Blocklength Regime', Springer International Publishing, pp. 425-437.
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Liang, L, Lin, X, Ma, B, Wang, X, He, Y, Liu, RP & Ni, W 1970, 'Leveraging Byte-Level Features for LSTM-based Anomaly Detection in Controller Area Networks', GLOBECOM 2022 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, Rio de Janeiro, Brazil.
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Lin, L-X, Tu, Z-H & Zhu, H 1970, 'Isolation Enhancement in Millimeter-wave MIMO Array Base on Array-Antenna Decoupling Surface', 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP), IEEE.
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Lin, X, Ma, B, Wang, X, He, Y, Liu, RP & Ni, W 1970, 'Multi-layer Reverse Engineering System for Vehicular Controller Area Network Messages', 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, pp. 1185-1190.
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The undisclosed Controller Area Network (CAN) decoding specification is important to the in-vehicle network (IVN) research for both industry and academia. Researchers have developed several CAN reverse engineering systems to predict signal boundaries and labels in order to map out CAN signal decoding specifications. Existing works mainly use one parameter (i.e., bit flip rate) to determine CAN signals boundary, which results in biased slicing and labelling of CAN signals. In this paper, we propose a multi-layer CAN reverse engineering system to cluster signal boundary at byte-level and label sliced CAN signal blocks at bit-level. The proposed system avoids biased signal slicing and labelling by introducing multiple parameters in signal classification, while existing works only use the bit flip rate and the number of unique value. The feasibility and adaptability of the proposed system is assessed by deploying it into a web application as a functionality module. We evaluate the proposed system with CAN messages from real cars. Compared with existing reverse engineering models, the proposed system introduces multi-layer signal processing to avoid over-slicing and over-labelling problem.
Lin, Y, Li, L, Zhang, J & Wang, J 1970, 'A model predictive control approach for cotton farm microgrid operation under uncertainties', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE.
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Liu, C, Chao, Z, Wang, S, Wang, Y, Lei, G, Guo, Y & Zhu, J 1970, 'Design and Performance Analysis of a Permanent Magnet Claw Pole Machine with Hybrid Cores', 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), IEEE, pp. 1-2.
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Permanent magnet claw pole machine (PMCPM) is a new type of transverse flux permanent magnet machine (TFPMM). Due to its special structure, its stator core is usually made by soft magnetic composite (SMC) materials. Based on PMCPM with SMC cores, a new PMCPM with hybrid cores is proposed in this paper. The hybrid core is produced by the combination of silicon sheet cores and SMC cores. Compared with the PMCPM with SMC cores, the PMCPM with hybrid cores has higher average torque and efficiency. However, as the permeability of silicon sheets is higher than that of SMC material and it replaced the SMC in part of stator yoke, the power factor of PMCPM with hybrid cores is lower than that with SMC cores. For performance evaluation, 3-D finite element method (FEM) is adopted, and the PMCPM with SMC cores is used as the benchmark comparison.
Liu, J, Choong, DSW, Goh, DJ, Merugu, S, Zhang, QX, Chang, P, Leotti, A, Tan, H-S, Hidayat, A, Ghosh, S, Ramegowda, PC, Chen, DS-H, Giusti, D, Quaglia, F, Pedrini, C, Barabani, L, Castoldi, L, Ng, EJ & Lee, JE-Y 1970, 'Sputtered PZT pMUT with Bias-Tunable Electromechanical Coupling Coefficient for Air-coupled Ranging Applications', 2022 IEEE International Ultrasonics Symposium (IUS), 2022 IEEE International Ultrasonics Symposium (IUS), IEEE, Venice, Italy, pp. 1-4.
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We report preliminary wafer level measurement results of air coupled phase vapor deposition PVD PZT pMUTs for ranging applications recently fabricated in our Lab in Fab 8 inch line Dual port PVD PZT pMUTs designed to resonate in the 100kHz range were fabricated based on a boldsymbol 2 mu mathbf m thick PZT film on a boldsymbol 4 mu mathbf m thick silicon diaphragm The standard deviation of the PZT stack capacitance was 2 across the wafer Despite the sensitivity of compliant diaphragms to film residual stress the standard deviation of frequency was 5 Out of 16 sites across wafer probed all devices were verified to be in good working condition Given the strong piezoelectric constant of the PZT film mathbf e boldsymbol 31 mathbf f boldsymbol sim 16 mathbf C mathbf m boldsymbol 2 and compliance of the diaphragm designed for ranging applications we demonstrate large tunability in frequency 120 210kHz mathbf K mathbf t boldsymbol 2 1 7 3 and quality factor 50 100 for a bias voltage range of0 40V Results have been obtained without poling of the PVD PZT film No unresponsive devices were found
Liu, L, Guo, Y, Lei, G & Zhu, J 1970, 'Multi-level Design Optimization of an IPMSM Drive System Considering an Improved Loss Model', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE.
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Liu, L, Guo, Y, Lei, G, Yin, W, Ba, X & Zhu, J 1970, 'Construction Method and Application Prospect of Electrical Machine Digital Twin', 2022 25th International Conference on Electrical Machines and Systems (ICEMS), 2022 25th International Conference on Electrical Machines and Systems (ICEMS), IEEE, pp. 1-6.
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The digital twin (DT) technique, defined as a dynamic mapping for the physical system to virtual replica, has attracted worldwide attention, which can realize the near real-time interaction among human, machine and environment. This paper aims to present an overview on the research status and technologies of the DT technique and propose an overall DT modeling framework for electrical machines. Among them, property modeling for advanced electrical machine design and analysis is one of the most important research topics which will then be fully investigated. Finally, the key problems that need to be broken through are summarized and the application prospect is also discussed. All the above-mentioned works may trigger in-depth thinking and bring research directions for the application of the DT technique in the field of electrical machines adhering to Industry 4.0 'informatization, digitalization and interaction' concepts.
Liu, Y, Liu, J, Ni, W & Song, L 1970, 'Abnormal Event Detection with Self-guiding Multi-instance Ranking Framework', 2022 International Joint Conference on Neural Networks (IJCNN), 2022 International Joint Conference on Neural Networks (IJCNN), IEEE.
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Lu, D & Aljarajreh, H 1970, 'Non-isolated Multiport DC/DC Converters: Applications, Challenges, and Solutions', 2022 IEEE 9th International Conference on Power Electronics Systems and Applications (PESA), 2022 9th International Conference on Power Electronics Systems and Applications (PESA), IEEE, pp. 1-5.
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This paper presents an overview of different aspects of non-isolated Multiport Converters (MPCs). A systematic converter circuit derivation tool based on power flow graphs is presented. It assists power electronic engineers and researchers in creating and identifying multiport converter topologies that are more reliable and less complex. Then, major challenges with MPCs and some solutions are presented. These challenges are power-sharing and cross-regulation issues, limited or range-reduced operation modes, heavy computational burden and sensor requirement, and multi-parametric optimization and compromises. Reliability of MPCs and how component stress translates into failure calculations are discussed, followed by the Fault Tolerance (FT) feature to increase the reliability of MPCs.
Lu, DD-C, Aljarajreh, H & Hassan, W 1970, 'Reliability Assessment of Selected DC/DC Boost-Converter-Based Multiport Converter Topologies', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE.
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Multiport power converters (MPCs) have the unique feature of interfacing with multiple sources and loads, which are nearby, simultaneously and effectively. They have found potential newer applications in such as hybrid energy systems, electric transportation and portable electronic devices. However, there are concerns about their reliability as compared with conventional single-input, single-output (SISO) and cascaded or paralleled converter structure due to sharing of the same components for multiple power flow paths within the converter circuit and associated higher component stresses. In this paper, six different boost-converter-derived topologies are selected and studied, which include conventional and newer designs. The converters are configured to operate for a standalone solar PV-battery application which demonstrates five distinctive operation modes. The reliability assessment is based on the MIL-HBDK-217F Standard information and LTSpice simulation. The analytical results have shown that while conventional cascaded design generally offers lower failure rates across the board, some alternative designs may offer better MPC reliability.
Lu, L, Xiao, J, Ni, W, Du, H & Zhang, D 1970, 'Deep-Reinforcement-Learning-based User-Preference-Aware Rate Adaptation for Video Streaming', 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), IEEE.
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Ma, B, Lin, X, Wang, X, Liu, B, He, Y, Ni, W & Liu, RP 1970, 'New Cloaking Region Obfuscation for Road Network-Indistinguishability and Location Privacy', Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses, RAID 2022: 25th International Symposium on Research in Attacks, Intrusions and Defenses, ACM, Cyprus.
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Mei, G, Huang, X, Liu, J, Zhang, J & Wu, Q 1970, 'Unsupervised Point Cloud Pre-Training Via Contrasting and Clustering', 2022 IEEE International Conference on Image Processing (ICIP), 2022 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 66-70.
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The annotation for large-scale point clouds is still time-consuming and unavailable for many complex real-world tasks. Point cloud pre-training is a promising direction to auto-extract features without labeled data. Therefore, this paper proposes a general unsupervised approach, named ConClu for point cloud pre-training by jointly performing contrasting and clustering. Specifically, the contrasting is formulated by maximizing the similarity feature vectors produced by encoders fed with two augmentations of the same point cloud. The clustering simultaneously clusters the data while enforcing consistency between cluster assignments produced different augmentations. Experimental evaluations on downstream applications outperform state-of-the-art techniques, which demonstrates the effectiveness of our framework.
Mei, G, Huang, X, Zhang, J & Wu, Q 1970, 'Overlap-Guided Coarse-to-Fine Correspondence Prediction for Point Cloud Registration', 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Taipei, Taiwan, pp. 1-6.
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Establishing reliable correspondences between a pair of point clouds is essential for registration with partial overlaps. However, existing correspondence estimation works usually struggle to distinguish the points in overlap and non-overlap regions. This paper thus proposes an Overlap-guided Coarse-to-Fine Network, named OCFNet, which first establishes correspondences at a coarse level and then refines them at a point level. Specifically, at the coarse level, our model first aggregates two point clouds into smaller sets of super-points with associated features and overlap scores, followed by establishing coarse-level correspondences between the two sets of super-points under the guidance of overlap scores. On the fine stage, a decoder recovers the raw points while jointly learning the associated features and overlap scores. Coarse-level proposals are then expanded to patches, and point-level correspondences are sequentially refined from the corresponding patches. We conducted comprehensive experiments on 3DMatch, 3DLoMatch, and KITTI benchmarks to show the effectiveness of the proposed method. [code]
Mei, G, Huang, X, Zhang, J & Wu, Q 1970, 'Partial Point Cloud Registration Via Soft Segmentation', 2022 IEEE International Conference on Image Processing (ICIP), 2022 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 681-685.
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Most existing correspondence-free registration methods suffer from performance degradation in partial overlapped point clouds. To solve the partial overlapped point cloud registration, this paper proposes, SegReg, a soft Segmentationbased correspondence-free Registration approach. Specifically, we first softly segment both source and target point clouds into a discrete number of geometric partitions, respectively. Then registration is achieved through iteratively using the IC-LK algorithm to minimize the distance between the feature descriptors of the corresponded partitions. Extensive experiments on synthetic synthetic dataset ModelNet40 and real dataset 7Scene show that the proposed method achieves state-of-the-art performance.
Mei, G, Saltori, C, Poiesi, F, Zhang, J, Ricci, E, Sebe, N & Wu, Q 1970, 'Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding', BMVC 2022 - 33rd British Machine Vision Conference Proceedings, British Machine Vision Conference, London, UK.
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Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods. However, data augmentation is not ideal as it requires a careful selection of the type of augmentations to perform, which in turn can affect the geometric and semantic information learned by the network during self-training. To overcome this issue, we propose an augmentation-free unsupervised approach for point clouds to learn transferable point-level features via soft clustering, named SoftClu. SoftClu assumes that the points belonging to a cluster should be close to each other in both geometric and feature spaces. This differs from typical contrastive learning, which builds similar representations for a whole point cloud and its augmented versions. We exploit the affiliation of points to their clusters as a proxy to enable self-training through a pseudo-label prediction task. Under the constraint that these pseudo-labels induce the equipartition of the point cloud, we cast SoftClu as an optimal transport problem. We formulate an unsupervised loss to minimize the standard cross-entropy between pseudo-labels and predicted labels. Experiments on downstream applications, such as 3D object classification, part segmentation, and semantic segmentation, show the effectiveness of our framework in outperforming state-of-the-art techniques [code].
Migalin, M, Keshavarz, R & Shariati, N 1970, 'mm-Wave Polarization Insensitive Spiral Antenna for 5G Energy Harvesting Applications', 2022 Wireless Power Week (WPW), 2022 Wireless Power Week (WPW), IEEE.
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Nabeel, MI, Ahmed, F, Afzal, MU, Thalakotuna, DN & Esselle, KP 1970, 'A Dual Band Resonant-Cavity Antenna for Satellite Communication', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Ngo, QT, Phan, KT, Mahmood, A & Xiang, W 1970, 'DRL-Based Secure Beamforming for Hybrid-RIS Aided Satellite Downlink Communications', 2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), 2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), IEEE.
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Nguyen, CT, Hoang, DT, Nguyen, DN & Dutkiewicz, E 1970, 'MetaChain: A Novel Blockchain-based Framework for Metaverse Applications', 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), IEEE, Helsinki, Finland.
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Metaverse has recently attracted paramount attention due to its potential for future Internet. However, to fully realize such potential, Metaverse applications have to overcome various challenges such as massive resource demands, interoperability among applications, and security and privacy concerns. In this paper, we propose MetaChain, a novel blockchain-based framework to address emerging challenges for the development of Metaverse applications. In particular, by utilizing the smart contract mechanism, MetaChain can effectively manage and automate complex interactions among the Metaverse Service Provider (MSP) and the Metaverse users (MUs). In addition, to allow the MSP to efficiently allocate its resources for Metaverse applications and MUs’ demands, we design a novel sharding scheme to improve the underlying blockchain’s scalability. Moreover, to leverage MUs’ resources as well as to attract more MUs to support Metaverse operations, we develop an incentive mechanism using the Stackelberg game theory that rewards MUs’ contributions to the Metaverse. Through numerical experiments, we clearly show the impacts of the MUs’ behaviors and how the incentive mechanism can attract more MUs and resources to the Metaverse.
Nguyen, CT, Nguyen, DN, Hoang, DT, Pham, H-A & Dutkiewicz, E 1970, 'Optimize Coding and Node Selection for Coded Distributed Computing over Wireless Edge Networks', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Austin, TX, pp. 1248-1253.
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This paper aims to develop a highly-effective framework to significantly enhance the efficiency in using coded computing techniques for distributed computing tasks over heterogeneous wireless edge networks. In particular, we first formulate a joint coding and node selection optimization problem to minimize the expected total processing time for computing tasks, taking into account the heterogeneity in the nodes' computing resources and communication links. The problem is shown to be NP-hard. To circumvent it, we leverage the unique characteristic of the problem to develop a linearization approach and a hybrid algorithm based on binary search and branch-and-bound (BB) algorithms. This hybrid algorithm can not only guarantee to find the optimal solution, but also significantly reduce the computational complexity of the BB algorithm. Simulations based on real-world datasets show that the proposed approach can reduce the total processing time up to 2.4 times compared with that of state-of-the-art approach, even without perfect knowledge regarding the node's performance and their straggling parameters.
Nguyen, D-A, Tran, X-T & Iacopi, F 1970, 'GAQ-SNN: A Genetic Algorithm based Quantization Framework for Deep Spiking Neural Networks', 2022 International Conference on IC Design and Technology (ICICDT), 2022 International Conference on IC Design and Technology (ICICDT), IEEE.
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Nguyen, LV, Torres Herrera, I, Le, TH, Phung, DM, Aguilera, RP & Ha, QP 1970, 'Stag hunt game-based approach for cooperative UAVs', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 39th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), pp. 367-374.
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Unmanned aerial vehicles (UAVs) are being employed in many areas such as photography, emergency, entertainment, defence, agriculture, forestry, mining and construction. Over the last decade, UAV technology hasfound applicationsin numerous construction project phases, ranging from site mapping, progress monitoring, building inspection, damage assessments, and material delivery. While extensive studies have been conducted on the advantages of UAVs for various construction-related processes, studies on UAV collaboration to improve the task capacity and efficiency are still scarce. This paper proposes a new cooperative path planning algorithm for multiple UAVs based on the stag hunt game and particle swarm optimization (PSO). First, a cost function for each UAV is defined, incorporating multiple objectives and constraints. The UAV game framework is then developed to formulate the multi-UAV path planning into the problem of finding payoff-dominant e quilibrium. Next, a PSO-based algorithm is proposed to obtain optimal paths for the UAVs. Simulation results for a large construction site inspected by three UAVs indicate the effectiveness of the proposed algorithm in generating feasible and efficient flight paths for UAV formation during the inspection task.
Nguyen, N-T, Yu, H, Tuan, HD, Nguyen, DN & Dutkiewicz, E 1970, 'Maximization of Geometric Mean of Secrecy Rates in RIS-aided Communications Networks', 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), 2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS), IEEE.
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Ni, Z, Zhang, JA, Huang, X & Yang, K 1970, 'Asynchronous Uplink Sensors Fused in Perceptive Mobile Networks', 2022 IEEE International Conference on Communications Workshops (ICC Workshops), 2022 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, Seoul, SOUTH KOREA, pp. 824-829.
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This paper proposes a scheme that solves two challenging problems in parameter estimation using communication signals: (1) asynchronous transmitter and receiver; and (2) sensing receiver with a small number of antennas. These problems exist in parameter estimation for perceptive mobile networks and WiFi. The geometrically-separated transmitter and receiver in communications are typically asynchronous at clock level. For a small base-station or WiFi, the number of antenna elements in an array is usually limited, which limits the resolution of estimating the angle-of-arrivals (AOAs) of multipath signals. In this paper, we employ cross-antenna cross-correlation (CACC) operation to resolve the asynchronous issue and use the CACC outputs to generate a multi-domain signal block that combines three-domain receive samples to efficiently increase the resolution of AOAs. The proposed scheme enables the direct use of uplink communication signals for radio sensing, without requiring any modifications on infrastructure or advanced hardware, such as a full-duplex transceiver. It also enables the estimation of more number of paths than the number of antennas, hence sensing in a small base-station or WiFi becomes possible.
Ni, Z, Zhang, JA, Yang, K & Liu, R 1970, 'Frequency-Hopping Based Joint Automotive Radar-Communication Systems Using A Single Device', 2022 IEEE International Conference on Communications Workshops (ICC Workshops), 2022 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, Seoul, SOUTH KOREA, pp. 480-485.
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Dual-functional radar-communication (DFRC), integrating the two functions into one system and sharing one transmitted signal, shows its great potential in self-driving networks. In this paper, we develop a single-device based multi-input single-output (MISO) DFRC vehicular system. Modulations of un-slotted ALOHA frequency-hopping (UA-FH) and fast FH, commonly used in automotive radar, are adopted to transmit the DFRC waveforms and to address severe interferences caused by an interfering vehicle that serves as a communication transmitter. Due to the asynchrony between vehicles, the FH sequences of the interfering vehicle are chosen from a fixed codebook. All channel parameters are then extracted via FH decoding from radar backscattered channels and communication channels, respectively. To further increase the accuracy, we proceed to propose an iterative algorithm that divides the signals into short segments and jointly obtains all parameters with high resolution. Finally, simulation results are provided and validate the proposed DFRC vehicular system.
Nikkhah, N, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 1970, 'Efficient Dual-Band Single-Port Rectifier for RF Energy Harvesting at FM and GSM Bands', 2022 Wireless Power Week (WPW), 2022 Wireless Power Week (WPW), IEEE, Bordeaux, France, pp. 141-145.
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This paper presents an efficient dual-band rectifier for radiofrequency energy harvesting (RFEH) applications at FM and GSM bands. The single-port rectifier circuit, which comprises a 3-port network, optimized T-matching circuits and voltage doubler, is designed, simulated and fabricated to obtain a high RF-to-DC power conversion efficiency (PCE). Measurement results show PCE of26% and 22% at -20dBm, and also 58% and 51% at -10dBm with a maximum amount of 69% and 65% at -2.5dBm and -5dBm, with single tone at 95 and 925 MHz, respectively. Besides, the fractional bandwidth of 21% at FM and 11% at GSM band is achieved. The measurement and simulation results are in good agreement. Consequently, the proposed rectifier can be a potential candidate for ambient RF energy harvesting and wireless power transfer (WPT). It should be noted that a 3-port network as a duplexer is designed to be integrated with single-port antennas which cover both FM and GSM bands as a low-cost solution. Moreover, based on simulation results, PCE has small variations when the load resistor varies from 10 to 18 k$\Omega$. Therefore, this rectifier can be utilized for any desired resistance within the range, such as sensors and IoT devices.
Oliveira, FT, Tong, BW, Garcia, JA & Gay, VC 1970, 'CogWorldTravel: Design of a Game-Based Cognitive Screening Instrument', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Joint Conference on Serious Games (JCSG), Springer International Publishing, Bauhaus Univ Weimar, Weimar, GERMANY, pp. 125-139.
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Cognitive Screening Instruments are helpful in the early detection of cognitive changes and possible underlying dementia. These instruments test all major cognitive domains of an individual. Serious games have been investigated as an alternative approach for cognitive assessment because of their ability to motivate. Previous work mostly focused on finding out whether it is feasible to use a serious game for such purpose. We decided to investigate further how a serious game can be engaging and fun while prioritizing the cognitive assessment. In this paper, we describe the design, development, and evaluation of CogWorldTravel, a serious game that has the potential to be used for cognitive screening as it measures at least one aspect of each cognitive domain. CogWorldTravel features six game tasks that involve recognition memory, attention, working memory, language, immediate memory span, processing speed, inhibition, recognition of emotions, visuoconstructional, perceptual-motor, and planning abilities. The serious game also accommodates age-related changes and considers the gameplay preferences of older adults.
Oliveira, FTV, Garcia, JA & Gay, VC 1970, 'Evaluation of CogWorldTravel: A Serious Game for Cognitive Screening', 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), 2022 IEEE 10th International Conference on Serious Games and Applications for Health(SeGAH), IEEE, pp. 1-8.
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As the world population is growing older, there is an urge to develop new technologies to support older adults, who are at a greater risk for the onset of dementia. Cognitive Screening Instruments (CSIs) can be used to screen for dementia. While there are a significant number of available well-researched and accepted CSIs, they are associated with drawbacks. Serious games have been investigated as an alternative instrument to overcome the constraints of traditional methods. The use of serious games for cognitive screening is still a relatively new field of research, with previous works mostly focusing on finding out whether there is a correlation or not between games and cognitive performance. Serious games that engage older adults and meet the criteria of CSIs remain an open challenge. To address this challenge, we developed CogWorldTravel, a serious game for the cognitive screening of older adults. In this paper, we describe the results of the evaluation of CogWorldTravel, which consisted of conducting semi-structured interviews with five experts in dementia assessment. Results suggest that the game involves recognition memory, attention, working memory, language, immediate memory span, processing speed, inhibition, recognition of emotions, visuoconstructional, perceptual-motor, and planning abilities.
Parnell, J, Jauregi Unanue, I & Piccardi, M 1970, 'A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization', Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, IRELAND, pp. 5112-5128.
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Pearce, A, Zhang, JA & Xu, R 1970, 'Regional Trajectory Analysis through Multi-Person Tracking with mmWave Radar', 2022 IEEE Radar Conference (RadarConf22), 2022 IEEE Radar Conference (RadarConf22), IEEE, New York, NY.
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Poblete, P, Syasegov, YY, Farhangi, M, Aguilera, RP, Siwakoti, YP, Lu, D & Pereda, J 1970, 'Optimal Switching Sequence Direct Power Control for AC/DC Converters with Enhanced Converter Model for Lower Switching Frequencies', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE.
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Roshandel, E, Mahmoudi, A, Soong, WL, Kahourzade, S, Lei, G, Guo, Y & Kalisch, N 1970, 'Design of a 100 kW Axial Flux Permanent Magnet Direct Drive Machine for a Hybrid Electric Vehicle', 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), IEEE.
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Samal, PB, Chen, SJ & Fumeaux, C 1970, 'Miniaturized Wearable Antennas using Resonant Current Path Length Manipulation', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Samal, PB, Chen, SJ, Zhang, Q & Fumeaux, C 1970, 'A PDMS-Based Low-Profile Monopole Antenna for Wearable Applications', 2022 IEEE MTT-S International Microwave Biomedical Conference (IMBioC), 2022 IEEE MTT-S International Microwave Biomedical Conference (IMBioC), IEEE.
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Saputra, YM, Nguyen, DN, Hoang, DT & Dutkiewicz, E 1970, 'In-Network Caching and Learning Optimization for Federated Learning in Mobile Edge Networks', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, Seoul, Korea, Republic of, pp. 1653-1658.
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In this paper, we develop a novel privacy-aware framework to address straggling problem in a federated learning (FL)-based mobile edge network through maximizing profit for the mobile service provider (MSP). In particular, unlike the conventional FL process when participating mobile users (MUs) have to train their all data locally, we propose a highly-effective solution that allows MUs to encrypt parts of local data and upload/cache the encrypted data to nearby mobile edge nodes (MENs) and/or a cloud server (CS) to perform additional training processes. In this way, we can not only mitigate the straggling problem caused by limited computing/communications resources at MUs but also enhance the usage efficiency of learning data from all MUs in the FL process. To optimize portions of encrypted data cached and trained at MENs/CS given constraints from MUs and the MSP while considering data privacy and training costs, we first formulate the profit maximization problem for the MSP as an optimal in-network encrypted data caching and learning optimization. We then prove that the objective function is concave, and thus an interior-point method algorithm can be effectively adopted to quickly find the optimal solution. The numerical results demonstrate that our proposed framework can enhance the profit of the MSP up to 5.39 times compared with other FL methods.
Sharma, J, Koh, Y, Ghosh, S, Wong, HX & Joshua, LE-Y 1970, 'In-line test structures for yield improvement in MEMS/NEMS device', 2022 IEEE 24th Electronics Packaging Technology Conference (EPTC), 2022 IEEE 24th Electronics Packaging Technology Conference (EPTC), IEEE.
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Shaw, P, Alam, MM, Ul Hasan, S, Siwakoti, YP & Dah-Chuan Lu, D 1970, 'A New Dual-Input Single-Output Step-up DC-DC Converter for Grid-Connected Photovoltaic Applications', 2022 4th International Conference on Smart Power & Internet Energy Systems (SPIES), 2022 4th International Conference on Smart Power & Internet Energy Systems (SPIES), IEEE.
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Shrestha, S, Zahra, H, Ali, H, Abbas, SM, Asadnia, M & Esselle, KP 1970, 'Conical Rotation of Beam using Three Dimensional Printable Prototype', 2022 International Workshop on Antenna Technology (iWAT), 2022 International Workshop on Antenna Technology (iWAT), IEEE.
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Singandhupe, A, La, HM & Ha, QP 1970, 'Single Frame Lidar-Camera Calibration Using Registration of 3D Planes', 2022 Sixth IEEE International Conference on Robotic Computing (IRC), 2022 Sixth IEEE International Conference on Robotic Computing (IRC), IEEE.
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Singh, K, Afzal, MU & Esselle, KP 1970, 'Efficient Near-Field Meta-Steering Systems for Connectivity-On-The-Move Applications using Hybrid Metasurfaces', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE.
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Singh, K, Ahmed, F, Esselle, KP & Thalakotuna, D 1970, 'Cross-Entropy Method for Combinatorial Mixed-Parameter Optimization of Waveguide Polarizers for Ku-Band', 2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA), IEEE, pp. 1-7.
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Singh, K, Ahmed, F, Thalakotuna, D & Esselle, KP 1970, 'A Modified Approach to Optimize Phase-Gradient Metasurface-Based Beam-Steering Systems', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Song, L-Z, Qin, P-Y & Du, J 1970, 'E-Band Multibeam Conformal Transmitarrays for Beyond 5G Wireless Networks', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Syasegov, YY, Barzegarkhoo, R, Hasan, S, Li, L & Siwakoti, YP 1970, 'A 5-Level Mid-Point Clamped HERIC Inverter', 2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), IEEE, pp. 1-6.
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Multilevel inverter demonstrates advantages over conventional topologies to meet the grid codes and standards. Compared to 3-level inverters, multilevel inverter exhibits significantly better harmonic performance, better efficiency, lower filter size, and less dv/dt and di/dt in the switches. In this regard, a modified HERIC-based topology with a 5-level modulation technique is derived from the conventional 3-level passive midpoint clamped HERIC-based topology and introduced in this paper. A circuit description and working principles of the proposed converter with simulation results, and experimental results are given to demonstrate the feasibility of the concept.
Tabandeh, A, Hossain, MJ & Khalilpour, K 1970, 'A Planning Framework for Integration of Distribution Systems with Grid-Connected Hydrogen Refuelling Stations', 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC), IEEE.
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Tan, Y, Long, Y, Zhao, S, Gong, S, Hoang, DT & Niyato, D 1970, 'Energy Minimization for Wireless Powered Data Offloading in IRS-assisted MEC for Vehicular Networks', 2022 International Wireless Communications and Mobile Computing (IWCMC), 2022 International Wireless Communications and Mobile Computing (IWCMC), IEEE, pp. 731-736.
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In this paper, we consider an IRS-assisted and wireless-powered mobile edge computing (MEC) system that allows both edge users and the IRS to harvest energy from the hybrid access point (HAP), co-located with the MEC server. Each edge user uses the harvested energy to offload its data to the MEC server. The IRS not only assists downlink energy transfer to the edge users, but also improves the users' uplink offloading rates. To minimize the overall energy consumption, we jointly optimize the users' offloading decisions, the HAP's active beamforming, as well as the IRS's energy harvesting and passive beamforming strategies. The energy minimization problem is intractable due to complicated couplings in both the objective function and constraints. We decompose this problem into the downlink energy transfer and the uplink data offloading phases. The uplink phase can be efficiently optimized by the conventional semi-definite relaxation (SDR) method, while the downlink phase depends on the alternating optimization between the users' offloading decisions and the joint active and passive beamforming strategies. Numerical results demonstrate that the proposed offloading scheme can significantly reduce the HAP's energy consumption compared with typical benchmarks.
Thalakotuna, DN, Esselle, KP & Koli, NY 1970, 'A Planar Patch Antenna Array for 5G Millimeter Wave Extender', 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/USNC-URSI), IEEE, pp. 866-867.
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A low cost, simple to fabricate, multi-layer printed patch antenna array that can be used as the outdoor antenna system in the 5G Millimeter wave Extender. The antenna is designed to operate in the n258 (24.25 GHz-27.5 GHz) and has a size of 2.3λx2.3λ. The antenna shows 94% efficiency with typical gain of 15.5 dBi with only a 0.6dB gain variation in the band of interest.
Tofigh, F, Sepehrirahnama, S, Lai, JCS & Oberst, S 1970, 'CHARACTERISING AND CALIBRATING PIEZO ACTUATORS FOR MICRO-EXCITATION FOR VIBRATION PLAYBACK IN BI-OASSAYS OF INSECTS', Proceedings of the International Congress on Sound and Vibration, 28th Intenational Congress on Sound and Vibration, Singapore.
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Micro-vibration signals in bioassays under controlled environmental conditions in biotremology require a device that can generate a similar level of vibration response as caused by the insect. Since bioassays often need to be run in environmental cabinets, the space available is limited, and structures to be excited should not be mass loaded. Considering the properties of piezo actuators in generating very short strokes with high frequency and fast response times, stacked arrangements were found suitable for micro-excitation based on a given approximation of a Dirac delta impulse, approximating in the first instance the impact signal of a walking insect. However, at below the current limit of miniaturised force and displacement actuators, it is essential to characterise and calibrate the piezo actuators to ensure they are producing the desired signal at the point of contact on a given structure. Here we established a methodology for driving piezo actuators at the order of μm/s to generate low-amplitude impulsive excitations. The methodology includes finding the transfer function of the piezo actuator and an aluminum and a wood beam (Pinus radiata) of 20x10mm2 cross section and 200mm length. The reaction force from the piezo actuator was measured from about 40mN down to 2mΝ for travel ranges between 1.2μm and 11μm. The results showed that the force varies linearly from 5-19μm for the ceramic, and 0.6μm to 1.4μm for the PI and the MTK actuators with an input voltage ranging from 2-10V. The measurement setup improved using an anechoic chamber to reduce the noise level by one order of magnitude, compared to reported results in literature, and ensure excitation amplitudes as low as ±10nm/s can be measured. The presented methodology allows developing affordable micro-excitors in the future for playback bioassays in confined spaces which cause minimal mass loading on the test specimen.
Tong, M, Huang, X & Zhang, JA 1970, 'Frame-based Decision Directed Successive Interference Cancellation for FTN Signaling', 2022 IEEE Globecom Workshops (GC Wkshps), 2022 IEEE Globecom Workshops (GC Wkshps), IEEE, pp. 1670-1674.
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In this paper, we propose a frame-based decision directed successive interference cancellation to improve the detection performance of Faster-than-Nyquist (FTN) signaling. The main idea of this method is to directly decide all data symbols in a complete transmission frame after minimum-mean-square-error (MMSE) equalization and regenerate the noise-free signal with the decided symbols. The difference between the equalized and regenerated signals represents the residual inter-symbol interference (ISI) which depends on the bit-error-rate (BER) of the decision. After adding the normalized residual ISI to the decided symbols, the date symbols in the transmission frame are decided recursively, leading to a decision directed successive interference cancellation (DDSIC) scheme. The simulation results in both Gaussian and multipath fading channels demonstrate that our proposed method enables lower complexity and better performance FTN systems compared with existing symbol-by-symbol interference cancellation methods.
Vu, TT, Hoang, DT, Phan, KT, Nguyen, DN & Dutkiewicz, E 1970, 'Energy-based Proportional Fairness for Task Offloading and Resource Allocation in Edge Computing', ICC 2022 - IEEE International Conference on Communications, ICC 2022 - IEEE International Conference on Communications, IEEE, Seoul, Korea, Republic of, pp. 1912-1917.
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By executing offloaded tasks from mobile users, edge computing augments mobile devices with computing/communications resources from edge nodes (ENs), enabling new services/applications (e.g., real-time gaming, virtual/augmented reality). However, despite being more resourceful than mobile devices, allocating ENs’ computing/communications resources to given favorable sets of users may block other devices from their service. This is often the case for most existing task offloading and resource allocation approaches that only aim to maximize the network social welfare (e.g., minimizing the total energy consumption) but not consider the computing/battery status of each mobile device. This work develops a proportional fair task offloading and resource allocation framework for a multi-layer cooperative edge computing network to serve all user equipment (UEs) while considering both their service requirements and individual energy/battery levels. The resulting optimization involves both binary (offloading decisions) and real variables (resource allocations), making it NP-hard. To tackle it, we leverage the fact that the relaxed problem is convex and propose a distributed algorithm, namely the dynamic branchand-bound Benders decomposition (DBBD). DBBD decomposes the original problem into a master problem (MP) for the offloading decision and subproblems (SPs) for resource allocation. The SPs can either find their closed-form solutions or be solved in parallel at ENs, thus help reduce the complexity. The numerical results show that the DBBD returns the optimal solution of the problem maximizing the fairness between UEs. The DBBD has higher fairness indexes, i.e., Jain’s index and min-max ratio, in comparing with the existing ones that minimize the total consumed energy.
Waheed, N, Ikram, M, Hashmi, SS, He, X & Nanda, P 1970, 'An Empirical Assessment of Security and Privacy Risks of Web-Based Chatbots', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 325-339.
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Web-based chatbots provide website owners with the benefits of increased sales, immediate response to their customers, and insight into customer behaviour. While Web-based chatbots are getting popular, they have not received much scrutiny from security researchers. The benefits to owners come at the cost of users’ privacy and security. Vulnerabilities, such as tracking cookies and third-party domains, can be hidden in the chatbot’s iFrame script. This paper presents a large-scale analysis of five Web-based chatbots among the top 1-million Alexa websites. Through our crawler tool, we identify the presence of chatbots in these 1-million websites. We discover that 13,392 out of the top 1- million Alexa websites (1.58%) use one of the five analysed chatbots. Our analysis reveals that the top 300k Alexa ranking websites are dominated by Intercom chatbots that embed the least number of third-party domains. LiveChat chatbots dominate the remaining websites and embed the highest samples of third-party domains. We also find that 721 (5.38%) web-based chatbots use insecure protocols to transfer users’ chats in plain text. Furthermore, some chatbots heavily rely on cookies for tracking and advertisement purposes. More than two-thirds (68.92%) of the identified cookies in chatbot iFrames are used for ads and tracking users. Our results show that, despite the promises for privacy, security, and anonymity given by most websites, millions of users may unknowingly be subject to poor security guarantees by chatbot service providers.
Wen, Y & Qin, P-Y 1970, 'Yagi-Uda Monopoles with Elevated-Angle Suppression for Endfire Radiation', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Wong, HX, Koh, Y, Goh, DJ, Sharma, J, Merugu, S & Lee, JE-Y 1970, 'Silicon Electrothermal Microactuators as Zero Standby Power Local Temperature Switches', 2022 IEEE Sensors, 2022 IEEE Sensors, IEEE, Dallas, TX, USA, pp. 1-4.
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The bent beam structure is a well known elec trothermal actuator based on asymmetric thermal expansion but is less commonly used for sensors In this work a silicon based bent beam structure was fabricated on Silicon on Insulator SOl wafers to realise a local temperature sensitive switch The novel sensor switch closes a 0 7 mu mathrm m gap when locally heated above 80 C yet is unperturbed when the ambient temperature is raised up to 150 C We used thermal imaging to analyse the temperature distributions for different heating schemes and modelled the results with finite element simulations The results show that these structures are attractive candidates for zero standby power surface sensitive temperature switches
Wu, K, Qin, P & Chen, S-L 1970, 'A High-Efficiency 3D-Printed E-Band Dielectric Transmitarray For Integrated Space and Terrestrial Networks', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 1970, 'Removing False Targets For Cyclic Prefixed OFDM Sensing With Extended Ranging', 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), IEEE, Helsinki, Finland.
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Employing cyclic prefixed OFDM (CP-OFDM) communication waveform for sensing has attracted extensive attention in vehicular integrated sensing and communications (ISAC). A unified sensing framework is developed recently, greatly extending the ranging capability of CP-OFDM sensing. However, a false target issue still remains unsolved. In this paper, we investigate and solve this issue. Specifically, we unveil that false targets are caused by periodic cyclic prefixes (CPs) in CP-OFDM waveform. We also derive the relation between the locations of false and true targets, and other features, e.g., strength, of false targets. Moreover, we develop an effective solution to removing false targets. Simulations are provided to confirm the validity of our analysis and the effectiveness of the proposed solution.
Xia, J, Qu, W, Huang, W, Zhang, J, Wang, X & Xu, M 1970, 'Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.
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Xiao, D, Ni, W, Zhang, JA, Liu, R, Chen, S & Qu, Y 1970, 'AI-Enabled Automated and Closed-Loop Optimization Algorithms for Delay-Aware Network', 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Austin, TX, pp. 806-811.
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Network slicing is one of the core techniques of the current 5G networks. To accommodate as many network slices as possible with limited hardware resources, service providers need to avoid over-provisioning of resources. In this paper, we first propose a Deep Q-Network (DQN) based network slicing algorithm to maximize the acceptance ratio and ensure prior placement of higher-priority requests for Ultra-Reliable Low-Latency Communication (URLLC) services. Specifically, we model the network slicing as a Markov Decision Process (MDP), where we consider Virtual Network Function (VNF) placements to be the actions of the MDP, and define a reward function based on service priority. For every service request, we use the DQN to choose an MDP action for performing the VNF placement. The placement results in an MDP reward that we can use to train the DQN. Once trained, the DQN approximates the optimal solution of the MDP. Considering the over-provisioning of resources, we then propose a Binary Search Assisted Transfer Learning algorithm (BSATL), in which the available hardware resources are scaled down/up and the knowledge learned from the source task is transferred to the target task in each iteration, to achieve automated and closed-loop optimization for the ever changing infrastructure, a scenario of 6G Event Defined uRLLC (EDuRLLC). Numerical evaluations show that our proposed scheme can significantly improve cost-utility while maintaining the optimal acceptance ratio.
Xu, H, Nanda, P, Liang, J & He, X 1970, 'The Force of Compensation, a Multi-stage Incentive Mechanism Model for Federated Learning', Springer Nature Switzerland, pp. 357-373.
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Yang, S, Sun, P, Jiang, Y, Xia, X, Zhang, R, Yuan, Z, Wang, C, Luo, P & Xu, M 1970, 'OBJECTS IN SEMANTIC TOPOLOGY', ICLR 2022 - 10th International Conference on Learning Representations.
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A more realistic object detection paradigm, Open-World Object Detection, has arised increasing research interests in the community recently. A qualified open-world object detector can not only identify objects of known categories, but also discover unknown objects, and incrementally learn to categorize them when their annotations progressively arrive. Previous works rely on independent modules to recognize unknown categories and perform incremental learning, respectively. In this paper, we provide a unified perspective: Semantic Topology. During the life-long learning of an open-world object detector, all object instances from the same category are assigned to their corresponding pre-defined node in the semantic topology, including the 'unknown' category. This constraint builds up discriminative feature representations and consistent relationships among objects, thus enabling the detector to distinguish unknown objects out of the known categories, as well as making learned features of known objects undistorted when learning new categories incrementally. Extensive experiments demonstrate that semantic topology, either randomly-generated or derived from a well-trained language model, could outperform the current state-of-the-art open-world object detectors by a large margin, e.g., the absolute open-set error (the number of unknown instances that are wrongly labeled as known) is reduced from 7832 to 2546, exhibiting the inherent superiority of semantic topology on open-world object detection.
Yang, S, Yang, E, Han, B, Liu, Y, Xu, M, Niu, G & Liu, T 1970, 'Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network', Proceedings of Machine Learning Research, pp. 25302-25312.
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In label-noise learning, estimating the transition matrix is a hot topic as the matrix plays an important role in building statistically consistent classifiers. Traditionally, the transition from clean labels to noisy labels (i.e., clean-label transition matrix (CLTM)) has been widely exploited to learn a clean label classifier by employing the noisy data. Motivated by that classifiers mostly output Bayes optimal labels for prediction, in this paper, we study to directly model the transition from Bayes optimal labels to noisy labels (i.e., Bayes-label transition matrix (BLTM)) and learn a classifier to predict Bayes optimal labels. Note that given only noisy data, it is ill-posed to estimate either the CLTM or the BLTM. But favorably, Bayes optimal labels have less uncertainty compared with the clean labels, i.e., the class posteriors of Bayes optimal labels are one-hot vectors while those of clean labels are not. This enables two advantages to estimate the BLTM, i.e., (a) a set of examples with theoretically guaranteed Bayes optimal labels can be collected out of noisy data; (b) the feasible solution space is much smaller. By exploiting the advantages, we estimate the BLTM parametrically by employing a deep neural network, leading to better generalization and superior classification performance.
Yang, Y, Li, M, Esselle, K & Thalakotuna, D 1970, '3D Printed Millimetre-Wave and Sub-Terahertz Devices: Prospects, Challenges, and Solutions', 2022 16th European Conference on Antennas and Propagation (EuCAP), 2022 16th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-4.
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Advanced additive manufacturing (AM) technology enjoys the advantages of fast-prototyping, low-entry-cost, and in-house short-run manufacturing, which empowers millions of start-ups and companies with demanding confidentiality and accelerated innovation. The advancement of AM technology will circumvent the limitations of traditional 3D printed microwave circuits and antennas. This talk aims to present the fundamental knowledge about AM technology and its capability in microwave/millimeter-wave/terahertz circuits and antenna designs. This article presents the state-of-the-art 3D printing technologies and their applications in the millimeter-wave, and sub-terahertz designs, including meta lenses, frequency selective surfaces, waveguides, and antennas.
Yin, Q, Wang, Z, Song, Y, Xu, Y, Niu, S, Bai, L, Guo, Y & Yang, X 1970, 'Improving Deep Embedded Clustering via Learning Cluster-level Representations', Proceedings - International Conference on Computational Linguistics, COLING, pp. 2226-2236.
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Driven by recent advances in neural networks, various Deep Embedding Clustering (DEC) based short text clustering models are being developed. In these works, latent representation learning and text clustering are performed simultaneously. Although these methods are becoming increasingly popular, they use pure cluster-oriented objectives, which can produce meaningless representations. To alleviate this problem, several improvements have been developed to introduce additional learning objectives in the clustering process, such as models based on contrastive learning. However, existing efforts rely heavily on learning meaningful representations at the instance level. They have limited focus on learning global representations, which are necessary to capture the overall data structure at the cluster level. In this paper, we propose a novel DEC model, which we named the deep embedded clustering model with cluster-level representation learning (DECCRL) to jointly learn cluster and instance level representations. Here, we extend the embedded topic modelling approach to introduce reconstruction constraints to help learn cluster-level representations. Experimental results on real-world short text datasets demonstrate that our model produces meaningful clusters.
Yu, X, Xiao, B, Ni, W & Wang, X 1970, 'Optimal Power Control for Over-The-Air Federated Edge Learning Using Statistical Channel Knowledge', 2022 14th International Conference on Wireless Communications and Signal Processing (WCSP), 2022 14th International Conference on Wireless Communications and Signal Processing (WCSP), IEEE.
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Zahra, H, Shrestha, S, Kiyani, A, Abbas, SM, Mukhopadhyay, S & Esselle, KP 1970, 'Switchable Frequency Selective Surface Based on Polydimethyl-siloxane Composite Flexible Substrate for WLAN and 5G Sub-6GHz Applications', 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), IEEE.
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Zhang, L, Cook, K, Szmalenberg, A, Liu, B, Ding, L, Wang, F & McGloin, D 1970, 'Dual beam optical fiber traps for aerosols with angular deviation', Complex Light and Optical Forces XVI, Complex Light and Optical Forces XVI, SPIE.
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Zhang, M, Pan, S, Chang, X, Su, S, Hu, J, Haffari, G & Yang, B 1970, 'BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule', 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New Orleans, LA, pp. 11861-11870.
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Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and continuous relaxation. However, more recent works find that existing differentiable NAS techniques struggle to outperform naive baselines, yielding deteriorative architectures as the search proceeds. Rather than directly optimizing the architecture parameters, this paper formulates the neural architecture search as a distribution learning problem through relaxing the architecture weights into Gaussian distributions. By leveraging the natural-gradient variational inference (NGVI), the architecture distribution can be easily optimized based on existing codebases without incurring more memory and computational consumption. We demonstrate how the differentiable NAS benefits from Bayesian principles, enhancing exploration and improving stability. The experimental results on NAS benchmark datasets confirm the significant improvements the proposed framework can make. In addition, instead of simply applying the argmax on the learned parameters, we further leverage the recently-proposed training-free proxies in NAS to select the optimal architecture from a group architectures drawn from the optimized distribution, where we achieve state-of-the-art results on the NAS-Bench-201 and NAS-Bench-1shot1 benchmarks. Our best architecture in the DARTS search space also obtains competitive test errors with 2.37%, 15.72%, and 24.2% on CIFAR-10, CIFAR-100, and ImageNet, respectively.
Zhang, Z, Wang, X, Yu, G, Ni, W, Liu, RP, Georgalas, N & Reeves, A 1970, 'A Community Detection-Based Blockchain Sharding Scheme', Springer Nature Switzerland, pp. 78-91.
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Zhao, S & Burnett, IS 1970, 'Time-Domain Acoustic Contrast Control with A Spatial Uniformity Constraint for Personal Audio Systems', ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Singapore, Singapore, pp. 1061-1065.
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Personal audio systems with multiple sound zones for listeners to enjoy different music/audio contents privately in a shared physical space have attracted great research interest in the past two decades. Acoustic Contrast Control (ACC) is one of the most popular methods for generating multiple personal sound zones because it produces the minimum inter-zone interference. However, the ACC method has been found to be inferior to the pressure matching method in terms of sound quality due to an uneven frequency response and nonuniform spatial sound field distribution in the bright zone. This paper proposes a spatial uniformity constraint on time-domain broadband ACC in addition to the frequency response trend estimation constraint with the aim of ensuring a uniform sound field distribution in the bright zone. Simulation results with measured room impulse responses demonstrate that the proposed algorithm reduces the magnitude variations in the bright zone to be less than 1 dB higher than the just noticeable level difference at a cost of a perceptually negligible degradation in acoustic contrast.
Zheng, J, Sun, K, Ma, B, Zhu, J & Lei, G 1970, 'An Efficient Decoupling Approach for Non-probabilistic Reliability-Based Design Optimization of Electrical Machines Considering Interval Uncertainties', 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), IEEE.
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Zheng, J, Sun, K, Ma, B, Zhu, J & Lei, G 1970, 'Non-probabilistic Reliability-based Robust Design Optimization of Electrical Machines Considering Interval Uncertainties', 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), 2022 IEEE 20th Biennial Conference on Electromagnetic Field Computation (CEFC), IEEE.
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Zhou, C, Lyu, B, Hoang, DT & Gong, S 1970, 'Reconfigurable Intelligent Surface Assisted Secure Symbiotic Radio Multicast Communications', 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), IEEE, London, United Kingdom, pp. 1-6.
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In this paper we propose a reconfigurable intelligent surface RIS assisted secure transmission scheme for a symbiotic radio multicast system where the RIS not only assists the confidential information multicasting from a primary transmitter PT to multiple primary users PUs to against the information interception by eavesdroppers but also delivers its own signal to a secondary user SU by passive reflections We formulate a signal to noise ratio SNR maximization problem for the SU by jointly optimizing the active beamforming at the PT amplitude reflection coefficients and phase shifts of the RIS To address the non convexity of the formulated problem we propose to decompose the original problem into two sub problems and solve them independently in an iteratively alternating manner For the first sub problem we adopt the successive convex approximation SCA and semidefinite relaxation SDR techniques to design the active beamforming by proving the tightness of SDR For the second sub problem the sequential rank one constraint relaxation SROCR technique is adopted to handle the rank one constraint for reflection coefficients optimization Numerical results show that compared to the benchmark schemes the proposed scheme can achieve up to 68 3 performance gain in terms of SNR
Zhu, H & Guo, YJ 1970, 'Compact and Wideband Filtering Power Dividers with Arbitrary and Constant Output Phase Difference', 2022 Asia-Pacific Microwave Conference (APMC), 2022 Asia-Pacific Microwave Conference (APMC), IEEE.
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Zhu, H, Song, L-Z & Guo, YJ 1970, 'Wideband Hybrid Couplers and Their Applications to Multi-beam Antenna Feed Networks', 2022 International Symposium on Antennas and Propagation (ISAP), 2022 International Symposium on Antennas and Propagation (ISAP), IEEE.
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