Publications

Hoang, DT, Huynh, NV, Nguyen, DN, Hossain, E & Niyato, D 2023, Deep Reinforcement Learning for Wireless Communications and Networking, Wiley, New Jersey.
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Deep Reinforcement Learning for Wireless Communications and Networking Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems Deep Reinforcement Learning for Wireless Communications and Networking presents an overview of the development of DRL while providing fundamental knowledge about theories, formulation, design, learning models, algorithms and implementation of DRL together with a particular case study to practice. The book also covers diverse applications of DRL to address various problems in wireless networks, such as caching, offloading, resource sharing, and security. The authors discuss open issues by introducing some advanced DRL approaches to address emerging issues in wireless communications and networking. Covering new advanced models of DRL, e.g., deep dueling architecture and generative adversarial networks, as well as emerging problems considered in wireless networks, e.g., ambient backscatter communication, intelligent reflecting surfaces and edge intelligence, this is the first comprehensive book studying applications of DRL for wireless networks that presents the state-of-the-art research in architecture, protocol, and application design. Deep Reinforcement Learning for Wireless Communications and Networking covers specific topics such as: Deep reinforcement learning models, covering deep learning, deep reinforcement learning, and models of deep reinforcement learning Physical layer applications covering signal detection, decoding, and beamforming, power and rate control, and physical-layer security Medium access control (MAC) layer applications, covering resource allocation, channel access, and user/cell association Network layer applications, covering traffic routing, network classification, and network slicing With comprehensive coverage of an exciting and noteworthy new technology, Deep Reinforcement Learning for Wireless Communications and Networking is an essential learning resource for ...

Aljumaie, M, Chi Nguyen, H, Chu, NH, Nguyen, CT, Nguyen, DN, Thai Hoang, D & Dutkiewicz, E 2023, 'Potential Applications and Benefits of Metaverse', Wiley, pp. 17-37.
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Chen, C, Chen, Z, Li, Y, Liu, X & Guo, Y 2023, 'A Novel Self-tuning Speed Control Scheme for the Permanent Magnet Synchronous Motor' in Lecture Notes in Electrical Engineering, Springer Nature Singapore, pp. 649-661.
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For the permanent magnet synchronous motor (PMSM), it is challenging to control the speed under the varying conditions, such as wide-speed range and sudden load. Therefore, a novel self-tuning speed controller is proposed for the current source inverter fed three-phase PMSM. This scheme includes two parts: the single-neuron based controller and the parameter self-tuning algorithm in real time. The controller takes the error as the input, and combines the proportional item and the modified active function to meet the actual requirements. Based on the stochastic gradient descent algorithm and the PMSM mathematical model, the real-time online self-tuning of the parameters including the weight and bias is achieved, and the other parameters like the proportional coefficient and learning rate can be easily obtained. With the proposed speed control scheme, the dynamic response and adaptive ability can be improved. Especially under the condition of the wide speed range, the settle time changes little, and the overshoot of the system decreases with the increase of speed. And the parameters of this scheme can be self-tuned in online. The comparison experiments have been executed to verify the proposed ST controller.

Hanna, B, Xu, G, Wang, X & Hossain, J 2023, 'Blockchain-Based Energy Efficient Supply Chain Management' in Blockchain in Supply Chain Digital Transformation, CRC Press, USA, pp. 195-218.
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This book explores real-world supply chain use cases from a range of industries to demonstrate the digital transformative capabilities of blockchain and DLT technologies"--

Hanna, B, Xu, G, Wang, X & Hossain, J 2023, 'Blockchain-enabled humanitarian supply chain management: sustainability and responsibility' in Blockchain in a Volatile-Uncertain-Complex-Ambiguous World, Elsevier, Amsterdam, Netherlands, pp. 251-276.
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Blockchain technology has lately garnered traction as a viable supply chain management system. This paper aims to create a complete theoretical framework for blockchain adoption in supply chain management by identifying the drivers and empirically analyzing their interdependencies and influence on adoption. This paper fills the present research gap on the Blockchain's potential implications for HSCM by proposing a theoretical framework built on the foundations of 10 organizational theories: social exchange theory, resource-based view, transaction cost theory, knowledge-based view, strategic choice theory, agency theory, institutional theory, systems theory, network theory, network perspective theory, and materials logistics theory. This theoretical framework can help decision-makers develop HSCM that can react adaptively to both desirable and undesirable changes (the agility angle), recuperate, and endure in the face of global shocks (short term and long term) accompanied by social and economic crises.

Hoang, DT, Nguyen, DN, Nguyen, CT, Hossain, E & Niyato, D 2023, 'Prefce', pp. xxiv-xxiv.

Iacopi, F & Balestra, F 2023, 'Preface', pp. v-x.

Ma, B, Zhao, Y, Wang, X, Liu, Z, Lin, X, Wang, Z, Ni, W & Liu, RP 2023, 'Vehicle Trajectory Obfuscation and Detection' in Advanced Sciences and Technologies for Security Applications, Springer International Publishing, pp. 121-134.
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Mishra, DK, Li, L, Hossain, MJ & Zhang, J 2023, 'Controller design for load frequency control: Shortcomings and benefits' in Power System Frequency Control, Elsevier, pp. 17-39.
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The controller is the key element of a control system to compare the controlled value to the desired value. From the comparison, if any deviation exists, the controller adjusts it. It is basically a device that generates the control signals to alleviate the deviation of the real value from the required value to either zero or an inconsiderable level. The controller ensures the plant’s efficiency and smooth run. Indeed, the frequency is a sensitive parameter of the power system, which needs to be maintained within rigid limits. Thus to control the frequency, AGC is considered an essential part of the power system, where the controller plays an important role. This chapter explains various control techniques with mathematical modeling and block diagram illustrations. Further, error functions are discussed, which have been used as an objective function in AGC.

Mishra, DK, Li, L, Zhang, J & Hossain, MJ 2023, 'Challenges and viewpoints of load frequency control in deregulated power system' in Power System Frequency Control, Elsevier, pp. 117-132.
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In the last few decades, the power system restructure is undergoing a significant phenomenon due to the participation of different power producers in the energy market. It helps to operate the power delivery in an economical manner. While 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 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 unified power flow controller have been incorporated. Three different test networks have been formed according to the contract value, such as unilateral, bilateral, and contract 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.

Mishra, DK, Li, L, Zhang, J & Hossain, MJ 2023, 'Fundamentals of load frequency control in power system' in Power System Frequency Control, Elsevier, pp. 1-16.
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Automatic generation control (AGC) plays a significant role in the control process in a power network to achieve the equilibrium between generation and load in the most cost-effective manner. Moreover, the AGC approach deals with frequency regulation and control of the power exchange and economic dispatch. This chapter discusses the fundamentals of load frequency control with mathematical modeling and error functions. In addition, the transfer function of the single and multiarea AGC models is presented.

Poostchi, H & Piccardi, M 2023, '6 Persian Named Entity Recognition with Structural Prediction Methods' in Persian Computational Linguistics and NLP, De Gruyter, pp. 149-184.
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Shafei, H, Li, L & Aguilera, RP 2023, 'A Comprehensive Review on Cyber-Attack Detection and Control of Microgrid Systems' in Power Systems Cybersecurity, Springer International Publishing, pp. 1-45.
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Due to the fast progress of Microgrid (MG) systems and the development of advanced computing technologies and communication networks—all of which enhance the efficiency and reliability of power networks—MGs are at the risk of various cyber-attacks which can eventually lead to different glitches in the power distribution networks. There are many different kinds of cyber-attacks, some of which are the False Data Injection Attack, Denial of Service, Stealth Attack, and Covert Attack. The common goals of these attacks are to cause power outage, economic loss, and even system instability. Cyber-attacks could infiltrate MGs through the communication links, local controllers, or master control channels. In this chapter, a thorough review of the types of cyber-attacks and the problems caused by them in MGs has been presented, and some methods of cyber-attack detection, resilient control system design, and countermeasures against such attacks have been discussed. Numerous research works have already investigated the subject of cyber-attacks on both the Direct-Current (DC) and Alternating-Current (AC) MG systems. These studies can be divided into two main categories: (a) detection and mitigation approaches, and (b) resilient control system designs. Several subclasses of each of these categories, along with their advantages and disadvantages has been thoroughly investigated in this chapter. In the first category, after detecting a compromised agent, an active or passive mitigation mechanism is activated to prevent the spread of the agent’s destructive effects to the whole system. This may impose some strict limitations on the MGs. In the second category, by developing the distributed attack-resilient control protocols, the resilience of a MG system against potential attacks/faults/noises is enhanced to the point where no detection and mitigation action will be required.

Vu, L, Nguyen, QU, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2023, 'A Novel Transfer Learning Model for Intrusion Detection Systems in IoT Networks' in Emerging Trends in Cybersecurity Applications, Springer International Publishing, Switzerland, pp. 45-65.
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Internet of Things, or IoT has been playing an important part in human’s lives. Nonetheless, the rapid growth of IoT-based services may lead to an increase in IoT-based attacks that compromise sensitive user data. IoT-based attack detection and prevention is thus essential for the development of IoT-based network technologies. However, this is a difficult task because the data transmitted between IoT devices is often complex and heterogeneous while IoT-based threats/attacks change rapidly over time. Machine learning-based threat detection methods, which are trained with old/existing labelled data, may become less effective in the future. For that, it is critical to develop effective learning models that can leverage both labelled and unlabelled data to timely adapt with the fast-varying attack methods. To this end, this chapter proposes a Deep Transfer Learning (DTL) model to build an effective IoT attack detection model from both labelled and unlabelled data collected from multiple IoT devices. The proposed solution combines the Multi-Maximum Mean Discrepancy (M2D) distance and two AutoEncoder (AE) networks named as Multi-Maximum Mean Discrepancy AutoEncoder (M2DA). The first AE is trained on labelled IoT data, while the second AE is trained on unlabelled IoT data. The purpose of the training process is to transfer the learned label information from the first AE to the second AE by the M2D distance. As a result, the second AE can efficiently classify and detect anomaly (attacks) even on unlabelled IoT network data. We further study the performance of the proposed M2DA framework using nine well-known commercial IoT datasets with different botnet families and attacks.

Zhang, JA, Ni, Z, Wu, K, Huang, X & Guo, YJ 2023, 'Perceptive Mobile Networks' in Integrated Sensing and Communications, Springer Nature Singapore, pp. 355-384.
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Abu, SM, Hannan, MA, Hossain Lipu, MS, Mannan, M, Ker, PJ, Hossain, MJ & Mahlia, TMI 2023, 'State of the art of lithium-ion battery material potentials: An analytical evaluations, issues and future research directions', Journal of Cleaner Production, vol. 394, pp. 136246-136246.
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Ahmed, F, Afzal, MU, Thalakotuna, DN & Esselle, KP 2023, 'Novel Dual-Band Metascreen for Dual-Band Near-Field Phase Correction', IEEE Transactions on Antennas and Propagation, vol. 71, no. 7, pp. 5591-5604.
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Ahmed, F, Hayat, T, Afzal, MU, Zhang, S, Esselle, KP & Whittow, WG 2023, '3-D Printable Synthetic Metasurface to Realize 2-D Beam-Steering Antenna', IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 506-519.
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This article presents highly radio-frequency (RF) transparent phase gradient synthetic metasurfaces made of sub-wavelength-sized 3D printable meta-atoms with tailored permittivity that cannot be achieved with off-the-shelf, commercially available materials. The synthesized meta-atoms design uses one dielectric block of PREPERM ® ABS 1000 material with air and metallic inclusions to make low- and high-permittivity materials. The inclusions' size and height are varied to achieve a complete phase range from 0 to 360°, while maintaining transmission magnitudes greater than -3.0 dB. A two-dimensional array of meta-atoms forms a metasurface, which can be used for phase-shifting structures. Such metasurfaces can manipulate RF waves by introducing progressive phase delay into array elements. The proposed meta-atoms are employed to create highly RF transparent phase-gradient metal-dielectric composite metasurfaces (MDCMs) operating at 11 GHz. These MDCMs can be implemented through 3D printed technology using low-cost thermoplastics or polymers with composite filaments and minimal human intervention. A pair of MDCMs are combined with an array of microstrip patches to demonstrate 2D beam steering functionality numerically. The antenna system provides a peak directivity of 19.9 dBi with a maximum conical scanning angle of 114° and a directivity variation of less than 3 dB.

Ahmed, F, Singh, K & Esselle, KP 2023, 'State-of-the-Art Passive Beam-Steering Antenna Technologies: Challenges and Capabilities', IEEE Access, vol. 11, pp. 69101-69116.
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This article reviews the latest developments of beam steering antennas that are entirely passive to realize interference-free, power-efficient, and highly secured end-to-end wireless communication. We briefly introduce metamaterials and metasurfaces, a timely advanced topic in electromagnetics (EM) and optics. Mathematical formulas associated with the design of beam steering metasurfaces have been numerically explained. In addition, reflect and transmit array antennas are also discussed for an in-depth understanding of beam scanning principles in elevation and azimuth planes. We then provide intuitive design examples and discuss three broad classes of the latest beam scanning antenna systems, namely i) Reflectarrays, ii) Transmitarrays, and iii) Near-Field Meta-Steering antennas that are available in up-to-date literature. The third category’s unprecedented scanning performance and aesthetically compact size are elucidated compared to previous antenna systems, such as reflector dishes or large phased arrays. Alongside the working principles, the trade-offs for the scanning techniques, operation, and physical size of each antenna type are also discussed. Towards the end, an evaluative conclusion with a comparative discussion on the beam-steering antenna systems is provided. Future research directions considering mass-market demands are also indicated.

Ahsan, F, Dana, NH, Sarker, SK, Li, L, Muyeen, SM, Ali, MF, Tasneem, Z, Hasan, MM, Abhi, SH, Islam, MR, Ahamed, MH, Islam, MM, Das, SK, Badal, MFR & Das, P 2023, 'Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review', Protection and Control of Modern Power Systems, vol. 8, no. 1.
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AbstractMeteorological changes urge engineering communities to look for sustainable and clean energy technologies to keep the environment safe by reducing CO2 emissions. The structure of these technologies relies on the deep integration of advanced data-driven techniques which can ensure efficient energy generation, transmission, and distribution. After conducting thorough research for more than a decade, the concept of the smart grid (SG) has emerged, and its practice around the world paves the ways for efficient use of reliable energy technology. However, many developing features evoke keen interest and their improvements can be regarded as the next-generation smart grid (NGSG). Also, to deal with the non-linearity and uncertainty, the emergence of data-driven NGSG technology can become a great initiative to reduce the diverse impact of non-linearity. This paper exhibits the conceptual framework of NGSG by enabling some intelligent technical features to ensure its reliable operation, including intelligent control, agent-based energy conversion, edge computing for energy management, internet of things (IoT) enabled inverter, agent-oriented demand side management, etc. Also, a study on the development of data-driven NGSG is discussed to facilitate the use of emerging data-driven techniques (DDTs) for the sustainable operation of the SG. The prospects of DDTs in the NGSG and their adaptation challenges in real-time are also explored in this paper from various points of view including engineering, technology, et al. Finally, the trends of DDTs towards securing sustainable and clean energy evolution from the NGSG technology in order to keep the environment safe is also studied, while some major future issues are highlighted. This paper can offer extended support for engineers and researchers in the context of data-driven technology and the SG.

Alam, MM, Farhangi, M, D.-C.LU, D, Siwakoti, YP & Aljarajreh, H 2023, 'Design, Implementation and Reliability Assessment of a Fault-Tolerant Three-Port Converter', IEEE Transactions on Industry Applications, pp. 1-12.
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Al-Maliki, S, Bouanani, FE, Ahmad, K, Abdallah, M, Hoang, DT, Niyato, D & Al-Fuqaha, A 2023, 'Toward Improved Reliability of Deep Learning Based Systems Through Online Relabeling of Potential Adversarial Attacks', IEEE Transactions on Reliability, vol. 72, no. 4, pp. 1367-1382.
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Deep neural networks have shown vulnerability to well-designed inputs called adversarial examples. Researchers in industry and academia have proposed many adversarial example defense techniques. However, they offer partial but not full robustness. Thus, complementing them with another layer of protection is a must, especially for mission-critical applications. This article proposes a novel online selection and relabeling algorithm (OSRA) that opportunistically utilizes a limited number of crowdsourced workers to maximize the machine learning (ML) system's robustness. The OSRA strives to use crowdsourced workers effectively by selecting the most suspicious inputs and moving them to the crowdsourced workers to be validated and corrected. As a result, the impact of adversarial examples gets reduced, and accordingly, the ML system becomes more robust. We also proposed a heuristic threshold selection method that contributes to enhancing the prediction system's reliability. We empirically validated our proposed algorithm and found that it can efficiently and optimally utilize the allocated budget for crowdsourcing. It is also effectively integrated with a state-of-the-art black box defense technique, resulting in a more robust system. Simulation results show that the OSRA can outperform a random selection algorithm by 60% and achieve comparable performance to an optimal offline selection benchmark. They also show that OSRA's performance has a positive correlation with system robustness.

Altaf, T, Wang, X, Ni, W, Liu, RP & Braun, R 2023, 'NE-GConv: A lightweight node edge graph convolutional network for intrusion detection', Computers & Security, vol. 130, pp. 103285-103285.
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Altaf, T, Wang, X, Ni, W, Yu, G, Liu, RP & Braun, R 2023, 'A new concatenated Multigraph Neural Network for IoT intrusion detection', Internet of Things, vol. 22, pp. 100818-100818.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2023, 'RF-Self-Powered Sensor for Fully Autonomous Soil Moisture Sensing', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 3, pp. 1374-1387.
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Soil moisture monitoring and irrigation scheduling are essential parameters in farming efficiency. Internet-of-Things (IoT) technology is a promising solution for automating irrigation procedures and improving farming efficiency by removing human faults. In this article, a new method is introduced to measure soil moisture level along with providing energy to run a low-power transmitter as an alarm signal. A combination of a metamaterial perfect absorber (MPA) and two rectifiers that are designed at different frequencies specifies 5% and 25% soil moisture levels. The sensor monitors the soil moisture continuously without consuming energy. Once the soil moisture becomes 5% of the first rectifier starts working and provides 65 $\text{uW}$ dc output, while the second rectifier is off. Increasing the soil moisture to 25%, the second rectifier creates 100 uW dc output when the first rectifier is off. The designed structure is fabricated on RO4003 in a $4$ $\times$ $4$ array. The measurement results are provided by performing a set of different experiments. Initially, the MPA’s absorption characteristics are validated facing different polarization and incident angles. Then, the sensing capability is proven by burying the proposed sensor under sand and measuring the dc outputs of rectifiers. A strong correlation between simulation and measurement results validates the design procedure.

An, Y, Lam, H-K & Ling, SH 2023, 'Multi-classification for EEG motor imagery signals using data evaluation-based auto-selected regularized FBCSP and convolutional neural network.', Neural Comput. Appl., vol. 35, no. 16, pp. 12001-12027.
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AbstractIn recent years, there has been a renewal of interest in brain–computer interface (BCI). One of the BCI tasks is to classify the EEG motor imagery (MI). A great deal of effort has been made on MI classification. What seems to be lacking, however, is multiple MI classification. This paper develops a single-channel-based convolutional neural network to tackle multi-classification motor imagery tasks. For multi-classification, a single-channel learning strategy can extract effective information from each independent channel, making the information between adjacent channels not affect each other. A data evaluation method and a mutual information-based regularization parameters auto-selection algorithm are also proposed to generate effective spatial filters. The proposed method can be used to tackle the problem of an inaccurate mixed covariance matrix caused by fixed regularization parameters and invalid training data. To illustrate the merits of the proposed methods, we used the tenfold cross-validation accuracy and kappa as the evaluation measures to test two data sets. BCI4-2a and BCI3a data sets have four mental classes. For the BCI4-2a data set, the average accuracy is 79.01%, and the kappa is 0.7202 using data evaluation-based auto-selected filter bank regularized common spatial pattern voting (D-ACSP-V) and single-channel series convolutional neural network (SCS-CNN). Compared to traditional FBRCSP, the proposed method improved accuracy by 7.14% for the BCI4-2a data set. By using the BCI3a data set, the proposed method improved accuracy by 9.54% compared with traditional FBRCSP, the average accuracy of the proposed method is 83.70%, and the kappa is 0.7827.

Anand, V, Singh, V, Guo, X, Sathik, MAJ, Siwakoti, YP, Mekhilef, S & Blaabjerg, F 2023, 'Seventeen Level Switched Capacitor Inverters With the Capability of High Voltage Gain and Low Inrush Current', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 4, pp. 1138-1150.
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Arsad, AZ, Hannan, MA, Al-Shetwi, AQ, Begum, RA, Hossain, MJ, Ker, PJ & Mahlia, TMI 2023, 'Hydrogen electrolyser technologies and their modelling for sustainable energy production: A comprehensive review and suggestions', International Journal of Hydrogen Energy, vol. 48, no. 72, pp. 27841-27871.
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Arsad, AZ, Hannan, MA, Al-Shetwi, AQ, Hossain, MJ, Begum, RA, Ker, PJ, Salehi, F & Muttaqi, KM 2023, 'Hydrogen electrolyser for sustainable energy production: A bibliometric analysis and future directions', International Journal of Hydrogen Energy, vol. 48, no. 13, pp. 4960-4983.
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Azizivahed, A, Gholami, K, Li, L & Zhang, J 2023, 'Accurate optimal power flow for active distribution networks via floating tangent surface', Electric Power Systems Research, vol. 217, pp. 109167-109167.
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Ba, X, Sun, X, Gong, Z, Guo, Y, Zhang, C & Zhu, J 2023, 'A Generalized Per-Phase Equivalent Circuit Model of the PMSM With Predictable Core Loss', IEEE/ASME Transactions on Mechatronics, vol. 28, no. 3, pp. 1512-1521.
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Bagirov, A, Seifollahi, S, Piccardi, M, Zare Borzeshi, E & Kruger, B 2023, 'SMGKM: An Efficient Incremental Algorithm for Clustering Document Collections', pp. 314-328.
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Bahrami, H, Sichetti, F, Puppo, E, Vettori, L, Liu Chung Ming, C, Perry, S, Gentile, C & Pietroni, N 2023, 'Physically-based simulation of elastic-plastic fusion of 3D bioprinted spheroids', Biofabrication, vol. 15, no. 4, pp. 045021-045021.
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Abstract Spheroids are microtissues containing cells organized in a spherical shape whose diameter is usually less than a millimetre. Depending on the properties of the environment they are placed in, some nearby spheroids spontaneously fuse and generate a tissue. Given their potential to mimic features typical of body parts and their ability to assemble by fusing in permissive hydrogels, they have been used as building blocks to 3D bioprint human tissue parts. Parameters controlling the shape and size of a bioprinted tissue using fusing spheroid cultures include cell composition, hydrogel properties, and their relative initial position. Hence, simulating, anticipating, and then controlling the spheroid fusion process is essential to control the shape and size of the bioprinted tissue. This study presents the first physically-based framework to simulate the fusion process of bioprinted spheroids. The simulation is based on elastic-plastic solid and fluid continuum mechanics models. Both models use the ‘smoothed particle hydrodynamics’ method, which is based on discretizing the continuous medium into a finite number of particles and solving the differential equations related to the physical properties (e.g. Navier–Stokes equation) using a smoothing kernel function. To further investigate the effects of such parameters on spheroid shape and geometry, we performed sensitivity and morphological analysis to validate our simulations with in-vitro spheroids. Through our in-silico simulations by changing the aforementioned parameters, we show that the proposed models appropriately simulate the range of the elastic-plastic behaviours of in-vitro fusing spheroids to generate tissues of desired shapes and sizes. Altogether, this study presented a physically-based simulation that can provide a framework for monitoring and controlling the ...

Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Blaabjerg, F & Siwakoti, YP 2023, 'A Novel Active Neutral Point-Clamped Five-Level Inverter With Single-Stage-Integrated Dynamic Voltage Boosting Feature', IEEE Transactions on Power Electronics, vol. 38, no. 6, pp. 7796-7809.
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Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Liserre, M & Siwakoti, YP 2023, 'New Family of Dual-Mode Active Neutral Point-Clamped Five-Level Converters', IEEE Transactions on Power Electronics, vol. 38, no. 10, pp. 12236-12253.
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Bi, S, Wang, C, Shen, J, Xiang, W, Ni, W, Wang, X, Wu, B & Gong, Y 2023, 'A Novel RFID Localization Approach to Smart Self-Service Borrowing and Returning System', Computer Modeling in Engineering & Sciences, vol. 135, no. 1, pp. 527-538.
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Brown, A, Lamb, E, Deo, A, Pasin, D, Liu, T, Zhang, W, Su, S & Ueland, M 2023, 'The use of novel electronic nose technology to locate missing persons for criminal investigations', iScience, vol. 26, no. 4, pp. 106353-106353.
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The search for missing persons is a major challenge for investigations involving presumed deceased individuals. Currently, the most effective tool is the use of cadaver-detection dogs; however, they are limited by their cost, limited operation times, and lack of granular information reported to the handler. Thus, there is a need for discrete, real-time detection methods that provide searchers explicit information as to whether human-decomposition volatiles are present. A novel e-nose (NOS.E) developed in-house was investigated as a tool to detect a surface-deposited individual over time. The NOS.E was able to detect the victim throughout most stages of decomposition and was influenced by wind parameters. The sensor responses from different chemical classes were compared to chemical class abundance confirmed by two-dimensional gas chromatography - time-of-flight mass spectrometry. The NOS.E demonstrated its ability to detect surface-deposited individuals days and weeks since death, demonstrating its utility as a detection tool.

Cai, P, Li, H, Guo, Q & Huang, X 2023, 'UAMP-Based Equalization for Dual Pulse Shaping Transmission Systems', IEEE Wireless Communications Letters, vol. 12, no. 7, pp. 1164-1168.
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Chen, L, Chen, L, Ge, Z, Sun, Y & Zhu, X 2023, 'A 40-GHz Load Modulated Balanced Power Amplifier Using Unequal Power Splitter and Phase Compensation Network in 45-nm SOI CMOS', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 8, pp. 3178-3186.
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Chen, L, Chen, L, Zhu, H, Gomez-Garcia, R & Zhu, X 2023, 'A Wideband Balanced Amplifier Using Edge-Coupled Quadrature Couplers in 0.13-μm SiGe HBT Technology', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 2, pp. 631-641.
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Chen, L, Liu, Y, Yang, S, Hu, J & Guo, YJ 2023, 'Synthesis of Wideband Frequency-Invariant Beam Patterns for Nonuniformly Spaced Arrays by Generalized Alternating Projection Approach', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 1099-1104.
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Chen, S-L, Song, L-Z, Karmokar, DK, Jones, B & Guo, YJ 2023, 'Wideband Fixed-Beam Single-Piece Leaky Wave Antenna With Controlled Dispersion Slope', IEEE Transactions on Antennas and Propagation, vol. 71, no. 11, pp. 8429-8440.
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Chen, X, Feng, Z, Wei, Z, Yuan, X, Zhang, P, Zhang, JA & Yang, H 2023, 'Multiple Signal Classification Based Joint Communication and Sensing System', IEEE Transactions on Wireless Communications, vol. 22, no. 10, pp. 6504-6517.
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Chen, Y, Huang, X, An, P & Wu, Q 2023, 'Enhanced Light Field Reconstruction by Combining Disparity and Texture Information in PSVs via Disparity-Guided Fusion', IEEE Transactions on Computational Imaging, vol. 9, pp. 665-677.
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Chen, Y, Li, C, Yang, T, Ekimov, EA, Bradac, C, Ha, ST, Toth, M, Aharonovich, I & Tran, TT 2023, 'Real-Time Ratiometric Optical Nanoscale Thermometry', ACS Nano, vol. 17, no. 3, pp. 2725-2736.
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Chen, Y, White, S, Ekimov, EA, Bradac, C, Toth, M, Aharonovich, I & Tran, TT 2023, 'Ultralow-Power Cryogenic Thermometry Based on Optical-Transition Broadening of a Two-Level System in Diamond', ACS Photonics, vol. 10, no. 8, pp. 2481-2487.
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Cryogenic temperatures are the prerequisite for many advanced scientific applications and technologies. The accurate determination of temperature in this range and at the submicrometer scale is, however, nontrivial. This is due to the fact that temperature reading in cryogenic conditions can be inaccurate due to optically induced heating. Here, we present an ultralow-power, optical thermometry technique that operates at cryogenic temperatures. The technique exploits the temperature-dependent linewidth broadening measured by resonant photoluminescence of a two-level system: a germanium-vacancy color center in a nanodiamond host. The proposed technique achieves a relative sensitivity of ∼20% K-1, at 5 K. This is higher than any other all-optical nanothermometry method. Additionally, it achieves such sensitivities while employing excitation powers of just a few tens of nanowatts, several orders of magnitude lower than other traditional optical thermometry protocols. To showcase the performance of the method, we demonstrate its ability to accurately read out local differences in temperatures at various target locations of a custom-made microcircuit. Our work is a step toward the advancement of nanoscale optical thermometry at cryogenic temperatures.

Chen, Y, Zhou, X, Ni, W, Hossain, E & Wang, X 2023, 'Optimal Power Allocation for Multiuser Photon-Counting Underwater Optical Wireless Communications Under Poisson Shot Noise', IEEE Transactions on Communications, vol. 71, no. 4, pp. 2230-2245.
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Chen, Y, Zhou, X, Ni, W, Wang, X & Hanzo, L 2023, 'Underwater Photon-Counting Systems Under Poisson Shot Noise: Rate Analysis and Power Allocation', IEEE Transactions on Communications, vol. 71, no. 9, pp. 5152-5168.
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Photon counting is an effective detection technique for weak optical signals in underwater optical wireless communications (UOWC). This paper proposes a new approach for power allocation in an uplink M-ary pulse position modulation (PPM), photo-counting non-orthogonal multiple-access (PhC-NOMA) system. Different from existing techniques in photon-counting systems, the new approach supports consistent duty cycles across underwater devices and adjusts the transmit rates of the devices through their transmit powers, thereby avoiding the delays of duty cycle adjustments and supporting high-speed transmissions. Power allocation is non-trivial in photon-counting systems due to signal-dependent Poisson shot noises. As a key contribution, we derive the exact and asymptotic expressions for the achievable rate of the M-ary PPM PhC-NOMA system with the signal-dependent Poisson shot noise and multiuser interference considered. With the expressions, we reveal the received power at the base station (BS) is minimized when their minimum data rate requirements are delivered and can be solved using an incremental algorithm. We also asymptotically maximize the photon efficiency of the devices while preventing the saturation of the receiving photon detector, using Karush-Kuhn-Tucker (KKT) conditions. Simulations show that our approach can reduce the received power at the BS by up to 25% and double the photon efficiency, as compared to the existing techniques.

Chen, Y-N, Ding, C, Zhu, H & Liu, Y 2023, 'A ±45°-Polarized Antenna System With Four Isolated Channels for In-Band Full-Duplex (IBFD)', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3000-3010.
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Chu, NH, Hoang, DT, Nguyen, DN, Van Huynh, N & Dutkiewicz, E 2023, 'Joint Speed Control and Energy Replenishment Optimization for UAV-Assisted IoT Data Collection With Deep Reinforcement Transfer Learning', IEEE Internet of Things Journal, vol. 10, no. 7, pp. 5778-5793.
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Unmanned aerial vehicle (UAV)-assisted data collection has been emerging as a prominent application due to its flexibility, mobility, and low operational cost. However, under the dynamic and uncertainty of IoT data collection and energy replenishment processes, optimizing the performance for UAV collectors is a very challenging task. Thus, this paper introduces a novel framework that jointly optimizes the flying speed and energy replenishment for each UAV to significantly improve the overall system performance (e.g., data collection and energy usage efficiency). Specifically, we first develop a Markov decision process to help the UAV automatically and dynamically make optimal decisions under the dynamics and uncertainties of the environment. Although traditional reinforcement learning algorithms such as Q-learning and deep Q-learning can help the UAV to obtain the optimal policy, they often take a long time to converge and require high computational complexity. Therefore, it is impractical to deploy these conventional methods on UAVs with limited computing capacity and energy resource. To that end, we develop advanced transfer learning techniques that allow UAVs to “share” and “transfer” learning knowledge, thereby reducing the learning time as well as significantly improving learning quality. Extensive simulations demonstrate that our proposed solution can improve the average data collection performance of the system up to 200% and reduce the convergence time up to 50% compared with those of conventional methods.

Chu, NH, Nguyen, DN, Hoang, DT, Pham, Q-V, Phan, KT, Hwang, W-J & Dutkiewicz, E 2023, 'AI-Enabled mm-Waveform Configuration for Autonomous Vehicles With Integrated Communication and Sensing', IEEE Internet of Things Journal, vol. 10, no. 19, pp. 16727-16743.
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Integrated Communications and Sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to Autonomous Vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a Coherent Processing Interval (CPI) is, the greater sensing task’s performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS’s waveform optimization problem even more challenging. To that end, this paper develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment’s dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.

Cui, Y, Lv, T, Ni, W & Jamalipour, A 2023, 'Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems', IEEE Journal on Selected Areas in Communications, vol. 41, no. 10, pp. 3175-3190.
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Dinh, TH, Anh, VTT, Nguyen, T, Hieu Le, C, Trung, NL, Duc, ND & Lin, C-T 2023, 'Toward Vision-Based Concrete Crack Detection: Automatic Simulation of Real-World Cracks', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-15.
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Dinh, TQ, Nguyen, DN, Hoang, DT, Pham, TV & Dutkiewicz, E 2023, 'In-Network Computation for Large-Scale Federated Learning Over Wireless Edge Networks', IEEE Transactions on Mobile Computing, vol. 22, no. 10, pp. 5918-5932.
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Most conventional Federated Learning (FL) models are using a star network topology where all users aggregate their local models at a single server (e.g., a cloud server). That causes significant overhead in terms of both communications and computing at the server, delaying the training process, especially for large scale FL systems with straggling nodes. This paper proposes a novel edge network architecture that enables decentralizing the model aggregation process at the server, thereby significantly reducing the training delay for the whole FL network. Specifically, we design a highly-effective in-network computation framework (INC) consisting of a user scheduling mechanism, an in-network aggregation process (INA) which is designed for both primal- and primal-dual methods in distributed machine learning problems, and a network routing algorithm with theoretical performance bounds. The in-network aggregation process, which is implemented at edge nodes and cloud node, can adapt two typical methods to allow edge networks to effectively solve the distributed machine learning problems. Under the proposed INA, we then formulate a joint routing and resource optimization problem, aiming to minimize the aggregation latency. The problem turns out to be NP-hard, and thus we propose a polynomial time routing algorithm which can achieve near optimal performance with a theoretical bound. Simulation results showed that the proposed algorithm can achieve more than 99$\%$ of the optimal solution and reduce the FL training latency, up to 5.6 times w.r.t other baselines. The proposed INC framework can not only help reduce the FL training latency but also significantly decrease cloud’s traffic and computing overhead. By embedding the computing/aggregation tasks at the edge nodes and leveraging the multi-layer edge-network architecture, the INC framework can liberate FL from the star topology to enable ...

Du, G, Pu, T, Zhou, Q, Wang, L, Lei, G & Zhu, J 2023, 'Multiphysics Comparative Study of High Speed PM Machines for Ring PM Rotor and Solid PM Rotor', IEEE Transactions on Energy Conversion, vol. 38, no. 2, pp. 1421-1432.
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Duan, L, Yang, L, Liu, C, Ni, W & Wang, W 2023, 'A New Smart Contract Anomaly Detection Method by Fusing Opcode and Source Code Features for Blockchain Services', IEEE Transactions on Network and Service Management, vol. 20, no. 4, pp. 4354-4368.
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Eklund, M, Khalilpour, K, Voinov, A & Hossain, MJ 2023, 'Understanding the community in community microgrids: A conceptual framework for better decision-making', Energy Research & Social Science, vol. 104, pp. 103260-103260.
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A community microgrid comes with the introduction of non-conventional distributed renewable energy infrastructure, affecting the behaviour of community members and their relationship with energy. The aspects of ownership, trust, collaboration and its often-discursive structure will be reflected in the cultural and social factors, such as norms and values in a community. The success of specific community microgrids is widely dependent on the community's ability to engage in various activities connected to the microgrid installation and operation. This paper conceptualises existing literature on community microgrids, focusing on the representation and inclusion of community preferences, needs and behaviour across the development stages. From this analysis, a conceptual-theoretical framework is proposed based on social capital theory for identifying community characteristics to determine key needs and considerations for microgrid adoption. The framework is divided into four components: social capital, community capability, community type and microgrid impact. Social capital, including its dimensions such as structural, cognitive, and relational capital forms the foundation of the framework and serves to evaluate the community capability and determine its type, which in turn affects its impact on the community microgrid. Finally, we present an initial step in operationalising our conceptual framework as a practical tool to guide further research in the development of community microgrids. Ultimately, this research can benefit both academia and industry by providing a comprehensive and practical approach to understanding the importance of social factors in community microgrid success.

Eskandari, M, Savkin, AV & Ni, W 2023, 'Consensus-Based Autonomous Navigation of a Team of RIS-Equipped UAVs for LoS Wireless Communication With Mobile Nodes in High-Density Areas', IEEE Transactions on Automation Science and Engineering, vol. 20, no. 2, pp. 923-935.
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Faisal, SN, Do, T-TN, Torzo, T, Leong, D, Pradeepkumar, A, Lin, C-T & Iacopi, F 2023, 'Noninvasive Sensors for Brain–Machine Interfaces Based on Micropatterned Epitaxial Graphene', ACS Applied Nano Materials, vol. 6, no. 7, pp. 5440-5447.
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Fang, C, Hu, Z, Meng, X, Tu, S, Wang, Z, Zeng, D, Ni, W, Guo, S & Han, Z 2023, 'DRL-Driven Joint Task Offloading and Resource Allocation for Energy-Efficient Content Delivery in Cloud-Edge Cooperation Networks', IEEE Transactions on Vehicular Technology, vol. 72, no. 12, pp. 16195-16207.
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Fang, Z, Wu, Z, Ni, W, Wang, X & Hossain, E 2023, 'Beamforming Design for Novel Relay-Assisted Multi-User Multi-Tag Symbiotic Radios', IEEE Wireless Communications Letters, vol. 12, no. 12, pp. 2253-2257.
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Farhangi, M, Barzegarkhoo, R, Aguilera, RP, Lu, DD-C, Liserre, M & Siwakoti, YP 2023, 'A Single-Stage Switched-Boost Grid-Connected Five-Level Converter With Integrated Active Power Decoupling Under Polluted Grid Voltage Condition', IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 328-345.
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Fei, Z, Wang, X, Wu, N, Huang, J & Zhang, JA 2023, 'Air-Ground Integrated Sensing and Communications: Opportunities and Challenges', IEEE Communications Magazine, vol. 61, no. 5, pp. 55-61.
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Fernandez, E, Hossain, MJ, Nawazish Ali, SM & Sharma, V 2023, 'An efficient P2P energy trading platform based on evolutionary games for prosumers in a community', Sustainable Energy, Grids and Networks, vol. 34, pp. 101074-101074.
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Ghadi, MJ, Mishra, DK, Azizivahed, A, Li, L & Zhang, J 2023, 'Mobile compressed air energy storage for active distribution systems', International Journal of Electrical Power & Energy Systems, vol. 154, pp. 109434-109434.
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Gholami, K, Abbasi, M, Azizivahed, A & Li, L 2023, 'An efficient bi-objective approach for dynamic economic emission dispatch of renewable-integrated microgrids', Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 8, pp. 10695-10714.
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AbstractTo overcome the challenges of conventional power systems, such as increasing power demand, requirements of stability and reliability, and increasing integration of renewable energy sources, the concept of microgrids was introduced and is currently one of the most important solutions for solving the mentioned problems. Generally, microgrids have two operating modes, namely grid-connected and islanded modes. Based on the literature and its unique characteristics, the islanded mode is more challenging than the other one. In this paper, a new self-adaptive comprehensive differential evolution (SACDE) algorithm is proposed for solving economic load dispatch (ELD) and combined economic emission dispatch (CEED) problems, achieving optimal power consumption in isolated microgrids. Initially, SACDE is employed for solving the ELD problem as a single-objective function, meaning that the operational cost is just considered as the objective function, and thereby, the resources are scheduled accordingly. Then, a multi-objective platform based on SACDE is also proposed to solve the CEED problem. It means two objective functions, including operational cost and emission, are simultaneously optimized. For evaluating the performance of the proposed method, three different scenarios under various cases are considered. According to the results, when SACDE is employed to solve the single objective function (cost minimization) problem, it has better performance than other methods. In terms of the bi-objective scheme (cost and emission minimization), SACDE is significantly superior to the price penalty factor technique which is frequently used in previous studies.

Gong, S, Cui, L, Gu, B, Lyu, B, Hoang, DT & Niyato, D 2023, 'Hierarchical Deep Reinforcement Learning for Age-of-Information Minimization in IRS-Aided and Wireless-Powered Wireless Networks', IEEE Transactions on Wireless Communications, vol. 22, no. 11, pp. 8114-8127.
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In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP’s signal beamforming. We aim to minimize the average age-of-information (AoI) by adapting the nodes’ scheduling and the transmission control strategies jointly. To reduce the transmission delay, an intelligent reflecting surface (IRS) is used to enhance the channel conditions by controlling the AP’s beamforming strategy and the IRS’s phase shifting matrix. Considering dynamic data arrivals at different sensing nodes, we propose a hierarchical deep reinforcement learning (DRL) framework to for AoI minimization in two steps. The users’ transmission scheduling is firstly determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and then the inner-loop optimization is used to adapt either the uplink information transmission or downlink energy transfer to all nodes. A simple and efficient approximation is also proposed to reduce the inner-loop rum time overhead. Numerical results verify that the hierarchical learning framework outperforms typical baselines in terms of the average AoI and proportional fairness among different nodes.

Gong, S, Wang, M, Gu, B, Zhang, W, Hoang, DT & Niyato, D 2023, 'Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks', IEEE Transactions on Vehicular Technology, vol. 72, no. 8, pp. 10933-10948.
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In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.

Guo, B, Zhang, X, Su, M, Ma, H, Yang, Y & Siwakoti, YP 2023, 'A Single-Phase Common-Ground Five-Level Transformerless Inverter With Low Component Count for PV Applications.', IEEE Trans. Ind. Electron., vol. 70, no. 3, pp. 2662-2674.
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Multilevel inverters (MLIs) featuring reactive power ability and common ground (CG) have become much popular in transformerless grid-tied photovoltaic applications. A switched-capacitor (SC) based five-level transformerless inverter configuration is thus proposed in this article, which requires only six switches, one diode, three capacitors, and one input dc voltage supply. Notably, only four power switches are operated in high frequency, thus, the switching losses are reduced. Moreover, the presented topology can effectively tackle the leakage current problem using a CG architecture. A multicarrier phase disposition pulsewidth modulation strategy is employed to achieve reactive power regulation and SC voltage self-balancing. To achieve the neutral point voltage balance of the presented five-level inverter, a simple closed-loop voltage-balancing control method is proposed. The operation principles with modulation strategy and voltage self-balancing of SC are discussed in depth. Comparisons between the proposed and the state-of-the-art MLIs are presented in detail. Finally, experimental tests on a single-phase 1.1-kW prototype validate the appropriate performance of the proposed inverter.

Guo, CA, Guo, YJ, Zhu, H, Ni, W & Yuan, J 2023, 'Optimization of Multibeam Antennas Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 215-224.
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Currently, there is increasing interest in analog multibeam antennas whose beams can be flexibly steered to arbitrary directions. In a previous paper, we presented the theoretical framework for synthesizing individually steerable multiple beams using generalized joined coupler (GJC) matrices. The synthesis method was to optimize the array excitation vectors to approximate known distributions. In this article, we present a more robust optimization method to optimize the multibeams directly in order to control the half-power beamwidth, the sidelobe levels, and nulls for mitigating system interference. The effectiveness of the proposed method is demonstrated by numerical examples. We reveal how the quality of the multiple beams is inherently determined by the dimensions of the GJC matrix. Experimental results of a 3 10 Nolen-like GJC matrix are presented for the first time to validate the proposed method in realizing low sidelobe multibeams.

Guo, K & Guo, Y 2023, 'Design and Optimization of Linear Rotary Drilling Motor', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Guo, Y, Liu, L, Ba, X, Lu, H, Lei, G, Yin, W & Zhu, J 2023, 'Measurement and Modeling of Magnetic Materials under 3D Vectorial Magnetization for Electrical Machine Design and Analysis', Energies, vol. 16, no. 1, pp. 417-417.
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The magnetic properties of magnetic cores are essential for the design of electrical machines, and consequently appropriate mathematical modeling is needed. Usually, the design and analysis of electrical machines consider only the one-dimensional (1D) magnetic properties of core materials, i.e., the relationship of magnetic flux density (B) versus magnetic field strength (H), and their associated power loss under 1D magnetization, in which the B and H are constrained in the same orientation. Some studies have also been performed with the two-dimensional (2D) magnetizations in which the B and H are vectorial, rotating on a plane, and they may not be in the same direction. It has been discovered that the 2D rotational property is very different from its 1D alternating counterpart. However, the magnetic fields in an electrical machine, in particular claw pole and transverse flux machines, are naturally three-dimensional (3D), and the B and H vectors are rotational and may not lie on the same plane. It can be expected that the 3D vectorial property might be different from its 2D or 1D counterpart, and hence it should be investigated for the interests of both academic research and engineering application. This paper targets at a general summary about the magnetic material characterization with 3D vectorial magnetization, and their application prospect in electrical machine design and analysis.

Hasanpour, S, Nouri, T, Blaabjerg, F & Siwakoti, YP 2023, 'High Step-Up SEPIC-Based Trans-Inverse DC-DC Converter With Quasi-Resonance Operation for Renewable Energy Applications.', IEEE Trans. Ind. Electron., vol. 70, no. 1, pp. 485-497.
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Hasanpour, S, Siwakoti, YP & Blaabjerg, F 2023, 'A New High Efficiency High Step-Up DC/DC Converter for Renewable Energy Applications.', IEEE Trans. Ind. Electron., vol. 70, no. 2, pp. 1489-1500.
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This article introduces a new nonisolated soft-switching coupled-inductor (CI) step-up dc/dc converter. The presented topology utilizes a three-winding CI (TWCI) along with a voltage multiplier circuit to increase the voltage conversion ratio without needing a high duty cycle. Using this circuit, a high voltage gain can be achieved without requiring a large number of turns ratio in the CI. The input current ripple of the introduced converter is very low, which is very desirable for renewable energy sources applications. The TWCI also creates an additional design freedom to increase the voltage gain, which indicates more circuit flexibility. Additionally, the voltage stress across the single power switch is limited with the help of a regenerative clamp capacitor. The leakage inductor of the CI is used to create a resonant tank to reduce power losses further. The leakage inductances help provide the zero-current switching conditions for the single power switch and decrease the reverse-recovery issues for all diodes, leading to an efficiency improvement. The operation principle, steady-state analysis, and design considerations are discussed thoroughly. Finally, the theoretical analysis is validated through experimental results obtained from a 200 W prototype with 250 V output voltage.

Hasanpour, S, Siwakoti, YP & Blaabjerg, F 2023, 'A New Soft-Switched High Step-Up Trans-Inverse DC/DC Converter Based on Built-In Transformer', IEEE Open Journal of Power Electronics, vol. 4, pp. 381-394.
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He, T, Wu, M, Aguilera, RP, Lu, DD-C, Liu, Q & Vazquez, S 2023, 'Low Computational Burden Model Predictive Control for Single-Phase Cascaded H-Bridge Converters Without Weighting Factor', IEEE Transactions on Industrial Electronics, vol. 70, no. 3, pp. 2396-2406.
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In this article, a low computational burden model predictive control (MPC) strategy without weighting factor is proposed for the single-phase cascaded H-bridge CHB converters. To reduce the switching state candidates, a hierarchy control algorithm is proposed. The grid current is controlled by selecting a subregion from the designed 2-D control plane, instead of the entire area. Two vectors are chosen in one sampling period for more accurate tracking. Then, the voltage balancing is achieved by selecting the optimal switching state from the subregion candidates to form the above two vectors. The cost function can be constructed of one variable: load voltage. Therefore, the weighting factor can be eliminated. No tuning or retuning processes are required in the proposed method. To reduce the computational time further, the principle of eliminating the switching state candidates operating the same voltage balancing performance is proposed. Conventional and proposed MPC methods are verified by experimental tests via a laboratory setup of a three-cell connected CHB converter. Steady-state and transient operations demonstrate that the proposed method guarantees less distortion grid current and shorter execution time (reduced from 15 to 3 μ s). Fast response speed to variations in voltage reference and load resistance can be achieved t.

Helalian, H, Zhu, X & Atarodi, M 2023, 'A Multioutput and Highly Efficient GaN Distributed Power Amplifier for Compact Subarrays in Wideband Phased Array Antennas', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 11, pp. 4800-4813.
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Hieu, NQ, Hoang, DT, Niyato, D, Nguyen, DN, Kim, DI & Jamalipour, A 2023, 'Joint Power Allocation and Rate Control for Rate Splitting Multiple Access Networks With Covert Communications', IEEE Transactions on Communications, vol. 71, no. 4, pp. 2274-2287.
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Rate Splitting Multiple Access (RSMA) has recently emerged as a promising technique to enhance the transmission rate for multiple access networks. Unlike conventional multiple access schemes, RSMA requires splitting and transmitting messages at different rates. The joint optimization of the power allocation and rate control at the transmitter is challenging given the uncertainty and dynamics of the environment. Furthermore, securing transmissions in RSMA networks is a crucial problem because the messages transmitted can be easily exposed to adversaries. This work first proposes a stochastic optimization framework that allows the transmitter to adaptively adjust its power and transmission rates allocated to users, and thereby maximizing the sum-rate and fairness of the system under the presence of an adversary. We then develop a highly effective learning algorithm that can help the transmitter to find the optimal policy without requiring complete information about the environment in advance. Extensive simulations show that our proposed scheme can achieve non-saturated transmission rates at high SNR values with infinite blocklength. More significantly, our proposed scheme can achieve positive covert transmission rates in the finite blocklength regime, compared with zero-valued covert rates of a conventional multiple access scheme.

Hieu, NQ, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2023, 'When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach', IEEE Journal on Selected Areas in Communications, vol. 41, no. 5, pp. 1536-1548.
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Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages in rate enhancement and spectral efficiency, it has yet not to be ready for latency-sensitive applications such as virtual reality streaming, which is an essential building block of future 6G networks. Unlike conventional High-Definition streaming applications, streaming virtual reality applications requires not only stringent latency requirements but also the computation capability of the transmitter to quickly respond to dynamic users' demands. Thus, conventional RSMA approaches usually fail to address the challenges caused by computational demands at the transmitter, let alone the dynamic nature of the virtual reality streaming applications. To overcome the aforementioned challenges, we first formulate the virtual reality streaming problem assisted by RSMA as a joint communication and computation optimization problem. A novel multicast approach is then proposed to cluster users into different groups based on a Field-of-View metric and transmit multicast streams in a hierarchical manner. After that, we propose a deep reinforcement learning approach to obtain the solution for the optimization problem. Extensive simulations show that our framework can achieve the millisecond-latency requirement, which is much lower than other baseline schemes.

Hoang, T-D, Huang, X & Qin, P 2023, 'Gradient Descent-Based Direction-of-Arrival Estimation for Lens Antenna Array', IEEE Signal Processing Letters, vol. 30, pp. 838-842.
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In this letter, we investigate a novel optimization approach to direction-of-arrival (DoA) estimation for a lens antenna array. Inspired by a property of the sinc function and ${\ell _{2}}$-norm optimization, we develop the gradient descent-based spatial spectrum reconstruction (GD-SSR) to estimate the DoAs based on the sum signal covariance vector (SSCV). Our proposed algorithm does not require a priori knowledge of signal number and has a lower complexity compared with existing techniques while achieving a better estimation performance, even in a low-SNR regime. In addition, the proposed model does not require any pretraining process as prior learning-based methods. The simulation results show that our scheme not only outperforms other techniques but also resolves the angular ambiguity problem.

Hoang, TM, Xu, C, Vahid, A, Tuan, HD, Duong, TQ & Hanzo, L 2023, 'Secrecy-Rate Optimization of Double RIS-Aided Space–Ground Networks', IEEE Internet of Things Journal, vol. 10, no. 15, pp. 13221-13234.
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Hossain, MA, Islam, MR, Hossain, MA & Hossain, MJ 2023, 'Control strategy review for hydrogen-renewable energy power system', Journal of Energy Storage, vol. 72, pp. 108170-108170.
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Hu, S, Yuan, X, Ni, W, Wang, X & Jamalipour, A 2023, 'RIS-Assisted Jamming Rejection and Path Planning for UAV-Borne IoT Platform: A New Deep Reinforcement Learning Framework', IEEE Internet of Things Journal, vol. 10, no. 22, pp. 20162-20173.
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Hu, S, Yuan, X, Ni, W, Wang, X & Jamalipour, A 2023, 'Visual Camouflage and Online Trajectory Planning for Unmanned Aerial Vehicle-Based Disguised Video Surveillance: Recent Advances and a Case Study', IEEE Vehicular Technology Magazine, vol. 18, no. 3, pp. 48-57.
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Hu, Y, Jin, P, Guo, Y, Lei, G & Zhu, J 2023, 'A New SVM Strategy to Suppress Total Harmonic Distortion and Current Stress in HFLMCs', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Huang, X, Mei, G & Zhang, J 2023, 'Cross-source point cloud registration: Challenges, progress and prospects', Neurocomputing, vol. 548, pp. 126383-126383.
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Huang, X, Tuyen Le, A & Guo, YJ 2023, 'Joint Analog and Digital Self-Interference Cancellation for Full Duplex Transceiver With Frequency-Dependent I/Q Imbalance', IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2364-2378.
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An effective and practical joint analog and digital self-interference cancellation (SIC) scheme without additional signalling overhead for an I/Q imbalanced full duplex transceiver is proposed in this paper. This scheme combines an I/Q imbalanced analog least mean square (ALMS) loop at the transceiver radio frequency frontend and a two-stage digital signal processing (DSP) at the digital baseband to achieve excellent SIC performance with low complexity. The steady state weighting coefficients of the I/Q imbalanced ALMS loop with periodical transmitted signal and the loop’s convergence behaviour are firstly analysed. The residual SI is then modelled as the output of a time-varying widely linear system. With a track/hold control mechanism applied to the ALMS loop, the system model for digital SIC is further presented, followed by the DSP algorithms suitable for real-time implementation. The noise enhancement in each stage digital cancellation is also analysed and formulated. Finally, simulation results are provided to verify the theoretical analyses and demonstrate the overall SIC performance.

Huang, Y, Li, Y, Jourjon, G, Seneviratne, S, Thilakarathna, K, Cheng, A, Webb, D & Xu, RYD 2023, 'Calibrated reconstruction based adversarial autoencoder model for novelty detection', Pattern Recognition Letters, vol. 169, pp. 50-57.
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Ibrahim, IA & Hossain, MJ 2023, 'Short-term multivariate time series load data forecasting at low-voltage level using optimised deep-ensemble learning-based models', Energy Conversion and Management, vol. 296, pp. 117663-117663.
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Islam, MT & Hossain, MJ 2023, 'Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review', Energies, vol. 16, no. 4, pp. 1864-1864.
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Distribution network operators face technical and operational challenges in integrating the increasing number of distributed energy resources (DER) with the distribution network. The hosting capacity analysis quantifies the level of power quality violation on the network due to the high penetration of the DER, such as over voltage, under voltage, transformer and feeder overloading, and protection failures. Real-time monitoring of the power quality factors such as the voltage, current, angle, frequency, harmonics and overloading that would help the distribution network operators overcome the challenges created by the high penetration of the DER. In this paper, different conventional hosting capacity analysis methods have been discussed. These methods have been compared based on the network constraints, impact factors, required input data, computational efficiency, and output accuracy. The artificial intelligence approaches of the hosting capacity analysis for the real-time monitoring of distribution network parameters have also been covered in this paper. Different artificial intelligence techniques have been analysed for sustainable integration, power system optimisation, and overcoming real-time monitoring challenges of conventional hosting capacity analysis methods. An overview of the conventional hosting capacity analysis methods, artificial intelligence techniques for overcoming the challenges of distributed energy resources integration, and different impact factors affecting the real-time hosting capacity analysis has been summarised. The distribution system operators and researchers will find the review paper as an easy reference for planning and further research. Finally, it is evident that artificial intelligence techniques could be a better alternative solution for real-time estimation and forecasting of the distribution network hosting capacity considering the intermittent nature of the DER, consumer loads, and network constraints.

Jafari, M, Shoeibi, A, Khodatars, M, Ghassemi, N, Moridian, P, Alizadehsani, R, Khosravi, A, Ling, SH, Delfan, N, Zhang, Y, Wang, S-H, Górriz, JM, Alinejad-Rokny, H & Acharya, UR 2023, 'Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review.', Comput. Biol. Medicine, vol. 160, pp. 106998-106998.
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Jafarizadeh, S & Veitch, D 2023, 'Robust Weighted-Average Continuous-Time Consensus With Communication Time Delay', IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2074-2086.
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Jauregi Unanue, I, Haffari, G & Piccardi, M 2023, 'T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification', Transactions of the Association for Computational Linguistics, vol. 11, pp. 1147-1161.
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Jia, Z, Liu, Q, He, Y, Wu, Q, Liu, RP & Sun, Y 2023, 'Efficient end-to-end failure probing matrix construction in data center networks', Journal of Communications and Networks, vol. 25, no. 4, pp. 532-543.
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Jin, P, Zhang, J, Lu, Y, Guo, Y, Lei, G & Zhu, J 2023, 'Variable Frequency Isolated Bidirectional CLLC Resonant Converter With Voltage Controlled Variable Capacitors', IEEE Transactions on Industrial Electronics, vol. 70, no. 9, pp. 8907-8917.
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Kaim, V, Singh, N, Kanaujia, BK, Matekovits, L, Esselle, KP & Rambabu, K 2023, 'Multi-Channel Implantable Cubic Rectenna MIMO System With CP Diversity in Orthogonal Space for Enhanced Wireless Power Transfer in Biotelemetry', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 200-214.
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Kang, J, Jia, W & He, X 2023, 'Toward extracting and exploiting generalizable knowledge of deep 2D transformations in computer vision', Neurocomputing, vol. 562, pp. 126882-126882.
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Karmaker, AK, Behrens, S, Hossain, MJ & Pota, H 2023, 'Multi-stakeholder perspectives for transport electrification: A review on placement and scheduling of electric vehicle charging infrastructure', Journal of Cleaner Production, vol. 427, pp. 139145-139145.
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Katzmarek, DA, Mancini, A, Maier, SA & Iacopi, F 2023, 'Direct synthesis of nanopatterned epitaxial graphene on silicon carbide', Nanotechnology, vol. 34, no. 40, pp. 405302-405302.
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Abstract This article introduces a straightforward approach for the direct synthesis of transfer-free, nanopatterned epitaxial graphene on silicon carbide on silicon substrates. A catalytic alloy tailored to optimal SiC graphitization is pre-patterned with common lithography and lift-off techniques to form planar graphene structures on top of an unpatterned SiC layer. This method is compatible with both electron-beam lithography and UV-lithography, and graphene gratings down to at least ∼100 nm width/space can be realized at the wafer scale. The minimum pitch is limited by the flow of the metal catalyst during the liquid-phase graphitization process. We expect that the current pitch resolution could be further improved by optimizing the metal deposition method and lift-off process.

Katzmarek, DA, Yang, Y, Ghasemian, MB, Kalantar-Zadeh, K, Ziolkowski, RW & Iacopi, F 2023, 'Characteristics of Epitaxial Graphene on SiC/Si Substrates in the Radio Frequency Spectrum', IEEE Electron Device Letters, vol. 44, no. 2, pp. 297-300.
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Keshavarz, R, Shariati, N & Miri, M-A 2023, 'Real-Time Discrete Fractional Fourier Transform Using Metamaterial Coupled Lines Network', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 8, pp. 3414-3423.
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Khan, TA, Ling, SH & Rizvi, AA 2023, 'Optimisation of electrical Impedance tomography image reconstruction error using heuristic algorithms.', Artif. Intell. Rev., vol. 56, pp. 15079-15099.

Khoa, TV, Hoang, DT, Trung, NL, Nguyen, CT, Quynh, TTT, Nguyen, DN, Ha, NV & Dutkiewicz, E 2023, 'Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks', IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8578-8589.
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Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. However, one of the biggest challenges for deploying FL in IoT networks is the unavailability of labeled data and dissimilarity of data features for training. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn “knowledge” from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning “knowledge” among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.

Khodasevych, I, Wesemann, L, Roberts, A & Iacopi, F 2023, 'Tunable nonlocal metasurfaces based on graphene for analogue optical computation', Optical Materials Express, vol. 13, no. 5, pp. 1475-1475.
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Meta-optical devices have recently emerged as ultra-compact candidates for real-time computation in the spatial domain. The use of meta-optics for applications in image processing and wavefront sensing could enable an order of magnitude increase in processing speed and data throughput, while simultaneously drastically reducing the footprint of currently available solutions to enable miniaturisation. Most research to date has focused on static devices that can perform a single operation. Dynamically tunable devices, however, offer increased versatility. Here we propose graphene covered subwavelength silicon carbide gratings as electrically tunable optical computation and image processing devices at mid-infrared wavelengths.

Kurdkandi, NV, Marangalu, MG, Husev, O, Aghaie, A, Islam, MR, Siwakoti, YP, Muttaqi, KM & Hosseini, SH 2023, 'A New Seven-Level Transformer-Less Grid-Tied Inverter With Leakage Current Limitation and Voltage Boosting Feature', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 1, pp. 228-241.
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Kurdkandi, NV, Marangalu, MG, Naderi, Y, Husev, O, Hosseini, SH, Siwakoti, Y & Mehrizi‐Sani, A 2023, 'An improved nine‐level switched capacitor‐based inverter with voltage boosting capability and limitation of capacitor current spikes for PV applications', IET Renewable Power Generation, vol. 17, no. 3, pp. 725-749.
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Lee, SS, Cao, S, Barzegarkhoo, R, Farhangi, M & Siwakoti, YP 2023, 'Single-Phase 5-Level Split-Midpoint Cross-Clamped (5L-SMCC) Inverter: An Alternative to the Two-Stage ANPC Topology', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 11, no. 2, pp. 1995-2003.
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Lee, SS, Ho, A-V, Barzegarkhoo, R, Grigoletto, FB, Siwakoti, YP & Cao, S 2023, 'Single-Phase Boost Inverters Designed Using Half-Bridges', IEEE Transactions on Industrial Electronics, pp. 1-6.
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Lee, SS, Lim, RJS, Barzegarkhoo, R, Lim, CS, Grigoletto, FB & Siwakoti, YP 2023, 'A Family of Single-Phase Single-Stage Boost Inverters', IEEE Transactions on Industrial Electronics, vol. 70, no. 8, pp. 7955-7964.
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H-bridge inverter is a common topology used for single-phase applications. Due to its limited voltage gain, a two-stage power conversion with a front-end dc-dc converter is usually adopted to accommodate the low dc source voltage. Recently, single-stage boost inverters are gaining significant interest due to their higher power efficiency and compactness. This paper presents a family of boost inverters with continuous dc source current. By the incorporation of merely a power switch and a boost inductor to the first leg of H-bridge, voltage-boosting and 3-level generation can be simultaneously achieved within a single-stage operation. All potential topologies using the same number of components are derived. An extension to generate five voltage levels with voltage gain enhancement is also proposed. The operation of the proposed boost inverters is thoroughly analyzed. Simulation and experimental results are presented for verification.

Lee, SS, Lim, RJS, Barzegarkhoo, R, Lim, CS, Grigoletto, FB & Siwakoti, YP 2023, 'A Family of Single-Phase Single-Stage Boost Inverters.', IEEE Trans. Ind. Electron., vol. 70, pp. 7955-7964.

Lee, SS, Siwakoti, YP, Barzegarkhoo, R & Lee, K-B 2023, 'Five-Level Unity-Gain Active Neutral-Point-Clamped Inverters Designed Using Half-Bridges', IEEE Transactions on Industry Applications, vol. 59, no. 3, pp. 3520-3529.
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Li, B, Zhu, JG, Liu, CC, Lei, G & Li, YJ 2023, 'Design and optimization of dual-stator FSPMM for integrated compressor', Dianji yu Kongzhi Xuebao/Electric Machines and Control, vol. 27, no. 1, pp. 101-109.
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An integrated flux-switching permanent magnet machine (FSPMM) was proposed based on the 6 / 4 dual stator structure for the issue of sizing and cooling in axial flow compressor. Firstly, the analytical expression of cogging torque considering the rotor skew was derived and the model of flow trajectory is built. An E-core and C-core dual-stator FSPMM were designed and finite element model was built by the three-dimensional finite-element method. The electromagnetic performance was calculated and compared, including the distribution of magnetic density, no-load back-EMF, cogging torque, electromagnetic torque and torque ripple. The results show that the performance of E-core FSPMM is obviously better than that of C-core FSPMM. The rotor of E-core FSPMM is optimized considering electromagnetic and hydrodynamic performance. The results show that the torque ripple of optimized 6 / 4 dual E-core stator FSPMM reduces by 15. 25% and the outlet velocity is pushed up to 113. 27 m / s for driving multistage axial-flow compressors.

Li, F, Zheng, J, Zhang, Y-F, Jia, W, Wei, Q & He, X 2023, 'Cross-domain learning for underwater image enhancement', Signal Processing: Image Communication, vol. 110, pp. 116890-116890.
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The poor quality of underwater images has become a widely-known cause affecting the performance of the underwater development projects, including mineral exploitation, driving photography, and navigation for autonomous underwater vehicles. In recent years, deep learning-based techniques have achieved remarkable successes in image restoration and enhancement tasks. However, the limited availability of paired training data (underwater images and their corresponding clear images) and the requirement for vivid color correction remain challenging for underwater image enhancement, as almost all learning-based methods require paired data for training. In this study, instead of creating the time-consuming paired data, we explore the unsupervised training strategy. Specifically, we introduce a universal cross-domain GAN-based framework to generate high-quality images to address the dependence on paired training data. To ensure the vivid colorfulness, the color loss is designed to constrain the training process. Also, a feature fusion module (FFM) is proposed to increase the capacity of the whole model as well as the dual discriminator channel adopted in the architecture. Extensive quantitative and perceptual experiments show that our approach overcomes the limitation of paired data and obtains superior performance over the state-of-the-art on several underwater benchmarks in terms of both accuracy and model deployment.

Li, J, Jiang, M, Qin, Y, Zhang, R & Ling, SH 2023, 'Intelligent depression detection with asynchronous federated optimization', Complex & Intelligent Systems, vol. 9, no. 1, pp. 115-131.
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AbstractThe growth of population and the various intensive life pressures everyday deepen the competitions among people. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. The development of social networks such as Facebook, Twitter, Weibo, and QQ provides more convenient communication and provides a new emotional vent window. People communicate with their friends, sharing their opinions, and shooting videos to reflect their feelings. It provides an opportunity to detect depression in social networks. Although depression detection using social networks has reflected the established connectivity across users, fewer researchers consider the data security and privacy-preserving schemes. Therefore, we advocate the federated learning technique as an efficient and scalable method, where it enables the handling of a massive number of edge devices in parallel. In this study, we conduct the depression analysis on the basis of an online microblog called Weibo. A novel algorithm termed as CNN Asynchronous Federated optimization (CAFed) is proposed based on federated learning to improve the communication cost and convergence rate. It is shown that our proposed method can effectively protect users' privacy under the premise of ensuring the accuracy of prediction. The proposed method converges faster than the Federated Averaging (FedAvg) for non-convex problems. Federated learning techniques can identify quality solutions of mental health problems among Weibo users.

Li, K, Lau, BPL, Yuan, X, Ni, W, Guizani, M & Yuen, C 2023, 'Toward Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities', IEEE Internet of Things Journal, vol. 10, no. 24, pp. 21855-21872.
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Li, L, Guo, R, You, P, Bai, J, Qin, P-Y & Liu, Y 2023, 'Pattern-Reconfigurable Sparse Linear Array Synthesis Under Minimum Element Spacing Control by Alternating Sequential Quadratic Programming', IEEE Antennas and Wireless Propagation Letters, vol. 22, no. 6, pp. 1271-1275.
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A new method called alternating sequential quadratic programming is proposed to synthesize pattern-reconfigurable sparse linear arrays with minimum element spacing control. The method can find the common element positions and multiple sets of excitations for generating multiple reconfigurable patterns which accurately meet their given upper and lower pattern bounds. In addition, by introducing auxiliary weighting coefficients and collective excitation coefficient vectors and choosing them as optimization variables alternately, the proposed method can appropriately incorporate the minimum element spacing constraint into the pattern synthesis. Synthesized results show that the proposed method can give satisfactory reconfigurable pattern performance but save much more elements compared with some existing methods.

Li, M, Chen, S-L, Liu, Y & Guo, YJ 2023, 'Wide-Angle Beam Scanning Phased Array Antennas: A Review', IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 695-712.
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Li, M, Liu, Y, Bao, Z, Chen, L, Hu, J & Guo, YJ 2023, 'Efficient Phase-Only Dual- and Multi-Beam Pattern Synthesis With Accurate Beam Direction and Power Control Employing Partitioned Iterative FFT', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3719-3724.
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Li, R, Millist, L, Foster, E, Yuan, X, Guvenc, U, Radfar, M, Marendy, P, Ni, W, O’Brien, TJ & Casillas-Espinosa, PM 2023, 'Spike and wave discharges detection in genetic absence epilepsy rat from Strasbourg and patients with genetic generalized epilepsy', Epilepsy Research, vol. 194, pp. 107181-107181.
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Li, R, Yuan, X, Radfar, M, Marendy, P, Ni, W, O'Brien, TJ & Casillas-Espinosa, P 2023, 'Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review', IEEE Reviews in Biomedical Engineering, vol. 16, pp. 109-135.
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Li, T, Lian, S, Zhao, S, Lu, J & Burnett, IS 2023, 'Distributed Active Noise Control Based on an Augmented Diffusion FxLMS Algorithm', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 1449-1463.
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Multichannel active noise control (ANC) systems have been widely investigated for low-frequency noise attenuation over a spatial region. Using a conventional centralized control strategy based on the multichannel filtered-x least mean square (FxLMS) algorithm has been demonstrated to be effective for multichannel ANC systems, but the high computational burden restricts its practical applications. Meanwhile, a decentralized control strategy suffers from stability problems although it has been successful in reducing the computational load. Recently, distributed control strategies, such as the multitask diffusion adaptation scheme, have been introduced to ANC systems and shown to mitigate the stability problems in decentralized systems. However, distributed ANC systems using the diffusion FxLMS algorithm require strong symmetry of acoustic paths because of the dependence on node-based adaptation and neighborhood-based combination. To overcome this limitation, this paper proposes an Augmented Diffusion FxLMS algorithm with neighborhood-based adaptation and node-based combination. A theoretical formulation and convergence analysis are presented and simulations are performed to compare the proposed algorithm with existing ones under different system configurations for tonal, multi-tonal, narrowband and broadband signals. Simulation results demonstrate that the proposed algorithm has the same noise reduction performance as centralized method even if the acoustic paths are strongly asymmetrical, which is superior over existing distributed multitask diffusion strategy.

Li, T, Rao, L, Zhao, S, Duan, H, Lu, J & Burnett, IS 2023, 'An augmented diffusion algorithm with bidirectional communication for a distributed active noise control system', The Journal of the Acoustical Society of America, vol. 154, no. 6, pp. 3568-3579.
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Recent studies on diffusion adaptation for distributed active noise control (DANC) systems have attracted significant research interest due to their balance between computational burden and stability compared to conventional centralized and decentralized adaptation schemes. The conventional multitask diffusion FxLMS algorithm assumes that the converged solutions of all control filters are consistent to each other, which is unrealistic in practice hence results in inferior performance in noise reduction. An augmented diffusion FxLMS algorithm has been proposed to overcome this problem, which adopts a neighborhood-wide adaptation and node-based combination approach to mitigate the bias in the converged solution of the multitask diffusion algorithms. However, the improvement comes at the expense of a higher computational burden and communication cost. All existing DANC systems, including the multitask and augmented diffusion algorithms, assume one-way communication between nodes. By contrast, this paper proposes a bidirectional communication scheme for the augmented diffusion algorithm to further reduce the memory requirement, computational burden, and communication cost. Simulation results in the free field and with measured room impulse responses both demonstrate that the proposed augmented diffusion algorithm with bidirectional communication can achieve a faster convergence speed than that based on one-way communication with a lower memory, computation, and communication burden.

Li, W, Lv, T, Cao, Y, Ni, W & Peng, M 2023, 'Multi-Carrier NOMA-Empowered Wireless Federated Learning With Optimal Power and Bandwidth Allocation', IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 9762-9777.
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Li, X, Cui, Y, Zhang, JA, Liu, F, Zhang, D & Hanzo, L 2023, 'Integrated Human Activity Sensing and Communications', IEEE Communications Magazine, vol. 61, no. 5, pp. 90-96.
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Li, X, Peng, Y & Xu, M 2023, 'Patch-shuffle-based semi-supervised segmentation of bone computed tomography via consistent learning', Biomedical Signal Processing and Control, vol. 80, pp. 104239-104239.
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Li, Y, Chen, Z, Sun, X, Gao, C, Liu, X & Guo, Y 2023, 'Back propagation neural network‐based torque ripple reduction strategy for high frequency square‐wave voltage injection‐based interior permanent magnet synchronous motor sensorless control', IET Electric Power Applications, vol. 17, no. 2, pp. 195-205.
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In interior permanent magnet synchronous motor (IPMSM) position-sensorless drives, the high-frequency (HF) square-wave voltage injection method is often used to estimate the rotor position and speed in low-speed range by tracking the salient polarity of the motor. In order to reduce the torque ripple caused by HF signal injection, a strategy to update the magnitude of the injected signal online by back propagation neural network is proposed in this paper. With the proposed method, the neural network can update the magnitude of the injected signal online according to the d-axis current and the position error information. It can not only ensure the accuracy of position extraction but also effectively reduce the current harmonics caused by the injected signal, and then the torque ripple can be reduced. In addition, the proposed method is easy to implement, resulting in low computation burden. Finally, the experiments are implemented on a 1-kW IPMSM drive. The experimental results show that compared with the conventional fixed magnitude injection, the peak-to-peak value of the torque ripple is reduced by nearly half along with the decrease of the injected magnitude.

Li, Y, Ma, B, Zheng, J, Zhu, J & Lei, G 2023, 'Electromagnetic and Mechanical Topology Optimization for SynRM Rotors Considering High Dimensional Constraints', IEEE Transactions on Industrial Electronics, vol. 70, no. 12, pp. 12048-12059.
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Lian, J-W, Ansari, M, Hu, P, Guo, YJ & Ding, D 2023, 'Wideband and High-Efficiency Parallel-Plate Luneburg Lens Employing All-Metal Metamaterial for Multibeam Antenna Applications', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3193-3203.
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Lin, J-Y, Yang, Y, Wong, S-W, Li, X, Wang, L & Dutkiewicz, E 2023, 'Two-Way Waveguide Diplexer and Its Application to Diplexing In-Band Full-Duplex Antenna', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 3, pp. 1171-1179.
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A design of a diplexing in-band full-duplex (IBFD) slot antenna based on the quadruple-mode resonator (QMR) is presented for the first time. First, a two-way waveguide diplexer integration using QMR is designed. Four waveguide modes, namely, TE $_{011}$ , TE $_{101}$ , TM $_{\mathrm{210,}}$ and TM $_{120}$ , are primarily used. These four modes are modal orthogonal to each other in a single QMR. Each of the quadruple modes can be independently manipulated with low mutual interference with other modes. Taking advantage of this characteristic, a two-way waveguide diplexer integration can be implemented with four independent frequency channels. In each diplexer, the downlink channel is dominated by fundamental mode TE $_{011}$ /TE $_{101}$ , while the uplink channel is dominated by high-order mode TM $_{210}$ /TM $_{120}$ . By cascading QMRs, higher order frequency responses are achieved with expected coupling coefficient ( $K)$ and external quality factor ( $Q_{{\text{e}}})$ . Based on the proposed two-way diplexer concept, a diplexing IBFD antenna with a turnstile junction is designed by replacing the inputs with a cross-coupled radiating slot. It integrates the filtering, diplexing, orthomode transducing, and radiating functions into a single element. Fou...

Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2023, 'Decentralized, Privacy-Preserving Routing of Cellular-Connected Unmanned Aerial Vehicles for Joint Goods Delivery and Sensing', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 9, pp. 9627-9641.
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Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2023, 'Optimal Routing of Unmanned Aerial Vehicle for Joint Goods Delivery and in-Situ Sensing', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 3, pp. 3594-3599.
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Liu, L, Ba, X, Guo, Y, Lei, G, Sun, X & Zhu, J 2023, 'Improved Iron Loss Prediction Models for Interior PMSMs Considering Coupling Effects of Multiphysics Factors', IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 416-427.
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This paper presents improved iron loss analytical prediction models for interior permanent magnet synchronous motors (IPMSMs) used in electric vehicles. The effects of slotting harmonics, pulse-width modulation (PWM) carrier harmonics, temperature rise and mechanical stress are considered in the proposed models. Specifically, by investigating the stator flux density as piecewise linear with trapezoidal waveform, the iron losses in the teeth and yoke regions are calculated separately, considering the different magnetic field distributions and waveforms. To deliberate the PWM harmonic influence, a correction coefficient is added to the hysteresis loss models, while the eddy current loss models are updated by summing all the eddy current losses caused by the power supplying current harmonics. Moreover, the coupling interaction effects of magnetic, thermal, and stress fields on the empirical coefficients of hysteresis and eddy current losses are analyzed in detail and also implemented in the iron loss prediction process. The feasibility and superiority of the proposed models are verified by numerical and experimental case studies on an IPMSM prototype.

Liu, Y & Piccardi, M 2023, 'Topic-Based Unsupervised and Supervised Dictionary Induction', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 22, no. 3, pp. 1-21.
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Word translation is a natural language processing task that provides translation between the words of a source and a target language. As a task, it reduces to the induction of a bilingual dictionary, which is typically performed by aligning word embeddings of the source language to word embeddings of the target language. To date, all the existing approaches have focused on performing a single, global alignment in word embedding space. However, semantic differences between the various languages, in addition to differences in the content of the corpora used for training the word embeddings, can hinder the effectiveness of such a global alignment. For this reason, in this article we propose conducting the alignment between the source and target embedding spaces by multiple mappings at topic level. The experimental results show that our approach has been able to achieve an average accuracy improvement of +3.30 percentage points over a state-of-the-art approach in unsupervised dictionary induction from languages as diverse as German, French, Italian, Spanish, Finnish, Turkish, and Chinese to English, and +3.95 points average improvement in supervised dictionary induction.

Liu, Y, Wen, S, Wang, F, Zuo, C, Chen, C, Zhou, J & Jin, D 2023, 'Population Control of Upconversion Energy Transfer for Stimulation Emission Depletion Nanoscopy', Advanced Science, vol. 10, no. 20.
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AbstractUpconverting stimulated emission depletion microscopy (U‐STED) is emerging as an effective approach for super‐resolution imaging due to its significantly low depletion power and its ability to surpass the limitations of the square‐root law and achieve higher resolution. Though the compelling performance, a trade‐off between the spatial resolution and imaging quality in U‐STED has been recognized in restricting the usability due to the low excitation power drove high depletion efficiency. Moreover, it is a burden to search for the right power relying on trial and error as the underpinning mechanism is unknown. Here, a method is proposed that can easily predict the ideal excitation power for high depletion efficiency with the assistance of the non‐saturate excitation based on the dynamic cross‐relaxation (CR) energy transfer of upconversion nanoparticles. This allows the authors to employ the rate equation model to simulate the populations of each relevant energy state of lanthanides and predict the ideal excitation power for high depletion efficiency. The authors demonstrate that the resolution of STED with the assistance of nonsaturated confocal super‐resolution results can easily achieve the highest resolution of sub‐40 nm, 1/24th of the excitation wavelengths. The finding on the CR effect provides opportunities for population control in realizing low‐power high‐resolution nanoscopy.

Loureiro, PHCDSB, Toebe, A, Siwakoti, YP & Andrade, AMSS 2023, 'Comparison of Coupled Inductor With Three Windings and Two Windings.', IEEE Trans. Circuits Syst. II Express Briefs, vol. 70, no. 6, pp. 1996-2000.
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Lu, X, Qiu, J, Lei, G & Zhu, J 2023, 'Degradation Mode Knowledge Transfer Method for LFP Batteries', IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 1142-1152.
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Lu, X, Qiu, J, Lei, G & Zhu, J 2023, 'State of Health Estimation of Lithium Iron Phosphate Batteries Based on Degradation Knowledge Transfer Learning', IEEE Transactions on Transportation Electrification, vol. 9, no. 3, pp. 4692-4703.
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Lu, Y, Xiao, W & Lu, DD-C 2023, 'Improved Voltage Regulation of PV System With Current-Sensorless Active Damping Technique', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 1, pp. 60-69.
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Lu, Y, Yu, H, Ni, W & Song, L 2023, '3D real-time human reconstruction with a single RGBD camera', Applied Intelligence, vol. 53, no. 8, pp. 8735-8745.
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Luong, NT & Hoang, D 2023, 'BAPRP: a machine learning approach to blackhole attacks prevention routing protocol in vehicular Ad Hoc networks', International Journal of Information Security, vol. 22, no. 6, pp. 1547-1566.
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Luu, HM, Mai, HS, Pham, XL, Le, QA, Le, QK, Walsum, TV, Le, NH, Franklin, DR, Le, HV, Moelker, A, Duc, TC & Trung, NL 2023, 'Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration.', Comput. Methods Programs Biomed., vol. 233, pp. 107453-107453.
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Lyu, B, Zhou, C, Gong, S, Hoang, DT & Liang, Y-C 2023, 'Robust Secure Transmission for Active RIS Enabled Symbiotic Radio Multicast Communications', IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 8766-8780.
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In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.

Ma, Y, Wu, N, Wu, K & Zhang, JA 2023, 'VAMP-Based Iterative Equalization for Index-Modulated Multicarrier FTN Signaling', IEEE Transactions on Communications, vol. 71, no. 4, pp. 2304-2316.
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Makhdoom, I, Abolhasan, M, Franklin, DR, Lipman, J, Zimmermann, C, Piccardi, M & Shariati, N 2023, 'Detecting compromised IoT devices: Existing techniques, challenges, and a way forward.', Comput. Secur., vol. 132, pp. 103384-103384.
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Ngo, QT, Jayawickrama, BA, He, Y & Dutkiewicz, E 2023, 'Multi-Agent DRL-Based RIS-Assisted Spectrum Sensing in Cognitive Satellite–Terrestrial Networks', IEEE Wireless Communications Letters, vol. 12, no. 12, pp. 2213-2217.
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This letter presents a novel approach for spectrum sensing in cognitive satellite-terrestrial networks. The approach uses multi-agent deep reinforcement learning (DRL) and reconfigurable intelligent surface to address the problem of under-utilization of terrestrial network spectrum in remote areas. Unlike previous studies that rely only on current sensing data, this approach utilizes historical data to improve spectrum detection accuracy and post-decision state to accelerate agent learning speed. Simulation results show that it outperforms existing DRL methods in terms of faster agent learning convergence and more effective detection of primary network spectrum occupancy.

Ngo, QT, Phan, KT, Mahmood, A & Xiang, W 2023, 'Physical Layer Security in IRS-Assisted Cache-Enabled Satellite Communication Networks', IEEE Transactions on Green Communications and Networking, vol. 7, no. 4, pp. 1920-1931.
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This paper presents a comprehensive analysis of the physical layer security performance of a cache-enabled satellite communication network that incorporates intelligent reflecting surfaces (IRS) in the presence of a passive eavesdropper. In the proposed system, content caches are deployed at both the ground station and the satellite, which can improve system performance by reducing latency and transmission overhead. Moreover, the use of IRS provides an additional layer of security by enabling the manipulation of the reflected signals to impede eavesdropping. Practical channel models are used to derive connection probability and secrecy probability for both the ground station-IRS-user and the satellite-IRS-user links. The obtained results are then used to evaluate the system’s secure transmission probability, which is maximized subject to the caching probabilities and transmission rate constraints. The paper presents numerical results to demonstrate the accuracy of the analysis and the effectiveness of deploying IRS and caching to support secure content delivery. The findings provide valuable insights into the potential benefits of utilizing IRS and caching technologies in satellite communication networks for improved physical layer security.

Nguyen, CC, Thai, MT, Hoang, TT, Davies, J, Phan, PT, Zhu, K, Wu, L, Brodie, MA, Tsai, D, Ha, QP, Phan, H-P, Lovell, NH & Nho Do, T 2023, 'Development of a soft robotic catheter for vascular intervention surgery', Sensors and Actuators A: Physical, vol. 357, pp. 114380-114380.
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Nguyen, CT, Hoang, DT, Nguyen, DN, Xiao, Y, Pham, H-A, Dutkiewicz, E & Tuong, NH 2023, 'FedChain: Secure Proof-of-Stake-Based Framework for Federated-Blockchain Systems', IEEE Transactions on Services Computing, vol. 16, no. 4, pp. 2642-2656.
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In this paper, we propose FedChain, a novel framework for federated-blockchain systems, to enable effective transferring of tokens between different blockchain networks. Particularly, we first introduce a federated-blockchain system together with a cross-chain transfer protocol to facilitate the secure and decentralized transfer of tokens between chains. We then develop a novel PoS-based consensus mechanism for FedChain, which can satisfy strict security requirements, prevent various blockchain-specific attacks, and achieve a more desirable performance compared to those of other existing consensus mechanisms. Moreover, a Stackelberg game model is developed to examine and address the problem of centralization in the FedChain system. Furthermore, the game model can enhance the security and performance of FedChain. By analyzing interactions between the stakeholders and chain operators, we can prove the uniqueness of the Stackelberg equilibrium and find the exact formula for this equilibrium. These results are especially important for the stakeholders to determine their best investment strategies and for the chain operators to design the optimal policy to maximize their benefits and security protection for FedChain. Simulations results then clearly show that the FedChain framework can help stakeholders to maximize their profits and the chain operators to design appropriate parameters to enhance FedChain’s security and performance.

Nguyen, CT, Nguyen, DN, Hoang, DT, Phan, KT, Niyato, D, Pham, H-A & Dutkiewicz, E 2023, 'Elastic Resource Allocation for Coded Distributed Computing Over Heterogeneous Wireless Edge Networks', IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2636-2649.
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Coded distributed computing (CDC) has recently emerged to be a promising solution to address the straggling effects in conventional distributed computing systems. By assigning redundant workloads to the computing nodes, CDC can significantly enhance the performance of the whole system. However, since the core idea of CDC is to introduce redundancies to compensate for uncertainties, it may lead to a large amount of wasted energy at the edge nodes. It can be observed that the more redundant workload added, the less impact the straggling effects have on the system. However, at the same time, the more energy is needed to perform redundant tasks. In this work, we develop a novel framework, namely CERA, to elastically allocate computing resources for CDC processes. Particularly, CERA consists of two stages. In the first stage, we model a joint coding and node selection optimization problem to minimize the expected processing time for a CDC task. Since the problem is NP-hard, we propose a linearization approach and a hybrid algorithm to quickly obtain the optimal solutions. In the second stage, we develop a smart online approach based on Lyapunov optimization to dynamically turn off straggling nodes based on their actual performance. As a result, wasteful energy consumption can be significantly reduced with minimal impact on the total processing time. Simulations using real-world datasets have shown that our proposed approach can reduce the system’s total processing time by more than 200% compared to that of the state-of-the-art approach, even when the nodes’ actual performance is not known in advance. Moreover, the results have shown that CERA’s online optimization stage can reduce the energy consumption by up to 37.14% without affecting the total processing time.

Nguyen, HAD & Ha, QP 2023, 'Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey', Robotica, vol. 41, no. 2, pp. 486-510.
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AbstractThere has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.

Nguyen, HAD, Ha, QP, Duc, H, Azzi, M, Jiang, N, Barthelemy, X & Riley, M 2023, 'Long Short-Term Memory Bayesian Neural Network for Air Pollution Forecast', IEEE Access, vol. 11, pp. 35710-35725.
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Ni, Z, Zhang, JA & Liu, RP 2023, 'Waveform Optimizations Using Virtual Arrays in Broadband Radar Communications', IEEE Wireless Communications Letters, vol. 12, no. 5, pp. 912-916.
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Ni, Z, Zhang, JA, Wu, K & Liu, RP 2023, 'Uplink Sensing Using CSI Ratio in Perceptive Mobile Networks', IEEE Transactions on Signal Processing, vol. 71, pp. 2699-2712.
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Ni, Z, Zhang, JA, Wu, K, Yang, K & Liu, RP 2023, 'Receiver Design in Full-Duplex Joint Radar-Communication Systems', IEEE Transactions on Communications, vol. 71, no. 7, pp. 4234-4246.
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Nie, X, Chai, B, Wang, L, Liao, Q & Xu, M 2023, 'Learning enhanced features and inferring twice for fine-grained image classification', Multimedia Tools and Applications, vol. 82, no. 10, pp. 14799-14813.
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AbstractFine-Grained Visual Categorization (FGVC) aims to distinguish between extremely similar subordinate-level categories within the same basic-level category. Existing research has proven the great importance of the discriminative features in FGVC but ignored the contributions for correct classification from other features, and the extracted features always contain more information about the obvious regions but less about subtle regions. In this paper, firstly, a novel module named forcing module is proposed to force the network to extract more diverse features for FGVC, which generates a suppression mask based on the class activation maps to suppress the most distinguishable regions, so as to force the network to extract other secondary distinguishable features as the final features. The forcing module consists of the original branch and the forcing branch. The original branch focuses on the primary discriminative regions while the forcing branch focuses on secondary discriminative regions. Secondly, in order to solve the problem that information of small-scale distinguishable features is lost seriously after multi-layer down-sampling, according to the class activation maps of the first prediction, the object is cropped and scaled as the second input. To reduce the prediction error, the first and second prediction probabilities are fused as the final prediction result. Experimental results indicate that the proposed method not only outperforms the baseline model by a large margin (3.7%, 5.9%, 3.1% respectively) on CUB-200-2011, Stanford-Cars, and FGVC-Aircraft, but also achieves state-of-the-art performance on FGVC-Aircraft.

Niwa, K, Ueda, N, Sawada, H, Fujino, A, Takeda, S, Zhang, G & Kleijn, WB 2023, 'CoordiNet: Constrained Dynamics Learning for State Coordination Over Graph', IEEE Transactions on Signal and Information Processing over Networks, vol. 9, pp. 242-257.
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Parsaei, K, Keshavarz, R, Boroujeni, RM & Shariati, N 2023, 'Compact Pixelated Microstrip Forward Broadside Coupler Using Binary Particle Swarm Optimization', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 12, pp. 5265-5274.
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Poursafar, N, Taghizadeh, S, Hossain, MJ & Blaabjerg, F 2023, 'An enhanced control strategy for an ultra-fast EV charging station in a DC microgrid', International Journal of Electrical Power & Energy Systems, vol. 146, pp. 108727-108727.
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Poursafar, N, Taghizadeh, S, Jahangir Hossain, M & Karimi-Ghartemani, M 2023, 'A Voltage-supportive Controller for Ultra-fast Electric Vehicle Chargers in Islanded DC Microgrids', Journal of Modern Power Systems and Clean Energy, vol. 11, no. 3, pp. 896-906.
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Pradeepkumar, A, Cheng, HH, Liu, KY, Gebert, M, Bhattacharyya, S, Fuhrer, MS & Iacopi, F 2023, 'Low-leakage epitaxial graphene field-effect transistors on cubic silicon carbide on silicon', Journal of Applied Physics, vol. 133, no. 17.
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Epitaxial graphene (EG) on cubic silicon carbide (3C-SiC) on silicon holds the promise of tunable nanoelectronic and nanophotonic devices, some uniquely unlocked by the graphene/cubic silicon carbide combination, directly integrated with the current well-established silicon technologies. Yet, the development of graphene field-effect devices based on the 3C-SiC/Si substrate system has been historically hindered by poor graphene quality and coverage, as well as substantial leakage issues of the heteroepitaxial system. We address these issues by growing EG on 3C-SiC on highly resistive silicon substrates using an alloy-mediated approach. In this work, we demonstrate a field-effect transistor based on EG/3C-SiC/Si with gate leakage current 6 orders of magnitude lower than the drain current at room temperature, which is a vast improvement on the current literature, opening the possibility for dynamically tunable nanoelectronic and nanophotonic devices on silicon at the wafer level.

Qi, T, Lyu, B & Hoang, DT 2023, 'Pilot Sequences With Low Coherence and PAPR for Grant-Free Massive Access', IEEE Wireless Communications Letters, vol. 12, no. 7, pp. 1254-1258.
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To accommodate massive devices and facilitate activity detection in a grant-free access system, it is necessary to design non-orthogonal pilot sequences with low coherence. In this letter, we study the optimization problem to minimize the worst-case coherence among sequences under the peak-to-average power ratio (PAPR) constraint of each sequence. An efficient method is proposed to iteratively construct sequences by the conjugate gradient descent and space projection. Simulation results demonstrate that the proposed sequences can decrease the coherence under strict PAPR constraints by up to 62% compared with the benchmarks.

Qian, J, Lan, H, Huang, L, Zheng, S, Hu, X, Chen, M, Lee, JE-Y & Zhang, W 2023, 'Acoustofluidics for simultaneous droplet transport and centrifugation facilitating ultrasensitive biomarker detection', Lab on a Chip, vol. 23, no. 19, pp. 4343-4351.
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An orthogonal tunable acoustic tweezer enables simultaneous droplet transport and centrifugation facilitating ultrasensitive miRNA biomarker detection.

Ray, MK, Mandal, K, Moloudian, G & Lalbakhsh, A 2023, 'Axial ratio beamwidth enhancement of a low-profile circularly polarized antenna using defected ground structures', International Journal of Microwave and Wireless Technologies, vol. 15, no. 8, pp. 1434-1442.
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AbstractThis article explores the possibilities of incorporating a defected ground structure (DGS) for widening the 3 dB axial ratio beamwidth (ARBW) of a simple low-profile planar circularly polarized (CP) antenna. Two pairs of DGSs are etched symmetrically along the diagonals of the ground plane to overcome the limited 3 dB ARBW performance of a crossed-slotted circular-shaped planar CP antenna. Here, one pair of DGSs is orthogonal to another pair of DGSs. The distance between the pairs of DGSs plays a key role in enhancing the 3 dB ARBW by 40.62%. A gap of 0.18λ0 is considered between the DGSs to maintain the symmetrical electric field distribution in the substrate. It also helps to reduce orthogonal current components and provides uniformly distributed patch surface currents. These phenomena collectively help the proposed CP antenna to exhibit right-handed circular polarization radiation with the 3 dB ARBW of 186° and 188° in the E-plane and H-plane, respectively. The measured results yield impedance bandwidth (S11 ≤ −10 dB) of 2.6% (2.283–2.342 GHz), CP bandwidth of 0.9% (2.294–2.315 GHz), gain higher than 4.8 dBic, and total antenna efficiency more than 64% across the entire CP band.

Raza, A, Keshavarz, R, Dutkiewicz, E & Shariati, N 2023, 'Compact Multiservice Antenna for Sensing and Communication Using Reconfigurable Complementary Spiral Resonator', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-9.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2023, 'Statistical Learning-Based Adaptive Network Access for the Industrial Internet of Things', IEEE Internet of Things Journal, vol. 10, no. 14, pp. 12219-12233.
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Ren, Y, Ren, B, Zhang, X, Lv, T, Ni, W & Lu, G 2023, 'Impartial Cooperation in SWIPT-Assisted NOMA Systems With Random User Distribution', IEEE Transactions on Vehicular Technology, vol. 72, no. 8, pp. 10488-10504.
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Rennie, C, Huang, Y, Siwakoti, P, Du, Z, Padula, M, Bao, G, Tuch, BE, Xu, X & McClements, L 2023, 'In vitroevaluation of a hybrid drug-delivery nanosystem for fibrosis prevention in cell therapy for Type 1 diabetes', Nanomedicine, vol. 18, no. 1, pp. 53-66.
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Background: Implantation of insulin-secreting cells has been trialed as a treatment for Type 1 diabetes mellitus; however, the host immunogenic response limits their effectiveness. Methodology: The authors developed a core-shell nanostructure of upconversion nanoparticle-mesoporous silica for controlled local delivery of an immunomodulatory agent, MCC950, using near-infrared light and validated it in in vitro models of fibrosis. Results: The individual components of the nanosystem did not affect the proliferation of insulin-secreting cells, unlike fibroblast proliferation (p < 0.01). The nanosystem is effective at releasing MCC950 and preventing fibroblast differentiation (p < 0.01), inflammation (IL-6 expression; p < 0.05) and monocyte adhesion (p < 0.01). Conclusion: This MCC950-loaded nanomedicine system could be used in the future together with insulin-secreting cell implants to increase their longevity as a curative treatment for Type 1 diabetes mellitus.

Reza, MS, Hannan, MA, Ker, PJ, Mansor, M, Lipu, MSH, Hossain, MJ & Mahlia, TMI 2023, 'Uncertainty parameters of battery energy storage integrated grid and their modeling approaches: A review and future research directions', Journal of Energy Storage, vol. 68, pp. 107698-107698.
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Ruan, Z, Song, W, Zhang, Y, Yao, G & Guo, Y 2023, 'A Variable Switching Frequency Space Vector Pulsewidth Modulation Technique Using Virtual Flux Ripple', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 11, no. 2, pp. 2051-2060.
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Ruan, Z, Song, W, Zhao, L, Zhang, Y & Guo, Y 2023, 'A Variable Switching Frequency Space Vector Pulse Width Modulation Control Strategy of Induction Motor Drive System With Torque Ripple Prediction', IEEE Transactions on Energy Conversion, vol. 38, no. 2, pp. 993-1003.
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Rubina Aktar, M, Shamim Anower, M, Zahurul Islam Sarkar, M, Sayem, ASM, Rashedul Islam, M, Akash, AI, Rumana Akter Rume, M, Moloudian, G & Lalbakhsh, A 2023, 'Energy-Efficient Hybrid Powered Cloud Radio Access Network (C-RAN) for 5G', IEEE Access, vol. 11, pp. 3208-3220.
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Saleem, R, Ni, W, Ikram, M & Jamalipour, A 2023, 'Deep-Reinforcement-Learning-Driven Secrecy Design for Intelligent-Reflecting-Surface-Based 6G-IoT Networks', IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8812-8824.
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The sixth-generation (6G) wireless communication has called for higher bandwidth and massive connectivity of Internet-of-Things (IoT) devices. The increased connectivity also demands advanced levels of network security, which are critical to maintaining due to severe signal attenuation at higher frequencies. Intelligent reflecting surface (IRS) is an increasingly popular, efficient, solution to cater to higher data rates, better coverage range, and reduced signal blockages. In this paper, an IRS-based model is proposed to address the issue of network security under trusted-untrusted device diversity, where the untrusted devices may potentially eavesdrop on the trusted devices. A mathematical design of the system model is presented, and an optimization problem is formulated. The secrecy rate of the trusted devices is maximized while guaranteeing Quality-of-Service (QoS) to all the legitimate, trusted and untrusted devices. A deep deterministic policy gradient (DDPG) algorithm is devised to jointly optimize the active and passive beamforming matrices owing to the complex and continuous nature of action and state spaces. The results confirm a maximum gain of 2-2.5 times in the sum secrecy rate of trusted devices under the proposed model, as compared to the benchmark cases. The results also ensure the throughput performance of all trusted and untrusted devices. The performance of the proposed DDPG model is evaluated under meticulously selected hyper-parameters.

Samal, PB, Chen, SJ & Fumeaux, C 2023, 'Wearable Textile Multiband Antenna for WBAN Applications', IEEE Transactions on Antennas and Propagation, vol. 71, no. 2, pp. 1391-1402.
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Samal, PB, Chen, SJ, Tung, TT, Losic, D & Fumeaux, C 2023, 'Efficiency-Driven Design for Planar Antennas With Lossy Materials', IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 23-33.
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Sang, L, Xu, M, Qian, S & Wu, X 2023, 'Adversarial Heterogeneous Graph Neural Network for Robust Recommendation', IEEE Transactions on Computational Social Systems, vol. 10, no. 5, pp. 2660-2671.
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Santra, SB, Chatterjee, D & Siwakoti, YP 2023, 'Coupled Inductor Based Soft Switched High Gain Bidirectional DC-DC Converter With Reduced Input Current Ripple.', IEEE Trans. Ind. Electron., vol. 70, no. 2, pp. 1431-1443.
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Bidirectional dc-dc converter (BDC) is an integral part of energy storage interface, where high efficiency, high voltage transfer ratio and small input ripple current are essential for application, such as dc microgrid and electric vehicle. In this article, a new approach is proposed to achieve reduced input ripple current at the low voltage side, which potentially replaces the necessity of using interleaved circuit structure. Parallel coupled inductor and capacitor circuit are utilized for charging and discharging the two parallel paths in a complementary fashion using a mosfet. The proposed solution helps to increase the voltage conversion ratio unlike interleaved structure. Additionally, in proposed BDC, zero voltage switching turn on operation of all active switches are possible utilizing synchronous rectification principle in both the operating modes. Parallel path structure not only reduce the input current ripple, but also helps to share the input current, thereby reducing the coil size and associated losses. A 250 W BDC is designed to validate performance in 1kW dc microgrid system

Saputra, YM, Hoang, DT, Nguyen, DN, Tran, L-N, Gong, S & Dutkiewicz, E 2023, 'Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles', IEEE Transactions on Mobile Computing, vol. 22, no. 4, pp. 2100-2115.
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Federated learning (FL) can empower Internet-of-Vehicles (IoV) to help the vehicular service provider (VSP) improve the global model accuracy for road safety and better profits for both VSP and participating smart vehicles (SVs). Nonetheless, there exist major challenges when implementing FL in IoV including dynamic activities and diverse quality-of-information (QoI) from a large number of SVs, VSP's limited payment budget, and profit competition among SVs. In this paper, we propose a novel dynamic FL-based economic framework for an IoV network to address these challenges. Specifically, the VSP first implements an SV selection method to determine a set of the best SVs for the FL process according to the significance of their current locations and information at each learning round. Then, each selected SV can collect on-road information and offer a payment contract to the VSP based on its collected QoI. For that, we develop a multi-principal one-agent contract-based policy to maximize the profits of the VSP and learning SVs under the asymmetric information between them. Through experimental results using real-world on-road datasets, we show that our framework can converge 57% faster and obtain social welfare of the network up to 27.2 times compared with those of other baseline FL methods.

Savkin, AV, Huang, C & Ni, W 2023, 'Collision-Free 3-D Navigation of a UAV Team for Optimal Data Collection in Internet-of-Things Networks With Reconfigurable Intelligent Surfaces', IEEE Systems Journal, vol. 17, no. 3, pp. 4070-4077.
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Savkin, AV, Huang, C & Ni, W 2023, 'Joint Multi-UAV Path Planning and LoS Communication for Mobile-Edge Computing in IoT Networks With RISs', IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2720-2727.
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Savkin, AV, Huang, C & Ni, W 2023, 'On-Demand Deployment of Aerial Base Stations for Coverage Enhancement in Reconfigurable Intelligent Surface-Assisted Cellular Networks on Uneven Terrains', IEEE Communications Letters, vol. 27, no. 2, pp. 666-670.
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Savkin, AV, Verma, SC & Ni, W 2023, 'Autonomous UAV 3D trajectory optimization and transmission scheduling for sensor data collection on uneven terrains', Defence Technology, vol. 30, pp. 154-160.
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Sayem, ASM, Lalbakhsh, A, Esselle, KP, Moloudian, G, Buckley, JL & Simorangkir, RBVB 2023, 'Advancements, Challenges, and Prospects of Water-Filled Antennas', IEEE Access, vol. 11, pp. 8301-8323.
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Seifollahi, S & Piccardi, M 2023, 'Taxonomy-Based Feature Extraction for Document Classification, Clustering and Semantic Analysis', pp. 575-586.
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Shan, B, Ni, W, Yuan, X, Yang, D, Wang, X & Liu, RP 2023, 'Graph learning from band-limited data by graph Fourier transform analysis', Signal Processing, vol. 207, pp. 108950-108950.
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Shan, B, Yuan, X, Ni, W, Wang, X, Liu, RP & Dutkiewicz, E 2023, 'Novel Graph Topology Learning for Spatio-Temporal Analysis of COVID-19 Spread', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 6, pp. 2693-2704.
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Shan, B, Yuan, X, Ni, W, Wang, X, Liu, RP & Dutkiewicz, E 2023, 'Preserving the Privacy of Latent Information for Graph-Structured Data', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 5041-5055.
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Shephard, RW & Maloney, SK 2023, 'A review of thermal stress in cattle', Australian Veterinary Journal, vol. 101, no. 11, pp. 417-429.
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Cattle control body temperature in a narrow range over varying climatic conditions. Endogenous body heat is generated by metabolism, digestion and activity. Radiation is the primary external source of heat transfer into the body of cattle. Cattle homeothermy uses behavioural and physiological controls to manage radiation, convection, conduction, and evaporative exchange of heat between the body and the environment, noting that evaporative mechanisms almost exclusively transfer body heat to the environment. Cattle control radiation by shade seeking (hot) and shelter (cold) and by huddling or standing further apart, noting there are intrinsic breed and age differences in radiative transfer potential. The temperature gradient between the skin and the external environment and wind speed (convection) determines heat transfer by these means. Cattle control these mechanisms by managing blood flow to the periphery (physiology), by shelter‐seeking and standing/lying activity in the short term (behaviourally) and by modifying their coats and adjusting their metabolic rates in the longer term (acclimatisation). Evaporative heat loss in cattle is primarily from sweating, with some respiratory contribution, and is the primary mechanism for dissipating excess heat when environmental temperatures exceed skin temperature (~36°C). Cattle tend to be better adapted to cooler rather than hotter external conditions, with Bos indicus breeds more adapted to hotter conditions than Bos taurus. Management can minimise the risk of thermal stress by ensuring appropriate breeds of suitably acclimatised cattle, at appropriate stocking densities, fed appropriate diets (and water), and with access to suitable shelter and ventilation are better suited to their expected farm environment.

Shi, T, Xiang, X, Lei, J, Liu, B, Wang, F, Chen, M, Yang, H, Li, L & Li, W 2023, 'Nondetection Zone Elimination and Detection Speed Improvement for DC Microgrids Islanding Detection With Adaptive Resonant Frequency', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 11, no. 6, pp. 5750-5765.
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Shoeibi, A, Khodatars, M, Jafari, M, Ghassemi, N, Moridian, P, Alizadehsani, R, Ling, SH, Khosravi, A, Alinejad-Rokny, H, Lam, H-K, Fuller-Tyszkiewicz, M, Acharya, UR, Anderson, D, Zhang, Y & Górriz, JM 2023, 'Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review.', Inf. Fusion, vol. 93, pp. 85-117.
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Siddiqui, SA, Mahmood, A, Sheng, QZ, Suzuki, H & Ni, W 2023, 'Trust in Vehicles: Toward Context-Aware Trust and Attack Resistance for the Internet of Vehicles', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 9, pp. 9546-9560.
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Singh, K, Saikia, M, Thiyagarajan, K, Thalakotuna, D, Esselle, K & Kodagoda, S 2023, 'Multi-Functional Reconfigurable Intelligent Surfaces for Enhanced Sensing and Communication', Sensors, vol. 23, no. 20, pp. 8561-8561.
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In this paper, we propose a reconfigurable intelligent surface (RIS) that can dynamically switch the transmission and reflection phase of incident electromagnetic waves in real time to realize the dual-beam or quad-beam and convert the polarization of the transmitted beam. Such surfaces can redirect a wireless signal at will to establish robust connectivity when the designated line-of-sight channel is disturbed, thereby enhancing the performance of wireless communication systems by creating an intelligent radio environment. When integrated with a sensing element, they are integral to performing joint detection and communication functions in future wireless sensor networks. In this work, we first analyze the scattering performance of a reconfigurable unit element and then design a RIS. The dynamic field scattering manipulation capability of the RIS is validated by full-wave electromagnetic simulations to realize six different functions. The scattering characteristics of the proposed unit element, which incorporates two p-i-n diodes have been substantiated through practical implementation. This involved the construction of a simple prototype and the subsequent examination of its scattering properties via the free-space measurement method. The obtained transmission and reflection coefficients from the measurements are in agreement with the anticipated outcomes from simulations.

Siva, V, Raghuram, M, Singh, A, Singh, SK & Siwakoti, YP 2023, 'Switching Strategy to Reduce Inductor Current Ripple and Common Mode Voltage in Quasi Z-Source Ultra Sparse Matrix Converter', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 4, pp. 1159-1169.
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Siwakoti, YP & Pilawa-Podgurski, RCN 2023, 'Guest Editorial Special Section on Switched Capacitor Converters (SCCs)', IEEE Transactions on Power Electronics, vol. 38, no. 12, pp. 14980-14981.
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Siwakoti, YP & Pilawa-Podgurski, RCN 2023, 'Guest Editorial Special Section on Switched Capacitor Converters (SCCs)', IEEE Transactions on Power Electronics, vol. 38, no. 11, pp. 13482-13483.
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Siwakoti, YP & Pilawa-Podgurski, RCN 2023, 'Guest Editorial: Special Section on Switched Capacitor Converters (SCCs)', IEEE Transactions on Power Electronics, vol. 38, no. 10, pp. 12219-12220.
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Song 宋, B波, Wu 吴, H-M惠, Song 宋, Y-R玉, Jiang 蒋, G-P国, Xia 夏, L-L玲 & Wang 王, X旭 2023, 'Robustness of community networks against cascading failures with heterogeneous redistribution strategies', Chinese Physics B, vol. 32, no. 9, pp. 098905-098905.
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Network robustness is one of the core contents of complex network security research. This paper focuses on the robustness of community networks with respect to cascading failures, considering the nodes influence and community heterogeneity. A novel node influence ranking method, community-based Clustering–LeaderRank (CCL) algorithm, is first proposed to identify influential nodes in community networks. Simulation results show that the CCL method can effectively identify the influence of nodes. Based on node influence, a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks. Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process. The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities. When the initial load distribution and the load redistribution strategy based on the node influence are the same, the network shows better robustness against node failure.

Song, L-Z, Ansari, M, Qin, P-Y, Maci, S, Du, J & Guo, YJ 2023, 'Two-Dimensional Wide-Angle Multibeam Flat GRIN Lens With a High Aperture Efficiency', IEEE Transactions on Antennas and Propagation, vol. 71, no. 10, pp. 8018-8029.
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High-aperture-efficiency 2-dimensional (2-D) multi-beam flat gradient-index (GRIN) lenses are developed in this work. New methods based on bifocal analysis are found to determine the refractive-index profile of the lens as well as feed positions along a circular feed locus to enable independent wide-angle multi-beam radiations. Distinct formulas for the GRIN profile are provided for two different purposes: i) multi-beam with good average performance in all azimuthal planes or ii) radiation performance optimized in a specific azimuthal plane. The latter solution requires a GRIN variation in both azimuthal and radial variables. Subwavelength triple-metal-layer unit cells are designed to emulate the local refractive indices. A 2-D multi-beam GRIN lens, fed by 13-modules of 2×2 patch arrays displaced along xoz and yoz focus loci, has been successfully simulated, fabricated, and measured. Wide-angle multi-beam radiations have been obtained with a beam coverage of around ±45° in both xoz and yoz planes. The multi-beam radiation patterns are stable in a 22.2% bandwidth from 12 GHz to 15 GHz. The beam-scanning losses in this operating band are 1-2.6 dB and 2.1-3.9 dB in xoz and yoz planes, respectively. The measured peak realized gain is 22.3 dBi at 13.4 GHz, corresponding to an aperture efficiency of 66.4%.

Song, X, Lu, X, Fang, G, He, X, Fan, X, Cai, L, Jia, W & Wang, Z 2023, 'ABUSDet: A Novel 2.5D deep learning model for automated breast ultrasound tumor detection', Applied Intelligence, vol. 53, no. 21, pp. 26255-26269.
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Automated Breast Ultrasound is a highly advanced breast tumor detection modality that produces hundreds of 2D slices in each scan. However, this large number of slices poses a significant burden for physicians to review. This paper proposes a novel 2.5D tumor detection model, named “ABUSDet,” to assist physicians in automatically reviewing ABUS images and predicting the locations of breast tumors in images. At the core of this approach, a sequence of data blocks partitioned from a pre-processed 3D volume are fed to a 2.5D tumor detection model, which outputs a sequence of 2D tumor candidates. An aggregation module then clusters the 2D tumor candidates to produce the ultimate 3D coordinates of the tumors. To further improve the accuracy of the model, a novel mechanism for training deep learning models, called “Deliberate Training,” is proposed. The proposed model is trained and tested on a dataset of 87 patients with 235 ABUS volumes. It achieves sensitivities of 77.94%, 75.49%, and 65.19% at FPs/volume of 3, 2, and 1, respectively. Compared with the 2D and 3D object detection models, the proposed ABUSDet model achieves the highest sensitivity with relatively low false-positive rates. Graphical abstract: [Figure not available: see fulltext.]

Soomro, WA, Guo, Y, Lu, H, Jin, J, Shen, B & Zhu, J 2023, 'AC Loss in High-Temperature Superconducting Bulks Subjected to Alternating and Rotating Magnetic Fields', Materials, vol. 16, no. 2, pp. 633-633.
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High-temperature superconductor (HTS) bulks have demonstrated extremely intriguing potential for industrial and commercial applications due to their capability to trap significantly larger magnetic fields than conventional permanent magnets. The magnetic field in electrical rotating machines is a combination of alternating and rotational fields. In contrast, all previous research on the characterization of electromagnetic properties of HTS have solely engrossed on the alternating AC magnetic fields and the associated AC loss. This research paper gives a thorough examination of the AC loss measurement under various conditions. The obtained results are compared to the finite element-based H-formulation. The AC loss is measured at various amplitudes of circular flux density patterns and compared with the AC loss under one-dimensional alternating flux density. The loss variation has also been studied at other frequencies. The findings in this research paper provide more insights into material characterization, which will be useful in the design of future large-scale HTS applications.

Spina, I, Rogers, DJ, Brando, G, Chatzinikolaou, E & Siwakoti, YP 2023, 'Maximum Power per Ampere Modulation for Cascaded H-Bridge Converters', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 11, no. 1, pp. 264-275.
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Stone, DG, Chen, Y, Ekimov, EA, Tran, TT & Bradac, C 2023, 'Diamond Nanothermometry Using a Machine Learning Approach', ACS Applied Optical Materials, vol. 1, no. 4, pp. 898-905.
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Sun, C, Xiong, X, Zhai, Z, Ni, W, Ohtsuki, T & Wang, X 2023, 'Max–Min Fair 3D Trajectory Design and Transmission Scheduling for Solar-Powered Fixed-Wing UAV-Assisted Data Collection', IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 8650-8665.
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Sun, S-Y, Ding, C, Jiang, W & Guo, YJ 2023, 'Simultaneous Suppression of Cross-Band Scattering and Coupling Between Closely Spaced Dual-Band Dual-Polarized Antennas', IEEE Transactions on Antennas and Propagation, vol. 71, no. 8, pp. 6423-6434.
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Sun, Y, Zhang, JA, Wu, K & Liu, RP 2023, 'Frequency-Domain Sensing in Time-Varying Channels', IEEE Wireless Communications Letters, vol. 12, no. 1, pp. 16-20.
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Joint Communications and Sensing (JCAS) in mobile networks are typically based on Orthogonal Frequency Division Multiplexing (OFDM) systems. For time-varying channels, large Doppler frequencies in OFDM JCAS can cause notable intercarrier interference, which has not been considered for sensing. In this paper, we propose a frequency-domain sensing framework for OFDM JCAS systems. We first derive a frequency-domain closed-form expression of the received signals, to characterise the delay and Doppler frequency impact within and across OFDM blocks. We then develop intra-block and inter-block sensing algorithms, based on the expression. The framework is further completed with exemplified pilot design and periodogram sensing algorithm. Simulation results demonstrate the effectiveness of the proposed framework.

Sunku Mohan, V, Sankaran, S, Nanda, P & Achuthan, K 2023, 'Enabling secure lightweight mobile Narrowband Internet of Things (NB-IoT) applications using blockchain', Journal of Network and Computer Applications, vol. 219, pp. 103723-103723.
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Swaminathan, GV, Periasamy, S & Lu, DD-C 2023, 'Capacitor Current Control Based Virtual Inertia Control of Autonomous DC Microgrid', IEEE Transactions on Industrial Electronics, vol. 70, no. 7, pp. 6908-6918.
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Syasegov, YY, Farhangi, M, Barzegarkhoo, R, Li, L, Lu, DD-C, Aguilera, RP & Siwakoti, YP 2023, 'HERIC-Clamped and PN-NPC Inverters With Five-Level Output Voltage and Reduced Grid-Interfaced Filter Size', IEEE Open Journal of Power Electronics, vol. 4, pp. 306-318.
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Syasegov, YY, Farhangi, M, Barzegarkhoo, R, Siwakoti, YP, Li, L, Lu, DD-C, Aguilera, RP & Pou, J 2023, 'A 5-Level HERIC Active-Clamped Inverter With Full Reactive Power Capability for Grid-Connected Applications', IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 135-148.
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Tang, K, Yang, C, Guo, Y, Wang, N, Zhu, Y, Zhang, Y, Ng, EJ, Lee, JE-Y, Fang, Z, Wang, W, Jiang, H, Heng, C-H & Zheng, Y 2023, 'A 107 pJ/b TX 260 pJ/b RX Ultralow-Power MEMS-Based Transceiver With Wake-Up in ISM-Bands for IoT Applications', IEEE Journal of Solid-State Circuits, vol. 58, no. 5, pp. 1337-1349.
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Tong, M, Huang, X & Zhang, JA 2023, 'Faster-Than-Nyquist Transmission With Frame-by-Frame Decision-Directed Successive Interference Cancellation', IEEE Transactions on Communications, vol. 71, no. 8, pp. 4851-4861.
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Tran, LC, Le, AT, Huang, X, Dutkiewicz, E, Ngo, D & Taparugssanagorn, A 2023, 'Complexity Reduction for Hybrid TOA/AOA Localization in UAV-Assisted WSNs', IEEE Sensors Letters, vol. 7, no. 11, pp. 1-4.
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Tuan, HD, Nasir, AA, Chen, Y, Dutkiewicz, E & Poor, HV 2023, 'Quantized RIS-Aided Multi-User Secure Beamforming Against Multiple Eavesdroppers', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 4695-4706.
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Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2023, 'Multiservice Compact Pixelated Stacked Antenna With Different Pixel Shapes for IoT Applications', IEEE Internet of Things Journal, vol. 10, no. 22, pp. 19883-19897.
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Unanue, IJ, Borzeshi, EZ & Piccardi, M 2023, 'Regressing Word and Sentence Embeddings for Low-Resource Neural Machine Translation', IEEE Transactions on Artificial Intelligence, vol. 4, no. 3, pp. 450-463.
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Unanue, IJ, Haffari, G & Piccardi, M 2023, 'T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification', Transactions of the Association for Computational Linguistics, vol. 11, pp. 1147-1161.
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Abstract Cross-lingual text classification leverages text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning (zero/ few-shots cross-lingual transfer). Nowadays, cross-lingual text classifiers are typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest. However, the performance of these models varies significantly across languages and classification tasks, suggesting that the superposition of the language modelling and classification tasks is not always effective. For this reason, in this paper we propose revisiting the classic “translate-and-test” pipeline to neatly separate the translation and classification stages. The proposed approach couples 1) a neural machine translator translating from the targeted language to a high-resource language, with 2) a text classifier trained in the high-resource language, but the neural machine translator generates “soft” translations to permit end-to-end backpropagation during fine-tuning of the pipeline. Extensive experiments have been carried out over three cross-lingual text classification datasets (XNLI, MLDoc, and MultiEURLEX), with the results showing that the proposed approach has significantly improved performance over a competitive baseline.

Uzair, M, Li, L, Eskandari, M, Hossain, J & Zhu, JG 2023, 'Challenges, advances and future trends in AC microgrid protection: With a focus on intelligent learning methods', Renewable and Sustainable Energy Reviews, vol. 178, pp. 113228-113228.
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Volpe, G, Maragò, OM, Rubinsztein-Dunlop, H, Pesce, G, Stilgoe, AB, Volpe, G, Tkachenko, G, Truong, VG, Chormaic, SN, Kalantarifard, F, Elahi, P, Käll, M, Callegari, A, Marqués, MI, Neves, AAR, Moreira, WL, Fontes, A, Cesar, CL, Saija, R, Saidi, A, Beck, P, Eismann, JS, Banzer, P, Fernandes, TFD, Pedaci, F, Bowen, WP, Vaippully, R, Lokesh, M, Roy, B, Thalhammer-Thurner, G, Ritsch-Marte, M, García, LP, Arzola, AV, Castillo, IP, Argun, A, Muenker, TM, Vos, BE, Betz, T, Cristiani, I, Minzioni, P, Reece, PJ, Wang, F, McGloin, D, Ndukaife, JC, Quidant, R, Roberts, RP, Laplane, C, Volz, T, Gordon, R, Hanstorp, D, Marmolejo, JT, Bruce, GD, Dholakia, K, Li, T, Brzobohatý, O, Simpson, SH, Zemánek, P, Ritort, F, Roichman, Y, Bobkova, V, Wittkowski, R, Denz, C, Kumar, GVP, Foti, A, Donato, MG, Gucciardi, PG, Gardini, L, Bianchi, G, Kashchuk, AV, Capitanio, M, Paterson, L, Jones, PH, Berg-Sørensen, K, Barooji, YF, Oddershede, LB, Pouladian, P, Preece, D, Adiels, CB, De Luca, AC, Magazzù, A, Bronte Ciriza, D, Iatì, MA & Swartzlander, GA 2023, 'Roadmap for optical tweezers', Journal of Physics: Photonics, vol. 5, no. 2, pp. 022501-022501.
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Abstract Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects, ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in the life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nano-particle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration.

Vu, L, Nguyen, QU, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2023, 'Deep Generative Learning Models for Cloud Intrusion Detection Systems', IEEE Transactions on Cybernetics, vol. 53, no. 1, pp. 565-577.
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Intrusion detection (ID) on the cloud environment has received paramount interest over the last few years. Among the latest approaches, machine learning-based ID methods allow us to discover unknown attacks. However, due to the lack of malicious samples and the rapid evolution of diverse attacks, constructing a cloud ID system (IDS) that is robust to a wide range of unknown attacks remains challenging. In this article, we propose a novel solution to enable robust cloud IDSs using deep neural networks. Specifically, we develop two deep generative models to synthesize malicious samples on the cloud systems. The first model, conditional denoising adversarial autoencoder (CDAAE), is used to generate specific types of malicious samples. The second model (CDAEE-KNN) is a hybrid of CDAAE and the K-nearest neighbor algorithm to generate malicious borderline samples that further improve the accuracy of a cloud IDS. The synthesized samples are merged with the original samples to form the augmented datasets. Three machine learning algorithms are trained on the augmented datasets and their effectiveness is analyzed. The experiments conducted on four popular IDS datasets show that our proposed techniques significantly improve the accuracy of the cloud IDSs compared with the baseline technique and the state-of-the-art approaches. Moreover, our models also enhance the accuracy of machine learning algorithms in detecting some currently challenging distributed denial of service (DDoS) attacks, including low-rate DDoS attacks and application layer DDoS attacks.

Wang, F, Guo, W & Li, J 2023, 'Numerical Simulation Study on Braking Performance of a New Eddy Current-Hydrodynamic Hybrid Retarder', World Electric Vehicle Journal, vol. 14, no. 12, pp. 319-319.
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Based on the principle of eddy current braking and hydraulic braking, a new eddy current-hydrodynamic hybrid retarder (ECHHR) is proposed. Based on the introduction of the working principle and structure of the ECHHR, the finite element analysis models of the electromagnetic field and the flow field of the ECHHR were established, respectively. The electromagnetic field distribution, the flow field velocity, and the flow field pressure in the ECHHR were numerically simulated. The air gap magnetic density, the eddy current braking torque, and the hydraulic braking torque under different excitation currents and different liquid filling rates were calculated. Finally, the braking performance of the EHHR was tested via experiments, and the effectiveness of the finite element analysis method was verified. The test results indicated that as the speed increased, the composite braking torque of the ECHHR increased approximately linearly. When the speed was 1000 r/min, the composite braking torque reached 2100 N·m. Compared to separate hydraulic braking and eddy current braking, the composite braking torque was relatively high in the full-speed range.

Wang, H, Ma, J & Zhu, J 2023, 'Identifying household EV models via weighted power recurrence graphs', Electric Power Systems Research, vol. 217, pp. 109121-109121.
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Wang, J, Li, L & zhang, J 2023, 'Deep Reinforcement Learning for Energy Trading and Load Scheduling in Residential Peer-to-Peer Energy Trading Market', International Journal of Electrical Power & Energy Systems, vol. 147, pp. 108885-108885.
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Wang, J, Mishra, DK, Li, L & Zhang, J 2023, 'Demand Side Management and Peer-to-Peer Energy Trading for Industrial Users Using Two-Level Multi-Agent Reinforcement Learning', IEEE Transactions on Energy Markets, Policy and Regulation, vol. 1, no. 1, pp. 23-36.
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Wang, J, Zhang, J, Li, L & Lin, Y 2023, 'Peer-to-Peer Energy Trading for Residential Prosumers With Photovoltaic and Battery Storage Systems', IEEE Systems Journal, vol. 17, no. 1, pp. 154-163.
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The popularization of solar generation enables residential households to supply their loads and trade the surplus energy through residential peer-to-peer (P2P) energy trading market. Facing the increasing complexity of the market structure and decision-making strategies, this article proposes a P2P energy trading model for residential households, and the objective is to help the centralized market coordinator optimize the benefit of participants under such a P2P market. To this end, a new mathematical model, including the rules for buying and selling energy, is presented. In this model, a supply function bidding mechanism is formulated to match the power supply imbalance and calculate the market-clearing price. An optimization problem is formulated to identify the optimal strategies for energy buying and selling, which consists of two parts: the first part is to maximize the social welfare; the second part is to minimize the unfair benefit distribution that participants can gain through P2P energy trading. The case study based on the real data for four different household categories has revealed that households can achieve 26.38% net cost reduction, and the proposed fair benefit distribution function also can fairly allocate the benefit by enforcing households' benefit variance indexes at a low level.

Wang, S, Wang, Y, Liu, C, Lei, G, Guo, Y & Zhu, J 2023, 'Performance Comparison of Tubular Flux-Switching Permanent Magnet Machines Using Soft Magnetic Composite Material and Hybrid Material Magnetic Cores', IEEE Transactions on Energy Conversion, vol. 38, no. 2, pp. 1118-1129.
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The magnetic core of a conventional tubular flux-switching permanent magnet machine (TFSPMM) is made of axially laminated silicon steel sheets. The laminated direction is consistent with the motion direction of the mover. According to the flux-switching principle, a large amount of eddy current loss will be generated when the alternating flux passes through the silicon steel sheets vertically. The soft magnetic composite (SMC) material is magneto-thermal isotropy and has low eddy current loss, which can be used in TFSPMM. However, the permeability of SMC is much lower than that of silicon steel sheets, and the hysteresis loss is large at low frequency. When the two materials are used as the hybrid material magnetic core in the machine, their respective advantages can be used to improve the thrust force of the machine and reduce core loss. In order to study the effect of SMC core and hybrid material magnetic core on the machine, the performances of TFSPMM with different kinds of cores are calculated and compared by finite element method (FEM), including magnetic density distribution, permanent magnet (PM) flux linkage, back-electromotive force (EMF), inductance, detent force, thrust force and core loss. Finally, a prototype machine with hybrid material magnetic core is made and experimental verification is carried out.

Wang, S, Yang, Z, Tao, J, Qiu, X & Burnett, IS 2023, 'Transmission loss and directivity of sound transmitted through a slit on ground', The Journal of the Acoustical Society of America, vol. 153, no. 1, pp. 224-235.
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An analytical model is proposed for sound transmission through a slit on a rigid ground based on the modal superposition method to investigate the transmission loss (TL). A simple formula is derived for estimation of the TL for plane waves with and without the ground, which gives a more precise prediction than existing approaches. It is found that a larger slit height generally decreases the TL, except at the resonant frequencies of the slit. The slit width has little effect on the TL at high frequencies, and the slit depth affects the resonant frequencies significantly even though it has little effect on the overall TL. Compared with the same size slit in the free field, the rigid ground reduces the TL at most frequencies, and that reduction is a constant between 3 and 9 dB in the low frequency range. It is also found that the sound transmitted through the slit is almost omnidirectional at low frequencies, while most of the sound energy at high frequencies falls within the range where the long side of the slit is located. The experimental results demonstrate the validity of the analytical model and the findings in numerical simulations.

Wang, X, Qin, P-Y, Song, L-Z, Jin, R & Guo, YJ 2023, 'Tightly Coupled Huygens Element-Based Conformal Transmitarray for E-Band Airborne Communication Systems', IEEE Transactions on Antennas and Propagation, vol. 71, no. 3, pp. 2467-2475.
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In this article, a wideband conformal transmitarray employing dual-layer tightly coupled Huygens elements is proposed at E-band. The element consists of five pairs of partly overlapped metallic strips with different lengths printed on two sides of a dielectric substrate. It can support tightly coupled Huygens resonances with a high transmission efficiency and a nearly full phase coverage in a wide bandwidth from 71 to 87 GHz. Equivalent circuit models are created to analyze the tightly coupled Huygens element, which has good agreement with that from full-wave simulations. In order to validate the proposed element, a cylindrically conformal transmitarray at 78 GHz is designed, fabricated, and measured. Good agreement between the measured and simulated results has been obtained, showing a peak realized gain of 26.6 dBi with an aperture efficiency of 37.2% from measurement. A measured 3 dB gain bandwidth of 20.4% is achieved from 71 to 87 GHz, fully covering the E-band spectrum.

Wang, Y, Sun, T, Li, S, Yuan, X, Ni, W, Hossain, E & Vincent Poor, H 2023, 'Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey', IEEE Communications Surveys & Tutorials, vol. 25, no. 4, pp. 2245-2298.
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Wang, Z, Zhang, JA, Xu, M & Guo, YJ 2023, 'Single-Target Real-Time Passive WiFi Tracking', IEEE Transactions on Mobile Computing, vol. 22, no. 6, pp. 3724-3742.
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Wei, F, Liu, X, Ding, X-Z, Zhao, X-B & Qin, P-Y 2023, 'A Balanced Filtering Antenna Array With High Gain, Steep Selectivity, and Multiradiation Nulls Parallel-Fed by Differential Broadband Network', IEEE Transactions on Antennas and Propagation, vol. 71, no. 12, pp. 9926-9931.
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In this communication, a 2 × 2 balanced filtering antenna array parallel-fed by a differential broadband network is presented. The array has four identical stacked filtering antenna elements, each of which consists of a main patch etched with a folded U-shaped slot, a pair of ear-shaped parasitic patches, a stacked patch, and a folded T-shaped strip. The conceived antenna topology can enhance the gain flatness, improve selectivity, and increase the number of radiation nulls. Meanwhile, by introducing a novel ring-shaped transition structure (RSTS) to the balanced-to-single-ended (BTSE) four-way feeding network, a broad bandwidth, low insertion losses, low phase difference errors, and high common-mode (CM) suppression are realized. To validate the method, an integrated antenna array with a center frequency at 2.5 GHz is fabricated and measured. Experimental results exhibit a boresight gain of 13.7 dBi, four radiation nulls, and a square factor (SF₁₀) of 1.21.

Wen, Y, Qin, P-Y, Maci, S & Guo, YJ 2023, 'Low-Profile Multibeam Antenna Based on Modulated Metasurface', IEEE Transactions on Antennas and Propagation, vol. 71, no. 8, pp. 6568-6578.
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Linearly polarized (LP) multiport multibeam antennas (MBAs) based on modulated metasurface (MTS) are presented in this article. A key challenge for MTS to generate multiple independent beams is the mutual interference of different impedance modulations. This interference can lead to reduced directivities of main beams and high sidelobe levels (SLLs). A full-wave-based optimization for a large number of beams is significantly time-consuming and practically ineffective. The source locations are optimized here by employing the aperture field calculated from a zeroth-order approximation (ZOA) of the currents on the surface. This provides a good preliminary accuracy without any full-wave analysis. The relevant design method is verified by two examples of three-beam and seven-beam MTS antennas. Furthermore, the seven-beam MTS antenna is fabricated and successfully measured. The measurements show a measured realized gain of 20 dBi at 14.10 GHz with a beam coverage (10-dB overlap) up to ±36°. It is seen that the same performance requires larger areas with conventional summation of apertures.

Wu, J, Li, L & Zhang, J 2023, 'Maximum demand flexibility from the demand response of a big group of residential homes', International Journal of Electrical Power & Energy Systems, vol. 147, pp. 108800-108800.
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Wu, K & Guo, YJ 2023, 'Deterministic Solutions to Improved Generalized Joined Coupler Matrix for Multibeam Antennas', IEEE Transactions on Antennas and Propagation, vol. 71, no. 12, pp. 9454-9466.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 2023, 'OTFS-Based Joint Communication and Sensing for Future Industrial IoT', IEEE Internet of Things Journal, vol. 10, no. 3, pp. 1973-1989.
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Effective wireless communications are increasingly important in maintaining the successful closed-loop operation of mission-critical industrial Internet-of-Things (IIoT) applications. To meet the ever-increasing demands on better wireless communications for IIoT, we propose an orthogonal time-frequency space (OTFS) waveform-based joint communication and radio sensing (JCAS) scheme -an energy-efficient solution for not only reliable communications but also high-accuracy sensing. OTFS has been demonstrated to have higher reliability and energy efficiency than the currently popular IIoT communication waveforms. JCAS has also been highly recommended for IIoT, since it saves cost, power and spectrum compared to having two separate radio frequency systems. Performing JCAS based on OTFS, however, can be hindered by a lack of effective OTFS sensing. This paper is dedicated to filling this technology gap. We first design a series of echo pre-processing methods that successfully remove the impact of communication data symbols in the time-frequency domain, where major challenges, like inter-carrier and inter-symbol interference and noise amplification, are addressed. Then, we provide a comprehensive analysis of the signal-to-interference-plus-noise ratio (SINR) for sensing and optimize a key parameter of the proposed method to maximize the SINR. Extensive simulations show that the proposed sensing method approaches the maximum likelihood estimator with respect to the estimation accuracy of target parameters and manifests applicability to wide ranges of key system parameters. Notably, the complexity of the proposed method is only dominated by a two-dimensional Fourier transform.

Wu, K, Zhang, JA, Huang, X, Guo, YJ & Hanzo, L 2023, 'Simultaneous Beam and User Selection for the Beamspace mmWave/THz Massive MIMO Downlink', IEEE Transactions on Communications, vol. 71, no. 3, pp. 1785-1797.
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Wu, K, Zhang, JA, Huang, X, Heath, RW & Guo, YJ 2023, 'Green Joint Communications and Sensing Employing Analog Multi-Beam Antenna Arrays', IEEE Communications Magazine, vol. 61, no. 7, pp. 172-178.
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Joint communications and sensing (JCAS) is potentially a hallmark technology for the sixth generation mobile network (6G). Most existing JCAS designs are based on digital arrays, analog arrays with tunable phase shifters, or hybrid arrays, which are effective but are generally complicated to design and power inefficient. This article introduces the energyefficient and easy-to-design multi-beam antenna arrays (MBAAs) for JCAS. Using pre-designed and fixed analog devices, such as lens or Butler matrix, MBAA can simultaneously steer multiple beams yet with negligible power consumption compared with other techniques. Moreover, MBAAs enable flexible beam synthesis, accurate angle-of-arrival estimation, and easy handling/ utilization of the beam squint effect. All these features have not been well captured by the JACS community yet. To promote the awareness of them, we intuitively illustrate them and also exploit them for constructing a multi-beam JCAS framework. Finally, the challenges and opportunities are discussed to foster the development of green JCAS systems.

Xia, J, Xu, M, Zhang, H, Zhang, J, Huang, W, Cao, H & Wen, S 2023, 'Robust Face Alignment via Inherent Relation Learning and Uncertainty Estimation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 10358-10375.
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Xiao, D, Zhang, JA, Liu, X, Qu, Y, Ni, W & Liu, RP 2023, 'A Two-Stage GCN-Based Deep Reinforcement Learning Framework for SFC Embedding in Multi-Datacenter Networks', IEEE Transactions on Network and Service Management, vol. 20, no. 4, pp. 4297-4312.
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Xiao, Y, Xia, R, Li, Y, Shi, G, Nguyen, DN, Hoang, DT, Niyato, D & Krunz, M 2023, 'Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach', IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-15.
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With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the distributions of data samples created by different generators, resulting in each generator producing synthetic data samples that follow the same distribution as one particular service type. Extensive simulation results show that the classification accuracy of FS-GAN achieves over $20\%$ improvement in average compared to the state-of-the-art clustering-based traffic analysis algorithms. FS-GAN also has the capability to synthesize highly complex mixtures of traffic types without requiring any human-labeled data samples.

Xiao, Y, Zhang, X, Li, Y, Shi, G, Krunz, M, Nguyen, DN & Hoang, DT 2023, 'Time-sensitive Learning for Heterogeneous Federated Edge Intelligence', IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-18.
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Real-time machine learning (ML) has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of application domains, including self-driving vehicles, intelligent transportation, and industry automation. In this paper, we investigate real-time ML in a federated edge intelligence (FEI) system, an edge computing system that implements federated learning (FL) solutions based on data samples collected and uploaded from decentralized data networks, e.g., Internet-of-Things (IoT) and/or wireless sensor networks. FEI systems often exhibit heterogenous communication and computational resource distribution, as well as non-i.i.d. data samples arrived at different edge servers, resulting in long model training time and inefficient resource utilization. Motivated by this fact, we propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model with desirable accuracy. Training acceleration solutions for both TS-FL with synchronous coordination (TS-FL-SC) and asynchronous coordination (TS-FL-ASC) are investigated. To address the straggler effect in TS-FL-SC, we develop an analytical solution to characterize the impact of selecting different subsets of edge servers on the overall model training time. A server dropping-based solution is proposed to allow some slow-performance edge servers to be removed from participating in the model training if their impact on the resulting model accuracy is limited. A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, the local epoch number (the number of model training iterations per coordination), and the data batch size (the number of data samples for each model training iteration). Motivated by the fact that data samples at the slowest edge server may exhibit special characteristi...

Xiong, X, Sun, C, Ni, W & Wang, X 2023, 'Three-Dimensional Trajectory Design for Unmanned Aerial Vehicle-Based Secure and Energy-Efficient Data Collection', IEEE Transactions on Vehicular Technology, vol. 72, no. 1, pp. 664-678.
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Xu, C, Jia, W, Cui, T, Wang, R, Zhang, Y-F & He, X 2023, 'Arbitrary-Shape Scene Text Detection via Visual-Relational Rectification and Contour Approximation', IEEE Transactions on Multimedia, vol. 25, no. 99, pp. 4052-4066.
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One trend in the latest bottom-up approaches for arbitrary-shape scene text detection is to determine the links between text segments using Graph Convolutional Networks (GCNs). However, the performance of these bottom-up methods is still inferior to that of state-of-the-art top-down methods even with the help of GCNs. We argue that a cause of this is that bottom-up methods fail to make proper use of visual-relational features, which results in accumulated false detection, as well as the error-prone route-finding used for grouping text segments. In this paper, we improve classic bottom-up text detection frameworks by fusing the visual-relational features of text with two effective false positive/negative suppression (FPNS) mechanisms and developing a new shape-approximation strategy. First, dense overlapping text segments depicting the ‘`characterness’' and ‘`streamline’' properties of text are constructed and used in weakly supervised node classification to filter the falsely detected text segments. Then, relational features and visual features of text segments are fused with a novel Location-Aware Transfer (LAT) module and Fuse Decoding (FD) module to jointly rectify the detected text segments. Finally, a novel multiple-text-map-aware contour-approximation strategy is developed based on the rectified text segments, instead of the error-prone route-finding process, to generate the final contour of the detected text. Experiments conducted on five benchmark datasets demonstrate that our method outperforms the state-of-the-art performance when embedded in a classic text detection framework, which revitalizes the strengths of bottom-up methods.

Xu, C, Jia, W, Wang, R, He, X, Zhao, B & Zhang, Y 2023, 'Semantic Navigation of PowerPoint-Based Lecture Video for AutoNote Generation', IEEE Transactions on Learning Technologies, vol. 16, no. 1, pp. 1-17.
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Xu, C, Jia, W, Wang, R, Luo, X & He, X 2023, 'MorphText: Deep Morphology Regularized Accurate Arbitrary-Shape Scene Text Detection', IEEE Transactions on Multimedia, vol. 25, no. 99, pp. 4199-4212.
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Bottom-up text detection methods play an important role in arbitrary-shape scene text detection but there are two restrictions preventing them from achieving their great potential, i.e., 1) the accumulation of false text segment detections, which affects subsequent processing, and 2) the difficulty of building reliable connections between text segments. Targeting these two problems, we propose a novel approach, named ``MorphText', to capture the regularity of texts by embedding deep morphology for arbitrary-shape text detection. Towards this end, two deep morphological modules are designed to regularize text segments and determine the linkage between them. First, a Deep Morphological Opening (DMOP) module is constructed to remove false text segment detections generated in the feature extraction process. Then, a Deep Morphological Closing (DMCL) module is proposed to allow text instances of various shapes to stretch their morphology along their most significant orientation while deriving their connections.Extensive experiments conducted on four challenging benchmark datasets (CTW1500, Total-Text, MSRA-TD500 and ICDAR2017) demonstrate that our proposed MorphText outperforms both top-down and bottom-up state-of-the-art arbitrary-shape scene text detection approaches.

Xu, H, Nanda, P, Liang, J & He, X 2023, 'FCH, an incentive framework for data-owner dominated federated learning', Journal of Information Security and Applications, vol. 76, pp. 103521-103521.
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Xu, K, Guo, Y, Lei, G, Sun, X & Zhu, J 2023, 'Electromagnetic Performance Analysis of a Bearingless Permanent Magnet Synchronous Motor by Model Order Reduction', IEEE Transactions on Magnetics, vol. 59, no. 11, pp. 1-5.
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Yang, L, Zhu, X & Gómez-García, R 2023, 'High-Order Quasi-Elliptic-Type Wideband Bandpass Filter With Ultrabroad Input-Reflectionless Stopband Range', IEEE Microwave and Wireless Technology Letters, vol. 33, no. 6, pp. 655-658.
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Yang, R, Huang, S, Zhang, Y, Zhang, C, Qian, J, Lam, RHW, Lee, JE-Y & Wang, Z 2023, 'Developing a multi-sample acoustofluidic device for high-throughput cell aggregation', Journal of Micromechanics and Microengineering, vol. 33, no. 5, pp. 055003-055003.
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Abstract Plug-and-play acoustofluidic devices are highly promising for dexterously aggregating microparticles owing to the advantages of being contactless, label-free, and cost-efficient. Despite the extensive progress, existing acoustofluidic devices are largely limited to addressing a single sample per device, lacking the ability to address multiple samples for high-throughput operations in a single acoustofluidic device. In this work, we report a high-throughput multi-sample acoustofluidic aggregation device that enables manipulation of up to 12 samples simultaneously using a single reusable acoustic tweezer. The key design of the multi-sample acoustofluidic device lies in the utilization of a polydimethylsiloxane frame as a selective acoustic-absorbing feature to create asymmetric acoustic waves over multiple detachable superstrates in a single device. This approach is distinct from conventional strategies which mostly have involved modifying the superstrates or tuning the settings for individual superstrates. We demonstrate that the proposed acoustofluidic device can efficiently aggregate multiple samples of various compositions ranging from non-bioactive microparticles to bioactive cells, as well as a range of object sizes spanning from 0.6 µm to 13 µm. Given its merits of simplicity, cost-efficiency and high throughput, the proposed platform could be useful for biomedical applications requiring large-scale operations, such as 3D tumor spheroids and bio-sensors.

Yang, S, Wu, S, Yang, E, Han, B, Liu, Y, Xu, M, Niu, G & Liu, T 2023, 'A Parametrical Model for Instance-Dependent Label Noise', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, pp. 14055-14068.
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Yao, L, Kusakunniran, W, Zhang, P, Wu, Q & Zhang, J 2023, 'Improving Disentangled Representation Learning for Gait Recognition Using Group Supervision', IEEE Transactions on Multimedia, vol. 25, no. 99, pp. 4187-4198.
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In decades, gait has been gathering extensive interest for the advantage that it can be measured from a distance without physical contact. However, for image/video-based gait recognition, its performance can be remarkably influenced by exterior factors, such as viewing angles and clothing changes. Thus, in this paper, a group-supervised disentangled representation learning network is proposed for gait recognition to extract features invariant to these factors. First, sequences are explicitly disentangled into pose, gait, appearance, and view features through a generic encoder-decoder framework. To ensure the feature adaptability and independency, a disentanglement swap module is specifically adopted during our encode-decoder process through a series of swap operations based on the feature attributes. Following the feature disentanglement, a disentanglement aggregation module is also specially proposed for pose, gait, and appearance features to enhance their effectiveness. Finally, the enhanced three features are concatenated together for gait recognition. Relevant experiments certify that compared with other disentangled representation learning-based gait recognition methods, our proposed method enables to obtain a more excellent recognition result, despite fewer gait frames being utilized.

Ye, Y, Wang, H, Xu, B & Zhang, J 2023, 'An imitation learning-based energy management strategy for electric vehicles considering battery aging', Energy, vol. 283, pp. 128537-128537.
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Ye, Y, Zhang, J, Pilla, S, Rao, AM & Xu, B 2023, 'Application of a new type of lithium‑sulfur battery and reinforcement learning in plug-in hybrid electric vehicle energy management', Journal of Energy Storage, vol. 59, pp. 106546-106546.
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Yu, G, Wang, X, Ni, W, Lu, Q, Xu, X, Liu, RP & Zhu, L 2023, 'Adaptive Resource Scheduling in Permissionless Sharded-Blockchains: A Decentralized Multiagent Deep Reinforcement Learning Approach', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 11, pp. 7256-7268.
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Yu, G, Wang, X, Sun, C, Yu, P, Ni, W & Liu, RP 2023, 'Obfuscating the Dataset: Impacts and Applications', ACM Transactions on Intelligent Systems and Technology, vol. 14, no. 5, pp. 1-15.
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Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties when dataset sharing is essential. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights —in terms of the model accuracy, ℓ 2 -distance-based model distance, and level of data privacy—and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a tradeoff between the model accuracy and privacy level and a tradeoff between the model difference and privacy level. The results indicate broad application prospects for training outsourcing and guarding against attacks in federated learning both of which have been increasingly attractive in many areas, particularly learning in edge computing.

Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2023, 'Low-Resolution Hybrid Beamforming in Millimeter-Wave Multi-User Systems', IEEE Transactions on Vehicular Technology, vol. 72, no. 7, pp. 8941-8955.
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Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2023, 'Regularized Zero-Forcing Aided Hybrid Beamforming for Millimeter-Wave Multiuser MIMO Systems', IEEE Transactions on Wireless Communications, vol. 22, no. 5, pp. 3280-3295.
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This paper considers hybrid beamforming consisting of analog beamforming (ABF) coupled with digital baseband beamforming (DBF) which is designed for multi-user (MU) multiple input multiple output (MIMO) millimeter-wave (mmWave) communications. ABF uses a limited number of radio frequency (RF) chains and finite-resolution phase-shifters to alleviate the power consumption at the base station (BS), while DBF uses either zero-forcing beamforming (ZFB) or regularized zero forcing beamforming (RZFB) to restrain MU interference. The joint design of ABF and DBF constitutes a computationally challenging mixed discrete continuous optimization problem. The paper develops efficient algorithms for its solution, which iterate scalable-complex expressions. Furthermore, we conceive a new class of MU RZFB for attaining higher rates. Simulations are provided to demonstrate the viability of the proposed algorithms and the advantages of the conceived RZFB.

Yu, X, Xiao, B, Ni, W & Wang, X 2023, 'Optimal Adaptive Power Control for Over-The-Air Federated Edge Learning Under Fading Channels', IEEE Transactions on Communications, vol. 71, no. 9, pp. 5199-5213.
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Yuan, C, Ni, W, Zhang, K, Bai, J, Shen, J & Jamalipour, A 2023, 'User Pairing and Power Allocation in Untrusted Multiuser NOMA for Internet of Things', IEEE Internet of Things Journal, vol. 10, no. 15, pp. 13155-13167.
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Yuan, X, Hu, S, Ni, W, Liu, RP & Wang, X 2023, 'Joint User, Channel, Modulation-Coding Selection, and RIS Configuration for Jamming Resistance in Multiuser OFDMA Systems', IEEE Transactions on Communications, vol. 71, no. 3, pp. 1631-1645.
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Yuan, X, Hu, S, Ni, W, Wang, X & Jamalipour, A 2023, 'Deep Reinforcement Learning-Driven Reconfigurable Intelligent Surface-Assisted Radio Surveillance With a Fixed-Wing UAV', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 4546-4560.
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Yuan, X, Ni, W, Ding, M, Wei, K, Li, J & Poor, HV 2023, 'Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 1884-1897.
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Zafra, E, Vazquez, S, Geyer, T, Aguilera, RP & Franquelo, LG 2023, 'Long Prediction Horizon FCS-MPC for Power Converters and Drives', IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 159-175.
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Zeng, FC, Ding, C, Chen, Y-N & Guo, YJ 2023, 'A Wide-Beam Antenna Array With Spatially Variable-Orthogonal-Polarizations (SVOP) Achieved by Polarization-Mixing', IEEE Transactions on Antennas and Propagation, vol. 71, no. 9, pp. 7626-7631.
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Zeng, J, Wu, T, Feng, W, Ni, W, Lv, T, Zhou, S, Wang, X & Guo, YJ 2023, 'Analysis of Massive Ultra-Reliable and Low-Latency Communications Over the κ-μ Shadowed Fading Channel', IEEE Transactions on Communications, vol. 71, no. 3, pp. 1798-1813.
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Zeng, J, Xiao, C, Wu, T, Ni, W, Liu, RP & Guo, YJ 2023, 'Uplink Non-Orthogonal Multiple Access With Statistical Delay Requirement: Effective Capacity, Power Allocation, and α Fairness', IEEE Transactions on Wireless Communications, vol. 22, no. 2, pp. 1298-1313.
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Zhang, C, Xu, RYD, Zhang, X & Huang, W 2023, 'Capture and control content discrepancies via normalised flow transfer', Pattern Recognition Letters, vol. 165, pp. 161-167.
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Zhang, G, Feng, S, Chen, Y, Yang, Y, Zhu, H, Zhou, X, Hong, J, Tang, W & Yang, J 2023, '3-D Printed Filtering Rat-Race Couplers Using Hemispherical Cavity Resonator', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 11, pp. 4922-4932.
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Zhang, H & Xu, M 2023, 'Multiscale Emotion Representation Learning for Affective Image Recognition', IEEE Transactions on Multimedia, vol. 25, pp. 2203-2212.
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Zhang, H & Xu, M 2023, 'Recognition of Emotions in User-Generated Videos through Frame-Level Adaptation and Emotion Intensity Learning', IEEE Transactions on Multimedia, vol. 25, pp. 881-891.
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Zhang, H, Liu, C, Zhang, S, Wang, Y, Lei, G & Zhu, J 2023, 'Analysis and Design Optimization of Axial–Radial Flux Hybrid Excitation Permanent Magnet Eddy Current Coupling', Journal of Electrical Engineering & Technology, vol. 18, no. 6, pp. 4231-4244.
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Zhang, JA, Wu, K, Huang, X & Guo, YJ 2023, 'Beam Alignment for Analog Arrays Based on Gaussian Approximation', IEEE Transactions on Vehicular Technology, vol. 72, no. 6, pp. 8152-8157.
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Beam alignment is a process for receive and transmit antenna arrays to find the correct beamforming directions. It is typically based on beam scanning and peak-energy searching, which lead to time-consuming beam training process in communication protocols, such as 802.11ad for vehicular networks. In this correspondence, we propose a fast beam alignment method for analog arrays, based on directly estimating the angle-of-arrival (AoA) of the incoming signals. We propose simple and highly efficient AoA estimators, by approximating the power of the array response as a Gaussian function. One estimator is based on the power ratio and can coherently combine multiple measurements scanned at arbitrary intervals and with different beam widths. The other two are based on an innovative idea of Gaussian curve fitting with weighted least square techniques, and one of them can even work without knowing the beam width. Simulation results validate the effectiveness of the proposed scheme.

Zhang, L, Cao, C, Tay, SS, Chen, C, Macmillan, A, Wen, S, Jin, D, McCarroll, J & Stenzel, MH 2023, 'Exploring the Effect of Drug Loading on the Biological Fate of Polymer-Coated Solid Nanoparticles', Chemistry of Materials, vol. 35, no. 11, pp. 4471-4488.
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Zhang, L, Szmalenberg, A, Cook, K, Liu, B, Ding, L, Wang, F & McGloin, D 2023, 'Trapped aerosol sizes under fiber-based counterpropagation optical trapping', Journal of the Optical Society of America B, vol. 40, no. 2, pp. 460-460.
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Quantifying the size range of aerosols that can be trapped in a counterpropagation dual-fiber trapping configuration is important in understanding how these particles can be manipulated and characterized in such traps. Here, we present simulations and experiments investigating the trapped aerosol size range variations in the intermediate position of two fibers under different fiber separations, aerosol particle sizes, fiber powers, and radial offset. By doing so, we establish a parametric space plot of stable aerosol trapping, and the parametric analysis provides insight into the tolerance of such traps to trapping fluctuations.

Zhang, X, Xia, W, Cui, Q, Tao, X & Liu, RP 2023, 'Efficient and Trusted Data Sharing in a Sharding-Enabled Vehicular Blockchain', IEEE Network, vol. 37, no. 2, pp. 230-237.
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Zhang, Z, Xu, Z, McGuire, HM, Essam, C, Nicholson, A, Hamilton, TJ, Li, J, Eshraghian, JK, Yong, K-T, Vigolo, D & Kavehei, O 2023, 'Neuromorphic cytometry: implementation on cell counting and size estimation', Neuromorphic Computing and Engineering, vol. 3, no. 4, pp. 044005-044005.
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Abstract Imaging flow cytometry (FC) is a powerful analytic tool that combines the principles of conventional FC with rich spatial information, allowing more profound insight into single-cell analysis. However, offering such high-resolution, full-frame feedback can restrain processing speed and has become a significant trade-off during development. In addition, the dynamic range (DR) offered by conventional photosensors can only capture limited fluorescence signals, which compromises the detection of high-velocity fluorescent objects. Neuromorphic photo-sensing focuses on the events of interest via individual-firing pixels to reduce data redundancy and latency. With its inherent high DR, this architecture has the potential to drastically elevate the performance in throughput and sensitivity to fluorescent targets. Herein, we presented an early demonstration of neuromorphic cytometry, demonstrating the feasibility of adopting an event-based resolution in describing spatiotemporal feedback on microscale objects and for the first time, including cytometric-like functions in object counting and size estimation to measure 8 µm, 15 µm microparticles and human monocytic cell line (THP-1). Our work has achieved highly consistent outputs with a widely adopted flow cytometer (CytoFLEX) in detecting microparticles. Moreover, the capacity of an event-based photosensor in registering fluorescent signals was evaluated by recording 6 µm Fluorescein isothiocyanate-marked particles in different lighting conditions, revealing superior performance compared to a standard photosensor. Although the current platform cannot deliver multiparametric measurements on cells, future endeavours will include further functionalities and increase the measurement parameters (granularity, cell condition, fluorescence analysis) to enrich cell interpretation.

Zhou, C, Lyu, B, Feng, Y & Hoang, DT 2023, 'Transmit Power Minimization for STAR-RIS Empowered Symbiotic Radio Communications', IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 6, pp. 1641-1656.
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In this paper, we propose a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) empowered transmission scheme for symbiotic radio (SR) systems to make more flexibility for network deployment and enhance system performance. The STAR-RIS is utilized to not only beam the primary signals from the base station (BS) towards multiple primary users on the same side of the STAR-RIS, but also achieve the secondary transmission to the secondary users on another side. We consider both the broadcasting signal model and unicasting signal model at the BS. For each model, we aim for minimizing the transmit power of the BS by designing the active beamforming and simultaneous reflection and transmission coefficients under the practical phase correlation constraint. To address the challenge of solving the formulated problem, we propose a block coordinate descent based algorithm with the semidefinite relaxation, penalty dual decomposition and successive convex approximation methods, which decomposes the original problem into one sub-problem about active beamforming and the other sub-problem about simultaneous reflection and transmission coefficients, and iteratively solve them until the convergence is achieved. Numerical results indicate that the proposed scheme can reduce up to 150.6% transmit power compared to the backscattering device enabled scheme.

Zhou, H, Long, Y, Gong, S, Zhu, K, Hoang, DT & Niyato, D 2023, 'Hierarchical Multi-Agent Deep Reinforcement Learning for Energy-Efficient Hybrid Computation Offloading', IEEE Transactions on Vehicular Technology, vol. 72, no. 1, pp. 986-1001.
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Mobile edge computing (MEC) provides an economical way for the resource-constrained edge users to offload computational workload to MEC servers co-located with the access point (AP). In this paper, we consider a hybrid computation offloading scheme that allows edge users to offload workloads by using active RF communications and backscatter communications. We aim to maximize the overall energy efficiency subject to the completion of all workload by jointly optimizing the AP's beamforming and the users' offloading decisions. Considering a dynamic environment, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework to solve this problem. The high-level agent resides in the AP and optimizes the beamforming strategy, while the low-level user agents learn and adapt individuals' offloading strategies. To further improve the learning efficiency, we propose a novel optimization-driven learning algorithm that allows the AP to estimate the low-level users' actions by solving approximate optimization problem efficiently. Then, the action estimation can be shared with all users and drive them to update individuals' actions independently. Simulation results reveal that our algorithm can improve the system performance by 50%. The learning efficiency and reliability are also improved significantly comparing to the model-free learning methods.

Zhou, I, Lipman, J, Abolhasan, M & Shariati, N 2023, 'Intelligent spatial interpolation-based frost prediction methodology using artificial neural networks with limited local data', Environmental Modelling & Software, vol. 165, pp. 105724-105724.
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Zhou, J, Peng, H, Su, S & Song, R 2023, 'Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation Robot', IEEE Transactions on Biomedical Engineering, vol. 70, no. 6, pp. 1858-1868.
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Zhu, J, Yang, Y, Hou, Z, Liao, S & Xue, Q 2023, 'Aperture-Shared All-Metal Endfire High-Gain Parabolic Antenna for Millimeter-Wave Multibeam and Sub-6-GHz Communication Applications', IEEE Transactions on Antennas and Propagation, vol. 71, no. 3, pp. 2784-2789.
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Zhu, Q & Ha, QP 2023, 'A Bidirectional Self-Rectifying Network With Bayesian Modeling for Vision-Based Crack Detection', IEEE Transactions on Industrial Informatics, vol. 19, no. 3, pp. 3017-3028.
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Zhu, W, Tuan, HD, Dutkiewicz, E, Fang, Y & Hanzo, L 2023, 'Low-Complexity Pareto-Optimal 3D Beamforming for the Full-Dimensional Multi-User Massive MIMO Downlink', IEEE Transactions on Vehicular Technology, vol. 72, no. 7, pp. 8869-8885.
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Zhu, W, Tuan, HD, Dutkiewicz, E, Fang, Y, Poor, HV & Hanzo, L 2023, 'Long-Term Rate-Fairness-Aware Beamforming Based Massive MIMO Systems', IEEE Transactions on Communications, pp. 1-1.
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Zuo, X, Xu, M, Huang, M, Li, M & Peng, N 2023, 'Maintenance Strategy for Elliptical Orbit Satellite With Air-Breathing Electric Propulsion', IEEE Transactions on Aerospace and Electronic Systems, vol. 59, no. 5, pp. 6863-6877.
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Abbasi, M, Li, L, Aguilera, RP, Lu, D & Wang, F 1970, 'A New Single-Switch Step-Up DC-DC Converter with High Gain, Reduced Voltage Stress, and Continuous Input Current', 2023 IEEE 14th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2023 IEEE 14th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), IEEE, pp. 1003-1007.
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Generally, renewable energy sources (RESs) such as solar and wind energy systems have variable output due to their dependence on the weather condition. Hence, numerous converter structures with variable voltage gain have been introduced, which can be used to regulate the output voltage of RESs. Providing a high dynamic voltage gain with even a small input voltage was realized by developing conventional DC-DC converters, such as Zeta, Sepic, and Cuk Converters. But these conventional converters cannot provide high voltage gains with non-extreme duty cycles. In this paper, it is aimed to design a new single-switch DC-DC power converter without employing a large number of components. This topology provides higher dynamic voltage gain with a lower duty cycle, continuous input current, and lower voltage stress on the components. The performance of the converter is proved by comparison and simulation results obtained in MATLAB/Simulink environment.

Abhijith, V, Hossain, MJ, Lei, G, Sreelekha, PA & Kadam, SB 1970, 'High Torque Capability Non-Permanent Magnet Hybrid Excited Switched Reluctance Motor for Electric Vehicle Application', 2023 IEEE Energy Conversion Congress and Exposition (ECCE), 2023 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE.
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Abhijith, V, Hossain, MJ, Lei, G, Sreelekha, PA & Kadam, SB 1970, 'Hybrid Topologies of Non-Permanent Magnet-Excited Switched-Reluctance Motors With High Torque Capability For Electric Vehicle Applications', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE.
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Abualsaud, A, Taghizadeh, F, Deilami, S, Hossain, J & Lu, J 1970, 'Smart Electric Vehicle Charging System with Flexible Charging and Voltage Stabilisation Service for Australian Household Network', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE.
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Ahmed, F, Singh, K, Esselle, KP & Thalakotuna, D 1970, 'Near-Field Metallic Metasurfaces for Enhancing Antenna Capabilities', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE, pp. 53-54.
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Alalmaie, AZ, Nanda, P, He, X & Alayan, MS 1970, 'Why Zero Trust Framework Adoption has Emerged During and After Covid-19 Pandemic', Springer International Publishing, pp. 181-192.
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Alam, MM, Shaw, P, Lu, DD-C & Siwakoti, YP 1970, 'A New Four-Port Dual-Input Dual-Output Quadratic DC-DC Boost Converter (FPQBC) with High Gain', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE.
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Alcaide, AM, Poblete, P, Vazquez, S, Aguilera, RP, Kouro, S, Leon, JI & Franquelo, LG 1970, 'Feed-Forward Technique to Emulate Natural Sampling Method for Cascaded H-Bridge Converters', IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, IEEE.
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Al-Soeidat, M, Khawaldeh, H & Lu, DD-C 1970, 'Investigation of Photovoltaic Panel Degradation Affected by Dust in Jordan', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE.
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An, Y, Wong, JK-W & Ling, SH 1970, 'An EEG-based brain-computer interface for real-time multi-task robotic control.', EMBC, 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, pp. 1-4.
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An, Y, Zhao, S & Zhang, G 1970, 'A Cycle Architecture Based on Policy Gradient for Unsupervised Video Summarization', Proceedings of the 15th International Conference on Digital Image Processing, ICDIP 2023: The 15th International Conference on Digital Image Processing, ACM.
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Ansari, M, Zetterstrom, O, Fonseca, NJG, Quevedo-Teruel, O & Guo, YJ 1970, 'A Lightweight Metalized-Insert Luneburg Lens', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Ariyarathna, T, Kularatna, N & Gunawardane, K 1970, 'Supercapacitor assisted extra low frequency power converters and surge protectors: Applying Supercapacitor Assisted Loss Management Concept in practical applications', 2023 IEEE Applied Power Electronics Conference and Exposition (APEC), 2023 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE.
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Arnaz, A, Lipman, J & Abolhasan, M 1970, 'A Multi-objective Reinforcement Learning Solution for Handover Optimization in URLLC', 2023 28th Asia Pacific Conference on Communications (APCC), 2023 28th Asia Pacific Conference on Communications (APCC), IEEE.
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Badar, AQH, Mangipudi, M, Panda, D & Hossain, MJ 1970, 'Optimal Solar Array Configuration for Multiple Shade Patterns', 2023 10th IEEE International Conference on Power Systems (ICPS), 2023 10th IEEE International Conference on Power Systems (ICPS), IEEE.
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Cabezas, V, Acuna, P, Lezana, P, Aguilera, RP & Garcia, C 1970, 'Selective Harmonic Mitigation — Model Predictive Control for a Grid-Connected Seven-Level Cascaded H-Bridge Converter', 2023 IEEE 8th Southern Power Electronics Conference and 17th Brazilian Power Electronics Conference (SPEC/COBEP), 2023 IEEE 8th Southern Power Electronics Conference and 17th Brazilian Power Electronics Conference (SPEC/COBEP), IEEE.
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Chen, K, Zhang, JA, Wang, Z & Guo, YJ 1970, 'Development of an Uplink Sensing Demonstrator for Perceptive Mobile Networks', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Chen, S-L & Guo, YJ 1970, 'Beam-Squint Mitigated Transverse-Slot Leaky-Wave Antenna for Wideband Wireless Communications', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE.
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Chen, S-L, Qin, P-Y, Liu, Y & Lin, W 1970, 'Recent Advances and Future Scopes of Multi-Linear Polarization Reconfigurable Antennas', 2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT), 2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT), IEEE.
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Chen, Y, Ding, C, Zhu, H, Liu, Y & Guo, YJ 1970, 'A Dual-Slant-Polarized In-band Full-duplex (IBFD) Antenna System with Four Isolated Channels', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Choong, DSW, Goh, DJ, Liu, J, Sarafianou, M, Merugu, S, Zhang, QX, Chang, P, Leotti, A, Koppisetti, G, Zakiyyan, N, Lin, H, Bhasetti, C, Ghosh, S, Ramegowda, PC, Chen, DS-H, Lee, JE-Y, Prelini, C, Giusti, D, Savoia, A & K, Y 1970, 'DC Bias Effects on Optimizing ScAlN Air-Coupled pMUT Performance Parameters', 2023 IEEE SENSORS, 2023 IEEE SENSORS, IEEE.
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Chu, NH, Nguyen, DN, Hoang, DT, Phan, KT, Dutkiewicz, E, Niyato, D & Shu, T 1970, 'Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning', 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6.
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This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.

Cuzmar, RH, Poblete, P, Aguilera, RP, Pereda, J & Lu, DD-C 1970, 'Power Balance of a Delta-Connected CHB Converter with MPC for Photovoltaic Systems', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE.
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Dassanayake, C, Kularatna, N, Steyn-Ross, A, Gunawardane, K & Gurusinghe, N 1970, 'Arc Absorption Options Based on Passive Components in DC Circuit Breakers', IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, IEEE.
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Dassanayake, C, Kularatna, N, Steyn-Ross, A, Gunawardane, K & Gurusinghe, N 1970, 'Plasma Absorption Techniques in Direct Current Circuit Breakers', 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), IEEE.
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Davies, J, Thai, MT, Hoang, TT, Nguyen, CC, Phan, PT, Zhu, K, Nhi Tran, DB, Ho, VA, La, HM, Ha, QP, Lovell, NH & Do, TN 1970, 'A Flexible 3D Force Sensor with In-Situ Tunable Sensitivity', 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.
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Deng, H, Wang, X, Yu, G, Dang, X & Liu, RP 1970, 'A Novel Weights-less Watermark Embedding Method for Neural Network Models', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Doan, QM, Dinh, TH, Trung, NL, Nguyen, DN, Singh, AK & Lin, C-T 1970, 'Extended Upscale and Downscale Representation with Cascade Arrangement', 2023 IEEE Statistical Signal Processing Workshop (SSP), 2023 IEEE Statistical Signal Processing Workshop (SSP), IEEE.
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Eklund, M, Khalilpour, K, Voinov, A & Hossain, MJ 1970, 'Barriers to Community Microgrids in Fragmented Communities: Insights from a Case Study', 2023 International Conference on Future Energy Solutions (FES), 2023 International Conference on Future Energy Solutions (FES), IEEE.
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Esselle, K, Singh, K, Thalakotuna, D, Koli, MNY & Ahmed, F 1970, 'Beam-Steering Antenna Technologies for Space-Related Applications', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Esselle, KP & Thalakotuna, DN 1970, 'Emerging Technologies for Steering Narrow Antenna Beams in Modern Radio Systems', 2023 IEEE Radio and Antenna Days of the Indian Ocean (RADIO), 2023 IEEE Radio and Antenna Days of the Indian Ocean (RADIO), IEEE.
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Farah, N, Lei, G, Zhu, J & Guo, Y 1970, 'Reinforcement Learning for Intelligent Control of AC Machine Drives: A Review', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE.
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Farasat, M, Thalakotuna, D, Yang, Y & Hu, Z 1970, 'A Novel Cross-Interspersed Design of Multiband Antennas for Base Station Applications', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE.
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Farhangi, M, Barzegarkhoo, R, Aguilera, RP, Lu, DD-C & Siwakoti, YP 1970, 'Flexible Active Power Decoupling Control Strategy for A Single-Stage Switched-Boost Grid-Connected Multilevel Inverter', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE.
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Faruque, MO, Hossain, MA, Alam, SMM, Islam, MR, Islam, MR & Guo, Y 1970, 'A Hybrid LSTM-LightGBM Model for Precise Short-Term Wind Power Forecasting', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE.
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Ghosh, S, Goh, DJ, Hu, Z, Sharma, J, Teo, YS, McCarthy, M, Ramegowda, PC, Toh, WD, Chen, W, Murugan, A, Zhang, Y, Tsuchiya, Y, Lal, A, Lee, JE-Y & Koh, Y 1970, 'Ultra-Low Power MEMS Inertial Switch Based Wake-Up Wireless Sensing Node for Door Lock Monitoring', 2023 IEEE SENSORS, 2023 IEEE SENSORS, IEEE.
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Ghosh, S, Goh, DJ, Koh, Y, Sharma, J, Da Toh, W, Chen, W, Zhang, Y, Ng, E, Lal, A & Lee, JE-Y 1970, 'Wake-Up IoT Wireless Sensing Node Based on a Low-G Threshold mems Inertial Switch with Reliable Contacts', 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS), 2023 IEEE 36th International Conference on Micro Electro Mechanical Systems (MEMS), IEEE.
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Ghosh, S, Ramegowda, PC, Jian Goh, D, Sharma, J, Koh, Y & Lee, JE-Y 1970, 'Extraction of Material Properties of A Thin Silicon Membrane Embedded in A Piezoelectric Stack', 2023 IEEE International Ultrasonics Symposium (IUS), 2023 IEEE International Ultrasonics Symposium (IUS), IEEE.
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Gunawan, R, Tran, Y, Zheng, J, Nguyen, H & Chai, R 1970, 'Implementing Natural Image Quality Evaluator for Performance Indicator on Noise Artefacts Recovery in CT Scan', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE.
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Guo, CA, Guo, YJ, Wu, K & Yuan, J 1970, 'A New Technique to Form Steerable Multibeams for Integrated Communications and Sensing', 2023 IEEE International Conference on Communications Workshops (ICC Workshops), 2023 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE.
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Guo, CA, Jay Guo, Y, Zhu, H & Yuanr, J 1970, 'Optimizing Multibeam Feed Networks for Antenna Arrays with Independent Beam Steering and Low Sidelobes', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Guo, K, Liu, C, Li, C & Guo, Y 1970, 'Comparison Analysis of Doubly Salient Permanent Magnet Machine', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE.
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Guo, Y, Zhu, J, Lei, G, Lu, H & Jin, J 1970, 'Characterization of Electromagnetic Materials under 2D/3D Rotational Magnetization', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE.
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Guo, YJ 1970, 'A New Circuit-Type Multibem Antenna Array Employing Generalized Joined Coupler Matrix', 2023 IEEE International Symposium On Antennas And Propagation (ISAP), 2023 IEEE International Symposium On Antennas And Propagation (ISAP), IEEE.
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Guo, YJ 1970, 'Phased Multibeam Antennas Employing Generalized Joined Coupler Matrix', 2023 International Workshop on Antenna Technology (iWAT), 2023 International Workshop on Antenna Technology (iWAT), IEEE.
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Hanna, B, Xu, G, Wang, X & Hossain, J 1970, 'Data-driven computational algorithms for predicting electricity consumption missing values: a comparative study', Australasian Universities Power Engineering Conference, Adelaide, Australia.

Hasan, M, Shah, R, Amjady, N, Hossain, MJ & Islam, S 1970, 'Impact of MMC-HVDC Control on Power System Dynamics: Various Concepts and Parameterization', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE.
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He, T, Zhang, W, Wu, M, Lu, DD-C & Zhu, J 1970, 'Virtual Vectors Based Model Predictive Control for Single-Phase Cascaded H-Bridge Converters', 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), IEEE.
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He, W, Zhao, J, Yang, L & Guo, Y 1970, 'Study on Magnetic Field Coupling in Integrated Magnetic Suspension Spherical Induction Motor', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE.
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Hieu, NQ, Chu, NH, Hoang, DT, Nguyen, DN & Dutkiewicz, E 1970, 'A Unified Resource Allocation Framework for Virtual Reality Streaming over Wireless Networks', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE.
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Huang, W, Liao, X, Qian, Y & Jia, W 1970, 'Learning Hierarchical Semantic Information for Efficient Low-Light Image Enhancement', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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Low-light environments can cause a variety of complex degradation problems, which result in poor visibility in images. As a classical vision task, low-light image enhancement has attracted an increasing interest in the research community. However, the existing methods tend to require a large number of parameters, making them difficult to implement and optimize, especially on resource-constrained devices. In this paper, we mainly focus on the lightweight of the method and propose a novel end-to-end two-stage CNN-ViT architecture (HSINet) to learn hierarchical semantic information (HSI) from low-light images efficiently. The HSINet consists of two stages: the first stage is a CNN-based low-level semantic (LS) Stage, and the second stage is ViT-based high-level semantic (HS) Stage. The LS Stage contains an efficient multi-scale convolution block, MLS Block, for low-level semantic information extraction. The HS stage, on the other hand, aims to learn the high-level semantic features via ViT's excellent global-learning capability. We propose a hierarchical Swin Transformer-based block, HS Block, to gradually enlarge Swin Transformer's window size as the network becomes deeper, to learn hierarchical high-level semantic information. Benefiting from the efficient architecture, our model only contains 0.6M parameters, far fewer than the existing SOTAs. We evaluated the method on three challenging benchmark datasets: LOL, VE-LOL, and MIT-Adobe FiveK, using three popular evaluation metrics. The quantitative and qualitative results both show that the proposed method not only outperforms the state of the arts in terms of PSNR, SSIM, LPIPS, and visual effects, but also with better efficiency.

Huynh, NV, Quang Hieu, N, Chu, NH, Nguyen, DN, Hoang, DT & Dutkiewicz, E 1970, 'Defeating Eavesdroppers with Ambient Backscatter Communications', 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6.
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Unlike conventional anti-eavesdropping methods that always require additional energy or computing resources (e.g., in friendly jamming and cryptography-based solutions), this work proposes a novel anti-eavesdropping solution that comes with mostly no extra power nor computing resource requirement. This is achieved by leveraging the ambient backscatter technology in which secret information can be transmitted by backscattering it over ambient radio signals. Specifically, the original message at the transmitter is first encoded into two parts: (i) active transmit message and (ii) backscatter message. The active transmit message is then transmitted by using the conventional wireless transmission method while the backscatter message is transmitted by backscattering it on the active transmit signals via an ambient backscatter tag. As the backscatter tag does not generate any active RF signals, it is intractable for the eavesdropper to detect the backscatter message. Therefore, secret information, e.g., a secret key for decryption, can be carried by the backscattered message, making the adversary unable to decode the original message. Simulation results demonstrate that our proposed solution can significantly enhance security protection for communication systems.

Ibanez-Hidalgo, I, Aguilera, RP, Sanchez-Ruiz, A, Perez-Basante, A, Zubizarreta, A & Ceballos, S 1970, 'SHC-PWM Closed-Loop Control Based on PI Controllers for Active Power Filters', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE.
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Indrajith, B, Gunawardane, K & Jayasinghe, H 1970, 'A Review: DC Microgrids for Sustainable Power Delivery in Offshore Industries', Volume 10: Professor Ian Young Honouring Symposium on Global Ocean Wind and Wave Climate; Blue Economy Symposium; Small Maritime Nations Symposium, ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering, American Society of Mechanical Engineers.
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Abstract Legacy Alternating Current (AC) based power systems, coupled with bulk-generated electrical energy from fossil fuels, are working against the achievement of sustainable development. Worldwide integration of renewable energy in power generation is growing rapidly. This is making a major contribution to the achievement of sustainable development goals and affordable clean energy. Direct Current (DC) operable products are becoming the most common type of internal power architecture in many application domains in both land and marine based systems. In these systems, power supplied from AC form is internally converted to DC within the appliance. Renewable-based power generation, including solar photovoltaics (PVs), wind, wave, and tidal generators, can generate DC power intrinsically or can be converted to DC power. In addition to that the supportive novel technologies developing around renewable energy generation such as hydrogen storages, fuel cells and associated other alternative energy storage technologies such as battery and supercapacitors all are intrinsically in DC form. Therefore, both the supply and the demand sides of power systems are in favor of moving towards DC systems called DC microgrids. With the elimination of DC/AC and AC/DC converter stages in DC grids, they create a highly efficient, low-cost platform. Also impacts such as skin effect, reactive power usage, power quality issues and grid synchronization are comprehensively reduced too. Since renewable sources like photovoltaics generate on-site DC power, eliminating the need for long distance AC transmission facilities, they are particularly attractive for remote communities and industrial and commercial sites. Since the minimum interaction on main power grids, offshore systems are more attractive to be operated as low voltage DC distribution. DC microgrids have the potential to revolutionize power systems by offering a versatile ...

Iqbal, H, Zheng, J, Chai, R & Chandrasekaran, S 1970, 'K-Nearest Neighbours and Ensemble Based Real-Time Hand Gesture Recognition for Powered Wheelchair', 2023 International Conference on Advanced Mechatronic Systems (ICAMechS), 2023 International Conference on Advanced Mechatronic Systems (ICAMechS), IEEE.
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Islam, MT, Hossain, MJ & Habib, MA 1970, 'Machine Learning-based Hosting Capacity Analysis and Forecasting in Low-Voltage Networks', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE.
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Jin, P, Wang, W, Guo, Y, Lei, G & Zhu, J 1970, 'Full Predictive Control of Permanent Magnet Synchronous Machine Based on Differential-Free Disturbance Position-Torque Observer and Online Parameter Identification', 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), IEEE.
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Karmaker, AK, Sturmberg, B, Behrens, S, Hossain, MJ & Pota, H 1970, 'Characterizing Electric Vehicle Plug-in Behaviors Using Customer Classification Approach', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE.
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Keshavarz, R, Winson, D, Lipman, J, Abolhasan, M & Shariati, N 1970, 'Dual-Band, Slant-Polarized MIMO Antenna Set for Vehicular Communication', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Khan, AF & Nanda, P 1970, 'C-Block: A Secure and Robust Framework for Authentication Handover in 5G HetNets based on Edge-enabled SDN/NFV Environments', 2023 International Wireless Communications and Mobile Computing (IWCMC), 2023 International Wireless Communications and Mobile Computing (IWCMC), IEEE, Marrakesh, Morocco.
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Lai, M, Cao, L, Lu, H, Ha, Q, Li, L, Hossain, J & Kennedy, P 1970, 'An Unsupervised Hierarchical Clustering Approach to Improve Hopfield Retrieval Accuracy', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE.
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Despite its efficiency, the classical Hopfield network was a highly impractical data-searching solution due to its limited storage capacity. While the recently released modern Hopfield variant has increased its storage capacity, its searching ability is heavily affected by local minima and saddle points, which prevented it from becoming a worthy successor of the classical Hopfield network. We propose a novel unsupervised clustering approach to bypass local minima and saddle points to enhance the overall robustness of the Hopfield network. Our experimental results on benchmark MNIST indicate that our algorithm can increase the retrieval accuracy by over (20%) in general against the Hopfield Update Rule, proving that it is a far superior modelling solution.

Lee, SS, Barzegarkhoo, R, Siwakoti, YP, Grigoletto, FB & Lee, K-B 1970, 'Single-Phase 3-Level and 5-Level Boost Inverters without High-Frequency Common-Mode Voltage', 2023 11th International Conference on Power Electronics and ECCE Asia (ICPE 2023 - ECCE Asia), 2023 11th International Conference on Power Electronics and ECCE Asia (ICPE 2023 - ECCE Asia), IEEE.
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Li, B, Zhu, J, Liu, C, Lei, G & Li, Y 1970, 'Impact of Soft Magnetic Material on Electromagnetic Force and Vibration of Permanent Magnet Electrical Machines', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE.
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Electromagnetic vibration is excited by the radial electromagnetic force generated by the air-gap magnetic field acting on the stator core. In this paper, an analysis and reduction of electromagnetic force in high-speed permanent magnet electrical machines with different stator cores are investigated. A fast and accurate calculation method of air gap flux density and radial force density distribution are derived. The radial force density including amplitude, frequency, and order is obtained by a two-dimension fast Fourier transform. Then, the effect of the different PM segmentation on radial force is summarized.

Li, C, Guo, K, Liu, C & Guo, Y 1970, 'High-Precision Spiral Motion Control of Linear Rotary Motor With E-shaped Stator', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE.
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Li, H, Wang, X, Yu, G, Ni, W & Liu, RP 1970, 'A Generative Adversarial Networks-Based Integer Overflow Detection Model for Smart Contracts', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Li, S, Unanue, IJ & Piccardi, M 1970, 'Improving Machine Translation and Summarization with the Sinkhorn Divergence', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Nature Switzerland, pp. 149-161.
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Important natural language processing tasks such as machine translation and document summarization have made enormous strides in recent years. However, their performance is still partially limited by the standard training objectives, which operate on single tokens rather than on more global features. Moreover, such standard objectives do not explicitly consider the source documents, potentially affecting their alignment with the predictions. For these reasons, in this paper, we propose using an Optimal Transport (OT) training objective to promote a global alignment between the model’s predictions and the source documents. In addition, we present an original implementation of the OT objective based on the Sinkhorn divergence between the final hidden states of the model’s encoder and decoder. Experimental results over machine translation and abstractive summarization tasks show that the proposed approach has been able to achieve statistically significant improvements across all experimental settings compared to our baseline and other alternative objectives. A qualitative analysis of the results also shows that the predictions have been able to better align with the source sentences thanks to the supervision of the proposed objective.

Lin, X, Sun, X, Yang, Z, Lei, G, Guo, Y & Zhu, J 1970, 'Torque Enhancement Scheme Based on Deadbeat Harmonic Current Control with Extended State Observer for Dual Three-Phase PMSM', 2023 26th International Conference on Electrical Machines and Systems (ICEMS), 2023 26th International Conference on Electrical Machines and Systems (ICEMS), IEEE.
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Liu, L, Guo, Y, Lei, G & Zhu, J 1970, 'Comparative Iron Loss Analysis of the Interior PMSMs for Electric Vehicles Considering the Effects of Temperature Variation', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE.
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Liu, L, Guo, Y, Lei, G & Zhu, J 1970, 'Review of Data-Driven Artificial Intelligence Applications in Electric Machines and Drive Systems', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE.
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Liu, L, Guo, Y, Lei, G, Yin, W & Zhu, J 1970, 'Iron Loss Analytical Prediction of IPMSMs Considering Multi-factor Effects Over the Drive Cycle of Electric Vehicles', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE.
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Liu, L, Guo, Y, Lei, G, Yin, W & Zhu, J 1970, 'Multi-objective Design Optimization of an IPMSM Drive System Based on Loss Minimization Control Strategy', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE.
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Liu, Y, Qi, M, Wu, Q, Yang, Y, Li, X & Zhang, J 1970, 'Camera Proxy based Contrastive Learning with Hard Sampling for Unsupervised Person Re-identification', 2023 IEEE International Conference on Multimedia and Expo (ICME), 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE.
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Mahajan, A, Wittwer, C & Siwakoti, Y 1970, 'Ultra-Wide Voltage Gain Hybrid Modulated CLLC Converter for Electric Vehicle', 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe), 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe), IEEE.
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Makhalfih, A, Hossain, MJ, Macana, C & Pota, HR 1970, 'Distributed Energy Resources Hosting Capacity Assessment - An Industry Perspective', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE.
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Mannan, M, Roslan, MF, Reza, MS, Mansor, M, Jern, KP, Hossain, MJ & Hannan, MA 1970, 'An Optimized Binary Scheduling Controller for Microgrid Energy Management Considering Real Load Conditions', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE.
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Manoharan, P, Pranata, A, Tse, KM & Chai, R 1970, 'Estimation of Lumbar Spine Loading of Low Back Pain Participant During Lifting Using an Open Source Musculoskeletal Model', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE.
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Mei, G, Poiesi, F, Saltori, C, Zhang, J, Ricci, E & Sebe, N 1970, 'Overlap-guided Gaussian Mixture Models for Point Cloud Registration', 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 4500-4509.
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Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations. However, registering point cloud pairs in the case of partial overlap is still a challenge. This paper proposes a novel overlap-guided probabilistic registration approach that computes the optimal transformation from matched Gaussian Mixture Model (GMM) parameters. We reformulate the registration problem as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized. We introduce a Transformer-based detection module to detect overlapping regions, and represent the input point clouds using GMMs by guiding their alignment through overlap scores computed by this detection module. Experiments show that our method achieves superior registration accuracy and efficiency than state-of-the-art methods when handling point clouds with partial overlap and different densities on synthetic and real-world datasets. https://github.com/gfmei/ogmm

Mei, G, Tang, H, Huang, X, Wang, W, Liu, J, Zhang, J, Van Gool, L & Wu, Q 1970, 'Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.
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Nabeel, MI, Afzal, MU, Thalakotuna, DN & Esselle, KP 1970, 'Improving Performance of Metasurfaces-based Beam-Steering 2D-Leaky Wave Resonant-Cavity Antennas', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Nabeel, MI, Singh, K, Afzal, MU, Thalakotuna, DN & Esselle, KP 1970, 'A Thin Transparent Phase Correction Surface for Patch Antenna Gain Enhancement', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE.
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Ngo, QT, Jayawickrama, B, He, Y & Dutkiewicz, E 1970, 'Machine Learning-Based Cyclostationary Spectrum Sensing in Cognitive Dual Satellite Networks', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Nguyen, HAD, Le, TH, Ha, QP & Azzi, M 1970, 'Deep learning for construction emission monitoring with low-cost sensor network', Proceedings of the 40th International Symposium on Automation and Robotics in Construction, 40th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC).
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Nguyen, HM, Chu, NH, Nguyen, DN, Hoang, DT, Hà, MH & Dutkiewicz, E 1970, 'Optimal Privacy Preserving in Wireless Federated Learning Over Mobile Edge Computing', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE.
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Nguyen, LV, Le, TH & Ha, QP 1970, 'Prototypical digital twin of multi-rotor UAV control and trajectory following', Proceedings of the 40th International Symposium on Automation and Robotics in Construction, 40th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC).
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Phan, TC, Pranata, A, Farragher, JB, Bryant, AL, Nguyen, HT & Chai, R 1970, 'Machine Learning Derived Lifting Technique in People without Low Back Pain', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE.
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Prazeres, J, Luo, Z, Pinheiro, AMG, da Silva Cruz, LA & Perry, S 1970, 'JPEG Pleno Call for Proposals Responses Quality Assessment', ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.
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Qaiser, G, Chandrasekaran, S, Chai, R & Zheng, J 1970, 'Classification of DDoS traffic for Industrial Internet of Services using Deep learning approaches', 2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), 2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE.
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Qaiser, G, Chandrasekaran, S, Chai, R & Zheng, J 1970, 'Classifying DDoS Attack in Industrial Internet of Services Using Machine Learning', 2023 15th International Conference on Computer and Automation Engineering (ICCAE), 2023 15th International Conference on Computer and Automation Engineering (ICCAE), IEEE.
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Ramegowda, PC, Choong, DSW, Goh, DJ, Jihang, L, Merugu, S, Chang, P, Leotti, A, Giusti, D, Prelini, C, Ghosh, S, Lee, JE-Y & Koh, Y 1970, 'Iterative Analysis Approach using interactive Python-FEM to estimate the residual stresses in PMUTs', 2023 IEEE SENSORS, 2023 IEEE SENSORS, IEEE.
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Reza, MS, Hannan, MA, Mansor, M, Ker, PJ, Tiong, SK & Hossain, MJ 1970, 'Gravitational Search Algorithm based Long Short-term Memory Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction with Uncertainty', 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG), IEEE.
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Rooholahi, B, Siwakoti, Y, Blaabjerg, F & Eckel, H-G 1970, 'Performance Analysis of A New High Step-Up Boost-Buck-Boost Interleaved Converter for Bipolar LVDC Applications', 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe), 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe), IEEE.
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Samal, PB, Chen, SJ & Fumeaux, C 1970, 'Time-Domain Performance of a Directional Wearable UWB Antenna under Bending', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE.
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Sarafianou, M, Chen, DS-H, Sze Wai Choong, D, Goh, DJ, Liu, J, Lee, JE-Y, Merugu, S, Zhang, QX, Chang, PHK, Giusti, D, Castoldi, L, Pedrini, C, Barabani, L, Leotti, A, Hur, YJ & Lung Lee, Y 1970, 'A Feasibility Study on Underwater 2D Localization Using PZT pMUTs', 2023 IEEE International Ultrasonics Symposium (IUS), 2023 IEEE International Ultrasonics Symposium (IUS), IEEE.
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Sarafianou, M, Wai Choong, DS, Jian Goh, D, Liu, J, Merugu, S, Zhang, QX, Chang, PHK, Giusti, D, Castoldi, L, D’Ercoli, F, Tacchini, R, Leotti, A, Sim, DH, Stuart Savoia, A & Lee, JE-Y 1970, 'DC-Bias Effects on Sputtered PZT pMUTs with High Transmit and Receive Sensitivities in Immersion for Imaging applications', 2023 IEEE International Ultrasonics Symposium (IUS), 2023 IEEE International Ultrasonics Symposium (IUS), IEEE.
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Savoia, AS, Giusti, D, Prelini, C, Chang, P, Leotti, A, Lee, J, Koh, Y & Ferrera, M 1970, 'Performance Analysis of Wideband PMUTs: a Comparative Study Between Sol-Gel PZT, PVD PZT, and 15% ScAlN-Based Arrays Through Experimental Evaluation', 2023 IEEE International Ultrasonics Symposium (IUS), 2023 IEEE International Ultrasonics Symposium (IUS), IEEE.
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Shafei, H, Li, L & Aguilera, RP 1970, 'Oberver-based Attack Detection and Mitigation in DC Microgrid Systems', 2023 IEEE 14th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2023 IEEE 14th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), IEEE.
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Shi, T, Liu, B, Lei, J, Wang, F, Xiang, X, Li, L & Li, W 1970, 'Virtual Damping Stabilizing for DC Microgrid with Current Disturbance of Selected Frequency Islanding Detection Method', 2023 IEEE PELS Students and Young Professionals Symposium (SYPS), 2023 IEEE PELS Students and Young Professionals Symposium (SYPS), IEEE.
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Shrestha, S, Abbas, SM, Asadnia, M & Esselle, KP 1970, 'Three Dimensional Printable Prototype to Realize Uniformity in Aperture Phase Distribution', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE.
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Singh, K, Ahmed, F & Esselle, KP 1970, 'Beam-Steering Antennas Using Semi-Periodic Metasurfaces', 2023 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2023 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE.
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Song, B, Hui-Ming, Song, Y-R, Jiang, G-P, Wang, X & Xia, L-L 1970, 'Identifying Influential Nodes in Community Networks', 2023 42nd Chinese Control Conference (CCC), 2023 42nd Chinese Control Conference (CCC), IEEE.
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Song, L, Qin, P, Du, J & Guo, YJ 1970, 'Multi-beam Conformal Transmitarray Synthesis for Advanced Wireless Systems', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Song, L, Qin, P, Zhang, T, Du, J & Jay Guo, Y 1970, 'High-Efficiency Multi-Beam GRIN Lens with 2-D Wide-Angle Coverages', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE, Melbourne, Australia, pp. 51-52.
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Multi beam gradient index GRIN lenses are investigated to enable high aperture efficiencies and 2 D wide angular coverages Metasurface composed of engineered elements with varied refractive indices is employed to implement the lens Innovative methodologies are developed to calculate refractive index distributions on the lens as well as the corresponding feed positions for multiple beams A three metal layer element is developed to enable the variation of effective refractive index Microstrip patch array antennas are designed as feed sources Thirteen feed arrays are arranged on two focal arcs in xoz and yoz planes A GRIN lens prototype is constructed radiating multiple beams in a wide range of 45 in two orthogonal planes with low scanning losses The peak realized gain is 21 8 dBi with an aperture efficiency of 62 8 The operating bandwidth is 22 2 from 12 GHz to 15 GHz

Stratton, PG, Hamilton, TJ & Wabnitz, A 1970, 'Unsupervised Feature Vector Clustering Using Temporally Coded Spiking Networks', 2023 International Joint Conference on Neural Networks (IJCNN), 2023 International Joint Conference on Neural Networks (IJCNN), IEEE.
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Sun, S, Ding, C & Jay Guo, Y 1970, 'An Electromagnetically Transparent Dipole for Cross-Band Scattering and Coupling Suppression', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE, pp. 57-58.
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In this paper, an electromagnetically transparent dipole working at the low-band (LB, 1.7-2.5 GHz) is proposed to simultaneously suppress the cross-band scattering and coupling to the high-band (HB, 4.4-5.0 GHz) antenna in a dual-band array. Dual-functional patches with vias are loaded on radiating arms of the LB dipole, which can introduce reversed induced-currents at the HB to restore the deteriorated HB radiation patterns and improve cross-band isolations at the HB. After the loading, HB patterns are well recovered, showing great consistency with the patterns of the HB dipoles operating alone. The transmission coefficients between the LB and HB ports are also greatly reduced.

Sun, S-Y, Ding, C & Guo, YJ 1970, 'Holistic Interaction Suppression in Dual-Band Antenna Array', 2023 IEEE International Symposium On Antennas And Propagation (ISAP), 2023 IEEE International Symposium On Antennas And Propagation (ISAP), IEEE.
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Susantha, K, Lu, D & Wang, X 1970, 'Lessons Learned from Previous Cyberattacks on Energy Systems – Global and Australian Context', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE.
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Tang, H, Zhao, Y, Jiang, Y, Gan, Z & Wu, Q 1970, 'Class-Aware Contextual Information for Semantic Segmentation', ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.
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Tun, W, Wong, JK-W & Ling, SH 1970, 'An Experimental HVAC Faults Data Generation and Detection Using One-Dimensional Convolutional Neural Networks.', CASE, IEEE, pp. 1-6.

Waheed, N, Khan, F, Mastorakis, S, Jan, MA, Alalmaie, AZ & Nanda, P 1970, 'Privacy-Enhanced Living: A Local Differential Privacy Approach to Secure Smart Home Data', 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 2023 IEEE International Conference on Omni-layer Intelligent Systems (COINS), IEEE, Berlin, Germany.
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Waheed, N, Ur Rehman, A, Nehra, A, Farooq, M, Tariq, N, Jan, MA, Khan, F, Alalmaie, AZ & Nanda, P 1970, 'FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare Through Federated Learning and Blockchain', Proceedings - IEEE Global Communications Conference, GLOBECOM, IEEE Global Communications Conference, Kuala Lumpur, pp. 3855-3860.
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The rapid adoption of Internet of Things (IoT) devices in healthcare has introduced new challenges in preserving data privacy, security and patient safety. Traditional approaches need to ensure security and privacy while maintaining computational efficiency, particularly for resource-constrained IoT devices. This paper proposes a novel hybrid approach by combining federated learning and blockchain technology to provide a secured and privacy-preserved solution for IoT-enabled healthcare applications. Our approach leverages a public-key cryptosystem that provides semantic security for local model updates, while blockchain technology ensures the integrity of these updates and enforces access control and accountability. The federated learning process enables a secure model aggregation without sharing sensitive patient data. We implement and evaluate our proposed framework using EMNIST datasets, demonstrating its effectiveness in preserving data privacy and security while maintaining computational efficiency. The results suggest that our hybrid approach can significantly enhance the development of secure and privacy-preserved IoT-enabled healthcare applications, offering a promising direction for future research in this field.

Wang, X, Qin, P-Y, Guo, YJ & Gupta, K 1970, 'Reconfigurable Dual-Layer Unit Cell Based Beam Steering Transmitarray', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE, Florence, Italy, pp. 1-4.
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A beam steering transmitarray utilizing reconfigurable dual layer unit cells is presented in this paper Two PIN diodes are used on each array unit cell to achieve a 1 bit phase change with a high transmission Compared to other electronically reconfigurable transmitarrays employing multilayer unit cells with metal vias the developed transmitarray has a much simpler configuration which is beneficial to larger aperture designs in high frequency bands To validate the unit cell design concept a transmitarray prototype at 13 GHz is designed The measured peak gain is 18 4 dBi and 2 D beam steering performances are 50 and 40 in E and H planes respectively

Wang, X, Qin, P-Y, Jin, R & Guo, YJ 1970, 'Wideband Conformal Transmitarray Employing Tightly Coupled Huygens Element at E Band', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE, pp. 485-486.
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A new method to achieve wideband conformal transmitarrays is presented by employing tightly coupled Huygens element in this paper. The element is composed of five pairs of metallic strips which are printed on two sides of a dielectric substrate. The tightly coupled elements can achieve a high transmission and a nearly full phase coverage in a wide bandwidth from 71 to 87 GHz. To further analyze the element, equivalent circuit models are developed. Good agreement is achieved between circuit and full-wave simulations. A cylindrical conformal transmitarray at 78 GHz is designed, fabricated and measured. The measured results have good agreement with the simulated ones. The measured peak realized gain is 26.6 dBi with an aperture efficiency of 37.2%. The measured 3-dB gain bandwidth is 20.4% from 71 to 87 GHz, which can fully cover the E-band spectrum.

Wang, Y, Ren, Y, Zhang, H, Li, H & Zhang, J 1970, 'An Intrusion Detection Method Based on Hash Function for Industrial Cloud Data', 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), IEEE.
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Wen, Y, Qin, P-Y & Guo, YJ 1970, 'A Low Profile Modulated Metasurface Antenna for Multi-Beam Applications', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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Wen, Y, Qin, P-Y & Jay Guo, Y 1970, 'A Multi-Beam Antenna Based on Modulated Metasurface', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE, Melbourne, Australia, pp. 55-56.
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In this paper modulated metasurface based multibeam antenna is presented The impedance superposition method of all impedance modulations for different beams is utilized in the design The current reported multi beam modulated metasurface has a very small number of beams due to the mutual interference of different impedance modulations It is found that the source locations for each beam play a key role in interference suppression Instead of using the time consuming full wave simulation based optimization in this paper the optimal source locations are obtained by using the aperture fields calculated from the zeroth order approximation of the currents on the metasurface A five beam modulated metasurface antenna has been designed providing an angular coverage up to 30 The peak realized gain for the broadside beam is 21 3 dBi at 14 30 GHz

Weththasinghe, K, Clark, N, Ngo, QT, Jayawickrama, B, He, Y, Dutkiewicz, E & Liu, RP 1970, 'L-Band Spectral Opportunities for Cognitive GEO-LEO Dual Satellite Networks', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Wu, J, Yang, F, Zheng, J, Nguyen, HT & Chai, R 1970, 'Microwave Imaging Based on a Subspace-based Two-step Iterative Shrinkage/Thresholding Method', 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 1970, 'A Low-Complexity CSI-Based Wifi Sensing Scheme for LoS-Dominant Scenarios', ICC 2023 - IEEE International Conference on Communications, ICC 2023 - IEEE International Conference on Communications, IEEE.
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Wu, Y, Yu, L & Zhang, J 1970, 'Adaptive Local Feature Matching for Few-shot Fine-grained Image Recognition', 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE.
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Xu, H, Nanda, P & Liang, J 1970, 'Designing Incentive Mechanisms for Fair Participation in Federated Learning', 2023 IEEE International Conference on High Performance Computing &amp; Communications, Data Science &amp; Systems, Smart City &amp; Dependability in Sensor, Cloud &amp; Big Data Systems &amp; Application (HPCC/DSS/SmartCity/DependSys), 2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), IEEE, Melbourne, Australia.
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Xu, K, Guo, Y, Lei, G & Zhu, J 1970, 'A Data-Driven Method for Iron Loss Estimation in Bearingless Permanent Magnet Synchronous Motors', 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023 International Future Energy Electronics Conference (IFEEC), IEEE.
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Xu, K, Guo, Y, Lei, G, Liu, L & Zhu, J 1970, 'Calculation of Iron Loss in Permanent Magnet Synchronous Motors Based on PSO-RNN', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE.
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Xu, K, Guo, Y, Lei, G, Zhu, J & Sun, X 1970, 'Electromagnetic Performance Analysis of a Bearingless Permanent Magnet Synchronous Motor by Model Order Reduction', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE.
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Xu, W, Huang, H, Cheng, M, Yu, L, Wu, Q & Zhang, J 1970, 'Masked Cross-image Encoding for Few-shot Segmentation', 2023 IEEE International Conference on Multimedia and Expo (ICME), 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE.
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Yang, CC-L, Lau, JH, Peng, AY-J, Teng, V, Chen, GC-F, Huang, JY-C, Chen, YH, Liu, H-N, Tseng, T-J & Li, M 1970, 'Reliability of Heterogeneous Integration on Hybrid Substrate With Ajinomoto Build-Up Film', 2023 IEEE 73rd Electronic Components and Technology Conference (ECTC), 2023 IEEE 73rd Electronic Components and Technology Conference (ECTC), IEEE.
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Yang, S, Xu, Z, Wang, K, You, Y, Yao, H, Liu, T & Xu, M 1970, 'BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency', 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Vancouver Convention Center.
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Yu, G, Wang, Q, Altaf, T, Wang, X, Xu, X & Chen, S 1970, 'Predicting NFT Classification with GNN: A Recommender System for Web3 Assets', 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), IEEE.
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Yu, L, Xu, W, Wu, Q & Zhang, J 1970, 'Automated Flock Density and Activity Recognition for Welfare Monitoring on Commercial Egg Farms', 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), IEEE.
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Zaqumi, MN, Lalbakhsh, A, Moloudian, G & Esselle, K 1970, 'Design of a Multi-layer Dual-band Frequency Selective Surface Bandpass Filter', 2023 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2023 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE.
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Zeng, FC, Ding, C & Guo, YJ 1970, 'A Polarization-Mixed Antenna Array with Wide Beamwidth and Orthogonal Polarizations', 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), IEEE.
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Zhang, H, Huang, X & Zhang, JA 1970, 'Fine Doppler Resolution Channel Estimation and Offset Gradient Descent Equalization for OTFS Transmission over Doubly Selective Channels', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Zhang, H, Liu, C, Zang, S, Lei, G & Wang, Y 1970, 'Design Optimization of an Interior Permanent Magnet Synchronous Machine with Asymmetric and Auxiliary Slot Structure', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE.
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Zhang, H, Liu, C, Zang, S, Lei, G & Wang, Y 1970, 'Rotor Auxiliary Slot Design Optimization for Permanent Magnet Synchronous Motor with Double-layer Rotor Structure for Electric Vehicle', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE.
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Zhang, H, Liu, C, Zang, S, Lei, G, Wang, Y & Zhu, J 1970, 'Rotor Auxiliary Slot Design Optimization for Interior Permanent Magnet Synchronous Machine for Electric Vehicle', 2023 IEEE International Magnetic Conference (INTERMAG), 2023 IEEE International Magnetic Conference (INTERMAG), IEEE.
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Zhang, N, Guo, K & Guo, Y 1970, 'Research on Stable Operation of PMSM at Low Speed Based on Improved Reaching Law', 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), IEEE.
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Zhang, S, Liu, C, Lei, G, Wang, Y & Zhu, J 1970, 'Design and Performance Analysis of a New Synchronous Reluctance Machine with Hybrid Cores', 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE.
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Zhang, T, Zhang, H, Huang, X, Suzuki, H, Pathikulangara, J, Smart, K, Du, J & Guo, JY 1970, 'Demonstration of a 245 GHz Real-Time Wireless Communication link with 30 Gbps Data Rate', 2023 48th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), 2023 48th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), IEEE.
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Zhang, T, Zhang, H, Zhu, H, Huang, X, Suzuki, HH, Pathikulangara, J, Smart, KW, Du, J & Guo, JY 1970, 'Demonstration of a 245 GHz Real-Time Wireless Communication link', 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), 2023 22nd International Symposium on Communications and Information Technologies (ISCIT), IEEE.
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Zhang, Z, Wang, Y, Yu, G, Wang, X, Zhang, M, Ni, W & Liu, RP 1970, 'A Community-based Strategy for Blockchain Sharding: Enabling More Budget-friendly Transactions', 2023 IEEE International Conference on Blockchain (Blockchain), 2023 IEEE International Conference on Blockchain (Blockchain), IEEE.
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Zhao, Y, Tang, H, Jiang, Y, A, Y, Wu, Q & Wang, J 1970, 'Parameter-Efficient Vision Transformer with Linear Attention', 2023 IEEE International Conference on Image Processing (ICIP), 2023 IEEE International Conference on Image Processing (ICIP), IEEE.
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Zheng, J, Li, K, Mhaisen, N, Ni, W, Tovar, E & Guizani, M 1970, 'Federated Learning for Online Resource Allocation in Mobile Edge Computing: A Deep Reinforcement Learning Approach', 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023 IEEE Wireless Communications and Networking Conference (WCNC), IEEE.
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Zhu, H & Jay Guo, Y 1970, 'Single-Ended-to-Balanced Hybrid Coupler for In-Band Full-Duplex Transceivers', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE.
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Zhu, H, Guo, CA & Jay Guo, Y 1970, 'Planar Beamforming Networks for Producing Multiple Independently Steerable Beams', 2023 17th European Conference on Antennas and Propagation (EuCAP), 2023 17th European Conference on Antennas and Propagation (EuCAP), IEEE.
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ashtarinakhaei, S, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2023, 'A Location-Based Multi-Hop Routing Protocol for Future Wireless Cellular Networks', Elsevier BV.
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Heusdens, R & Zhang, G 2023, 'Distributed Optimisation with Linear Equality and Inequality Constraints using PDMM'.

Hoang, TD, Huang, X & Qin, P 2023, 'Low-Complexity Compressed Sensing-Aided Coherent Direction-of-Arrival Estimation for Large-Scale Lens Antenna Array', Institute of Electrical and Electronics Engineers (IEEE).
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Hoang, TD, Huang, X & Qin, P 2023, 'Low-Complexity Compressed Sensing-Aided Coherent Direction-of-Arrival Estimation for Large-Scale Lens Antenna Array', Institute of Electrical and Electronics Engineers (IEEE).
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Hossain, MS, Bacaoco, M, Mai, TNA, Ponchon, G, Chen, C, Ding, L, Chen, Y, Ekimov, E, Xu, H, Solntsev, AS & Tran, TT 2023, 'Fiber-based Ratiometric Optical Thermometry with Silicon-Vacancy in Microdiamonds'.

Kielly, M, Caracciolo, A, Chacon, A, Vita, DD, Mohammadi, A, Tashima, H, Franklin, DR, Yamaya, T, Rosenfeld, A, Carminati, M, Fiorini, C, Guatelli, S & Safavi-Naeini, M 2023, 'First experimental demonstration of real-time neutron capture discrimination in helium and carbon ion therapy', Research Square Platform LLC.
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Kumar, A, Esmaili, N & Piccardi, M 2023, 'The Contextualized Regressive Topic Model', Elsevier BV.
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Liu, C, Zhang, H, Lei, G, Zhang, S, Wang, Y & Zhu, J 2023, 'Ultra-High-Dimensional Multi-Level Optimization Strategies for Electrical Machines', Research Square Platform LLC.
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Liu, J, Wu, K, Su, T & Zhang, JA 2023, 'Practical Frequency-Hopping MIMO Joint Radar Communications: Design and Experiment'.

Maier, M & Hoang, DT 2023, '6G and Onward to Next G: the Road to the Multiverse', Institute of Electrical and Electronics Engineers (IEEE), pp. 20-20.
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Ngo, Q, Phan, TK, Mahmood, A & Xiang, W 2023, 'Hybrid IRS-Assisted Secure Satellite Downlink Communications: A Fast Deep Reinforcement Learning Approach', Institute of Electrical and Electronics Engineers (IEEE).
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Nguyen, H, Saputra, YM, Hoang, D, Nguyen, D, Nguyen, V-D, Xiao, Y & Dutkiewicz, E 2023, 'Encrypted Data Caching and Learning Framework for Robust Federated Learning-based Mobile Edge Computing', Institute of Electrical and Electronics Engineers (IEEE).
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Singh, K, Saikia, M, Thiyagarajan, K, Thalakotuna, D, Esselle, K & Kodagoda, S 2023, 'Multi-Functional Reconfigurable Intelligent Surfaces For Enhanced Sensing and Communication', MDPI AG.
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Su, SW 2023, 'Special Stable Matrices and Their Non-square Counterpart'.

Unanue, IJ, Haffari, G & Piccardi, M 2023, 'T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification'.

Vachon, P, Merugu, S, Sharma, J, Lal, A, Ng, E, Koh, Y, Lee, J & Lee, C 2023, 'Cavity-Agnostic Acoustofluidic Functions Enabled by Guided Flexural Waves on a Membrane Acoustic Waveguide Actuator', Research Square Platform LLC.
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Zhang, G 2023, 'On Suppressing Range of Adaptive Stepsizes of Adam to Improve Generalisation Performance'.

Zhang, G, Kenta, N & Kleijn, WB 2023, 'Lookahead Diffusion Probabilistic Models for Refining Mean Estimation'.

Zhang, G, Kenta, N & Kleijn, WB 2023, 'On Accelerating Diffusion-Based Sampling Process via Improved Integration Approximation'.

Zhang, G, Lewis, JP & Kleijn, WB 2023, 'Exact Diffusion Inversion via Bi-directional Integration Approximation'.

Zhang, Z, Xu, Z, McGuire, HM, Essam, C, Nicholson, A, Hamilton, TJ, Li, J, Eshraghian, JK, Yong, K-T, Vigolo, D & Kavehei, O 2023, 'Neuromorphic Cytometry: Implementation on cell counting and size estimation', Cold Spring Harbor Laboratory.
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Zhu, W, Tuan, HD, Dutkiewicz, E, Fang, Y & Hanzo, L 2023, 'Low-Complexity Pareto-Optimal 3D Beamforming for the Full-Dimensional Multi-User Massive MIMO Downlink'.