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A Half-Bridge/Dual Stacked-Switches Structured AC/DC Electrolytic Capacitor-Less Bidirectional Converter with Soft-Switching for High-Voltage EV Charger

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IEEE Transactions on Power Electronics 2025年第8期40卷 10912-10930页

作者： Derakhshan, Siamak Lam, John York University Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada

This paper presents a Half-Bridge (HB)/dual stacked switches, fully soft-switched bidirectional electrolytic capacitor-less AC/DC resonant converter, tailored for High-Voltage (HV) Electric Vehicle (EV) systems. The proposed converter integrates a closed-loop Power Factor Correction (PFC) mechanism based on the $dq$ model of the input LCL filter, ensuring high-quality sinusoidal input current and reduced Total Harmonic Distortion (THD) in both AC/DC conversion and DC/AC inversion modes. The front-end HB PFC circuit operates in Continuous Conduction Mode (CCM) with soft-switching operation, which eliminates the high peak current issue that happens in the switches in Discontinuous Conduction Mode (DCM). Moreover, the dual stacked switches configuration of the proposed converter reduces the voltage stress on the switches to half of the DC-link and output voltage. A dedicated duty ratio-based closed-loop control system in both primary and secondary sides of the dual-stacked-switches resonant converter drastically reduces the double-line frequency voltage ripple, thereby eliminating the need for bulky electrolytic storage capacitors. Output voltage regulation in AC/DC conversion mode is achieved through Variable Frequency Modulation (VFM), and soft-switching is maintained for all switches for all the operating condition in both modes. The performance of the proposed bidirectional converter is validated through a 1kW, 120Vrms/750Vdc proof-of-concept prototype. © 1986-2012 IEEE.

关键词： Capacitor bank

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A robust image encryption method based on block diagonal chaotic architecture with CNN performance evaluation metric

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Multimedia Tools and Applications 2025年 1-35页

作者： Mamandi, Vida Ardalani, Nahid Ghalamkari, Behbod Department of Electrical Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Mechanical Electrical and Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

In this paper, a new block diagonal chaotic model (BDC) is investigated due to higher necessity of advanced secure data transmission method in wireless medium and considerable limitation on computational storage space. In this model, the image sparse coding using BDC matrix in noisy and Rayleigh fading channels is studied. The structured BDC model is designed with two schemes, including chaotic pseudo inverse matrix (CPI-m) and block diagonal chaotic structured matrix (BDC-m) using two chaotic maps, in which the initial values for generating nonlinear chaotic sequences are used as private key at the receiver side. The simulation results show that the retrieved data measured based on CPI and BDC schemes outperforms the chaotic compressive sensing (CCS) method over AWGN and Rayleigh fading channels by 6% and 22.7%, respectively. Also, the sensitivity of recovered BDC-based image increases to 10(−32) compared to that of 10(−16) for conventional CCS based image. Moreover, the encryption key space increases from 10(37) for standard CCS-based measurements to β×10111 for measured data based on the BDC model, which guarantees that the competitors access no information. Considering datasets from iterative reconstruction process, the image features are extracted using convolutional neural network (CNN) model by ResNet-18 and GoogleNet architectures. The improved performance of data based on CPI and BDC model is validated by retrieved images accuracy over the classifier support vector machine (SVM). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fading channels

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Error-Correcting Graph Codes 16

Error-Correcting Graph Codes

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16th Innovations in Theoretical computer science Conference, ITCS 2025

作者： Kopparty, Swastik Potukuchi, Aditya Sha, Harry Department of Mathematics Department of Computer Science University of Toronto Canada Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (纸本)9783959773614

In this paper, we construct Error-Correcting Graph Codes. An error-correcting graph code of distance δ is a family C of graphs, on a common vertex set of size n, such that if we start with any graph in C, we would have to modify the neighborhoods of at least δn vertices in order to obtain some other graph in C. This is a natural graph generalization of the standard Hamming distance error-correcting codes for binary strings. Yohananov and Yaakobi were the first to construct codes in this metric. We extend their work by showing 1. Combinatorial results determining the optimal rate vs distance trade-off nonconstructively. 2. Graph code analogues of Reed-Solomon codes and code concatenation, leading to positive distance codes for all rates and positive rate codes for all distances. 3. Graph code analogues of dual-BCH codes, yielding large codes with distance δ = 1 - o(1). This gives an explicit "graph code of Ramsey graphs". Several recent works, starting with the paper of Alon, Gujgiczer, Körner, Milojević, and Simonyi, have studied more general graph codes;where the symmetric difference between any two graphs in the code is required to have some desired property. Error-correcting graph codes are a particularly interesting instantiation of this concept. © Swastik Kopparty, Aditya Potukuchi, and Harry Sha.

关键词： Reed-Solomon codes

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Perception-Aware Attack Against Music Copyright Detection: Impacts and Defenses

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2959-2977页

作者： Duan, Rui Qu, Zhe Zhao, Shangqing Ding, Leah Liu, Yao Lu, Zhuo University of Missouri-Kansas City School of Science and Engineering Kansas CityMO64110 United States Central South University School of Computer Science and Engineering Changsha410017 China University of Oklahoma School of Computer Science TulsaOK74135 United States American University Department of Computer Science WashingtonDC20016 United States University of South Florida Department of Computer Science and Engineering TampaFL33620 United States University of South Florida Department of Electrical Engineering TampaFL33620 United States

Recently, adversarial machine learning attacks have posed serious security threats against practical audio signal classification systems, including speech recognition, speaker recognition, music copyright detection. Most existing studies have mainly fo cused on ensuring the effectiveness of attacking an audio signal classifier via creating a noise-like perturbation on the original signal, which remains a gap in preserving the human perception of adversarial audios. This paper presents a novel perspective to cre ate adversarial audios by integrating the human perception model into the attack formulation to generate well-perceived adversarial *** from conventional approaches which primarily focused on using Lp norm to preserve the audio quality, we adopt a human study to understand how human participants react to different types of music perturbations, build a Siamese Neural Network (SNN) based model to characterize the human percep tion. The new findings of the human perception study guide us to formulate a new computationally efficient, multiple-feature-based perception-aware (CEMF-PA)attack, which manipulates different audio signal features to find an optimal perturbed music signal against music copyright detection. This novel attack vector opens a new door to generating highly effective, well-perceived adversarial audio signals via manipulating the auditory features. Experimental results show that the proposed attack is effective against YouTube’s copyright detection. Finally, we propose the defense strategy design to make the copyright detection more robust to adversarial music signals generated by the CEMF-PA attack. © 2004-2012 IEEE.

关键词： Speech recognition

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A Frog-Shaped UWB MIMO Antenna Design for 5G

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Progress in Electromagnetics Research C 2025年 151卷 101-112页

作者： Yao, Shanhua Yang, Tianchu Qiu, Xiaorong Li, Xiang School of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China School of Computer Science Huainan Normal University Huainan232001 China

In this paper, a frog-shaped ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna is proposed for 5G applications in the n77, n78, n79, and 6 GHz bands with a compact antenna structure of 31 × 55 × 1.5 mm3. The designed antenna consists of two frog-shaped monopole antennas and a floor from which a part has been removed, and the operating bandwidths range from 3.05 to 13.38 GHz, which meets the design criteria for UWB. The T-shaped floor with two T-shaped slots impedes the flow of coupling currents and improves the isolation of the antenna. This results in an isolation of less than −17 dB over the entire operating bandwidth and less than −20 dB in the 5G band. In addition, the envelope correlation coefficient (ECC) is less than 0.007, the diversity gain (DG) more than 9.96, the total active reflection coefficient (TARC) less than −30 dB, and the channel capacity loss (CCL) less than 0.34 bit/s/Hz. The measured and simulated results agree with each other, demonstrating the antenna’s potential application in 5G communication systems. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Bandwidth

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CMOS Nanopore-Based DNA Sequencing Systems: Recent Advancements and Future Prospects: A Review

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IEEE Sensors Journal 2025年第9期25卷 14539-14556页

作者： Asgari, Sepideh Mohammadpanah, Amirhossein Ghafar-Zadeh, Ebrahim Magierowski, Sebastian York University Lassonde School of Engineering Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada

Recent advancements in electrochemical detection have created significant opportunities in biosensing, particularly in medical diagnostics and health monitoring, where real-time response, high sensitivity, and accuracy are paramount. Nanopore-based deoxyribonucleic acid (DNA) sequencing has emerged as a powerful tool for high-throughput genetic analysis, relying on precise sensing mechanisms and efficient readout electronics. This review provides a comprehensive overview of both nanopore sensor architectures and CMOS readout circuits used in modern DNA sequencing technologies. We discuss different nanopore structures, their advantages, limitations, and noise reduction techniques, alongside the critical role of CMOS readout circuits in converting weak electrochemical signals into actionable data. In addition, we explore the tradeoffs between different arrayed CMOS readout architectures, highlighting challenges in power consumption, noise performance, and scalability in multichannel nanopore arrays. By analyzing these advancements, this article underscores the importance of integrating innovative sensor designs with efficient readout electronics to enhance the accuracy and throughput of next-generation biosensing platforms. © 2001-2012 IEEE.

关键词： CMOS integrated circuits

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Correction to: Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images (Physical and engineering sciences in Medicine, (2024), 10.1007/s13246-024-01420-1)

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Physical and engineering sciences in Medicine 2025年第1期48卷 19-20页

作者： Mostafavi, Mohammadreza Ko, Seok-Bum Shokouhi, Shahriar Baradaran Ayatollahi, Ahmad Department of Electrical Engineering Electronic Engineering Iran University of Science and Technology Tehran Iran Division of Biomedical Engineering Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

In this article the legend of Fig. 6 was presented without a reference. The legend of Fig. 6 has been changed from "The general framework for knowledge distillation involving a teacher-student relationship" to "The generic teacher-student framework for knowledge distillation [33]." In Sharma et al.’s research, two results obtained from the "KNN with ten-fold cross-validation" and "KNN with leave-one-subject-out cross-validation (LOSOCV)" methods are reported as the final results. Unfortunately, we mistakenly attributed the accuracy of one method. While we correctly listed the name of the method used, which is KNN with ten-fold cross-validation, we inadvertently entered the precision of the KNN with leave-one-subject-out cross-validation (LOSOCV) method. Therefore, we updated our results table (Table 7). The correct Table 7 is shown below. (Table presented.) Comparison of the performance of the proposed method with existing methods Study Method Dataset Accuracy Sensitivity Specificity Shim et al. [38] SVM 34 SZ-34 healthy 88.24% – – Siuly et al. [7] EBT – EMD (Empirical Mode Decomposition) 49 SZ-32 healthy 89.59% 0.8976 0.8932 Phang et al. [39] Multi-domain connectome CNN 45 SZ-39 healthy 91.69% 0.9111 0.925 Jahmunah et al. [40] Non-linear features, decision-tree, neural network, SVM 14 SZ and 14 healthy 92.90% 0.9345 0.9224 Akbari et al. [41] Phase space dynamic & graphical features 14 SZ and 14 healthy 94.8% 0.943 0.952 Shalbaf et al. [20] ResNet-18-SVM 14 SZ and 14 healthy 94.84% 0.980 0.9239 Bagherzadeh et al. [42] DenseNet121 model 14 SZ and 14 healthy 96.26% 0.9548 0.9702 Sharma et al. [43] KNN with ten-fold 14 SZ and 14 healthy 99.21% 0.9942 0.9905 LOSOCV 97.20% 0.9649 0.9806 Proposed method CWT—Self distillation- VGG16 14 SZ and 14 healthy 97.81 0.970 0.986 The original article has been corrected. © Australasian College of Physical Scientists and Engineers in Medicine 2024.

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AC Optimal Power Flow Using a Learnable Weighted-Ensemble Neural Network

AC Optimal Power Flow Using a Learnable Weighted-Ensemble Ne...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Tran, Quan Mitra, Joydeep Nguyen, Nga University of Wyoming Electrical Engineering and Computer Science LaramieWY United States Michigan State University Electrical and Computer Engineering East LansingMI United States

ISBN: (纸本)9798331541125

Leveraging machine learning algorithms (ML) for AC optimal power flow (ACOPF) is an inevitable tendency in power system analysis and operation. A wide range of ML models' applications such as deep neural networks and convolutional neural networks are utilized to tackle the ACOPF more efficiently. For large-scale power systems, the disadvantages of deep neural networks (DNNs) are alternated by manipulating weighted-convolution neural networks (WCNNs) to input a whopping number of the entire system's parameters for efficient training. However, the strengths of DNNs are undeniable, and they should be exploited to improve their efficacy and efficiency. By combining DNNs and WCNNs within a model, named an ensemble-weighted convolutional neural network (EWCNN), this paper proposes a new machine-learning model based on DNNs and WCNNs to increase the model fidelity and accuracy. The proposed approach is tested on three IEEE case studies to exemplify its capability. The empirical experiments show an improvement in reducing the bias-variance tradeoff commonly found in machine learning problems and raising the prediction accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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Contract-Based Incentive Mechanisms for UAV Networks in Covert Communications

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7378-7391页

作者： Tian, Wen Fan, Hao Liu, Guangjie Du, Miao Liu, Yinqiu Niyato, Dusit Han, Zhu Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing University of Information Science and Technology Key Laboratory of Intelligent Support Technology In Complex Environment Ministry of Education Nanjing210044 China Southeat University School of Computer Science and Engineering China Southeast University Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Covert communication is considered a promising technology for hiding transmission processes and activities from malicious eavesdropping. With the development of detection technology, the traditional point-to-point covert communication method cannot effectively guarantee the reliability of covert communication. In order to solve this problem, unmanned aerial vehicle (UAV) are widely adopted in covert communication as common relay equipment. However, the UAV maximizes its benefits by taking advantage of the information asymmetry between itself and the covert communication sender. Therefore, it is necessary to encourage UAV-assisted potential users (UPU) to transmit covert data honestly by designing an information asymmetry-based incentive mechanism. In this paper, we propose a UAV-assisted contract-theoretic (UACT) model to improve the covert communication performance, where UPU honestly transmits covert data and the throughput of UACT can be increased. Specifically, we first divide UPU into finite types and model the data transfer contract between the sender, UPU, and receiver. Then, the contract feasibility of UACT is derived with sufficient and necessary conditions. Finally, we analyze the contract optimization in the continuous case of UPUs. Numerical simulations show that the UACT can incentivize UPUs to transmit covert data through UAV assistance and make covert communication performance close to the information symmetry case. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Accurate, Yet Scalable: A SPICE-based Design and Optimization Framework for eNVM-based Analog In-memory Computing 24

Accurate, Yet Scalable: A SPICE-based Design and Optimizatio...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Mojahidul Ahsan, S.M. Shahriar, Muhammad Sakib Chowdhury, Mrittika Hossain, Tanvir Hasan, Md Sakib Hoque, Tamzidul Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Department of Electrical and Computer Engineering University of Mississippi OxfordMS United States

ISBN: (纸本)9798400710773

This paper introduces a scalable SPICE-based tool infrastructure designed to optimize analog compute-in-memory (ACIM) architectures utilizing emerging non-volatile resistive memory (eNVM) technologies. The inherent efficiency of analog eNVM crossbar arrays in performing matrix-vector multiplications significantly enhances the power, performance, and area efficiency of edge AI devices and other applications. Our framework addresses the challenges of accurately simulating ACIM architectures, which are highly susceptible to variations in process, voltage, temperature, and analog noise. The framework uses SPICE for accurate analog and mixed-signal circuit simulation. It automates the generation of SPICE-level ACIM designs for deep neural networks. Additionally, it speeds up the simulation runtime by up to 35× for large DNN models while maintaining the same SPICE-level accuracy. Moreover, it ensures simulation convergence for large netlists, facilitating SPICE simulation of large-scale eNVM crossbars that were previously impractical. We demonstrated that our framework is capable of simulating inference using netlists for MLPs with over 800,000 parameters trained on the MNIST dataset within acceptable runtime, where contemporary SPICE simulators do not even converge. We validated the simulation results in terms of inference accuracy, which shows less than a 3.8% accuracy drop compared to software-based inference results. Lastly, we demonstrated the integration of our framework with an architectural simulator, facilitating comprehensive system-level simulation. © 2024 Copyright is held by the owner/author(s).

关键词： Deep neural networks

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