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Active Voltage Balancing of Series-Connected SiC MOSFETs During Body-Diode Turn-Off Using an Active Gate Driver

IEEE Transactions on Power Electronics 2025年第8期40卷 10609-10622页

作者： Rai, Ajay Ganesan, P. Hatua, Kamalesh Bhattacharya, Subhashish Department of Electrical Engineering Indian Institute of Technology Madras Chennai India Centre for Development of Advanced Computing (C-DAC) Thiruvananthapuram India Department of Electrical and Computer Engineering NC State University Raleigh NC USA

The series connection of fast-switching SiC MOSFETs presents a promising approach to enhance the efficiency, compactness, and simplicity of Medium Voltage (MV) power converters. However, managing voltage imbalances during the turn-off of MOSFETs and their body diodes introduces significant challenges. While existing literature covers passive and active voltage balancing strategies for switch turn-off (e.g., MOSFETs/IGBTs), the issue of uneven voltage distribution during body diode turn-off remains largely unexplored. This gap limits the widespread adoption of series-connected devices in MV power converters, as achieving reliable performance with minimal additional components and low switching losses remains difficult. This article presents an Active Voltage Balancing technique for handling body diode turn-off events by regulating the gate charge of parallel MOSFETs during voltage rise transients in the closedloop using Active Gate Drivers (AGD). This approach ensures uniform voltage distribution among the body diodes of seriesconnected MOSFETs. A comprehensive analysis and modelling of gate charge control during voltage transients is presented. The proposed method is experimentally validated on two seriesconnected SiC MOSFETs operating in buck mode (DC-DC) and half-bridge inverter mode (DC-AC) at a 1.5 kV DC bus voltage, with switching frequencies up to 40 kHz. © 1986-2012 IEEE.

关键词： Diodes

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Detection of Brain Tumor Using U-Net Model Approach

Detection of Brain Tumor Using U-Net Model Approach

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Patra, Arunima Paul, Krishnangshu Paul, Prithwineel Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India Department of Computer Science and Engineering University of Engineering and Management Kolkata India Department of Computer Science and Engineering Centre of Excellence for Quantum Computing Institute of Engineering & Management University of Engineering and Management Kolkata India

ISBN: (纸本)9789819613472

This study introduces an innovative deep learning methodology leveraging the U-Net framework for medical image segmentation and lesion detection in brain tumors. U-net architecture contains encoder and decoder blocks which in turn are capable of capturing and propagation of information of the features. In this paper, for brain tumor segmentation, a U-Net Convolutional Neural Network (CNN) is employed, utilizing multimodal magnetic resonance imaging (MRI) data and skip connections for precise tumor localization and segmentation. Through comprehensive experiments, we show that our method outperforms traditional techniques and current CNN models for medical image segmentation and lesion detection in brain tumors. Moreover, the proposed method can perform the task with higher accuracy. This tailored U-Net architecture demonstrates the potential for advancing medical image analysis, enabling precise segmentation and lesion detection, ultimately leading to improved patient care and treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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VITMST++: Efficient Hyperspectral Reconstruction Through Vision Transformer-Based Spatial Compression

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2025年 6卷 398-404页

作者： Silveira, Ana C. Caznok do Carmo, Diedre S. Ueda, Lucas H. Fantinato, Denis G. Costa, Paula D. P. Rittner, Leticia Universidade Estadual de Campinas Department of Computer Engineering and Automation School of Electrical and Computer Engineering Campinas13083-852 Brazil Universidade Estadual de Campinas CPQD Recod.AI Institute of Computing Campinas13083-852 Brazil

Hyperspectralchannel reconstruction transforms a subsampled multispectral image into hyperspectral imaging, providing higher spectral resolution without a dedicated acquisition hardware and camera. Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (MST++) is a state-of-the-art channel reconstruction technique, but it faces memory limitations for high spatial-resolution images. In this context, we introduced VITMST++, a novel architecture incorporating Vision Transformer embeddings for spatial compression, multi-resolution image context, and a custom channel-weighted loss. Developed for the ICASSP 2024 HyperSkin Challenge, VITMST++ outperforms the state-of-the-art MST++ in both performance and computational efficiency in channel reconstruction. In this work, we perform a deeper analysis on the main aspects of VITMST++ efficiency, quantitative performance, and generalization to other datasets. Results show that VITMST++ achieves similar values of SAM and SSIM hyperspectral reconstruction metrics when compared to state-of-the-art methods, while consuming up to three fold less memory and needing up to 10 times fewer multiply-add operations. © 2020 IEEE.

关键词： Image compression

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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Science China(Information Sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN 2

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Krishna, Tiruveedula Gopi Sreenivas, Nune Ajay Kumar, M. Reddy, Chandra Sekhar Rathee, Davinder Sing Urgessa, Teklu College of Electrical Engineering and Computing Adama Science and Technology University Department of Computer Science and Engineering Adama Ethiopia Andhra Loyala institute of Engineering and Technology Department of Electrical and Electronics Engineering Vijayawada India College of Electrical Engineering and Computing Adama Science and Technology University Department of Electrical Power and Control Engineering Adama Ethiopia College of Electrical Engineering and Computing Adama Science and Technology University Department of ECE Adama Ethiopia

ISBN: (纸本)9798331505745

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Dzyaloshinskii-Moriya-type spin-spin interaction from mixed-parity superconductivity

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Physical Review B 2025年第6期111卷 L060504-L060504页

作者： Jabir Ali Ouassou Takehito Yokoyama Jacob Linder Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences NO-5528 Haugesund Norway Department of Physics Tokyo Institute of Technology Meguro Tokyo 152-8551 Japan Center for Quantum Spintronics Department of Physics Norwegian University of Science and Technology NO-7491 Trondheim Norway

Interacting impurity spins adsorbed on surfaces have been suggested as basic components for applications in quantum computation and spintronics. Such spins usually prefer a parallel or antiparallel configuration, but weakly noncollinear alignments are possible due to the Dzyaloshinskii-Moriya interaction (DMI) that arises in the presence of relativistic spin-orbit coupling. Here, we show that an effective Dzyaloshinskii-Moriya-type interaction (DMTI) can emerge purely from superconducting correlations without any spin-orbit interaction. We give an analytical proof and provide a numerical study which shows that DMTI arises in mixed-parity superconductors solely from the superconducting pairing. Moreover, we show that the same effect can be realized in Josephson junctions between s-wave and p-wave superconductors, where a phase bias toggles the DMTI entirely on and off. These results enable a way to engineer spin textures using superconducting order.

关键词： Shear waves

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Monte Carlo modelling of Raman signal enhancement using power-efficient Apple processors 15

Monte Carlo modelling of Raman signal enhancement using powe...

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Biomedical Light Scattering XV 2025

作者： Vladyko, Ilya Yakovlev, Vladislav Doronin, Alexander School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Department of Biomedical Engineering Department of Electrical and Computer Engineering Department of Physics and Astronomy Institute for Quantum Science and Engineering Texas A&M University College StationTX77843 United States

ISBN: (纸本)9781510683884

Modern optical technologies encompass classical light phenomena and non-linear effects, crucial for biomedical imaging and therapies. Despite substantial interest and many experimental studies, non-linear optical effects in inhomogeneous scattering media require extensive computer modeling to understand light-material interactions. This report focuses on Monte Carlo simulations of Raman scattering in turbid media, such as biological tissues, essential for clinical applications. We developed a novel, open-source, power-efficient Monte Carlo algorithm optimized for Apple s M-series chips. By incorporating single-photon tracking, the algorithm accurately models time-resolved light-Tissue interactions. Validated against experiments, it matches traditional methods accuracy while offering superior efficiency, advancing Raman functional imaging. © 2025 SPIE.

关键词： Tissue

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Understanding Cybercrime: A Study of Malware Analysis, Phishing Attacks, and Sentiment Trends 3

Understanding Cybercrime: A Study of Malware Analysis, Phish...

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3rd International Conference on Intelligent and Innovative Technologies in computing, electrical and Electronics, IITCEE 2025

作者： Kumaran, U. Sreeraag, G. Narendran, Advik Meena, V.P. Gupta, Sushil Kumar Appaji, Abhishek Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bangalore India National Institute of Technology Department of Electrical Engineering Jharkhand Jamshedpur831014 India Bms College of Engineering Department of Medical Electronics Engineering Bangalore India

ISBN: (纸本)9798331515911

With the rapid evolution of computer systems and technologies, individuals face an increasing threat of cybercrime. This research explores the changing landscape of cybercrime by focusing on three key areas: malware analysis, phishing attack analysis, and sentiment analysis. The study aims to identify malware strategies for evading detection and analyze the prevalence of spyware by examining diverse malware samples. Additionally, trend analysis highlights the rising complexity and frequency of phishing attacks, with a notable shift toward targeting reputable platforms. Sentiment analysis of media headlines surrounding data breaches uncovers a paradox: while concerns about cybersecurity remain, there is a growing confidence in the ability to manage cybersecurity crises. These findings provide insights into emerging threats and evolving public perceptions, offering a comprehensive understanding of the current cyber- crime landscape. © 2025 IEEE.

关键词： Phishing

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Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets

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Neural computing and Applications 2025年第8期37卷 6217-6232页

作者： Nematzadeh, Hossein Mani, Joseph Nematzadeh, Zahra Akbari, Ebrahim Mohamad, Radziah Modern College of Business and Science Muscat Oman Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States Otolaryngology Department School of Medicine Emory University AtlantaGA30322 United States Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Faculty of Computing Universiti Teknologi Malaysia Skudai81310 Malaysia

Abstract: Feature selection poses a challenge in high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There is not a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response,this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature Selection

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