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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Ji-Hoon Suh Hyungjoo Cho Yeseul Jeon Minkyu Je School of Electrical Engineering Korea Advanced Institute of Science and Technology Dajeon South Korea Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA

A batteryless electrochemical sensing system IC for miniaturized wearable sensor nodes is presented. A battery-powered hub node transmits the power and control signal required for the operation of sensor nodes through a human body. The sensor data acquired by sensor nodes are transmitted to the hub node, again through a body channel, using frequency modulation (FM). The intra-body power and data transfer leads to batteryless sensor node operation, and the direct-FM-based sensor interface using a current-controlled oscillator (26.8 and 44.9 kHz/ $\mu\mathrm{A}$ ) simplifies sensor node hardware, which enables miniaturized wearable sensor nodes. By adding a proper multiplexing scheme among sensor nodes, the proposed system can be extended to a further advanced system consisting of distributed wearable sensor nodes.

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Detecting and Monitoring Bias for Subgroups in Breast Cancer Detection AI

arXiv

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arXiv 2025年

作者： Kundu, Amit Kumar Doo, Florence X. Patil, Vaishnavi Varshney, Amitabh Jaja, Joseph Department of Electrical and Computer Engineering University of Maryland United States Department of Diagnostic Radiology and Nuclear Medicine University of Maryland School of Medicine United States Department of Computer Science University of Maryland United States Institute for Health Computing University of Maryland United States University of Maryland Institute for Advanced Computer Studies United States

Automated mammography screening plays an important role in early breast cancer detection. However, current machine learning models, developed on some training datasets, may exhibit performance degradation and bias when deployed in real-world settings. In this paper, we analyze the performance of high-performing AI models on two mammography datasets—the Emory Breast Imaging Dataset (EMBED) and the RSNA 2022 challenge dataset. Specifically, we evaluate how these models perform across different subgroups, defined by six attributes, to detect potential biases using a range of classification metrics. Our analysis identifies certain subgroups that demonstrate notable underperformance, highlighting the need for ongoing monitoring of these subgroups’ performance. To address this, we adopt a monitoring method designed to detect performance drifts over time. Upon identifying a drift, this method issues an alert, which can enable timely interventions. This approach not only provides a tool for tracking the performance but also helps ensure that AI models continue to perform effectively across diverse populations. © 2025, CC BY.

关键词： Mammography

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Unsupervised 3D SAR Imaging Network Based on Generative Adversary Learning

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IEEE Transactions on Antennas and Propagation 2025年

作者： Wang, Mou Hu, Yifei Wei, Shunjun Li, Jiahui Shen, Rong Shi, Jun Cui, Guolong Kong, Lingjiang Guo, Yongxin University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China UESTC Shenzhen Institute for Advanced Study Shenzhen518000 China City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong National University of Singapore Department of Electrical and Computer Engineering 117576 Singapore

Reconstructing three-dimensional synthetic aperture radar (3D SAR) images from the sparsely sampled echo measurements holds significant importance in simplifying the system complexity and sensing costs. The conventional compressed sensing-based imaging algorithms address the problem by suspecting the sparsity of the imaging space, showing good reconstruction performance but failing to tackle the imaging tasks in weakly sparse scenes. Besides, traditional iterative imaging algorithms also suffer from high computational complexity, cumbersome parameter tuning, and poor adaptability. To address these issues, a novel unsupervised 3D SAR image reconstruction network is proposed for estimating 3D SAR images from the corresponding incomplete echo measurements. The proposed scheme contains two stages. Wherein, the first stage aims to estimate the missing echo elements from the incomplete observations by designing a generative adversary network based on partial convolution (PCGAN). The second phase focuses on reconstructing the target SAR image from the estimated echoes by constructing an unsupervised adaptive fast iterative shrinkage-thresholding algorithm (FISTA)-inspired deep unfolding network (AdaFIST-Net). Finally, simulations and real-measured experiments are carried out. Experimental results show that the proposed imaging network outperforms the current state-of-the-art algorithms in reconstructing 3D images from sparsely sampled echoes in various sampling conditions and SNR cases. © 1963-2012 IEEE.

关键词： Radar imaging

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Measurement of Absorption Current of Polyester Coated Enameled Wires with Q(t) meter

Measurement of Absorption Current of Polyester Coated Enamel...

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2023 International Symposium on electrical Insulating Materials, ISEIM 2023

作者： Watanabe, Akinao Fukuma, Masumi Sekiguchi, Yoitsu Advanced Electronic and Information Systems Course National Institute of Technology Matsue College Matsue Japan Department of Electrical Engineering and Computer Science National Institute of Technology Matsue College Matsue Japan Energy and Electronics Materials R&D Laboratories Sumitomo Electric Industries Osaka Japan

ISBN: (纸本)9784886864376

A Q(t) meter is used to measure electrical properties in insulating materials. Here, Q(t) is the integral value of circuit current with time t. It is possible to measure the Q(t) of the insulation material of a capacitor and a cable by using Q(t) meter. The current through an insulating material is divided into an instantaneous charging current, an absorption current and an equilibrium leakage current. It is considered that the absorption current is related to charge accumulation in the insulating materials. In this paper, an enameled wire (1.6 PEW) is used to prepare a cable sample, the Q(t) of the coated polyester (PEs) is measured, and extraction of absorption current components from measured Q(t) are reported. © 2023 The institute of electrical Engineers of Japan.

关键词： Insulating materials

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuefei Chen, Mingzhe Liu, Danpu Zhang, Zhilong Quek, Tony Q. S. Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs). © 2024 IEEE.

关键词： Reinforcement learning

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Skin-like Organic Optoelectronic System for Real-time Heart Rate Monitoring 59th

Skin-like Organic Optoelectronic System for Real-time Heart ...

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59th International Symposium, Seminar and Exhibition, Display Week 2022

作者： Yun, Youngjun Lee, Yeongjun Chung, Jong Won Lee, Gae Hwnag Kang, Hyunbum Kim, Joo-Young Kang, Sung-Gyu Jung, Ji Young Kuzumoto, Yasutaka Gam, Sangah Hahm, Suk Gyu Hong, Yongtaek Choi, Byoungki Samsung Advanced Institute of Technology Samsung Electronics Suwon Korea Republic of Department of Chemical Engineering Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of

Conventional wearable electronics do not have conformal contact with the skin, so they might produce unreliable detecting of physiological signals during continuous health monitoring. This leads to the skin-like healthcare patch that ensure secure contact with skin and maximizes user’s comfort during daily life. Here, we present skin-like stretchable and conformable healthcare patch in which stretchable organic light-emitting diode (OLED) matrix display and a stretchable organic photoplethysmography (PPG) sensor are implemented together. Thepatch,fabricated on elastomer substrate without pre-strained fabricating process, maintain stable operation up to 30% strain by the use of the stress relief layers and deformable micro-cracked interconnects that reduce the mechanical stress of the OLED matrix display and PPG sensor. The simple device structure and reliablefabrication process show promise for commercialization of healthcare patch as novel form-factor of wearable electronics. © 2022 SID.

关键词： Stress relief

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

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Intelligent Medicine 2024年第3期4卷 170-176页

作者： Bijan Roudini Boshra Khajehpiri Hamid Abrishami Moghaddam Mohamad Forouzanfar Department of Systems Engineering School of Advanced TechnologyUniversity of QuebecMontrealCanada Machine Vision and Medical Image Processing(MVMIP)Laboratory Faculty of Electrical and Computer EngineeringK.N.Toosi University of TechnologyTehranIran Research Center of the Montreal University Institute of Geriatrics MontrealCanada

Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new *** learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is *** study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term *** This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,*** collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September *** collected and analyzed mortality data up to December *** records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial *** the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline *** The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,a

关键词： Electrocardiogram Predication Mortality prediction Machine learning

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The Future of Smart Grids: Revolutionizing Energy Distribution with advanced IoT, and Renewable Integration 5

The Future of Smart Grids: Revolutionizing Energy Distributi...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Avinash, S. Vinitha, J.C. Dharinya, S. Sree Samuthira Pandi, V. Lakshmi Priya, J. Jaishika, S. St. Joseph's College of Engineering Department of Mba Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vit University School of Computer Science Engineering and Information Systems Vellore India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Saveetha Engineering College Department of Ece Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798350376685

Smart grids, which integrate modern Internet of Things (IoT) technology and renewable energy sources to boost efficiency, sustainability, and dependability, are poised to bring about a dramatic shift in the future of energy distribution. Intelligent grids are expected to bring about this shift. Focusing on the role of the Internet of Things (IoT) in enabling real-time monitoring and control, as well as the incorporation of renewable energy to support a low-carbon future, this research investigates the potential for smart grids to change the energy sector. This study gives insights into how smart grids can be efficiently deployed to satisfy the growing energy needs of society by studying the technological breakthroughs, obstacles, and benefits of smart grids. Specifically, the study's focus is on smart grids. The research highlights the essential role that smart grids play in aiding the shift towards a more sustainable and resilient energy infrastructure by conducting an in-depth analysis of case studies, pilot projects, and industry trends all around the world. Providing recommendations for policymakers, industry stakeholders, and utilities to exploit the full potential of smart grids in determining the future of energy distribution, the findings contribute to the continuing discourse on the strategic development and implementation of smart grid technologies. Both of these topics are currently being discussed. © 2024 IEEE.

关键词： Demand Response Management Distributed Generation Energy Efficiency Energy Storage Systems Grid Resilience Low-Carbon Future Real-Time Monitoring and Control

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La-substituted W-type barium-nickel ferrites for tunable and high-performance electromagnetic wave absorption

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International Journal of Minerals,Metallurgy and Materials 2025年第3期32卷 645-656页

作者： Long Wang Jiurong Liu Shenghui Xie Yanli Deng Zhou Wang Shanyue Hou Shengying Yue Gang Wang Na Wu Zhihui Zeng Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials(Ministry of Education) School of Materials Science and EngineeringShandong UniversityJinan 250061China Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education and International Center for Dielectric ResearchSchool of Electronic Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China Army Engineering University of PLA College of Field Engineering Nanjing 210007China Laboratory for Multiscale Mechanics and Medical Science State Key Laboratory for Strength and Vibration of Mechanical StructuresSchool of AerospaceXi’an Jiaotong UniversityXi’an 710049China Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbana 61801USA School of Chemistry and Chemical Engineering Shandong UniversityJinan 250100China

W-type barium-nickel ferrite(BaNi_(2)Fe_(16)O_(27))is a highly promising material for electromagnetic wave(EMW)absorption be-cause of its magnetic loss capability for EMW,low cost,large-scale production potential,high-temperature resistance,and excellent chemical ***,the poor dielectric loss of magnetic ferrites hampers their utilization,hindering enhancement in their EMW-absorption *** efficient strategies that improve the EMW-absorption performance of ferrite is highly desired but re-mains ***,an efficient strategy substituting Ba^(2+)with rare earth La^(3+)in W-type ferrite was proposed for the preparation of novel La-substituted ferrites(Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)).The influences of La^(3+)substitution on ferrites’EMW-absorption performance and the dissipative mechanism toward EMW were systematically explored and ***^(3+)efficiently induced lattice defects,enhanced defect-induced polarization,and slightly reduced the ferrites’bandgap,enhancing the dielectric properties of the ***^(3+)also enhanced the ferromagnetic resonance loss and strengthened magnetic *** effects considerably improved the EMW-absorption perform-ance of Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)compared with pure W-type *** x=0.2,the best EMW-absorption performance was achieved with a minimum reflection loss of-55.6 dB and effective absorption bandwidth(EAB)of 3.44 GHz.

关键词： electromagnetic wave absorption W-type barium ferrite rare earth dielectric

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Robust Control of Feeding Speed for Coal Mine Tunnel Drilling Machines 25

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold School of Automation China University of Geosciences Wuhan China School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed. © 2024 IEEE.

关键词： Robust control

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