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2024 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2024

作者： Xia, Shengxuan Khilkevich, Victor Beetner, Daryl Missouri University of Science and Technology Emc Laboratory Electrical and Computer Engineering Department RollaMO United States

ISBN: (纸本)9798350360394

Determining electromagnetic (EM) coupling to printed circuit boards (PCBs) is essential to finding potential EM susceptibilities early in the design process. For realistic PCB structures, analysis usually relies heavily on time-consuming full-wave simulations because of the complexity of the geometries and the lack of analytical solutions. In this paper, we adopt a segmentation approach based on far-field reciprocity which allows for rapid estimation of the voltage induced at trace terminations over frequency, and which is then used to estimate statistical characteristic of coupling across trace geometries. Frequency-domain results can then be used to estimate time-domain responses with appropriate transformations. Super-position is then used to separate the coupling contributed by different PCB structures, which provides better insight into the mechanisms responsible for susceptibility. © 2024 IEEE.

关键词： Time domain analysis

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Artificial sensory neurons and their applications

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Journal of Semiconductors 2025年第1期46卷 108-128页

作者： Jiale Shao Hongwei Ying Peihong Cheng Lingxiang Hu Xianhua Wei Zongxiao Li Huanming Lu Zhizhen Ye Fei Zhuge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingbo 315201China School of Electronic and Information Engineering Ningbo University of TechnologyNingbo 315211China State Key Laboratory of Environment-friendly Energy Materials Southwest University of Science and TechnologyMianyang 621010China Institute of Wenzhou Zhejiang UniversityWenzhou 325006China State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200072China

With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly *** limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a *** computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI *** neurons and synapses are the two core components of neuromorphic computing *** perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of *** work reviews recent advances in artificial sensory neurons and their ***,biological sensory neurons are briefly ***,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working ***,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and ***,challenges faced by artificial sensory neurons at both device and system levels are summarized.

关键词： artificial sensory neurons artificial perception systems neuromorphic computing artificial intelligence

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Wind Power Probability Density Prediction Based on Quantile Regression Model of Dilated Causal Convolutional Neural Network

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Chinese Journal of electrical engineering 2023年第1期9卷 120-128页

作者： Yunhao Yang Heng Zhang Shurong Peng Sheng Su Bin Li College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China State Grid Jining Electric Power Corporation Jining 272100China School of Electrical&Information Engineering Changsha University of Science&TechnologyChangsha 410114China

Aiming at the wind power prediction problem,a wind power probability prediction method based on the quantile regression of a dilated causal convolutional neural network is *** the developed model,the Adam stochastic gradient descent technique is utilized to solve the cavity parameters of the causal convolutional neural network under different quantile conditions and obtain the probability density distribution of wind power at various times within the following 200 *** presented method can obtain more useful information than conventional point and interval ***,a prediction of the future complete probability distribution of wind power can be *** to the actual data forecast of wind power in the PJM network in the United States,the proposed probability density prediction approach can not only obtain more accurate point prediction results,it also obtains the complete probability density curve prediction results for wind *** with two other quantile regression methods,the developed technique can achieve a higher accuracy and smaller prediction interval range under the same confidence level.

关键词： Dilated causal neural network nuclear density estimation wind power probability prediction quantile regression probability density distribution

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Monolithic Fabrication of On-Paper Self-Charging Power Systems Through Direct Ink Writing 60

Monolithic Fabrication of On-Paper Self-Charging Power Syste...

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60th Annual Microelectronics and Packaging Conference and Exhibition, NordPac 2024

作者： Su, Yingchun Fu, Yujie Chen, Shiqian Li, Zheng Xue, Han Li, Jiantong School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Electrum 229 Kista16440 Sweden

ISBN: (纸本)9789189896925

The rapid development of emerging electronics requires power sources with the advantages of lightweight, high efficiency, and portability. Considering the use of critical raw materials (such as Li, Co, etc.) and the increasing global concern of battery waste, self-charging power systems (SCPSs) integrating energy harvesting, power management, and energy storage devices have been envisioned as promising solutions to replace traditional batteries to avoid the use of toxic materials and the need of frequent recharging/replacement. Up to date, the reported SCPSs still hold the problem of large form factor, unscalable fabrication, noble materials, and material complexity. In our work, a highly stable and eco-friendly organic conductive ink based on poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS) has been developed for monolithic fabrication on-paper SCPSs almost fully through a simple direct ink writing (DIW) process. The ink possesses multiple functions and enables to directly print almost all the key components in the SCPSs, including electrodes for triboelectric nanogenerators (TENGs, mechanical energy harvesters), electrodes for micro-supercapacitors (MSCs, energy storage devices), and interconnects, on the same paper substrate in a monolithic manner without the need for "post-integration". The monolithic printing process exhibits excellent upscaling capability for manufacturing. In particular, the direct patterning merit of the DIW process offers great flexibility in optimizing the system performance through adjusting the cell number, electrode dimension, and thickness of the MSC arrays. By adjusting the cell numbers, the MSC arrays attain high-rate capability up to 50 V/s to match the pulsing electricity produced from the TENGs. For small-size printed SCPSs (~ 2 cm × 3 cm ×1 mm), after continuous press and release of the TENGs for ~79000 cycles, the 3-cell series-connected MSC array can be charged to 1.6 V while 6-cell array to 3.0 V. For a lar

关键词： Electrodes

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Lossy Compression of Multi-Channel EEG and PPG Signals Based on Golomb-Rice Coding with Parameter Estimation

Lossy Compression of Multi-Channel EEG and PPG Signals Based...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Wanasekara, Senura Hansaja Dung, Han Huy Nguyen, Ngoc Hung Nguyen, Van-Dinh College of Engineering and Computer Science Center for Environmental Intelligence VinUniversity Hanoi Viet Nam School of Electrical and Electronic Engineering Hanoi University of Science and Technology Viet Nam

ISBN: (纸本)9798350353983

The integration of electroencephalography (EEG) and photoplethysmography (PPG) in consumer-grade wearable devices is revolutionizing healthcare by enabling continuous health monitoring. However, these devices face significant challenges in terms of power consumption, computational complexity, and data storage, necessitating efficient data compression techniques. This paper proposes a modified Golomb-Rice coding method tailored for lossy compression of multi-channel EEG and PPG signals. By leveraging the statistical properties of filtered data and employing an optimized parameter estimation strategy, the proposed method achieves high compression ratios while minimizing computational overhead and preserving critical signal information. The proposed algorithm shifts data to handle negative values, scales and converts it into an appropriate format for encoding, and uses a novel parameter estimation technique to optimize the Golomb-Rice parameter. Extensive simulations on EEG and PPG datasets demonstrate that this parameter estimation method yields significantly better performance, improving both efficiency and processing speed. Results underscore the importance of preprocessing in data compression and provide a robust framework for future advancements in biomedical signal processing. © 2024 IEEE.

关键词： Encoding (symbols)

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Unveiling Marketing Potential: Harnessing Advanced Analytics and Machine Learning for Gold Membership Strategy Optimization in a Superstore

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SN computer science 2024年第4期5卷 374页

作者： Singh Mahala, Vikas Ranveer Garg, Neeraj Saxena, D. Kumar, Rajesh Department of Electrical Engineering Swami Keshvanand Institute of Technology Management & amp Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management & amp Department of Electrical Engineering Malaviya National Institute of Technology Rajasthan Jaipur 302017 India

This research paper presents a comprehensive case study conducted in a superstore, introducing a novel gold membership offer and employing sophisticated analytics and machine learning methodologies to identify potential customers. The primary objective of this study is to explore available data to discern the factors influencing customers’ responses to a new supermarket offering. Subsequently, a predictive model is developed to accurately gauge the likelihood of a favorable customer response. In pursuit of enhancing marketing strategies and bolstering sales, this study employs a suite of machine learning techniques, including decision trees, support vector machines, random forests, and XGBoost. Furthermore, the study incorporates metaheuristic optimization algorithms such as grey wolf optimization, slime mold algorithm, multi-verse optimizer, and particle swarm optimization to fine-tune hyperparameters of the machine learning models. These optimization algorithms serve as effective search mechanisms, facilitating the identification of optimal solutions and significantly improving classification performance in the context of the complex superstore problem. The research findings highlight the substantial impact of the metaheuristic strategy, specifically grey wolf optimization, on the performance of all machine learning models. Notably, the random forest model achieved the highest accuracy of 95% with the application of grey wolf optimization. Moreover, the decision tree model demonstrated remarkable improvement in accuracy following hyperparameter tuning with grey wolf optimization. Collectively, these results underscore the critical role of metaheuristic optimization in enhancing the performance of machine learning models for marketing strategies in the superstore industry. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Decision tree GWO Hyperparameter MVO Optimization PSO Random Forest SMA Supermarket SVM XGBoost

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Scalable Spintronic Synapses for Analog In-Memory Computing Based on Exchange-Coupled Nanostructures 9

Scalable Spintronic Synapses for Analog In-Memory Computing ...

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9th Annual IEEE International Conference on Rebooting Computing, ICRC 2024

作者： Gupte, Kshemal K. Mugdho, Sohan Salahuddin Wang, Cheng Department of Electrical and Computer Engineering Iowa State University of Science and Technology AmesIA50010 United States

ISBN: (纸本)9798331541279

The pursuit of high-performance and energy-efficient computing for data-intensive algorithms such as deep neural networks (DNN) opens up exciting opportunities for emerging non-volatile memories (NVM). Particularly, implementing such non-volatile memory units in crossbar arrays as weight matrix storage can provide highly parallel and efficient means of processing matrix-vector multiplications, providing synaptic functionality for the neuromorphic computing paradigm. While numerous memristive and phase-change device systems have been investigated for synaptic crossbar arrays, it remains challenging to provide robust and efficient device technology for multi-bit (analog) synapses. In this work, a multi-level spintronic device based on a magnetic tunnel junction (MTJ) device is proposed and studied. By integrating a standard MTJ free layer exchange coupled with a granular magnetic nanostructure, multiple near-continuous resistive states can be induced thanks to the distribution of the energy barrier among individual magnetic grains. Our simulation analysis demonstrated superior scalability with small variability compared to other means of multi-level devices. System-level simulation demonstrates that enabling 2-bit per cell MRAM crossbars leads up to 3.2x improvement in hardware efficiency while maintaining the inference accuracy. © 2024 IEEE.

关键词： Deep neural networks

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Time-Synchronized Formation Control of Unmanned Surface Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-9页

作者： Wang, Duansong Ge, Shuzhi Sam Liang, Xiaoling Li, Dongyu School of Electrical and Photoelectronic Engineering West Anhui University LuAn China Department of Electrical and Computer Engineering National University of Singapore Singapore College of Marine Engineering Dalian Maritime University Dalian China School of Cyber Science and Technology Beihang University Beijing China

Time-synchronization (TS) formation control for unmanned surface vehicles (USVs) presents several advantages, including precise execution of tasks, broadened combat capabilities, and improved information fusion quality. To achieve this performance, a time-synchronized formation control method is presented that takes into account direct topology, external disturbances, and system uncertainties (EDSU). In contrast to prior formation control strategies, we introduce the formalized time-synchronized formation control framework, where all state components of the formation system concurrently converge to the equilibrium point at a uniform time constant, independently of their initial states. To counteract the EDSU, a fixed-time disturbance observer is designed to guarantee the convergence of all observer error components to zero. System stability is corroborated through the application of Lyapunov-like theory. Simulations and comparative experiments on three USVs are conducted to demonstrate the proposed method's superiority. IEEE

关键词： Synchronization

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A Utility-frequency Skyline Itemsets Pattern Mining Algorithm with Threshold Restrictions

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1183-1202页

作者： Liu, Yadong Tayeb, Shahab Wu, Jimmy Ming-Tai College of Computer Science and Engineering Shandong University of Science and Technology Qianwangang Road 579 Qingdao China Department of Electrical and Computer Engineering California State University Engineering East 274 Fresno United States Department of Information Management National Kaohsiung University of Science and Technology No1 University Rd. Yanchao Dist Kaohsiung City824 Taiwan

Skyline frequent utility patterns have been extensively studied. However, the existing skyline pattern mining algorithms are inefficiency and time consuming, and can be inadequate in practical applications, as sometimes traditional skyline pattern may obtain points that are relatively extreme in one dimension. These one-sided extreme points are not the points that the user would like to see in many applications, and the user prefer to see the skyline itemsets with relatively balanced two dimensions. Therefore, this paper proposes a new pattern of skyline mining with threshold settings. In the process of new skyline pattern mining, multiple thresholds are used to exclude extreme points that do not satisfy the user’s needs in certain dimensions, while also greatly reducing the number of search size and improving the speed of searching the target itemsets. In addition, the intelligent recommendation system of an online shopping website is taken as an example to demonstrate the application potential of the new pattern in a service AI system. In this paper, the UFmax array structure is applied so that it can be used not only to get the itemsets with the highest utility at the same frequency, but also to exclude the set of items whose utility and frequency both do not satisfy the threshold. The SFUTPMiner algorithm is introduced to make skyline pattern mining more accurate and efficient. Experiments were conducted on six databases respectively, and the proposed algorithm was compared with the current popular mining algorithm. The results show that the designed new pattern of skyline mining with threshold settings not only provides a streamlined and optimised set of result items, but also surpasses the previous traditional pattern in the aspect of runtime, search space and memory consumption. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Data mining

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Efficient IRS-Aided Rate Optimizations for Dual Functional Radar and Communications

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IEEE Communications Letters 2024年第1期28卷 39-42页

作者： Sweta, S. Dubey, Ankit Nguyen, Van-Linh Indian Institute of Technology Jammu Department of Electrical Engineering Jammu and Kashmir Jammu181221 India National Chung Cheng University Department of Computer Science and Information Engineering Chiayi62102 Taiwan

This letter presents a novel method to enable beam steering optimization for secure communications and resilience against radio tracking in an intelligent reflecting surfaces-enabled multiple-input multiple-output dual-functional radar and communications system. The approach involves optimizing beam steering by solving two convex sub-problems: i) maximizing secrecy rate and ii) maximizing legitimate receiver rate, solved using Taylor series approximation. Evaluation results demonstrate that optimizing radar and beam steering enhances secure communications while maintaining quality of service. Furthermore, the utilization of IRS effectively prevents passive eavesdropping in both line-of-sight (LOS) and non-LOS scenarios. © 1997-2012 IEEE.

关键词： Convex optimization

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