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IEEE International Conference on Communications (ICC)

作者： Peixi Liu Guangxu Zhu Shuai Wang Miaowen Wen Wu Luo H. Vincent Poor Shuguang Cui State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Shenzhen Research Institute of Big Data Chinese Academy of Sciences Shenzhen Institute of Advanced Technology School of Electronic and Information Engineering South China University of Technology Department of Electrical and Computer Engineering Princeton University the Guangdong Provincial Key Laboratory of Future Networks of Intelligence School of Science and Engineering (SSE) Future Network of Intelligence Institute (FNii) the Chinese University of Hong Kong Shenzhen

Sensing, computation, and communication (SC 2 ) are highly coupled processes in federated edge learning (FEEL) and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC 2 resources jointly affect the FEEL performance. In this paper, we address the problem of joint SC 2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Specifically, the joint SC 2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time and energy supply of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determining a joint sensing and communication resource allocation that maximizes the total number of samples sensed during the entire training process; the second one concerns the partition of the total number of sensed samples over communication rounds to determine the batch size at each round for convergence speed maximization. Finally, extensive simulation results are provided to validate the superiority of the proposed scheme over several baseline schemes.

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Scalable Electrophysiology of Millimeter-Scale Animals with Electrode Devices

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Biomedical engineering Frontiers 2023年第1期4卷 3-20页

作者： Kairu Dong Wen-Che Liu Yuyan Su Yidan Lyu Hao Huang Nenggan Zheng John A.Rogers Kewang Nan College of Pharmaceutical Sciences Zhejiang UniversityHangzhou 310058China National Key Laboratory of Advanced Drug Delivery and Release Systems Zhejiang UniversityHangzhou 310058China College of Biomedical Engineering&Instrument Science Zhejiang UniversityHangzhou310027China Department of Gastroenterology Brigham and Women’s HospitalHarvard Medical SchoolBostonMA 02115USA College of Chemical and Biological Engineering Zhejiang UniversityHangzhou 310058China Qiushi Academy for Advanced Studies Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhou 310058China CCAI by MOE and Zhejiang Provincial Government(ZJU) Hangzhou 310027China Querrey Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Department of Biomedical Engineering Northwestern UniversityEvanstonIL 60208USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL 60208USA Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA Jinhua Institute of Zhejiang University Jinhua 321299China

Millimeter-scale animals such as Caenorhabditis elegans,Drosophila larvae,zebrafish,and bees serve as powerful model organisms in the fields of neurobiology and *** methods exist for recording large-scale electrophysiological signals from these *** approaches often lack,however,real-time,uninterrupted investigations due to their rigid constructs,geometric constraints,and mechanical mismatch in integration with soft *** recent research establishes the foundations for 3-dimensional flexible bioelectronic interfaces that incorporate microfabricated components and nanoelectronic function with adjustable mechanical properties and multidimensional variability,offering unique capabilities for chronic,stable interrogation and stimulation of millimeter-scale animals and miniature tissue *** review summarizes the most advanced technologies for electrophysiological studies,based on methods of 3-dimensional flexible bioelectronics.A concluding section addresses the challenges of these devices in achieving freestanding,robust,and multifunctional biointerfaces.

关键词： offering recording millimeter

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Modular Multilevel DC-DC Converter With Inherent Bipolar Operation Capability for Resilient Bipolar MVDC Grids

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CPSS Transactions on Power Electronics and Applications 2022年第1期7卷 37-48页

作者： Shanshan Zhao Yu Chen Shenghui Cui Jingxin Hu State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Department of Electrical and Computer Engineering Seoul National University Korea Institute for Power Generation and Storage Systems E. ON Energy Research Center RWTH Aachen University Germany

Bipolar medium-voltage dc (MVDC) distribution systems are of great interests nowadays due to its high availability and reliability. A bipolar MVDC grid can be established by the installation of power-balancers or directly by converters with inherent bipolar operation capability to avoid the extra cost and volume. Therefore, this paper proposes a modular multilevel dc-dc converter (MMDC) with inherent bipolar operation capability for the interconnection of bipolar MVDC grids and LVDC girds. Considering that the MMDC is normally only capable of operating with monopole MVDC distribution systems due to the symmetric structure and operation scheme, a center-tapped high-frequency interface transformer is employed on the MV side of the MMDC. Based on the concept of the flux dc-bias cancellation, a dedicated operation method is proposed, whereby the power flows of the two MVDC poles can be regulated independently with a simple control scheme, and no penalty of increased current rating is imposed on the semiconductor devices or the interface transformer compared to the conventional MMDC with monopole operation. Additionally, the MMDC can realize single-pole operation in case one pole is faulty and deliver at least 50% of the rated power capacity, which significantly enhance the reliability and availability of the power delivery. The validity of the proposed bipolar operation scheme of the MMDC has been verified by both simulations and experiments with a down-scale prototype.

关键词： Multilevel converters Semiconductor devices Prototypes DC-DC power converters Transformers Valves Power electronics

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IRS-Enhanced Anti-Jamming Precoding Against DISCO Physical Layer Jamming Attacks

arXiv

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arXiv 2024年

作者： Huang, Huan Zhang, Hongliang Cai, Yi Zhang, Yunjing Lee Swindlehurst, A. Han, Zhu Electronic Information School Soochow University Suzhou China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of Houston Houston United States Center for Pervasive Communications and Computing University of California Irvine United States

Illegitimate intelligent reflective surfaces (IRSs) can pose significant physical layer security risks on multi-user multiple-input single-output (MU-MISO) systems. Recently, a DISCO approach has been proposed an illegitimate IRS with random and time-varying reflection coefficients, referred to as a "disco" IRS (DIRS). Such DIRS can attack MU-MISO systems without relying on either jamming power or channel state information (CSI), and classical anti-jamming techniques are ineffective for the DIRS-based fully-passive jammers (DIRS-based FPJs). In this paper, we propose an IRS-enhanced anti-jamming precoder against DIRS-based FPJs that requires only statistical rather than instantaneous CSI of the DIRS-jammed channels. Specifically, a legitimate IRS is introduced to reduce the strength of the DIRS-based jamming relative to the transmit signals at a legitimate user (LU). In addition, the active beamforming at the legitimate access point (AP) is designed to maximize the signal-to-jamming-plus-noise ratios (SJNRs). Numerical results are presented to evaluate the effectiveness of the proposed IRS-enhanced anti-jamming precoder against DIRS-based FPJs. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement

arXiv

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arXiv 2023年

作者： Chen, Jinbiao Zhang, Zizhen Cao, Zhiguang Wu, Yaoxin Ma, Yining Ye, Te Wang, Jiahai School of Computer Science and Engineering Sun Yat-sen University China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC systems

arXiv

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arXiv 2023年

作者： Han, Runzhou Zheng, Mai Byna, Suren Tang, Houjun Dong, Bin Dai, Dong Chen, Yong Kim, Dongkyun Hassoun, Joseph Thorsley, David Wolf, Matthew Department of Electrical and Computer Engineering Iowa State University United States Department of Computer Science and Engineering The Ohio State University United States Scientific Data Management Group Lawrence Berkeley National Laboratory United States Computer Science Department University of North Carolina Charlotte United States Systems Architecture Lab Samsung United States Samsung Advanced Institute of Technology Samsung Korea Republic of

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance solutions cannot address the challenges due to their incompatible provenance models and/or system implementations. In this paper, we analyze four representative scientific workflows in collaboration with the domain scientists to identify concrete provenance needs. Based on the first-hand analysis, we propose a provenance framework called PROV-IO+, which includes an I/O-centric provenance model for describing scientific data and the associated I/O operations and environments precisely. Moreover, we build a prototype of PROV-IO+ to enable end-to-end provenance support on real HPC systems with little manual effort. The PROV-IO+ framework can support both containerized and non-containerized workflows on different HPC platforms with flexibility in selecting various classes of provenance. Our experiments with realistic workflows show that PROV-IO+ can address the provenance needs of the domain scientists effectively with reasonable performance (e.g., less than 3.5% tracking overhead for most experiments). Moreover, PROV-IO+ outperforms a state-of-the-art system (i.e., ProvLake) in our experiments. © 2023, CC BY-NC-SA.

关键词： Life cycle

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Geometric Parameterized Macromodel of Uniaxial Capacitance Accelerometer Based on Modal Superposition

Geometric Parameterized Macromodel of Uniaxial Capacitance A...

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International Conference on Actual Problems of Electronic Instrument engineering (APEIE)

作者： Aleksandr V. Gridchin Aleksei N. Koleda Evgenii S. Barbin Tamara G. Nesterenko Vladimir A. Kolchuzhin Department of Semiconductor Devices and Microelectronics Novosibirsk State Technical University Novosibirsk Russia Department of Electronic Engineering National Research Tomsk Polytechnic University Tomsk Russia Laboratory of Intelligent Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Advanced Cellular Group Qorvo Munich GmbH Munich Germany

The paper presents a piecewise geometric parameterization method and a parametric analysis of uniaxial capacitance accelerometer using a reduced model formulated on the modal superposition method. The extraction of parameters for the macromodel such as modal frequencies, modal masses, modal stiffnesses, strain energy, and capacitances are described. The reduced model consists of the two modes: torsion mode, and transversal mode, three electrodes in the electrostatic domain, and eight master nodes in the geometrical domain located at the edges of the plate. The generation of the discrete macromodels is executed using commercial software. The thickness of the plate has been selected as a design parameter. Linear piecewise interpolation is used for macromodel parameterization which have been done inside VHDL-AMS code. The accelerometer’s dynamic response simulated using of parametrized macromodel considering the mechanical contact phenomena and the nonlinear electrostatic interaction are presented and discussed. The developed approach can be applied for fast system level simulations in view of the variation of the geometric parameters.

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Reservoir computing with state-dependent time delay

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Physical Review E 2025年第3期111卷 035313-035313页

作者： G. O. Danilenko A. V. Kovalev D. S. Citrin A. Locquet D. Rontani E. A. Viktorov Institute of Advanced Data Transfer Systems ITMO University Birzhevaya Liniya 14 199034 Saint Petersburg Russia Georgia Tech-CNRS IRL 2958 Georgia Tech-Europe 2 Rue Marconi 57070 Metz France School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia 30332-0250 USA Chair in Photonics LMOPS UR 4423 Laboratory CentraleSupélec & Université de Lorraine 57070 Metz France

We examine a new design of reservoir computing based on an otherwise linear dynamical system subject to feedback in which a delay time linearly depends on the system's state. Despite the apparent linearity of the system under casual perusal, the system nonetheless possesses a nonlinearity that can be used for time-delay reservoir computing. We find that close multiple Hopf bifurcation points lead to a rich sawtooth-shaped transient response to input signals, which can be beneficial for computing capabilities. We benchmark the reservoir computing system's memory capacity and performance on solving delayed XOR, Iris flower classification tasks, and the Santa Fe time-series prediction task. We demonstrate how the nonlinearity and the memory capacity of the system can be tuned by changing the time-delay dependence.

关键词： Response time (computer systems)

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Hybrid Data-Free Knowledge Distillation

arXiv

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arXiv 2024年

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at https://***/tangjialiang97/HiDFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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A Comprehensive Review of Active EV Battery Cell Voltage Balancing systems: Current Issues and Prospective Solutions

A Comprehensive Review of Active EV Battery Cell Voltage Bal...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Chandan Chetri Alvin Huynh Sheldon S. Williamson Department of Electrical Computer and Software Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

The performance of a battery pack is greatly affected by an imbalance between the cells. Cell balancing is a very important criterion for Battery Management System (BMS) to operate properly. This paper presents the various cell balancing topologies which have been presented by various researchers in the past years. A novel topology has also been discussed, which improves the overall efficiency of the components being used. Various comparison parameters such as Control, Speed, Efficiency, Power Effects, Manufacturability, Size, and Cost have been considered.

关键词： Industrial electronics Electric potential Costs Equalizers Battery management systems Transformers Power electronics

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