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arXiv 2023年

作者： Zhang, Rui Leng, Luziwei Che, Kaiwei Zhang, Hu Cheng, Jie Guo, Qinghai Liao, Jianxing Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China ACSLab Huawei Technologies Co. Ltd China Advanced Computing and Storage Lab Huawei Technologies Co. Ltd. Shenzhen518055 China

Spiking neural networks (SNNs), known for their low-power, event-driven computation and intrinsic temporal dynamics, are emerging as promising solutions for processing dynamic, asynchronous signals from event-based sensors. Despite their potential, SNNs face challenges in training and architectural design, resulting in limited performance in challenging eventbased dense prediction tasks compared to artificial neural networks (ANNs). In this work, we develop an efficient spiking encoder-decoder network (SpikingEDN) for large-scale eventbased semantic segmentation tasks. To enhance the learning efficiency from dynamic event streams, we harness the adaptive threshold which improves network accuracy, sparsity and robustness in streaming inference. Moreover, we develop a dualpath Spiking Spatially-Adaptive Modulation module, which is specifically tailored to enhance the representation of sparse events and multi-modal inputs, thereby considerably improving network performance. Our SpikingEDN attains a mean intersection over union (MIoU) of 72.57% on the DDD17 dataset and 58.32% on the larger DSEC-Semantic dataset, showing competitive results to the state-of-the-art ANNs while requiring substantially fewer computational resources. Our results shed light on the untapped potential of SNNs in event-based vision applications. The source code will be made publicly available. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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From Centralized RAN to Open RAN: A Survey on the Evolution of Distributed Antenna Systems

arXiv

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arXiv 2024年

作者： Hasabelnaby, Mahmoud A. Obeed, Mohanad Saif, Mohammed Chaaban, Anas Hossain, M.J. Advanced Wireless Technology Lab Huawei Technologies Canada Co. Ltd ONK2K 3J1 Canada Department of Electrical and Computer Engineering University of Toronto TorontoONM5S Canada School of Engineering University of British Columbia KelownaBCV1V 1V7 Canada

Next-generation mobile networks require evolved radio access network (RAN) architectures to meet the demands of high capacity, massive connectivity, reduced costs, and energy efficiency, and to realize communication with ultra-low latency and ultra-high reliability. Meeting such requirements for both mobile users and vertical industries in the next decade requires novel solutions. One of the potential solutions that attracted significant research attention in the past 15 years is to redesign the radio access network (RAN). In this survey, we present a comprehensive survey on distributed antenna system (DAS) architectures that address these challenges and improve network performance. We cover the transition from traditional decentralized RAN to DAS, including cloud radio-access networks (C-RAN), fog radio-access networks (F-RAN), virtualized radio-access networks (V-RAN), cell-free massive multiple-input multiple-output (CF-mMIMO), and the most recent advances manifested in open radio-access network (O-RAN). In the process, we discuss the benefits and limitations of these architectures, including the impact of limited-capacity fronthaul links, various cooperative uplink and downlink coding strategies, cross-layer optimization, and techniques to optimize the performance of DAS. Moreover, we review key enabling technologies for next-generation RAN systems, such as multi-access edge computing, network function virtualization, software-defined networking, and network slicing;in addition to some crucial radio access technologies, such as millimeter wave, massive multi-input multi-output, device-to-device communication, and massive machine-type communication. Last but not least, we discuss the major research challenges in DAS and identify several possible directions for future research. © 2024, CC BY.

关键词： Radio access networks

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A Trustless Federated Framework for Decentralized and Confidential Deep Learning

A Trustless Federated Framework for Decentralized and Confid...

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Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Chao Li Qiuyu Shen Cheng Xiang Bharath Ramesh Department of Electrical and Computer Engineering Advanced Control Technology Lab National University of Singapore Singapore International Center for Neuromorphic Systems The MARCS Institute for Brain Behaviour and Development Western Sydney University Sydney Australia

Nowadays, deep learning models can be trained on large amounts of web data on power hungry servers and be deployment-ready for specific real-world applications. With a state-of-the-art model architecture and a large publicly available dataset for pre-training, convolutional neural network models can be further fine-tuned via transfer learning for a related task. Nonetheless, the training process required of privacy-sensitive applications needs to protect data confidentiality and simultaneously boost performance using limited training data. For such data-deprived and privacy-centric learning, we introduce a trustless federated learning framework that seamlessly integrates deep learning models from different edge nodes using a blockchain-based architecture. Our framework performs federated learning without the need of a central server by leveraging a smart contract blockchain platform with a distributed file system for model storage. Users can locally train on their data while routinely benefiting from an enhanced model obtained by merging the models from all users in a decentralized fashion. Most importantly, this framework is free of the potential single point of failure in centralized federated learning. Besides, our framework has in-built incentive mechanisms to prevent model corruption and temper bad actors. We tested our framework on various computer vision datasets. The experimental results show that the merged model accuracy is on-par compared to a centralized federated training setup. To the best of our knowledge, this work represents the first systematic attempt at building a blockchain-based federated deep learning framework for computer vision. The code is publicly made available at: https://***/s-elo/DNN-Blockchain.

关键词： Deep learning Training Data privacy Federated learning Computational modeling Smart contracts Data models

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Evolving Neuromorphic Systems on the Ethereum Smart Contract Platform

Evolving Neuromorphic Systems on the Ethereum Smart Contract...

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Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Hongchi Wu Binhao Fang Cheng Xiang Gregory Cohen André van Schaik Bharath Ramesh Department of Electrical and Computer Engineering Advanced Control Technology Lab National University of Singapore Singapore International Center for Neuromorphic Systems The MARCS Institute for Brain Behaviour and Development Western Sydney University Sydney Australia

Neuromorphic intelligent systems are motivated by the observation that biological organisms - from algae to primates - excel in swiftly sensing their environment, reacting promptly to its perils and opportunities. Furthermore, biological organisms function more resiliently than our most advanced machines, with a fraction of their power requirements. Taking inspiration from how primates and humans have successfully evolved higher cognitive intelligence within social constructs, this paper proposes neuromorphic systems to be built and governed on a public distributed ledger platform. However, following in the footsteps of generic AI research, neuromorphic benchmarks and algorithms are developed in isolation. Furthermore, as a relatively niche research field, there is limited access to the actual neuromorphic sensors and large publicly available curated data, exacerbating the slow research progress. Nonetheless, centralized neuromorphic datasets and algorithms pose a threat to secure closed-loop behavior and learning outcomes, both commonly modulated in biological organisms via social interactions. This paper makes the case for early adoption of distributed ledger technology by neuromorphic systems and benchmarks to avoid the pitfalls endured by AI research – showcasing competing event-based gesture recognition systems on the Ethereum smart contract platform. This shift towards real-world and dynamic systems on a distributed ledger platform will improve collaboration among neuromorphic researchers while enabling healthy competition via incentives. Smart contract protocols allow model behavior monitoring, setting new learning tasks and increase in baseline performance, and naturally provides a governance framework for evolving neuromorphic systems. The code is publicly made available at: https://***/BruceFang123.

关键词： Neuromorphics Distributed ledger Smart contracts Biological systems Benchmark testing Blockchains Sensors

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Unveiling a Multiplexed Sensing System for advanced Biosensing Applications with Thread-Based Electrochemical Sensors

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ECS Meeting Abstracts 2024年第49期MA2024-01卷 2675-2675页

作者： Atul Sharma Cihan Asci Sameer Sonkusale Nano Lab Advanced Technology Laboratory Tufts University Department of Electrical & Computer Engineering Tufts University Nano Lab Advanced Technology Laboratory Tufts University USA

The miniaturization and portability of electrochemical sensing devices play a crucial role in a wide range of applications, including medical diagnostics, environmental monitoring, and point-of-care testing. Integrating a pseudo-reference electrode addresses challenges linked to traditional counterparts, ensuring enhanced portability and miniaturization without performance compromise. Our research delves into the significance of this innovation, emphasizing its impact on signal stability, reproducibility, and overall sensor reliability. The tailored pseudo-reference for compact electrochemical devices contributes to a highly efficient, cost-effective, and user-friendly design, ideal for point-of-care diagnostics and field applications. This work showcases the design, implementation, and performance evaluation of a portable electrochemical sensing system with a pseudo-reference-integrated readout circuit, improving reliability under diverse operating conditions. In the proposed research, we present the design and implementation of a dual-channel configurable multiplexed sensing platform utilizing thread-based electrochemical sensors for continuous potentiometric and amperometric measurements.

For the pH sensor, the fabrication process initiates with the cleaning (using isopropyl alcohol) and subsequent plasma treatment of polysorb sutures. The cleaned sutures undergo multiple coatings (4 cycles) with conductive carbon ink, followed by baking at 60°C for 30 to 35 minutes to ensure uniform conductivity. Carbon-coated (CC) sutures undergo electrochemical pre-treatment in a solution of 0.50 M H₂SO₄ and 0.10 M KCl, followed by electrodeposition with polyaniline (PANI) using potentiometric deposition at 0.80 V in the presence of 0.50 M aniline in a solution containing 1.0 M H₂SO₄. An insulating polymer layer of 5% polyurethane (PU) is applied at the sensor's center, leaving ends exposed for electrical connections and

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Unsupervised Collaborative Metric Learning with Mixed-Scale Groups for General Object Retrieval

arXiv

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arXiv 2024年

作者： Kan, Shichao Deng, Yuhai Liang, Yixiong Cen, Lihui Qu, Zhe Cen, Yigang He, Zhihai School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Automation Central South University Hunan Changsha410083 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Pengcheng Lab Shenzhen518066 China

The task of searching for visual objects in a large image dataset is difficult because it requires efficient matching and accurate localization of objects that can vary in size. Although the segment anything model (SAM) offers a potential solution for extracting object spatial context, learning embeddings for local objects remains a challenging problem. This paper presents a novel unsupervised deep metric learning approach, termed unsupervised collaborative metric learning with mixed-scale groups (MS-UGCML), devised to learn embeddings for objects of varying scales. Following this, a benchmark of challenges is assembled by utilizing COCO 2017 and VOC 2007 datasets to facilitate the training and evaluation of general object retrieval models. Finally, we conduct comprehensive ablation studies and discuss the complexities faced within the domain of general object retrieval. Our object retrieval evaluations span a range of datasets, including BelgaLogos, Visual Genome, LVIS, in addition to a challenging evaluation set that we have individually assembled for open-vocabulary evaluation. These comprehensive evaluations effectively highlight the robustness of our unsupervised MS-UGCML approach, with an object level and image level mAPs improvement of up to 6.69% and 10.03%, respectively. The code is publicly available at https://***/dengyuhai/MSUGCML. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

Model-Based Reinforcement Learning for Quantized Federated L...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning Mathematical models

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Free form three dimensional integrated circuits and wearables on a thread using organic eutectogel gated electrochemical transistors

arXiv

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arXiv 2023年

作者： Owyeung, Rachel E. Zeng, Wenxin Panzer, Matthew J. Sonkusale, Sameer Department of Chemical and Biological Engineering Tufts University 4 Colby Street MedfordMA02155 United States Department of Electrical and Computer Engineering Tufts University 161 College Ave MedfordMA02155 United States Nano Lab Advanced Technology Laboratory Tufts University 200 Boston Ave. Suite 2600 MedfordMA02155 United States

Existing flexible electronics are planar, nonbreathable, and easily damaged. In this work, authors introduce a new paradigm of free-form three-dimensional integrated circuits assembled on a single textile thread for ultimate flexibility. Transistors are assembled as daisy chain on a thread and utilize deep eutectic solvent gels, or eutectogels as nonvolatile, breathable, and repairable dielectric for gating organic electrochemical transistor. The eutectogels provide stable transistor performance without the need for encapsulation, are air permeable to the skin, and make the Organic Eutectogel Gated Electrochemical Transistor (OEGET) a promising candidate for wearable electronics. Complex analog integrated circuits, such as single stage and multi-stage amplifiers can be realized on a single thread which can bend out of the plane of stretching for true three-dimensional flexibility. An all-thread based wearable that uses a thread-based strain sensor and thread-based integrated circuit for eye motion and respiration monitoring is also presented. © 2023, CC BY-NC-ND.

关键词： Flexible electronics

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FLaaS6G: Federated Learning as a Service in 6G Using Distributed Data Management Architecture

FLaaS6G: Federated Learning as a Service in 6G Using Distrib...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Wenxuan Ye Xueli Ant Xueqiang Yan Mohammad Hamad Sebastian Steinhorst Department of Electrical and Computer Engineering Technical University of Munich Advanced Wireless Technology Laboratory Munich Research Center Huawei Technologies Duesseldorf GmbH Wireless Technology Lab 2012 Laboratories Huawei Technologies Co. Ltd

ISBN: (纸本)9781665435413

AI/ML is envisioned to play an essential role in 6G mobile communication systems. The privacy-preserving capabil-ities of Federated Learning (FL) make it promising in vertical applications; however, the central server-based system and lack of trusted data management limit its widespread use. To effectively support FL as a service from a network architecture perspective, this work provides a comprehensive design including three key features: First, the network architecture enables transparent and traceable data management based on Distributed Ledger Technology (DLT) platform, and realizes distributed and off-chain data storage by adopting Distributed Data Storage Entity (DDSE). Second, the central aggregator of an FL service is decoupled from the data management scheme mentioned above, and is decentralized through smart contracts for aggregator selection among a set of aggregator candidates, with the selected aggregator subsequently responsible for client selection and model aggregation. Third, a completed set of procedures for FL services operations is defined. A simulation system is developed to verify the feasibility of the proposed architecture and to study the impact of introducing the data management mechanisms on the overall performance overhead. The results show that the impact is related to the FL settings, with a worst-case time overhead of 15% observed in selected test cases, i.e., 15% of the total time spent on the interactions with the DLT platform and DDSE.

关键词： 6G mobile communication Distributed ledger Federated learning Smart contracts Memory Distributed databases Network architecture

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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