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Real-time programmable metasurface for terahertz multifunctional wave front engineering

Light(Science & Applications) 2023年第9期12卷 1795-1806页

作者： Feng Lan Luyang Wang Hongxin Zeng Shixiong Liang Tianyang Song Wenxin Liu Pinaki Mazumder Ziqiang Yang Yaxin Zhang Daniel M.Mittleman Sichuan THz Communication Technology Engineering Research Center School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313000China Zhangjiang Laboratory Shanghai 201204China National Key Laboratory of Solid-State Microwave Devices and Circuits Hebei Semiconductor Research InstituteShijiazhuang 050051China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China University of Chinese Academy of Sciences School of ElectronicElectrical and Communication EngineeringBeijing 101408China Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA School of Engineering Brown UniversityProvidenceRI 02912USA

Terahertz(THz)technologies have become a focus of research in recent years due to their prominent role in envisioned future communication and sensing *** of the key challenges facing the field is the need for tools to enable agile engineering of THz wave ***,we describe a reconfigurable metasurface based on GaN technology with an array-of-subarrays *** subwavelength-spaced array,under the control of a 1-bit digital coding sequence,can switch between an enormous range of possible configurations,providing facile access to nearly arbitrary wave front control for signals near 0.34 *** demonstrate wide-angle beam scanning with 1°of angular precision over 70 GHz of bandwidth,as well as the generation of multi-beam and diffuse wave fronts,with a switching speed up to 100 *** device,offering the ability to rapidly reconfigure a propagating wave front for beam-forming or diffusively scattered wide-angle coverage of a scene,will open new realms of possibilities in sensing,imaging,and networking.

关键词： wave scattered arbitrary

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Age of Information Based User Scheduling and Data Assignment in Multi-User Mobile Edge Computing Networks:An Online Algorithm

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China Communications 2024年第5期21卷 153-165页

作者： Ge Yiyang Xiong Ke Dong Rui Lu Yang Fan Pingyi Qu Gang School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education Beijing 100044China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing 100044China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Department of Electrical and Computer Engineering College ParkMarylandUSA

This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading *** weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data *** to the non-analytic expression of the WSA *** optimization variables and the unknowability of future network information,the problem cannot be solved with known solution ***,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems *** analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of *** results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline *** is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.

关键词： age of information(aoi) mobile edge computing(mec) user scheduling

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Self-Immolative Electrochemical Redox Substrates: Emerging Artificial Receptors in Sensing and Biosensing

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ACS Measurement Science Au 2024年第2期4卷 163-183页

作者： Manibalan, Kesavan Arul, Ponnusamy Wu, Hsin-Jay Huang, Sheng-Tung Mani, Veerappan Department of Materials Science and Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Institute of Biochemical and Biomedical Engineering Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei 10608 Taiwan Advanced Membranes and Porous Materials Center (AMPMC) Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia High-Value Biomaterials Research and Commercialization Center National Taipei University of Technology No. 1 Sec. 3 Zhongxiao E. Rd. Taipei 10608 Taiwan

The development of artificial receptors has great significance in measurement science and technology. The need for a robust version of natural receptors is getting increased attention because the cost of natural receptors is still high along with storage difficulties. Aptamers, imprinted polymers, and nanozymes are some of the matured artificial receptors in analytical chemistry. Recently, a new direction has been discovered by organic chemists, who can synthesize robust, activity-based, self-immolative organic molecules that have artificial receptor properties for the targeted analytes. Specifically designed trigger moieties implant selectivity and sensitivity. These latent electrochemical redox substrates are highly stable, mass-producible, inexpensive, and eco-friendly. Combining redox substrates with the merits of electrochemical techniques is a good opportunity to establish a new direction in artificial receptors. This Review provides an overview of electrochemical redox substrate design, anatomy, benefits, and biosensing potential. A proper understanding of molecular design can lead to the development of a library of novel self-immolative redox molecules that would have huge implications for measurement science and technology. © 2024 The Authors. Published by American Chemical Society.

关键词： alkaline phosphatase artificial receptors electrochemical mediators electrochemical sensors and biosensors galactosidase hydrogen peroxide neuraminidase organic electrochemistry ratiometric sensors self-immolative sensors

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Optimizing the Ferroelectric Properties of Hf1-xZrxO2 Films via Crystal Orientation

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ACS Applied Electronic Materials 2023年第2期5卷 1114-1122页

作者： Teng, Chih-Yu Cheng, Chia-Chieh Li, Kai-Shin Hu, Chenming Lin, Jhih-Min Lin, Bi-Hsuan Tang, Mau-Tsu Tseng, Yuan-Chieh Department of Materials Science & Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Taiwan Semiconductor Research Institute Hsinchu30010 Taiwan Intelligent Semiconductor Nano-system Technology Research Center National Yang Ming Chiao Tung University 1001 Ta Hsueh Road Hsinchu30010 Taiwan Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States National Synchrotron Radiation Research Center Hsinchu30076 Taiwan

This study exploits the coupling of crystallographic and ferroelectric (FE) properties in an effort to enhance the FE performance of Hf1-xZrxO2 (HZO). The proposed scheme involved optimizing the orientation of orthorhombic (O)-phase crystals along the preferred polarization axis by applying an electric pulse during annealing. The proposed electric pulse annealing (EPA) process reduced the need for wake-up treatment;however, it imposed limited changes on the amount of the O-phase and leakage performance, compared with rapid thermal annealing (RTA). Excessively high EPA doses induced an O → M (monoclinic) phase transformation, which sacrificed FE polarization but preserved the wake-up-free-like properties. Our results demonstrate that optimizing the orientation of polycrystalline O-phase may be as important as maximizing the quantity of O-phase for HZO applications. © 2023 American Chemical Society.

关键词： Ferroelectricity

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Graph Neural Network-Based Joint Beamforming for Hybrid Relay and Reconfigurable Intelligent Surface Aided Multiuser Systems

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IEEE Wireless Communications Letters 2023年第10期12卷 1811-1815页

作者： Chen, Bing-Jia Chang, Ronald Y. Chien, Feng-Tsun Poor, H. Vincent Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

This letter examines a downlink multiple-input single-output (MISO) system, where a base station (BS) with multiple antennas sends data to multiple single-antenna users with the help of a reconfigurable intelligent surface (RIS) and a half-duplex decode-and-forward (DF) relay. The system's sum rate is maximized through joint optimization of active beamforming at the BS and DF relay and passive beamforming at the RIS. The conventional alternating optimization algorithm for handling this complex design problem is suboptimal and computationally intensive. To overcome these challenges, this letter proposes a two-phase graph neural network (GNN) model that learns the joint beamforming strategy by exchanging and updating relevant relational information embedded in the graph representation of the transmission system. The proposed method demonstrates superior performance compared to existing approaches, robustness against channel imperfections and variations, generalizability across varying user numbers, and notable complexity advantages. © 2012 IEEE.

关键词： Beamforming

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Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach

arXiv

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arXiv 2022年

作者： Yang, Yizhou Liu, Xin Zhejiang Lab China National Research Centre of Parallel Computer Engineering and Technology

Penetration Testing plays a critical role in evaluating the security of a target network by emulating real active adversaries. Deep Reinforcement Learning (RL) is seen as a promising solution to automating the process of penetration tests by reducing human effort and improving reliability. Existing RL solutions focus on finding a specific attack path to impact the target hosts. However, in reality, a diverse range of attack variations are needed to provide comprehensive assessments of the target network's security level. Hence, the attack agents must consider multiple objectives when penetrating the network. Nevertheless, this challenge is not adequately addressed in the existing literature. To this end, we formulate the automatic penetration testing in the Multi-Objective Reinforcement Learning (MORL) framework and propose a Chebyshev decomposition critic to find diverse adversary strategies that balance different objectives in the penetration test. Additionally, the number of available actions increases with the agent consistently probing the target network, making the training process intractable in many practical situations. Thus, we introduce a coverage-based masking mechanism that reduces attention on previously selected actions to help the agent adapt to future exploration. Experimental evaluation on a range of scenarios demonstrates the superiority of our proposed approach when compared to adapted algorithms in terms of multi-objective learning and performance efficiency. © 2022, CC BY.

关键词： Deep learning

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FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection 42

FedMoS: Taming Client Drift in Federated Learning with Doubl...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Xiong Chen, Yuxin Li, Yuqing Liao, Xiaofei Jin, Hai Li, Bo Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients' selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal O(T - 2/3) convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques. © 2023 IEEE.

关键词： Momentum

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Wearable pressure sensors based on antibacterial and porous chitosan hydrogels for full-range human motion detection

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Science China(Technological Sciences) 2024年第8期67卷 2475-2484页

作者： ZHANG YingXin LIU HanGuang JIN ShunYu LI YaJuan ZHANG Wan ZHOU Hang NIE ZanXiang HUANG Yuan School of Microelectronics Science and Technology Sun Yat-sen UniversityGuangzhou 510275China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 230022China Department of Clinical Laboratory the First Affiliated Hospital of Anhui Medical UniversityHefei 230022China School of Electronic and Computer Engineering Peking University Shenzhen Graduate SchoolShenzhen 518055China Zinergy Shenzhen Ltd. Shenzhen 518109China

Wearable pressure sensors made from conductive hydrogels hold significant potential in health ***,limited pressure range(Pa to hundreds of kPa)and inadequate antibacterial properties restrict their practical applications in diagnostic and health ***,a wearable high-performance pressure sensor was assembled using a facilely prepared porous chitosan-based hydrogel,which was constructed from commercial phenolphthalein particles as a sacrificial *** relationship between the porosity of hydrogels and sensing performance of sensors was systematically ***,the wearable pressure sensor,featuring an optimized porosity of hydrogels,exhibits an ultrawide sensing capacity from 4.83 Pa to 250 k Pa(range-to-limit ratio of 51,760)and high sensitivity throughout high pressure ranges(0.7 kPa~(-1),120–250 kPa).The presence of chitosan endows these hydrogels with outstanding antibacterial performance against *** and ***,making them ideal candidates for use in wearable *** features allow for a practical approach to monitor full-range human motion using a single device with a simple structure.

关键词： porous hydrogels antibacterial performances wearable pressure sensors full-range human motion chitosan

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A Fault Classification Prediction Model Based on Loose-Type Wavelet Neural Network for Server Health Management 2

A Fault Classification Prediction Model Based on Loose-Type ...

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2nd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2023

作者： Wei, Yi Nong, Yingxiong Li, Zhe Pan, Jian Yang, Zhenyu Lu, Xiaoqi Chen, Ningjiang China Tobacco Guangxi Industrial Co. Ltd Nanning China Guangxi University School of Computer Electronics and Information Nanning China Guangxi University School of Computer Electronics and Information China Guangxi Intelligent Digital Services Research Center of Engineering Technology China Key Laboratory of Parallel Nanning China

ISBN: (纸本)9798350331448

To prevent the serious impact on production efficiency caused by service interruption, data loss, and other issues resulting from server faults, this paper proposes a server fault prediction model named Wavelet Packet Transform Probabilistic Neural Network(WPT-PNN). WPT-PNN loosely mixes wavelet packet transform and probabilistic neural network to achieve quick fault localization and signal denoising effects. The proposed model is validated using server operational data gathered. The experimental results suggest that the WPT-PNN model can effectively manage the challenges of complicated, non-stationary, and noisy signals in the feature extraction stage and extract signal features reliably. In the fault classification prediction stage, our method improves fault prediction accuracy to 81%, and limits the error range within [-2, 4], better matching the requirements for precise and low false-positive fault prediction in servers. © 2023 IEEE.

关键词： Neural networks

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FSLNet: Leveraging Skeleton Cue Guided FBIC for Bionic Intelligent Surveillance Robot via Transformer

FSLNet: Leveraging Skeleton Cue Guided FBIC for Bionic Intel...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Liu, Hai Chen, Qiang Liu, Zhibing Deng, Yongjian Zhang, Zhaoli Li, You-Fu Central China Normal University National Engineering Research Center for E-Learning Wuhan430079 China College of Computer Science Beijing University of Technology Beijing100124 China City University of Hong Kong Department of Mechanical Engineering Kowloon Hong Kong

ISBN: (纸本)9781665481090

Fine-grained bird image classification (FBIC) plays a critical role in robotic vision tracking and monitoring of endangered bird species. Traditional surveillance robots often face major challenges when operating in complex environments, especially under extreme conditions involving occlusions and complex backgrounds. To overcome these limitations, we developed a novel bionic intelligent surveillance robot that incorporates FSLNet - a fine-grained skeletal learning network that integrates skeletal tokens into a Vision Transformer (ViT) framework. Unlike traditional CNN-based models, FSLNet leverages skeletal tokens to capture key invariant features, improving the ability to distinguish between visually similar bird species under challenging circumstances. The architecture of our model includes visual token construction, skeleton token construction, and a transformer-based backbone for thorough feature learning. Extensive experiments on CUB-200-2011 and NABirds datasets demonstrate that our method achieves superior performance compared to state-of-the-art approaches. © 2024 IEEE.

关键词： Birds

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