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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yuntao Wang Zhou Su Abderrahim Benslimane Qichao Xu Minghui Dai Ruidong Li School of Cyber Science and Engineering Xi'an Jiaotong University China Laboratory of Computer Sciences Avignon University France School of Mechatronic Engineering and Automation Shanghai University China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Department of Electrical and Computer Engineering Kanazawa University Japan

ISBN: (纸本)9781665435413

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs' participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the pres-ence of network dynamics and UAVs' multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs under both partial and complete information asymmetry scenarios. We then devise a contract-theoretic method to solve the optimal VDD-reward contract design problem with partial information asymmetry, while ensuring truthfulness, fair-ness, and computational efficiency. Furthermore, under complete information asymmetry, we devise a reinforcement learning based distributed method to dynamically design optimal contracts for distinct types of UAVs in the fast-changing network. Experimental simulations show that the proposed scheme can motivate UAV's collaboration in VDD sharing and enhance defensive effectiveness, compared with existing solutions.

关键词： Costs Heuristic algorithms Collaboration Games Resists Reinforcement learning Autonomous aerial vehicles

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Wideband Circularly Polarized Sequentially Rotated Microstrip Antenna Array with Sequential-Phase Feeding Network

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Journal of Communications and Information Networks 2020年第3期5卷 350-357页

作者： Meijiao Sun Nengwu Liu Lei Zhu Guang Fu National Key Laboratory of Antennas and Microwave Technology Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering Faculty of Science and TechnologyUniversity of MacaoMacao 999078China State Key Laboratory of Millimeter Waves Nanjing 210096China

A wideband 2×2 sequential-phase fed circularly polarized(CP)microstrip antenna array(MAA)with sequentially rotated elements is ***,the antenna element loaded with shorting pins is studied as compared to the traditional ***,a feeding network is designed to be connected together in a sequential rotation manner,resulting in forming a four-port network to allow symmetrical positioning of CP microstrip patch *** that,straight strips are added to the feeding network in order to increase the distance and reduce coupling between array *** also play a role in improving the impedance matching of the ***,the proposed antenna array is designed and *** results are presented that its impedance bandwidth is about 36.3%at 24.1~34.8 GHz for|S_(11)|<10 dB and axial-ratio(AR)bandwidth is about 20.4%at 24.2~29.7 GHz for AR<3 ***,a peak gain of 11.1 dBic is simulated over the operating frequency range,which is significantly improved compared to the classic and existing works.

关键词： microstrip antenna array sequential-phase feeding network straight strips shorting pins

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Self-supervised On-device Federated Learning from Unlabeled Streams

arXiv

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arXiv 2022年

作者： Shi, Jiahe Wu, Yawen Zeng, Dewen Tao, Jun Hu, Jingtong Shi, Yiyu The State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai200433 China The Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States

The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unlabeled data to continuously improve accuracy. Self-supervised representation learning has achieved promising performances using centralized unlabeled data. However, the increasing awareness of privacy protection limits centralizing the distributed unlabeled image data on edge devices. While federated learning has been widely adopted to enable distributed machine learning with privacy preservation, without a data selection method to efficiently select streaming data, the traditional federated learning framework fails to handle these huge amounts of decentralized unlabeled data with limited storage resources on edge. To address these challenges, we propose a Self-supervised On-device Federated learning framework with coreset selection, which we call SOFed, to automatically select a coreset that consists of the most representative samples into the replay buffer on each device. It preserves data privacy as each client does not share raw data while learning good visual representations. Experiments demonstrate the effectiveness and significance of the proposed method in visual representation learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Data privacy

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Evaluating and Constraining Hardware Assertions with Absent Scenarios

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Journal of computer science & Technology 2020年第5期35卷 1198-1216页

作者： Hui-Na Chao Hua-Wei Li Xiaoyu Song Tian-Cheng Wang Xiao-Wei Li State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518052China Department of Electrical and Computing Engineering Portland State UniversityPortlandOR 97207U.S.A.

Mining from simulation data of the golden model in hardware design verification is an effective solution to assertion *** the simulation data is inherently incomplete,it is necessary to evaluate the truth values of the mined *** paper presents an approach to evaluating and constraining hardware assertions with absent scenarios.A Belief-fail Rate metric is proposed to predict the truth/falseness of generated *** considering both the occurrences of free variable assignments and the conflicts of absent scenarios,we use the metric to sort true assertions in higher ranking and false assertions in lower *** Belief-failRate guided assertion constraining method leverages the quality of generated *** experimental results show that the Belief-failRate framework performs better than the existing *** addition,the assertion evaluating and constraining procedure can find more assertions that cover new design functionality in comparison with the previous methods.

关键词： hardware formal verification assertion generation data mining assertion evaluation assertion coverage

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A Lightweight Blockchain and Fog-enabled Secure Remote Patient Monitoring System

arXiv

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arXiv 2023年

作者： Cheikhrouhou, Omar Mershad, Khaleel Jamil, Faisal Mahmud, Redowan Koubaa, Anis Moosavi, Sanaz Rahimi Ces Laboratory University of Sfax Tunisia Computer Science and Mathematics Department Lebanese American University Beirut Lebanon Department of Computer Engineering Jeju National University Korea Republic of School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Robotics and Internet of Things Lab Prince Sultan University Riyadh Saudi Arabia United States Isima University of Monastir Mahdia Tunisia

IoT has enabled the rapid growth of smart remote healthcare applications. These IoT-based remote healthcare applications deliver fast and preventive medical services to patients at risk or with chronic diseases. However, ensuring data security and patient privacy while exchanging sensitive medical data among medical IoT devices is still a significant concern in remote healthcare applications. Altered or corrupted medical data may cause wrong treatment and create grave health issues for patients. Moreover, current remote medical applications' efficiency and response time need to be addressed and improved. Considering the need for secure and efficient patient care, this paper proposes a lightweight Blockchain-based and Fogenabled remote patient monitoring system that provides a high level of security and efficient response time. Simulation results and security analysis show that the proposed lightweight blockchain architecture fits the resource-constrained IoT devices well and is secure against attacks. Moreover, the augmentation of Fog computing improved the responsiveness of the remote patient monitoring system by 40%. © 2023, CC BY-NC-ND.

关键词： Fog computing

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Deep Neural Network Enhanced Chip-on-Board Hybrid-Packaged Photonic-electrical Silicon Transceiver for High-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-Board Hybrid-Packaged P...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Jun Qin Qipeng Yang Changhao Han Yan Zhou Yunhao Zhang Yichen WU Yu Sun Junde Lu Siming Liu Yueqin Li Jian Sun Miao Min Weiwei Hu Zhixue He Lei Wang Shaohua Yu Haowen Shu Xingjun Wang Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing 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 California Santa Barbara CA USA Peking University Yangtze Delta Institute of Optoelectronics Nantong China Peng Cheng Laboratory Shenzhen China State Key Laboratory of Information Photonics and Optical Communications BUPT Beijing China

We explore implementing a multilevel deep neural network to enhance the performance of a 4-channel photonic-electrical hybrid-packaged silicon transceiver. Stable transmission and reception of 150 Gbps/λ PAM4 signals... 详细信息

ISBN: (纸本)9798350369311

关键词： Electric potential Optical interconnections System performance Artificial neural networks Transceivers Silicon Electro-optical waveguides Photonics

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Reconfigurable Intelligence Surface Assisted Opportunistic Multiple Access in UAV-IoT Networks

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IEEE Internet of Things Journal 2025年

作者： Zhang, Xi-Ran Sun, Wen-Bin Zhang, Zhaolin Wang, Ling Gao, Ang Cheng, Nan Meng, Wei-Xiao Leung, Victor C. M. Northwestern Polytechnical University School of Electronics And Information Xi'an China Xidian University State Key Laboratory of ISN Xi'an710071 China Xidian University School of Cyber Engineering Xi'an710071 China Harbin Institute of Technology Communication Research Center Harbin150001 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

Due to the advantages of flexible deployment and strong environmental adaptability of an unmanned aerial vehicle (UAV), UAVs serve as aerial base stations (BSs) to meet Quality of Services (QoSs) of ground users in internet of things (IoT) networks. NOMA (Non-Orthogonal Multiple Access) is a potential technique in wireless communications area, which can significantly improve sum spectrum efficiency (SE) of systems. To avoid the limitation of perfect CSI, opportunistic beamforming (OBF) is proposed, where a set of randomly generated weights is used to preprocess transmitted signals. Due to multiuser diversity gain introduced by OBF, OBF-NOMA systems can achieve approximate sum SE to conventional NOMA systems. Additionally, reconfigurable intelligent surfaces (RISs) are involved to overcome obstruction and obtain further improvements of SE. Therefore, this paper proposes a RIS-aid OBF-NOMA system in UAV-IoT networks, where random weights and opportunistic phase matrix are respectively applied in a UAV and RIS. Statistical characteristics of equivalent channels are derived in Nakagami-m (m≥1) fading channels. Theoretical asymptotic analyses of SE and bit error rate (BER) are then presented. Furthermore, a non-convex optimization problem is formulated to maximize SE. To obtain the optimal solution, we divide the problem into two sub-optimization problems and apply a joint iterative algorithm. Numerical results show that the proposed method achieves a satisfactory SE without complex channel estimation and perfect CSI. © 2014 IEEE.

关键词： Micro air vehicle (MAV)

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Over-the-air Wireless Federated Learning Model for Generative AI

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IEEE Network 2025年

作者： Zheng, Jie Niyato, Dusit Zhang, Haijun Du, Hongyang Wang, Jiacheng Kang, Jiawen Xiong, Zehui Northwest University State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Shaanxi 710127 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore University of Science and Technology Beijing Institute of Artificial Intelligence Beijing100083 China University of Hong Kong Department of electrical and electronic engineering Hong Kong Hong Kong Guangdong University of Technology Automation of School Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

Generative artificial intelligence (GenAI) technologies represent an important advancement in the field of AI, particularly for their capabilities in text and image generation. Over-the-air wireless federated learning (OA-WFL) can utilize the wireless waveform superposition property to achieve efficient model aggregation, thereby providing support for GenAI training and deployment. Therefore, this article investigates the support of OA-WFL for GenAI, focusing on potential applications and specific examples. We first discuss the OA-WFL and GenAI models, emphasizing their functionalities and the potential benefits arising from their interaction. We then explore its application in various GenAI scenarios, including large-scale edge device content generation, efficient distributed training of GenAI models, and reduction of bandwidth requirements and device load. Next, a framework is proposed to apply OA-WFL to diffusion models, such as those used in image generation, and validate its effectiveness through simulation results. Finally, we discuss prospective research directions for the application of OA-WFL for GenAI. © 2025 IEEE.

关键词： Federated learning

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A Novel Robotic System Enabling Multiple Bilateral Upper Limb Rehabilitation Training via an Admittance Controller and Force Field

TechRxiv

引用

TechRxiv 2023年

作者： Jiao, Ran Liu, Wenjie Rashad, Ramy Li, Jianfeng Dong, Mingjie Stramigioli, Stefano Beijing Key Laboratory of Advanced Manufacturing Technology Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China The Robotics and Mechatronics Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7545VL Netherlands ITMO University Saint Petersburg Russia

Patients with hemiplegia are usually restricted to performing general bilateral activities of daily life (gbADLs). Bilateral training has been verified to contribute to the rehabilitation of physical functions. Although robotic systems are gradually being employed in the field of rehabilitation, few studies have performed simulations with regards to gbADLs for training. Therefore, a novel end-effector bilateral rehabilitation robotic system (EBReCS) for the upper limb is developed in this article for the task rendering of gbADLs, in which the gbADL-corresponding workspace is obtained via modularly designed bilateral parallelogram mechanisms, and interaction rendering of multiple bimanual modes (uncoupled, trans-soft-coupled, trans-semi-coupled, and rotation-coupled) is achieved by implementing the admittance model, the inner force field between robotic end-effectors, and the outer force field distributed around. Experiments of the proposed four rehabilitation training modes were carried out on the healthy subject, with the results showing a feasible method of the EBReCS in the simulation of multiple bimanual coordinated rehabilitation training tasks. In the future, the constructed EBReCS is expected to be clinically tested and exploited for home rehabilitation as a coordinated training device. © 2023, CC BY.

关键词： End effectors

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Observation of charge to spin conversion in p-orbital InBi alloy

arXiv

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arXiv 2025年

作者： Li, Gen Zhang, Ying Xu, Xiaoguang Shen, Lei Feng, Zheng Meng, Kangkang Li, Ang Cheng, Lu He, Kang Tan, Wei Wu, Yong Wu, Yihong Jiang, Yong Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education School of Materials Science and Engineering University of Science and Technology Beijing Beijing100083 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Research Institute Chongqing Liang Jiang New Area Chongqing401123 China Department of Mechanical Engineering National University of Singapore 117575 Singapore Microsystem & Terahertz Research Center CAEP Chengdu610200 China Faculty of Materials and Manufacturing Beijing Key Lab of Microstructure and Properties of Advanced Materials Beijing University of Technology Beijing100124 China Institute of Quantum Materials and Devices School of Electronic and Information Engineering State Key Laboratory of Separation Membranes and Membrane Processes Tiangong University Tianjin300387 China

High density data storage and spin-logic devices require highly efficient all-electric control of spin moment. So far, charge-to-spin conversion through the spin Hall effect limits to d-orbital materials associated with strong spin-orbit coupling, especially heavy metals. However, d-orbital heavy metals with strong spin-orbit coupling results in a short spin diffusion length, which restricts the spin transport and accumulation in spintronic devices. Therefore, the conflict between charge-to-spin conversion efficiency and spin transport ability is irreconcilable in d-orbital materials. Here, we report a large charge to spin conversion experimentally observed in the p-orbital In2Bi alloy, exhibiting the coexistence of a large spin Hall angle comparable to heave metal Pt and a long spin diffusion length (4 times that of Pt). First-principles calculations reveal that topological symmetry-protected gap opening near the Fermi level results in large Berry curvature-related spin Hall conductivity. Due to the delocalized nature of p-orbitals and semimetal properties of In2Bi, its spin current can overcome the physical barriers between spin Hall angle and spin diffusion length in d-orbital metals, thereby advancing the development of high-performance spintronic devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Heavy metals

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