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arXiv 2024年

作者： Quy, Vu Khanh Quy, Nguyen Minh Hoai, Tran Thi Shaon, Shaba Uddin, Md Raihan Nguyen, Tien Nguyen, Dinh C. Kaushik, Aryan Chatzimisios, Periklis Faculty of Information Technology Hung Yen University of Technology and Education Hungyen Viet Nam College of Sciences and Engineering University of Tasmania Sandy Bay TAS Hobart7001 Australia Department of Electrical and Computer Engineering University of Alabama in Huntsville United States Department of Electrical and Electronics Engineering Lac Hong University Viet Nam Department of Computing & Mathematics Manchester Metropolitan University United Kingdom Department of Information and Electronic Systems Engineering International Hellenic University Thessaloniki Greece

6G wireless networks are expected to provide seamless and data-based connections that cover space-air-ground and underwater networks. As a core partition of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) have been envisioned to provide countless real-time intelligent applications. To realize this, promoting AI techniques into SAGIN is an inevitable trend. Due to the distributed and heterogeneous architecture of SAGIN, federated learning (FL) and then quantum FL are emerging AI model training techniques for enabling future privacy-enhanced and computation-efficient SAGINs. In this work, we explore the vision of using FL/QFL in SAGINs. We present a few representative applications enabled by the integration of FL and QFL in SAGINs. A case study of QFL over UAV networks is also given, showing the merit of quantum-enabled training approach over the conventional FL benchmark. Research challenges along with standardization for QFL adoption in future SAGINs are also highlighted. © 2024, CC BY.

关键词： Federated learning

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Near-Field Wideband Secure Communications: An Analog Beamfocusing Approach

arXiv

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arXiv 2023年

作者： Zhang, Yuchen Zhang, Haiyang Xiao, Sa Tang, Wanbin Eldar, Yonina C. The National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China The School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

In the rapidly advancing landscape of 6G, characterized by ultra-high-speed wideband transmission in millimeter-wave and terahertz bands, our paper addresses the pivotal task of enhancing physical layer security (PLS) within near-field wideband communications. We introduce true-time delayer (TTD)-incorporated analog beamfocusing techniques designed to address the interplay between near-field propagation and wideband beamsplit, an uncharted domain in existing literature. Our approach to maximizing secrecy rates involves formulating an optimization problem for joint power allocation and analog beamformer design, employing a two-stage process encompassing a semi-digital solution and analog approximation. This problem is efficiently solved through a combination of alternating optimization, fractional programming, and block successive upper-bound minimization techniques. Additionally, we present a low-complexity beamsplit-aware beamfocusing strategy, capitalizing on geometric insights from near-field wideband propagation, which can also serve as a robust initial value for the optimization-based approach. Numerical results substantiate the efficacy of the proposed methods, clearly demonstrating their superiority over TTD-free approaches in fortifying wideband PLS, as well as the advantageous secrecy energy efficiency achieved by leveraging low-cost analog devices. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

引用

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

arXiv

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arXiv 2024年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Artificial Intelligence Technology Engineering Research Center Henan Open University Zhengzhou450002 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia School of Information Engineering Zhengzhou University Zhengzhou450002 China Hangzhou Institute of Technology Xidian University Hangzhou311200 China School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW2522 Australia College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China The International Joint Innovation Center Zhejiang University Haining314400 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Department of Electronic and Electrical Engineering University College London Torrington Place WC1E 7JE United Kingdom Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green’s function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method. © 2024, CC BY.

关键词： Channel estimation

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A quantum system control method based on enhanced reinforcement learning

arXiv

引用

arXiv 2023年

作者： Liu, Wenjie Wang, Bosi Fan, Jihao Ge, Yebo Zidan, Mohammed School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Hurghada Faculty of Computers and Artificial Intelligence South Valley University Egypt

Traditional quantum system control methods often face different constraints, and are easy to cause both leakage and stochastic control errors under the condition of limited resources. Reinforcement learning has been proved as an efficient way to complete the quantum system control task. To learn a satisfactory control strategy under the condition of limited resources, a quantum system control method based on enhanced reinforcement learning (QSC-ERL) is proposed. The states and actions in reinforcement learning are mapped to quantum states and control operations in quantum systems. By using a new enhanced neural networks, reinforcement learning can quickly achieve the maximization of long-term cumulative rewards, and a quantum state can be evolved accurately from an initial state to a target state. According to the number of candidate unitary operations, the three-switch control is used for simulation experiments. Compared with other methods, the QSC-ERL achieves close to 1 fidelity learning control of quantum systems, and takes fewer episodes to quantum state evolution under the condition of limited resources. © 2023, CC BY.

关键词： Reinforcement learning

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A computer Vision-Based System for Recognition and Classification of Urdu Sign Language Dataset for Differently Abled People Using Artificial Intelligence

引用

Mobile Information Systems 2023年第1期2023卷

作者： Zahid, Hira Syed, Sidra Abid Rashid, Munaf Hussain, Samreen Umer, Asif Waheed, Abdul Nasim, Shahzad Zareei, Mahdi Mansoor, Nafees Biomedical Engineering Department and Electrical Engineering Department Ziauddin University Karachi74600 Pakistan Biomedical Engineering Department Sir Syed University of Engineering and Technology Karachi75300 Pakistan Software Engineering Department Sir Syed University of Engineering and Technology Karachi74600 Pakistan Electronic Engineering Department Dawood University of Engineering & Technology Karachi74800 Pakistan Department of Computer Science & IT Hazara University Mansehra23100 Pakistan Department of Computer Science Women University Swabi Swabi23430 Pakistan School of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Begum Nusrat Bhutto Women University Faculty of Management Information Science and Technology Sukkur Pakistan Tecnologico de Monterrey School of Engineering and Sciences Monterrey64849 Mexico Dhaka Bangladesh

Communication between normal people and deaf people is the most difficult part of daily life worldwide. It is difficult for a normal person to understand a word from the deaf one in their daily routine. So, to communicate with deaf people, different countries developed different sign languages to make communication easy. In Pakistan, for deaf people, the government developed Urdu Sign Language to communicate with deaf people. Physical trainers and experts are difficult to provide everywhere in society, so we need such a computer/mobile-based system to convert the deaf sign symbol into voice and written alphabet that the normal person can easily get the intentions of the deaf one. In this paper, we provided an image processing and deep learning-based model for Urdu Sign Language. The proposed model is implemented in Python 3 and uses different image processing and machine techniques to capture the video and transform the symbols into voice and Urdu writing. First, we get a video from the deaf person, and then the model crops the frames into pictures. Then, the individual picture is recognized for the sign symbol such as if the deaf showed a symbol for one, then the model recognizes it and shows the letter which he/she wants to tell. Image processing techniques such as OpenCV are used for image recognition and classification while TensorFlow and linear regression are used for training the model to behave intelligently in the future. The results show that the proposed model increased accuracy from 80% to 97% and 100% accordingly. The accuracy of the previously available work was 80% when we implemented the algorithms, while with the proposed algorithm, when we used linear regression, we achieved the highest accuracy. Similarly, when we used the TensorFlow deep learning algorithm, we achieved 97% accuracy which was less than that of the linear regression model. © 2023 Hira Zahid et al.

关键词： Image recognition

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Detection of S100 Calcium Binding Protein A6 by Silicon Nitride Photonic Sensor

Detection of S100 Calcium Binding Protein A6 by Silicon Nitr...

引用

2022 International Electrical engineering Congress, iEECON 2022

作者： Udomsom, Suruk Rattakorn, Punramon Mankong, Ukrit Umezawa, Toshimasa Matsumoto, Atsushi Kanno, Atsushi Yamamoto, Naokatsu Mendez-Astudillo, Manuel Atthi, Nithi Yamwong, Wittawat Intuyod, Kitti Pinlaor, Somchai Kawanishi, Tetsuya Imai, Kazuo Chiang Mai University Biomedical Engineering Institute Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand National Institute of Information and Communications Network System Research Institute Tokyo Japan Ansys Inc. Tokyo Japan Thai Microelectronics Center National Electronics and Computer Technology Center Chachoengsao Thailand Khon Kaen University Faculty of Medicine Department of Pathology Khon Kaen Thailand Khon Kaen University Faculty of Medicine Department of Parasitology Khon Kaen Thailand Waseda University Department of Electronic and Physical Systems Tokyo Japan Imai Engineering Tokyo Japan

ISBN: (纸本)9781665402064

The detection of S100 Calcium Binding Protein A6, a useful cholangiocarcinoma marker, has been demonstrated for the first time using silicon nitride photonic resonator sensor. The sensor was designed and fabricated by low-cost 500 nm technology. Label-free sensing results based on the reactions between anti-S100A6 antibody and S100A6 antigen, showed good sensitivity and specificity. This sensing technique has potential in low-cost and low-level detection of the biomarker, deserving further study. © 2022 IEEE.

关键词： Silicon nitride

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Analytical Survey on the Security Framework of Cyber-Physical Systems for Smart Power System Networks

Analytical Survey on the Security Framework of Cyber-Physica...

引用

Cyber Resilience (ICCR), International Conference on

作者： Ahmed Hadi Ali AL-Jumaili Ravie Chandren Muniyandi Mohammad Kamrul Hasan Mandeep Jit Singh Johnny Koh Siaw Paw Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Computer Centre Department University of Fallujah Anbar Iraq Department of Electrical Electronic and System Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Department of Electronic & Communication Engineering Universiti Tenaga Nasional Kajang Selango Malaysia

ISBN: (数字)9781665461221

ISBN: (纸本)9781665461238

Cyber-Physical Power System (CPPS) is one of the most critical infrastructure systems due to deep integration between power grids and communication networks. In the power system, cascading failure is spreading more readily in CPPS, even leading to blackouts as well as there are new difficulties with the power system security simulation and faults brought by physical harm or network intrusions. The current study summarized the cross- integration of several fields such as computer and cyberspace security in terms of the robustness of Cyber-Physical Systems, viewed as Interconnected and secure network systems. Therefore, the security events that significantly influenced the power system were evaluated in this study, besides the challenges and future directions of power system security simulation technologies were investigated for posing both challenges and opportunities for simulation techniques of power system security like building a new power system to accelerate the transformation of the existing energy system to a clean, low-carbon, safe, and efficient energy system which is used to assure power system stability through fusion systems that combine the cyber-physical to integrate the battery power station, power generation and renewable energy resources through the internet with the cyber system that contains Smart energy system control and attacks.

关键词： Computational modeling Buildings Power system stability Network security Stability analysis Safety Security

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DRL-Based Pricing-Driven for Task Offloading and Dynamic Resource in Vehicle Edge Computing

引用

IEEE Transactions on Mobile Computing 2025年

作者： Wu, Sijun Yang, Liang Li, Junjie Guo, Hongzhi Ahmad, Ishtiaq Da Costa, Daniel Benevides Jiang, Hongbo Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Shaanxi Xi'an710072 China Czech Technical University in Prague Department of Telecommunication Engineering Faculty of Electrical Engineering Prague166 27 Czech Republic Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore

Vehicle Edge Computing (VEC) assists vehicles in performing latency-sensitive tasks by deploying resources near the vehicle. Designing an incentive mechanism for vehicles and VEC is crucial for realizing an intelligent transmission system. Considering the rationality of resource allocation, we model the utility functions of the VEC and the vehicle, which are used as optimization objectives. Specifically, the VEC allocates resources through pricing to maximize revenue under resource-constrained conditions, and the vehicle weighs payments against energy consumption to determine offloading and resource allocation. Given the vehicle movement and the variable channel state, we use the Deep Reinforcement Learning (DRL) algorithm to solve these optimization problems. To reduce the learning difficulty of the DRL algorithm in complex VEC scenarios with multiple optimization variables, we propose a Pricing-Driven Resource Allocation (PDRA) algorithm that performs mobility-aware task offloading and calculates the optimal values of the optimization variables in the utility function of the vehicle to reduce the decision dimension. Furthermore, we also propose a DRL-based Pricing-Driven Dynamic Resource Allocation (DPDDRA) algorithm to achieve efficient resource allocation. Extensive experimental results show that the proposed algorithms can reduce the learning difficulty while maximizing VEC and vehicle revenue in complex VEC scenarios. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Design and Performance Analysis of Rectangular Microstrip Patch Antenna for Wireless Applications

Design and Performance Analysis of Rectangular Microstrip Pa...

引用

International Conference on Circuits, Communication, Control and Computing

作者： Md. Sohel Rana Arunav Mallik Avi Sifat Hossain Shuvashis Biswas Rana AKM Ahasan Habib Md. Mostafizur Rahman Department of Electronics and Communication Engineering Khulna University of Engineering and Technology Khulna Bangladesh Department of Electrical and Electronic Engineering Northern University of Business and Technology Khulna Bangladesh Department of Computer Science and Engineering National University Gazipur Dhaka Bangladesh Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Malaysia

A rectangular microstrip patch antenna (RMPA) is designed and analyzed in this paper. FR-4 is used as a substrate material, whose dielectric permittivity is 4.3. This antenna is designed and simulated using CST software. Return loss, VSWR, directive gain, and bandwidth are obtained from the given simulation. The results after simulation are return loss, VSWR, directive gain, and bandwidth of -55.7 dB, 1.0032, 6.81 dBi, and 110 MHz, respectively. The main objective of that paper was to increase return loss, bring the VSWR closer to 1, and increase directive gain. This antenna can be used in radars, mobile phones, and wireless LAN applications.

关键词： Wireless communication Wireless LAN Patch antennas Computational modeling Bandwidth Microstrip antennas Directive antennas

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