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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Fang, Yuguang Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. © 2024, CC BY.

关键词： Integration

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Tunable Rectangular Waveguide Bandpass Filter Based on Plasma technology

Tunable Rectangular Waveguide Bandpass Filter Based on Plasm...

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European Conference on Antennas and Propagation, EuCAP

作者： Atefeh Ashrafian Fatemeh Sadeghikia Jalil A Rashed Mohassel Mohamed Himdi Mirko Magarotto School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Communication Group ARI Ministry of Science Research and Technology Tehran Iran Institute of Electronics and Telecommunications of Rennes (IETR) University of Rennes 1 Rennes Cedex France Department of Information Engineering University of Padova Padua Italy

This paper presents a novel tunable bandpass filter that exploits plasma conductors within a standard hollow waveguide framework. The operational principle of the filter leverages the reconfigurability of plasma and its tunable properties, allowing dynamic control over the lower frequency boundary. Through the adjustment of plasma frequency, the filter demonstrates remarkable adaptability, shifting the lower cutoff frequency within a range from 1.66 GHz to 1.75 GHz while maintaining a nearly fixed upper cutoff frequency at approximately 2 GHz. Additionally, the paper includes a parametric analysis that investigates the influence of post dimensions on filter performance. This analysis underscores the significance of initial post dimensions in shaping the bandwidth of the filter. Findings affirm the feasibility of this innovative filter concept.

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Secrecy Outage Probability Analysis for Downlink RIS-NOMA Networks with On-Off Control

arXiv

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arXiv 2024年

作者： Pei, Yingjie Yue, Xinwei Yi, Wenqiang Liu, Yuanwei Li, Xuehua Ding, Zhiguo Key Laboratory of Information and Communication Systems Ministry of Information Industry Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical Engineering Princeton University Princeton United States School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

Reconfigurable intelligent surface (RIS) has been regarded as a promising technology since it has ability to create the favorable channel conditions. This paper investigates the secure communications of RIS assisted non-orthogonal multiple access (NOMA) networks, where both external and internal eavesdropping scenarios are taken into consideration. More specifically, novel approximate and asymptotic expressions of secrecy outage probability (SOP) for the k-th legitimate user (LU) are derived by invoking imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC). To characterize the secrecy performance of RIS-NOMA networks, the diversity order of the k-th LU with ipSIC/pSIC is obtained in the high signal-to-noise ratio region. The secrecy system throughput of RIS-NOMA networks is discussed in delay-limited transmission mode. Numerical results are presented to verify theoretical analysis that: i) The SOP of RIS-NOMA networks is superior to that of RIS assisted orthogonal multiple access (OMA) and conventional cooperative communication schemes;ii) As the number of reflecting elements increases, the RIS-NOMA networks are capable of achieving the enhanced secrecy performance;and iii) The RIS-NOMA networks have better secrecy system throughput than that of RIS-OMA networks and conventional cooperative communication schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Cooperative communication

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

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IEEE Transactions on Wireless Communications 2025年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Henan Open University Artificial Intelligence Technology Engineering Research Center Zhengzhou450002 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University School of Information Engineering Zhengzhou450002 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul 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. © 2025 IEEE.

关键词： Green's function

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Zero-DCE With Global information for low-Light Image Enhancement in Coal Mine IoVT

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IEEE Internet of Things Journal 2025年

作者： Tian, Zijian Li, XinLong Zhang, HaiLan Chen, Wei Yang, Wei Wang, Zehua Yu, Fei Richard Leung, Victor C. M. School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Jiangsu Xuzhou221116 China School of Mechanical Electronic and Information Engineering Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering ON OttawaK1S 5B6 Canada

With the rapid advancement of technologies such as artificial intelligence and the Internet of Things, video surveillance—being a core component of video IoT systems—has been widely adopted for underground coal mine safety monitoring. However, the dim lighting and heavy coal dust in underground mines result in poor visibility and significant detail loss in monitoring images, posing a major challenge to coal mine safety management. To address these issues, we propose a low-light image enhancement method tailored for underground coal mine environments, based on *** our method, traditional convolutions are replaced with Ghost modules to reduce computational cost while maintaining feature extraction ***,we incorporate global context blocks and a Vision Transformer branch to integrate more global information into the ***, the global context blocks improve the model’s ability to correct uneven illumination and prevent ***, the Vision Transformer branch captures long-range dependencies and fuses local and global features to enhance brightness while mitigating color ***, we replace the original quadratic iterative function with a reciprocal illumination mapping function,enabling more stable and perceptually aligned brightness *** results on the coal mine underground personnel dataset demonstrate that our method outperforms several state-of-the-art low-light enhancement techniques, achieving superior results in both qualitative and quantitative *** findings indicate that our approach significantly improves the visibility and overall quality of underground coal mine monitoring images. © 2014 IEEE.

关键词： Coal dust

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MBPD: A Robust Algorithm for Polar-Domain Channel Estimation in Near-Field Wideband XL-MIMO Systems

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IEEE Internet of Things Journal 2024年

作者： Wang, Han Guo, Peiqing Li, Xingwang Wen, Fangqing Wang, Xianpeng Nallanathan, Arumugam Yichun University College of Physical Science and Engineering Yichun336000 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Henan Polytechnic University School of Physical and Electronic Information Engieering Jiaozuo454000 China China Three Gorges University College of Computer and Information Technology Yichang443002 China Hainan University School of Information and Communication Engineering Haikou570228 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

In the evolving landscape of wireless communications, extremely large-scale multiple-input multiple-output (XL-MIMO) systems offer promising enhancements in capacity and spectral efficiency, particularly in near-field scenarios. This paper investigates polar-domain channel estimation methods for near-field wideband XL-MIMO systems, proposing a novel approach based on the bilinear pattern detection (BPD) method. We introduce the multi-candidate BPD (MBPD) algorithm, which improves detection accuracy by incorporating adaptive weight matrix adjustments and evaluating multiple candidate modes per iteration. Comprehensive simulations validate the superiority of MBPD over traditional BPD in terms of estimation accuracy and robustness. Furthermore, a detailed complexity analysis demonstrates the computational feasibility of the proposed algorithm. The MBPD algorithm greatly improves polar-domain channel estimation, facilitating more efficient implementations of near-field wideband XL-MIMO systems. © 2024 IEEE.

关键词： Channel estimation

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Machine Learning Techniques Applied to Development of Flexible Electronic Antireflective Film

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Sensors and Materials 2024年第1期36卷 17-23页

作者： Lin, Shih-Hung Wang, Yuan-Ting Wang, Yao-Chin Department of Electronics Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Bachelor of Science and Technology National Chi Nan University 1 University Road Puli Town Nantou County545 Taiwan Department of Computer Science and Information Engineering Cheng Shiu University 840 Chengcing Road Niaosong District Kaohsiung City83347 Taiwan

In this study, we aim to optimize the process for developing antireflective and refractive-index-matching films on flexible substrates. The development of these films is crucial in light of the increasing demand in the market for flexible electronics, which are poised to be the next emerging technology and application after semiconductors and flat panel displays. Our research involves the production of these films by physical vapor deposition and sputtering techniques, which can also be applied to various optical thin films. The effectiveness of our coating process has been verified and refined on the basis of feedback. The results of this study are applicable to related industrial technologies and will contribute to improving industry competitiveness. © 2024 M Y U Scientific Publishing Division. All rights reserved.

关键词： Machine learning

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technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Vader protocol based sentiment analysis technique using LSTM for Stock Trend prediction

Vader protocol based sentiment analysis technique using LSTM...

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International Conference on Computing and Networking technology (ICCNT)

作者： Amrita Ticku Niva Tripathy Pankaj Kumar Mishra Anurag Sinha Shivangi Jadon Aditya Raj Harsh Kulshrestha Vipin Rai Rejuwan Shamim Bharti Vidyapeeth’s College of Engineering India Driems Autonomous Engineering College Cuttack India Department of Electronics and Telecommunication Engg. Rungta College of Engg. & TechnologyR1 Bhilai CG India School of Computing and Information Science IGNOU New Delhi India Dept. of Computer Science and Engineering Galgotias University Noida India Department of Computer Science and Engineering Galgotias University Noida India Dept. of Computer Science and Engineering With Data Science Maharishi University of Information Technology Noida India

Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and deep learning models such as Long Short-Term Memory (LSTM) to predict stock prices. We offer a stock prediction model that integrates sentimental analysis and LSTM in this research. The model considers the mood of stock market news items and use LSTM to forecast future stock prices. The results of our suggested model's evaluation on the S& amp; P 500 dataset reveal that it outperforms traditional stock prediction approaches.

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Performance Analysis of OMA/NOMA-aided Satellite Communication Networks: A Stochastic Geometry Approach

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IEEE Transactions on Communications 2024年

作者： Li, Kecheng Wang, Jun Hou, Tianwei Li, Anna Yue, Xinwei Liu, Yuanwei Chen, Wei School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom KU Leuven Leuven3001 Belgium School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom Key Laboratory of Information and Communication Systems Ministry of Information Industry China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

The increasing quality of service requirements and demand for satellite services necessitate higher data rates, spectral efficiency, and stability in satellite communication networks. Therefore, this paper investigates the non-orthogonal multiple access (NOMA) assisted satellite communication networks, where multiple users are uniformly distributed over a spherical hat according to the homogeneous Poisson point process (HPPP). Based on the characteristics of HPPP, we analyze the distance distributions of users. To evaluate the performance of the proposed networks, we first derive the closed-form expressions and approximated expressions of the outage probability (OP) for paired NOMA users. To obtain more insights into the proposed networks, the ergodic rate and diversity orders for paired NOMA users are also derived. Spectral efficiency is derived for NOMA and orthogonal multiple access (OMA) assisted satellite communication networks. Our analytical results demonstrate that the diversity order of the proposed networks is all one. Numerical results confirm that: 1) compared to OMA, the proposed NOMA-assisted satellite network demonstrates superior outage performance and spectral efficiency, especially in the higher power regimes;2) fading factors have negligible effects on OP and spectral efficiency;and 3) under certain target rates for both near and far users, the outage performance of far users in NOMA-assisted satellite communication networks is superior to that of near users. © 2024 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Satellite communication systems

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