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arXiv 2025年

作者： Zhang, Xu-Lu Yang, Zhen-Qun Jiang, Dong-Mei Liao, Ga Li, Qing Jain, Ramesh Wei, Xiao-Yong College of Computer Science Sichuan University Chengdu610065 China Department of Biomedical Engineering Chinese University of Hong Kong Kowloon Hong Kong Center for Artificial Intelligence Peng Cheng Lab Shenzhen518055 China School of Computer Science Northwestern Polytechnical University Xi’an China State Key Laboratory of Oral Diseases National Clinical Research Center for Oral Diseases West China Hospital of Stomatology Sichuan University Chengdu610041 China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Institute for Future Health University of California IrvineCA United States Donald Bren School of Information and Computer Sciences University of California IrvineCA United States

Eating monitoring has remained an open challenge in medical research for years due to the lack of non-invasive sensors for continuous monitoring and the reliable methods for automatic behavior detection. In this paper, we present a pilot study using the wearable 24-hour ECG for sensing and tailoring the sophisticated deep learning for ad-hoc and interpretable detection. This is accomplished using a collocative learning framework in which 1) we construct collocative tensors as pseudo-images from 1D ECG signals to improve the feasibility of 2D image-based deep models;2) we formulate the cardiac logic of analyzing the ECG data in a comparative way as periodic attention regulators so as to guide the deep inference to collect evidence in a human comprehensible manner;and 3) we improve the interpretability of the framework by enabling the backtracking of evidence with a set of methods designed for Class Activation Mapping (CAM) decoding and decision tree/forest generation. The effectiveness of the proposed framework has been validated on the largest ECG dataset of eating behavior with superior performance over conventional models, and its capacity of cardiac evidence mining has also been verified through the consistency of the evidence it backtracked and that of the previous medical studies. © 2025, CC BY.

关键词： Decision trees

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Spiral-Phased Laguerre-Gaussian Modes Generation in SWDM over Few Mode Fiber based on Electrical Equalization 2

Spiral-Phased Laguerre-Gaussian Modes Generation in SWDM ove...

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2nd Joint International Conference on Emerging computing Technology and Sports, JICETS 2019

作者： Ghazi, Alaan Fareed, Alaa Masunda, Tendai Aljunid, S.A. Idrus, Syed Zulkarnain Syed Amphawan, Angela Al-Dawoodi, Aras Rashidi, C.B.M. Fadhel, Ibrahim Eskandar Ibrahim School of Human Development and Techno-communication Universiti Malaysia Perlis Malaysia School of Computer and Communication Engineering Universiti Malaysia Perlis Malaysia Computer Technical Engineering Alkitab University Iraq School of Computing Universiti Utara Malaysia Malaysia Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA United States Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Department of Computer and Information Systems National Institute of Administrative Science Yemen

Space division multiplexing, in conjunction with wavelength division multiplexing, is a powerful mechanism that increases spatial channels significantly in a single optical fiber. Spiral Phased-Laguerre-Gaussian (SP-LG) modes indicate that they effectively reduce mode coupling, thereby increasing the channel response and performance positively. However, mode-coupling influence optical fiber system leads to inter-symbol interference (ISI) between the channels and reduces both capacity and distance. In this paper, we apply the SP-LG modes in a space wavelength division multiplexing over a few-mode fiber system. The results demonstrate significant improvements in reducing channels effects and ISI. The performance evaluations are based on the bit-error-rate (BER), eye diagram and spectrum analyzer. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Spectrum analyzers

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Graph Regularized L_p Smooth Non-negative Matrix Factorization for Data Representation

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IEEE/CAA Journal of Automatica Sinica 2019年第2期6卷 584-595页

作者： Chengcai Leng Hai Zhang Guorong Cai Irene Cheng Anup Basu the School of Mathematics Northwest University School of Mathematics and Information Sciences Nanchang Hangkong University Nanchang 330063 China the Department of Computing Science University of Alberta the Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines Macao University of Science and Technology the College of Computer Engineering Jimei University the Department of Computing Science University of Alberta

This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods.

关键词： Data clustering dimensionality reduction graph regularization Lp smooth non-negative matrix factorization(SNMF)

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Unity makes strength: Coalition Formation-based Group-buying for Timely UAV Data Collection

Unity makes strength: Coalition Formation-based Group-buying...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nan Qi Yeting Huang Wen Sun Shi Jin Theodoros A. Tsiftsis Qihui Wu Xiang Su National Mobile Communications Research Laboratory Southeast University Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Intelligent Systems Science & Engineering Jinan University Zhuhai China Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Center for Ubiquitous Computing University of Oulu Oulu Finland

ISBN: (纸本)9781665435413

With their high mobility, unmanned aerial vehicles (UAVs) become appealing data collectors in hard-to-reach wide-area distributed sensor networks. Different from existing works focusing on the perspective of UAVs for service order optimization and UAV utility maximization, we consider the utilities of both sensors and UAVs, and innovatively model the competition among sensors (buyers) for the service of UAVs (sellers) as an auction game. A “unity makes strength” strategy is exploited. That is, to strengthen the bidding competitiveness, a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV service is proposed. Besides, we propose a parallel variable neighborhood ascent search algorithm, we can quickly determine the approximately optimal group-buying coalition structure. Numerical results show that the proposed method outperforms the joint trajectory design-task scheduling (TDTS) UAV-to-community method and the single coalition formation game (CFG) method.

关键词： Wireless sensor networks Games Data collection Autonomous aerial vehicles Search problems information age Trajectory

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Wireless Large AI Model: Shaping the AI-Native Future of 6G and Beyond

arXiv

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

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Cross-Domain Dual-Functional OFDM Waveform Design for Accurate Sensing/Positioning

arXiv

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arXiv 2023年

作者： Zhang, Fan Mao, Tianqi Liu, Ruiqi Han, Zhu Chen, Sheng Wang, Zhaocheng Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China The State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China The MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China The Wireless and Computing Research Institute ZTE Corporation Beijing100029 China The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guarantee superior sensing/positioning accuracy while supporting high-speed communications simultaneously, the dual functions tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for effective convergence of OFDM-based communications and sensing/positioning, following communication- and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communications according to prior knowledge of the communication channel. The remaining REs are then employed for sensing/positioning, where the sidelobe level and peak-to-average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics for sensing performance improvement. For the sensing-centric design, a 'locally' perfect auto-correlation property is ensured for accurate sensing and positioning by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed waveform designs. Copyright © 2023, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

arXiv

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arXiv 2023年

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023, CC0.

关键词： Machine learning

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Global-and-Local Collaborative Learning for Co-Salient Object Detection

arXiv

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arXiv 2022年

作者： Cong, Runmin Yang, Ning Li, Chongyi Fu, Huazhu Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong The School of Computer Science and Engineering Nanyang Technological University Singapore A*STAR Singapore The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effectively extract inter-image correspondence is crucial for the CoSOD task. In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives. Firstly, we treat different images as different time slices and use 3D convolution to integrate all intra features intuitively, which can more fully extract the global group semantics. Secondly, we design a pairwise correlation transformation (PCT) to explore similarity correspondence between pairwise images and combine the multiple local pairwise correspondences to generate the local inter-image relationship. Thirdly, the inter-image relationships of the GCM and LCM are integrated through a global-and-local correspondence aggregation (GLA) module to explore more comprehensive inter-image collaboration cues. Finally, the intra- and inter-features are adaptively integrated by an intra-and-inter weighting fusion (AEWF) module to learn co-saliency features and predict the co-saliency map. The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets, demonstrating that our model trained on a small dataset (about 3k images) still outperforms eleven state-of-the-art competitors trained on some large datasets (about 8k̴200k images). © 2022, CC BY-NC-SA.

关键词： Convolution

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Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

arXiv

引用

arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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The Graph Structure of Chebyshev Permutation Polynomials over Ring pk

arXiv

引用

arXiv 2022年

作者： Li, Chengqing Lu, Xiaoxiong Tan, Kai Chen, Guanrong The School of Computer Science Xiangtan University Hunan Xiangtan411105 China The School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China The Key Laboratory of Intelligent Computing & Information Processing Ministry of Education Xiangtan University Hunan Xiangtan411105 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Understanding the underlying graph structure of a nonlinear map over a particular domain is essential in evaluating its potential for real applications. In this paper, we investigate the structure of the associated functional graph of Chebyshev permutation polynomials over a ring pk, with p being a prime number greater than three, where every number in the ring is considered as a vertex and the existing mapping relation between two vertices is regarded as a directed edge. Based on some new properties of Chebyshev polynomials and their derivatives, we disclose how the basic structure of the functional graph evolves with respect to parameter k. First, we present a complete and explicit form of the length of a path starting from any given vertex. Then, we show that the strong patterns of the functional graph that the number of cycles of any given length always remains constant as k increases. Moreover, we rigorously prove the rules on the elegant structure of the functional graph and verify them experimentally. Our results could be useful for studying the emergence of the complexity of a nonlinear map in digital computers and security analysis of its cryptographic *** Codes 94A55 Copyright © 2022, The Authors. All rights reserved.

关键词： Chebyshev polynomials

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