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arXiv 2022年

作者： Théate, Thibaut Ernst, Damien Department of Electrical Engineering and Computer Science University of Liège Liège Belgium Information Processing and Communications Laboratory Institut Polytechnique de Paris Paris France

Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk associated with the actions taken, which may be critical in certain applications. To address that issue, the present research work introduces a novel methodology based on distributional RL to derive sequential decision-making policies that are sensitive to the risk, the latter being modelled by the tail of the return probability distribution. The core idea is to replace the Q function generally standing at the core of learning schemes in RL by another function taking into account both the expected return and the risk. Named the risk-based utility function U, it can be extracted from the random return distribution Z naturally learnt by any distributional RL algorithm. This enables to span the complete potential trade-off between risk minimisation and expected return maximisation, in contrast to fully risk-averse methodologies. Fundamentally, this research yields a truly practical and accessible solution for learning risk-sensitive policies with minimal modification to the distributional RL algorithm, and with an emphasis on the interpretability of the resulting decision-making process. © 2022, CC BY.

关键词： Reinforcement learning

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Coverage analysis and handover strategy in RF/VLC heterogeneous networks

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Applied Optics 2025年第15期64卷 4160-4173页

作者： Feng, Renhai Guo, Yatong Yousaf, Muhammad Zain Khan, Baseem Luo, Liping School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Physics and Electronic Engineering Xinjiang Normal University Ürümqi830054 China Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhejiang Zhuji311816 China School of Electrical and Information Engineering Hubei University of Automotive Technology Shiyan China School of Aeronautics and Astronautics Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Hawassa University Hawassa Ethiopia School of Electronics and Information Guangxi Minzu University Nanning530006 China Guangxi Colleges Universities Engineering Research Center for Multi-modal Information Intelligent Sensing Processing and Application Nanning530006 China

Coverage analysis in heterogeneous networks (HetNets) remains insufficient, especially under user mobility and diverse association strategies. To address this, this paper proposes a contact probability and coverage approximation based on association strategy (CPCA-AS) to analyze the coverage probability (CPr) in visible light communication (VLC) and radio frequency (RF) HetNets. Based on the proposed stochastic geometry framework, CPCA-AS evaluates coverage performance under various configurations, including RF-only, VLC-only, and hybrid RF/VLC scenarios. The model incorporates a contact probability mechanism and considers two strategies: opportunistic VLC and maximum received signal power (MRSP). For VLC networks, CPCA-AS utilizes the Gil-Pelaez inversion theorem, and Laplace transforms to efficiently approximate CPr. For RF networks, Markov and Gamma inequalities form conservative accurate approximations and lower bounds. CPCA-AS effectively captures the influence of the photodetector’s field-of-view (FoV) on interference and association behavior. Monte Carlo simulations validate the accuracy of the CPCA-AS. Results demonstrate that CPCA-AS improves CPr. The opportunistic VLC strategy performs better in high-interference and large FoV scenarios. In contrast, the MRSP strategy is more effective in low-interference and low-rate environments. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Markov processes

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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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Exploration of Optimizing FPGA-based Qubit Controller for Experiments on Superconducting Quantum Computing Hardware

Exploration of Optimizing FPGA-based Qubit Controller for Ex...

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IEEE International Conference on Electro-Information Technology

作者： Hans Johnson Silvia Zorzetti Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A. Superconducting Quantum Materials and Systems Center (SQMS) Fermi National Accelerator Laboratory Batavia IL U.S.A.

This work explores avenues and target areas for optimizing FPGA-based control hardware for experiments conducted on superconducting quantum computing systems and serves as an introduction to some of the current research at the intersection of classical and quantum computing hardware. With the promise of building larger-scale error-corrected quantum computers based on superconducting qubit architecture, innovations to room-temperature control electronics are needed to bring these quantum realizations to fruition. The QICK (Quantum Instrumentation Control Kit) is one leading experimental FPGA-based implementations. However, its integration into other experimental quantum computing architectures, especially those using superconducting radiofrequency (SRF) cavities, is largely unexplored. We identify some key target areas for optimizing control electronics for superconducting qubit architectures and provide some preliminary results to the resolution of a control pulse waveform. With optimizations targeted at 3D superconducting qubit setups, we hope to bring to light some of the requirements in classical computational methodologies to bring out the full potential of this quantum computing architecture, and to convey the excitement of progress in this research.

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Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware

Improving Signal-to-Noise Ratio (SNR) for Readout Signals Us...

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Hans Johnson Silvia Zorzetti Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A. Fermi National Accelerator Laboratory Superconducting Quantum Materials and Systems Center (SQMS) Batavia IL U.S.A.

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.

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Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

arXiv

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arXiv 2023年

作者： Xu, Minrui Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Jamalipour, Abbas Kim, Dong In Shen, Xuemin Leung, Victor C.M. Poor, H. Vincent 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 Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States 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 Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia 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 The College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks. © 2023, CC BY.

关键词： Life cycle

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Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detection

arXiv

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arXiv 2024年

作者： Xiao, Chunjing Pang, Shikang Xu, Xovee Li, Xuan Trajcevski, Goce Zhou, Fan The School of Computer and Information Engineering Henan University China The Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China The University of Electronic Science and Technology of China Sichuan Chengdu610054 China The National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu610065 China The Department of Electrical and Computer Engineering Iowa State University AmesIA50011 United States

A critical aspect of Graph Neural Networks (GNNs) is to enhance the node representations by aggregating node neighborhood information. However, when detecting anomalies, the representations of abnormal nodes are prone to be averaged by normal neighbors, making the learned anomaly representations less distinguishable. To tackle this issue, we propose CAGAD - an unsupervised Counterfactual data Augmentation method for Graph Anomaly Detection - which introduces a graph pointer neural network as the heterophilic node detector to identify potential anomalies whose neighborhoods are normal-node-dominant. For each identified potential anomaly, we design a graph-specific diffusion model to translate a part of its neighbors, which are probably normal, into anomalous ones. At last, we involve these translated neighbors in GNN neighborhood aggregation to produce counterfactual representations of anomalies. Through aggregating the translated anomalous neighbors, counterfactual representations become more distinguishable and further advocate detection performance. The experimental results on four datasets demonstrate that CAGAD significantly outperforms strong baselines, with an average improvement of 2.35% on F1, 2.53% on AUC-ROC, and 2.79% on AUC-PR. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Deep Learning in Palmprint Recognition-A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

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Editorial

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2024年第3期6卷 289-289页

作者： Liao, Shengcai Liu, Xiaoming Tistarelli, Massimo Chellappa, Rama Kittler, Josef United Arab Emirates University College of Information Technology Al Ain United Arab Emirates Michigan State University Department of Computer Science and Engineering East LansingMI48824 United States University of Sassari Computer Vision Laboratory Sassari07100 Italy Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218 United States Johns Hopkins University Department of Biomedical Engineering BaltimoreMD21218 United States University of Surrey Centre for Vision Speech and Signal Processing GuildfordGU2 7XH United Kingdom

The 2022 International Joint Conference on Biometrics (IJCB 2022) was held at Abu Dhabi, United Arab Emirates, from October 10 to October 13, 2022. This marked the first biometrics conference held in the Middle East. The IJCB 2022 conference combined two major biometrics conferences: the IEEE Biometrics Theory, Applications, and Systems (BTAS) conference and the International Conference on Biometrics (ICB). The IJCB 2022 conference was technically co-sponsored by both IAPR TC4 (Technical Committee on Biometrics) and the IEEE Biometrics Council. © 2019 IEEE.

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Evaluating edge artificial intelligence-driven supply chain management platforms using collaborative large-scale fuzzy information fusion

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Applied Soft Computing 2024年 159卷

作者： Zhang, Chao Zhang, Jingjing Sangaiah, Arun Kumar Li, Deyu Li, Wentao School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan Shanxi030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon College of Artificial Intelligence Southwest University Chongqing400715 China

The rapid development of edge artificial intelligence (AI) has brought about revolutionary changes in supply chain management (SCM). It not only provides real-time data processing capabilities but also accelerates the decision-making process, injecting more innovative elements into SCM. In this context, SCM platforms require new technological developments and effective evaluations, as they collectively drive the efficient coordination of complex business processes. SCM platforms enable the seamless coordination of various supply chain elements, facilitating streamlined operations and enhanced decision-making processes. The evaluation of these platforms not only serves to validate their performance and effectiveness, but also contributes to continual improvements in design and application, and addressing the evolving demands within the realms of global commerce and dynamic market conditions. The ongoing assessment and enhancement of SCM platforms play a crucial role in optimizing resource allocation, improving production efficiency, and fostering adaptability to changing market dynamics. However, the acquisition of assessment data often introduces imprecise data. Additionally, the large-scale nature and bounded rationality of decision-makers (DMs) significantly impact the evaluation of SCM platforms. This article aims to explore a collaborative large-scale information fusion approach and provide a robust fuzzy framework for evaluating SCM platforms. This approach employs a combination of spherical fuzzy sets (SFSs), large-scale group decision-making (LSGDM), behavioral theories and three-way decisions (TWD) to thoroughly explore collaborative large-scale information fusion and its practical application in assessing SCM platforms. It introduces an innovative spherical fuzzy (SF) LSGDM technique and integrates TWD with the inclusion of prospect theory (PT) and regret theory (RT) to mitigate potential decision risks. The developed collaborative large-scale information f

关键词： Supply chain management

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