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Beamforming Design Towards Sum-Rate Maximization for Holographic Active RIS-Aided Uplink Near-Field Communications

IEEE Internet of Things Journal 2025年第12期12卷 18582-18595页

作者： Singh, Sandeep Singh, Keshav Singh, Sandeep Kumar Shin, Hyundong Duong, Trung Q. Kaohsiung80424 Taiwan National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Motilal Nehru National Institute of Technology Allahabad Department of Electronics and Communication Engineering Prayagraj India Kyung Hee University Department of Electronics and Information Convergence Engineering Deogyeong-daero Gyeonggi-do Yongin-si1732 Korea Republic of Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

Holographically-driven active reconfigurable intelligent surface (HARIS), leveraging densely packed sub-wavelength elements, overcomes the limitations of conventional RIS in signal processing, unlocking advanced capabilities for next-generation networks. Thus, to exploit its full potential, this work proposes the integration of HARIS into an Internet of Things (IoT) multi-user uplink near-field-driven wireless communication system. A sum-rate maximization problem is formulated to provide efficient resource utilization by jointly optimizing the equalizer design, power allocation at each IoT user, and the HARIS phase shift, while satisfying strict constraints of quality of service (QoS) requirement and limited power budget at each IoT user and HARIS. Due to the non-convex nature of the problem, we propose an alternating optimization (AO)-based algorithm, incorporating techniques such as minimum mean square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR). Then, extensive simulations validate the algorithm's efficacy and convergence, demonstrating up to 63% higher performance with HARIS than passive RIS. Additionally, we highlight that near-field communication yields up to 90% higher sum-rate than hybrid 76% and far-field model 73%. Moreover, we demonstrate the impact of imperfect channel state information (iCSI) on the system performance. © 2014 IEEE.

关键词： Mean square error

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EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection

arXiv

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arXiv 2023年

作者： Li, Xiang Lan, Long Lahza, Husam Yang, Shaowu Wang, Shuihua Yang, Wenjing Liu, Hengzhu Zhang, Yudong College of Computer Science and Technology National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Biological Sciences Xi'an Jiaotong-Liverpool University Jiangsu Suzhou215123 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing210096 China Department of Mathematical Sciences University of Liverpool LiverpoolL69 3BX United Kingdom

In the face of rapid advances in medical imaging, cross-domain adaptive medical image detection is challenging due to the differences in lesion representations across various medical imaging technologies. To address this issue, we draw inspiration from large language models to propose EAFP-Med, an efficient adaptive feature processing module based on prompts for medical image detection. EAFP-Med can efficiently extract lesion features of different scales from a diverse range of medical images based on prompts while being flexible and not limited by specific imaging techniques. Furthermore, it serves as a feature preprocessing module that can be connected to any model front-end to enhance the lesion features in input images. Moreover, we propose a novel adaptive disease detection model named EAFP-Med ST, which utilizes the Swin Transformer V2 – Tiny (SwinV2-T) as its backbone and connects it to EAFP-Med. We have compared our method to nine state-of-the-art methods. Experimental results demonstrate that EAFP-Med ST achieves the best performance on all three datasets (chest X-ray images, cranial magnetic resonance imaging images, and skin images). EAFP-Med can efficiently extract lesion features from various medical images based on prompts, enhancing the model's performance. This holds significant potential for improving medical image analysis and diagnosis. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Multiple Access Techniques for Intelligent and Multi-Functional 6G: Tutorial, Survey, and Outlook

arXiv

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arXiv 2024年

作者： Clerckx, Bruno Mao, Yijie Yang, Zhaohui Chen, Mingzhe Alkhateeb, Ahmed Liu, Liang Qiu, Min Yuan, Jinhong Wong, Vincent W.S. Montojo, Juan The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The School of Information Science and Technology ShanghaiTech University Shanghai201210 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Corporate R&D Qualcomm Inc. San DiegoCA United States

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, message, etc) to serve multiple users/devices/machines/services, ideally in the most efficient way. Given the increasing needs of multi-functional wireless networks for integrated communications, sensing, localization, computing, coupled with the surge of machine learning / artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this paper, we provide a tutorial, survey and outlook of past, emerging and future MA techniques and pay a particular attention to how wireless network intelligence and multi-functionality will lead to a re-thinking of those techniques. The paper starts with an overview of orthogonal, physical layer multicasting, space domain, power domain, rate-splitting, code domain MAs, and MAs in other domains, and highlight the importance of researching universal multiple access to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, receiver designs, user behavior predictions for different MA schemes, and MA for AI such as federated learning/edge intelligence and over the air computation. We then discuss MA for network multi-functionality and the interplay between MA and integrated sensing, localization, and communications, covering MA for joint sensing and communications, multimodal sensing-aided communications, multimodal sensing and digital twin-assisted communications, and communication-aided sensing/localization systems. We finish with studying MA for emerging intelligent applications such as semantic communications, metaverse, virtual realit

关键词： Wireless networks

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Correction of slanted text pictures based on modified opencv

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第2期6卷 238-246页

作者： Lu, Ran Xu, Yongjin Wu, Zhanhe Zheng, Yijie Ren, Lingxin Ding, Feifei Li, Jianjun Chang, Chin-Chen State Grid Zhejiang Marketing Service Center Hangzhou311100 China School of Coomputer Science and Technology Hangzhou Dianzi University No.1 of the 2nd street XiaSha Hangzhou310018 China Department of Information and Engineering Computer Science Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung40724 Taiwan

Correction of slanted or tilted text is very important in many applications, such as optical character recognition (OCR), text detection and recognition. It is not difficult to find that the recognition accuracy of OCR has a great relationship with the tilt angle of the recognized picture, resulting in reduced OCR adaptability and great limitations. As for the problem, we proposed an innovative optimization to estimate the angle of tilting texts in a picture based on Hough transform and modified OpenCV, which reduces the negative effect of background noises. Our algorithm outperforms the state-of-the-art methods in both performances and robustness. © 2021 Global Research Online. All rights reserved.

关键词： Optical character recognition

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A Novel Human-Based Meta-Heuristic Algorithm: Dragon Boat Optimization

arXiv

引用

arXiv 2023年

作者： Li, Xiang Lan, Long Lahza, Husam Yang, Shaowu Wang, Shuihua Yang, Wenjing Liu, Hengzhu Zhang, Yudong College of Computer Science and Technology National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Biological Sciences Xi’an Jiaotong-Liverpool University Jiangsu Suzhou215123 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing210096 China Department of Mathematical Sciences University of Liverpool LiverpoolL69 3BX United Kingdom

(Aim) Dragon Boat Racing, a popular aquatic folklore team sport, is traditionally held during the Dragon Boat Festival. Inspired by this event, we propose a novel human-based meta-heuristic algorithm called dragon boat optimization (DBO) in this paper. (Method) It models the unique behaviors of each crew member on the dragon boat during the race by introducing social psychology mechanisms (social loafing, social incentive). Throughout this process, the focus is on the interaction and collaboration among the crew members, as well as their decision-making in different situations. During each iteration, DBO implements different state updating strategies. By modelling the crew's behavior and adjusting the state updating strategies, DBO is able to maintain high-performance efficiency. (Results) We have tested the DBO algorithm with 29 mathematical optimization problems and 2 structural design problems. (Conclusion) The experimental results demonstrate that DBO is competitive with state-of-the-art meta-heuristic algorithms as well as conventional methods. © 2023, CC BY.

关键词： Structural optimization

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Mutual information Rate of Gaussian and Truncated Gaussian Inputs on Intensity-Driven Signal Transduction Channels

arXiv

引用

arXiv 2023年

作者： Chen, Xuan Ji, Fei Wen, Miaowen Huang, Yu Eckford, Andrew W. The Research Center of Intelligent Communication Engineering School of Electronics and Communication Engineering Guangzhou University Guangzhou510006 China The School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China The Department of Electrical Engineering and Computer Science York University TorontoM3J 1P3 Canada

In this letter, we investigate the mutual information rate (MIR) achieved by an independent identically distributed (IID) Gaussian input on the intensity-driven signal transduction channel. Specifically, the asymptotic expression of the continuous-time MIR is given. Next, aiming at low computational complexity, we also deduce an approximately numerical solution for this MIR. Moreover, the corresponding lower and upper bounds that can be used to find the capacity-achieving input distribution parameters are derived in closed-form. Finally, simulation results show the accuracy of our analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal transduction

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Detection of oral cancer lesions using hybrid classifier

引用

AIP Conference Proceedings 2023年第1期2591卷

作者： K. Karpagavadivu C. R. Rathish P. Sindhuja A. Kousalya 1Department of Computer Science Engineering Institute of Technology Coimbatore India 2Department of Computer Engineering New Horizon College of Engineering Bengaluru India 3Department of Computer Science Engineering United Institute of Technology Coimbatore India 4Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore India

Oral cancer is a main worldwide health problem accounting for 606,520 deaths in 2020, and it is most predominant in the middle-and low-income nations. Permitting computerization in identifying potentially malignant and malignant lesions in the oral cavity would possibly result in low-expense and early detection of the disease. The most important purpose of this research is to find the Oral Cancer Lesions affected region in the tongue images. The current work utilized the GVF algorithm to detect Oral Cancer Lesions using features involved in tongue images. This article offers a novel approach to merging bounding box annotations from different medical practitioners. Additionally, gradient vector flow was used to segment images, where the complicated patterns have been obtained for tackling this difficult task. Using the initial data gathered in this study, a hybrid classifier algorithm was assessed to detect Oral cancer lesions, and features like colour, texture and geometry were extracted. BioMed Chinese Medicine Repository collects the tongue images. Additionally, performances are described categorizing as per the kind of referral decision. Our initial findings establish support vector machine has the probability of challenging this stimulating task.

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Optics for terawatt-scale photovoltaics: review and perspectives

引用

Advances in Optics and Photonics 2025年第1期17卷 185-294页

作者： Jäger, Klaus Aeberhard, Urs Llado, Esther Alarcon Bläsi, Benedikt Burger, Sven Ehrler, Bruno Favre, Wilfried Fejfar, Antonín Gageot, Tristan Gordon, Ivan Helmers, Henning Höhn, Oliver Isabella, Olindo Jošt, Marko Ledinský, Martin Mandal, Jyotirmoy Manley, Phillip Muñoz, Delfina Omair, Zunaid Lizcano, Juan Camilo Ortiz Paetzold, Ulrich W. Raman, Aaswath P. Sai, Hitoshi Saive, Rebecca Schmid, Martina Yablonovitch, Eli Becker, Christiane Department Optics for Solar Energy Helmholtz-Zentrum Berlin für Materialien und Energie Kekuléstraße 5 Berlin12489 Germany Computational Nano Optics Zuse Institute Berlin Takustraße 7 Berlin14195 Germany Fluxim AG Katharina-Sulzer-Platz 2 Winterthur8400 Switzerland LMPV-Sustainable Energy Materials Department AMOLF Science Park 104 Amsterdam1098 XG Netherlands Fraunhofer Institute for Solar Energy Systems ISE Heidenhofstraße 2 Freiburg79100 Germany JCMwave GmbH Bolivarallee 22 Berlin14050 Germany Université Grenoble Alpes CEA LITEN Campus INES 50 avenue du Lac Léman Le Bourget-du-Lac73375 France FZU - Institute of Physics The Czech Academy of Sciences Na Slovance 1999/2 Prague 8182 00 Czech Republic IMEC/Energyville Thor Park 8320 Genk3600 Belgium Hasselt University Martelarenlaan 42 Hasselt3500 Belgium Photovoltaic Materials and Devices Laboratory Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands University of Freiburg Emmy-Noether-Str.2 Freiburg79110 Germany Faculty of Electrical Engineering University of Ljubljana Tržaška 25 Ljubljana1000 Slovenia Department of Civil and Environmental Engineering Princeton University E209A Engineering Quadrangle PrincetonNJ08544 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Institute of Microstructure Technology Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany Light Technology Institute Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Materials Science and Engineering University of California Los AngelesCA90095 United States Ibaraki Tsukuba305-8568 Japan MESA+ Institute for Nanotechnology Inorganic Materials Science University of Twente NB Enschede7522 Netherlands University of Duisburg-Essen Forsthausweg 2 Duisburg47057 Germany Faculty 1 School of Engineering – Energy and Information Hochschule für Technik und Wirtschaft Berlin

In this section, we described the various device architectures in production for crystalline-silicon PV as well as the various absorber materials being used and developed for thin-film PV. We also explained why multijunction PV technology is considered the future of PV technology. We identified four major technological challenges (Fig. 1) for future mass production and deployment of PV technology in order to achieve the goal of climate neutrality and indicated how optics could help resolve these challenges. The four main challenges (Fig. 1) are: • Sustainable production of PV systems;• Higher energy conversion efficiencies;• Higher energy output and operational lifetime;• Integration of PV systems in their environment. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Energy conversion efficiency

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Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching

Attention-Weighted Federated Deep Reinforcement Learning for...

引用

作者： Wang, Xiaofei Li, Ruibin Wang, Chenyang Li, Xiuhua Taleb, Tarik Leung, Victor C. M. College of Intelligence and Computing Tianjin University Tianjin China Ministry of Education China Department of Communications and Networking School of Electrical Engineering Aalto University Espoo Finland College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Information Technology and Electrical Engineering Oulu University Oulu90570 Finland Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic. © 1983-2012 IEEE.

关键词： Reinforcement learning

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Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis

arXiv

引用

arXiv 2024年

作者： Ren, Yuemei Liao, Liang Maybank, Stephen John Zhang, Yanning Liu, Xin School of Electronics and Information Zhongyuan University of Technology Henan Zhengzhou450007 China Birkbeck College University of London LondonWC1E 7HX United Kingdom Department of Electronic Information Engineering Henan Polytechnic Institute Nanyang473000 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China Information Center Yellow River Conservancy Commission Zhengzhou450000 China

This paper addresses the challenge of spectral-spatial feature extraction for hyperspectral image classification by introducing a novel tensor-based framework. The proposed approach incorporates circular convolution into a tensor structure to effectively capture and integrate both spectral and spatial information. Building upon this framework, the traditional Principal Component Analysis (PCA) technique is extended to its tensor-based counterpart, referred to as Tensor Principal Component Analysis (TPCA). The proposed TPCA method leverages the inherent multi-dimensional structure of hyperspectral data, thereby enabling more effective feature representation. Experimental results on benchmark hyperspectral datasets demonstrate that classification models using TPCA features consistently outperform those using traditional PCA and other state-of-the-art techniques. These findings highlight the potential of the tensor-based framework in advancing hyperspectral image analysis. © 2024, CC BY.

关键词： Principal component analysis

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