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arXiv 2023年

作者： Li, Gongyang Bai, Zhen Liu, Zhi Zhang, Xinpeng Ling, Haibin Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of Communication and Information Engineering Shanghai University Shanghai200444 China Wenzhou Institute of Shanghai University Wenzhou325000 China The Department of Computer Science Stony Brook University Stony BrookNY11794 United States

Existing methods for Salient Object Detection in Optical Remote Sensing Images (ORSI-SOD) mainly adopt Convolutional Neural networks (CNNs) as the backbone, such as VGG and ResNet. Since CNNs can only extract features within certain receptive fields, most ORSI-SOD methods generally follow the local-to-contextual paradigm. In this paper, we propose a novel Global Extraction Local Exploration Network (GeleNet) for ORSI-SOD following the global-to-local paradigm. Specifically, GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies. Then, GeleNet employs a Direction-aware Shuffle Weighted Spatial Attention Module (D-SWSAM) and its simplified version (SWSAM) to enhance local interactions, and a Knowledge Transfer Module (KTM) to further enhance cross-level contextual interactions. D-SWSAM comprehensively perceives the orientation information in the lowest-level features through directional convolutions to adapt to various orientations of salient objects in ORSIs, and effectively enhances the details of salient objects with an improved attention mechanism. SWSAM discards the direction-aware part of D-SWSAM to focus on localizing salient objects in the highest-level features. KTM models the contextual correlation knowledge of two middle-level features of different scales based on the self-attention mechanism, and transfers the knowledge to the raw features to generate more discriminative features. Finally, a saliency predictor is used to generate the saliency map based on the outputs of the above three modules. Extensive experiments on three public datasets demonstrate that the proposed GeleNet outperforms relevant state-of-the-art methods. The code and results of our method are available at https://***/MathLee/GeleNet. © 2023, CC BY-NC-ND.

关键词： Convolution

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Comparative Study of Quantum and Classical Error Correction for Future Quantum-6G networks

TechRxiv

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TechRxiv 2023年

作者： Das, Siddharth Bassoli, Riccardo Ferrara, Roberto Nötzel, Janis Deppe, Christian Fitzek, Frank H.P. Boche, Holger The Deutsche Telekom Chair of Communication Networks Institute of Communication Technology Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden Germany Cluster of Excellence Dresden Germany research group Technische Universität Dresden Dresden Germany The Department of Electrical and Computer Engineering Technische Universität München München Germany München Germany Ruhr University Bochum Bochum44801 Germany

The research on 6G communication networks has started. Currently, the proposed architectures envision the realisation of a fully softwarized network continuum, with in-network intelligence for network management and operations. Moreover, more stringent requirements than 5G have been set in order to support new use cases like telepresence and massive twinning. However, these requirements implies some critical tradeoffs, which are going to limit the promises of 6G. In order to go beyond the intrinsic limitations of 6G, new resources and technologies should be found. That is why, some research groups are now investigating how quantum technologies can be integrated into 6G. This works deals with the scenario of sending classical information via a quantum channel, using the so-called dense coding protocol. If this channel is not ideal, quantum errors can affect the reliability of the communication. Nevertheless, quantum error-correcting codes are still very demanding in terms of resources and redundancy. Then, this work investigates how classical error-correcting codes (in particular, Reed-Solomon codes and turbo codes) can be applied to make reliable classical communications via quantum channels. The preliminary results of this paper show that the employment of classical forward error correction in future hybrid 6G-quantum networks can be very promising. © 2023, CC BY.

关键词： Reed-Solomon codes

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Efficient Model Training in Edge networks with Hierarchical Split Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Songge Wu, Wen Song, Lingyang Shen, Xuemin Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen518055 China Pengcheng Laboratory Frontier Research Center Shenzhen518055 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Xiangtan University Hunan Institute of Advanced Sensing and Information Technology Xiangtan China University of Waterloo Department of Electrical and Computer Engineering WaterlooN2L 3G1 Canada

In this paper, we propose an efficient model training scheme, named Group-based Hierarchical Split Learning (GHSL), which can accelerate the artificial intelligence (AI) training process in edge networks in a "first-sequential-then-parallel" manner. Specifically, the proposed scheme hierarchically splits an AI model into a user-side and server-side model, while dividing a number of users into multiple groups. Users in each group train user-side models with the interaction of the shared server-side model sequentially;different groups perform the above training process parallelly;the AI models of each group are aggregated into a global model. We also carry out the convergence analysis for the proposed scheme over non-independent and identically distributed data, which reveals that the convergence rate depends on user grouping. Furthermore, we propose a data-driven two-stage user grouping algorithm to minimize the overall training delay, taking user resource heterogeneity and the black-box training process into account. The proposed algorithm first utilizes the Gaussian process regression approach to determine the number of groups, and then employs the coalition game theory to determine the optimal user grouping decision. Comprehensive simulation results demonstrate that the proposed scheme can reduce training delay, user-side computational workload, and communication overhead by up to 19%, 53%, and 54%, respectively, comparing to state-of-the-art benchmarks. © 2002-2012 IEEE.

关键词： Game theory

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

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

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

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

ISBN: (纸本)9798350302493

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

关键词： Optical resolving power

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Kerr effect in ultra-compact hybrid plasmonic metal-insulator-metal nano-focusing structure

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Chinese Optics Letters 2022年第3期20卷 106-110页

作者： Kejian Zhu Pengfei Sun F|engfei Xu Xingpeng Liu Tangyou Sun Haiou Li Zhiping Zhou State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic TechnologyGuilin 541004China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China

Nano-focusing structures based on hybrid plasmonic waveguides are likely to play a key role in strong nonlinear optical *** the insertion loss is considerable,a significant nonlinear phase shift may be achieved by decreasing the nano-focusing device footprint and careful parameter ***,we study the Kerr effect in hybrid plasmonic waveguides by analyzing the mode effective area,energy velocity,and insertion ***,by utilizing plasmonics to manipulate the effective index and mode similarity,the TM mode is reflected and absorbed,while the TE mode passes through with relatively low propagation *** providing a deep understanding of hybrid plasmonic waveguides for nonlinear applications,we indicate pathways for their future optimization.

关键词： silicon photonics plasmonics nonlinear optics nano-focusing

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Augmented V2V Radio Fingerprinting With Heterogeneous Federated Scattering Network for Autonomous Vehicle Road Cooperation

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Tiantian Xu, Dongyang Ma, Jing Alharbi, Abdullah Yu, Keping Guizani, Mohsen Leung, Victor C.M Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xi'an Jiaotong University School of Information and Communication Engineering Xi'an710049 China Xidian University State Key Laboratory of Integrated Services Networks China King Saud University Computer Science Department Community College Riyadh11437 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan 999041 United Arab Emirates Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Augmented Internet of vehicle (IoV) network, providing natural infrastructure support for autonomous vehicle road cooperation (VRC), has been deemed as one of key enablers for autonomous VRC while simultaneously facing the challenge of impersonation, tampering or replay attack towards the high layer digital certificate for critical information. In this paper, we propose a heterogeneous scattering fingerprinting network (HSFNet) which amalgamates wavelet scattering and deep learning to achieve lightweight, rapid and reliable physical layer authentication by radio frequency fingerprinting (RFF) which employs the unique inherent hardware impairments within for vehicle-to everything (V2X) communication. Particularly, we first exploit a wavelet scattering network to extract RFF coefficients and eliminate redundancies within heterogeneous datasets. The establishment of a high-information-density datasets facilitates the efficient processing of extensive heterogeneous data, especially for resource-limited vehicles nodes. To improve the interpretability, we combine the multi-scale coefficients of input waveform and shapley values, formulating a complete interpretable framework and completing parameters tuning. Finally, considering heterogeneous applications in practical scenarios, we design a distributed federated learning framework which not only completes distributed training but also protects privacy with the resources-constrained intelligent IoV nodes. Experimental results demonstrate that the proposed scheme maintains excellent performance while utilizing only about 25.7% of original samples, significantly improving identification accuracy. Furthermore, the identification accuracy of federated learning ultimately converges to 98.1%. © 1967-2012 IEEE.

关键词： Deep learning

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Simplified Framework for Diagnosis Brain Disease Using Functional Connectivity

Simplified Framework for Diagnosis Brain Disease Using Funct...

引用

communication & Computing (WCONF), World Conference on

作者： T. Swarnalatha B. Supraja Akhila Akula Raaid Alubady K Saikumar Patakottu Prasadareddy Department of CSE PBR Visvodaya Institute Of Technology And Science Kavali Department of CSE GNITC Hyderabad Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad Telangana India Technical Engineering College Al-Ayen University Thi-Qar Iraq College of Information Networks University of Babylon Babylon Iraq Computer Science Malla Reddy University Malla Reddy University School of Engineering Maisammaguda Dulapally Hyderabad Telangana Dept. of ECE Student MVGR College of Engineering Vijayaram Nagar campus Chintalavalasa Vizianagaram Andhra Pradesh

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Diagnosing brain infections is another region where artificial intelligence and neuroscience research meet. Functional connectivity (FC) examination has turned into the standard method for checking out fMRI information. The quantitative investigation of functional magnetic resonance imaging (fMRI) information might uncover significant variables that assistance in clinical assessment. Then again, the greater part of the review done in the past has zeroed in on placing mind sicknesses into bunches in light of low-request FC characteristics and has disregarded the conceivable significance of high-request utilitarian connections between cerebrum regions. To protect this issue, physicists formulated an unusual MFC a multi-level fusion classification paradigm for recognizing mental disorders. To begin, they built a model using a deep neural network (DNN) to collect and formulate vital fundamental portrayals for the depressed-request and extreme-request FC designs we fashioned. During the preparation of the DNN model, there were both uncontrolled and directed learning steps. During the managed calibrating, model learning was added to make the element portrayal more reduced inside each class and simpler to distinguish from different classes. Then, at that point, to characterize mind illnesses, we consolidated the theoretical staggered FC highlights we had learned and utilized various leveled stacking learning techniques to prepare a troupe classifier. Deliberate tests were finished with two genuine, enormous-scope fMRI datasets. The recommended MFC model worked effectively in ordering information for various preprocessing processes, different mind parcellations, and different cross-approval techniques, which shows that it works and can be utilized in a wide range of circumstances. By and large, this work gives an effective method for consolidating significant low-request and high-request FC examples to make it more straightforward to order mind sicknesses.

关键词： Neuroscience Reviews Mental disorders Stacking Cerebrum Artificial neural networks Functional magnetic resonance imaging Standards Medical diagnostic imaging Diseases

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Scheduling of wireless edge networks for feedback-based interactive applications

arXiv

引用

arXiv 2021年

作者： Zoppi, Samuele Champati, Jaya Prakash Gross, James Kellerer, Wolfgang Department of Communication Networks Technical University of Munich Germany IMDEA Networks Institute Spain School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Interactive applications with automated feedback will largely influence the design of future networked infrastructures. In such applications, status information about an environment of interest is captured and forwarded to a compute node, which analyzes the information and generates a feedback message. Timely processing and forwarding must ensure the feedback information to be still applicable;thus, the quality-of-service parameter for such applications is the end-to-end latency over the entire loop. By modelling the communication of a feedback loop as a two-hop network, we address the problem of allocating network resources in order to minimize the delay violation probability (DVP), i.e. the probability of the end-to-end latency exceeding a target value. We investigate the influence of the network queue states along the network path on the performance of semi-static and dynamic scheduling policies. The former determine the schedule prior to the transmission of the packet, while the latter benefit from feedback on the queue states as time evolves and reallocate time slots depending on the queue's evolution. The performance of the proposed policies is evaluated for variations in several system parameters and comparison baselines. Results show that the proposed semi-static policy achieves close-to-optimal DVP and the dynamic policy outperforms the state-of-the-art algorithms. © 2021, CC BY.

关键词： Interactive devices

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Silicon photonics for high-capacity data communications

引用

Photonics Research 2022年第9期10卷 I0002-I0030页

作者： YAOCHENG SHI YONG ZHANG YATING WAN YU YU YUGUANG ZHANG XIAO HU XI XIAO HONGNAN XU LONG ZHANG BINGCHENG PAN Centre for Optical and Electromagnetic Research State Key Laboratory for Modern Optical InstrumentationCollege of Optical Science and EngineeringZhejiang UniversityZijingang CampusHangzhou 310058China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Institute for Energy Efficiency University of California Santa BarbaraSanta BarbaraCalifornia 93106USA Electrical and Computer Engineering Department King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Wuhan National Laboratory for Optoelectronics&School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China National Information Optoelectronics Innovation Center China Information and Communication Technologies Group Corporation(CICT)Wuhan 430074China State Key Laboratory of Optical Communication Technologies and Networks China Information and Communication Technologies Group Corporation(CICT)Wuhan 430074China Department of Electronic Engineering The Chinese University of Hong KongShatinNew TerritoriesHong KongChina

In recent years,optical modulators,photodetectors,(de)multiplexers,and heterogeneously integrated lasers based on silicon optical platforms have been *** performance of some devices even surpasses the traditional III-V and photonic integrated circuit(PIC)platforms,laying the foundation for large-scale photonic *** photonic technology can overcome the limitations of traditional transceiver technology in high-speed transmission networks to support faster interconnection between data *** this article,we will review recent progress for silicon *** first part gives an overview of recent achievements in silicon *** second part introduces the silicon photonic building blocks,including low-loss waveguides,passive devices,modulators,photodetectors,heterogeneously integrated lasers,and so *** the third part,the recent progress on high-capacity silicon photonic transceivers is *** the fourth part,we give a review of high-capacity silicon photonic networks on chip.

关键词： interconnection passive networks

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Finger Knuckle Print classification: Leveraging vision Mamba for low complexity and high accuracy

Franklin Open

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Franklin Open 2025年 11卷

作者： Bang, Chiron Altaher, Ali Salem Altaher, Ahmed Moamin, Hawraa Alshammari, Thamer Alkanjr, Basmh Altaher, Hasan Al-Jassani, Mohammed G. Zhuang, Hanqi Department of Electrical Engineering and Computer Science Florida Atlantic University 777 Glades Road Boca Raton 33431 FL United States College of Medicine Ibn Sina University of Medical and Pharmaceutical Science Baghdad Iraq Electronic Computer Center Al-Nahrain University Jadriya Baghdad Iraq Ministry of Education Baghdad Iraq Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Al-Jawf Saudi Arabia Department of Computer Science College of Computer and Information Sciences Jouf University Al-Jawf Saudi Arabia Information and Communication Technology Department Baghdad Institute of Technology Middle Technical University Baghdad Iraq

Although FKP has been recognized as a viable alternative to other biometric modalities, its adoption remains in the early stages due to its accuracy being lower than that of more competitive options. This research aims to bridge this performance gap by presenting an advancement in the classification of Finger Knuckle Prints (FKPs) using the Vision Mamba (ViM) model. The experimental study, conducted on a dataset from Hong Kong Polytechnic university containing 7,920 images from 165 individuals, evaluated the ViM model's performance against several pretrained classification models. ViM achieved an impressive accuracy of 99.1%, outperforming other models such as AlexNet (96.2%), SCNN (98.3%), and EfficientNet (98.0%), highlighting its superior capability in FKP classification. With around 7 million parameters, ViM balances complexity and performance, engineered specifically to capture fine-grained FKP features, such as texture and line patterns. Its use of weight decay mitigates overfitting, and it demonstrates resilience in occlusion scenarios by maintaining performance despite missing FKP components. Spatial attention mechanisms further enhance classification accuracy by prioritizing the most informative regions. Seventeen pretrained deep neural networks were evaluated for their effectiveness in classifying FKPs, with experimental results consistently demonstrating the superior performance of the ViM model. ViM exemplifies an advanced deep learning approach for biometric applications, combining high precision with efficient resource usage. Its versatile design – incorporating bidirectional SSMs for global context modeling and position embeddings for spatial awareness – extends its applicability to visual tasks beyond FKP identification. However, users should take its complexity and resource requirements into account for practical implementation. © 2025 The Authors

关键词： Deep learning Finger Knuckle Print Transfer learning Vision Mamba

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