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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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MACCoM: A multiple attention and convolutional cross-mixer framework for detailed 2D biomedical image segmentation

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computers in Biology and Medicine 2024年 179卷 108847-108847页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Ejiyi, Makuachukwu Bennedith Ukwuoma, Chiagoziem Ejiyi, Thomas Ugochukwu Muoka, Gladys Wavinya Gyarteng, Emmanuel S.A. Bamisile, Olusola O. Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria Department of Pure and Industrial Chemistry University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Chengdu China

The UNet architecture, which is widely used for biomedical image segmentation, has limitations like blurred feature maps and over- or under-segmented regions. To overcome these limitations, we propose a novel network architecture called MACCoM (Multiple Attention and Convolutional Cross-Mixer) – an end-to-end depthwise encoder-decoder fully convolutional network designed for binary and multi-class biomedical image segmentation built upon deeperUNet. We proposed a multi-scope attention module (MSAM) that allows the model to attend to diverse scale features, preserving fine details and high-level semantic information thus useful at the encoder-decoder connection. As the depth increases, our proposed spatial multi-head attention (SMA) is added to facilitate inter-layer communication and information exchange, enabling the network to effectively capture long-range dependencies and global context. MACCoM is also equipped with a convolutional cross-mixer we proposed to enhance the feature extraction capability of the model. By incorporating these modules, we effectively combine semantically similar features and reduce artifacts during the early stages of training. Experimental results on 4 biomedical datasets crafted from 3 datasets of varying modalities consistently demonstrate that MACCoM outperforms or matches state-of-the-art baselines in the segmentation tasks. With Breast Ultrasound Image (BUSI), MACCoM recorded 99.06 % Jaccard, 77.58 % Dice, and 93.92 % Accuracy, while recording 99.50 %, 98.44 %, and 99.29 % respectively for Jaccard, Dice, and Accuracy on the Chest X-ray (CXR) images used. The Jaccard, Dice, and Accuracy for the High-Resolution Fundus (HRF) images are 95.77 %, 74.35 %, and 95.95 % respectively. The findings here highlight MACCoM's effectiveness in improving segmentation performance and its valuable potential in image analysis. © 2024 Elsevier Ltd

关键词： Mixers (machinery)

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Unsupervised Collaborative Metric Learning with Mixed-Scale Groups for General Object Retrieval

arXiv

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arXiv 2024年

作者： Kan, Shichao Deng, Yuhai Liang, Yixiong Cen, Lihui Qu, Zhe Cen, Yigang He, Zhihai School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Automation Central South University Hunan Changsha410083 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Pengcheng Lab Shenzhen518066 China

The task of searching for visual objects in a large image dataset is difficult because it requires efficient matching and accurate localization of objects that can vary in size. Although the segment anything model (SAM) offers a potential solution for extracting object spatial context, learning embeddings for local objects remains a challenging problem. This paper presents a novel unsupervised deep metric learning approach, termed unsupervised collaborative metric learning with mixed-scale groups (MS-UGCML), devised to learn embeddings for objects of varying scales. Following this, a benchmark of challenges is assembled by utilizing COCO 2017 and VOC 2007 datasets to facilitate the training and evaluation of general object retrieval models. Finally, we conduct comprehensive ablation studies and discuss the complexities faced within the domain of general object retrieval. Our object retrieval evaluations span a range of datasets, including BelgaLogos, Visual Genome, LVIS, in addition to a challenging evaluation set that we have individually assembled for open-vocabulary evaluation. These comprehensive evaluations effectively highlight the robustness of our unsupervised MS-UGCML approach, with an object level and image level mAPs improvement of up to 6.69% and 10.03%, respectively. The code is publicly available at https://***/dengyuhai/MSUGCML. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Multi-Task Semantic Communications via Large Models

arXiv

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arXiv 2025年

作者： Ni, Wanli Qin, Zhijin Sun, Haofeng Tao, Xiaoming Han, Zhu Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of Space Network and Communications Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Electrical and Computer Engineering University of Houston HoustonTX77004 United States

Artificial intelligence (AI) promises to revolutionize the design, optimization and management of next-generation communication systems. In this article, we explore the integration of large AI models (LAMs) into semantic communications (SemCom) by leveraging their multi-modal data processing and generation capabilities. Although LAMs bring unprecedented abilities to extract semantics from raw data, this integration entails multifaceted challenges including high resource demands, model complexity, and the need for adaptability across diverse modalities and tasks. To overcome these challenges, we propose a LAM-based multi-task SemCom (MTSC) architecture, which includes an adaptive model compression strategy and a federated split fine-tuning approach to facilitate the efficient deployment of LAM-based semantic models in resource-limited networks. Furthermore, a retrieval-augmented generation scheme is implemented to synthesize the most recent local and global knowledge bases to enhance the accuracy of semantic extraction and content generation, thereby improving the inference performance. Finally, simulation results demonstrate the efficacy of the proposed LAM-based MTSC architecture, highlighting the performance enhancements across various downstream tasks under varying channel conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Data integration

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Quantum Secret Sharing Enhanced: Utilizing W States for Anonymous and Secure Communication

arXiv

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arXiv 2024年

作者： Li, Guo-Dong Cheng, Wen-Chuan Wang, Qing-Le Cheng, Long Mao, Ying Jia, Heng-Yue School of Control and Computer Engineering North China Electric Power University Beijing102206 China Key Lab of Information Network Security Ministry of Public Security Shanghai200031 China Department of Computer and Information Science Fordham University New York City10458 United States School of Information Central University of Finance and Economics Beijing102206 China Engineering Research Center of State Financial Security Ministry of Education Central University of Finance and Economics Beijing102206 China

Quantum secret sharing (QSS) is the result of merging the principles of quantum mechanics with secret information sharing. It enables a sender to share a secret among receivers, and the receivers can then collectively recover the secret when the need arises. To enhance the practicality of these quantum protocols, an innovative concept of quantum anonymous secret sharing (QASS) is advanced. In this paper, we propose a QASS protocol via W states, which can share secrets while ensuring recover-ability, recover-security, and recover-anonymity. We have rigorously evaluated our protocols, verifying their accuracy and fortifying their security against scenarios involving the active adversary. This includes considerations for dishonest receivers and non-receivers. Moreover, acknowledging the imperfections inherent in real-world communication channels, we have also undertaken an exhaustive analysis of our protocol’s security and effectiveness in a quantum network where some form of noise is present. Our investigations reveal that W states exhibit good performance in mitigating noise interference, making them apt for practical applications. © 2024, CC BY-NC-ND.

关键词： Quantum theory

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Joint Space-Time Sparsity Based Jamming Detection for Mission-Critical mMTC networks

arXiv

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arXiv 2023年

作者： Wang, Shao-Di Wang, Hui-Ming Li, Zhetao Leung, Victor C.M. The School of Information and Communication Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'An710049 China The Key Laboratory of Hunnan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent Network School of Computer Science Xiangtan University Xiangtan411105 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

For mission-critical massive machine-type communications (mMTC) applications, the messages are required to be delivered in real-time. However, due to the weak security protection capabilities of the low-cost and low-complexity machine-type devices, active jamming attack in the uplink access is a serious threat. Uplink access jamming (UAJ) can increase the number of dropped/retransmitted packets and restrict or prevent the normal device access. To tackle this vital and challenging problem, we propose a novel UAJ detection method based on the joint space-time sparsity (JSTS). Our key insight is that the JSTS-based feature will be significantly impacted if UAJ happens, since only a small fraction of the devices are active and the traffic pattern for each device is sporadic in the normal state. Unlike the existing detection methods under batch mode (i.e., all sample observations are collected before making a decision), the JSTS-based detection is performed in a sequential manner by processing the received signals one by one, which can detect UAJ as quickly as possible. Moreover, the proposed JSTS-based method does not rely on the prior knowledge of the attackers, since it only cares the abrupt change in the JSTS-based feature on each frame. Numerical results evaluate and confirm the effectiveness of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation

Incentive Mechanism Design for Unbiased Federated Learning w...

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International Conference on Distributed Computing Systems

作者： Bing Luo Yutong Feng Shiqiang Wang Jianwei Huang Leandros Tassiulas Electrical and Computer Engineering Division of Natural and Applied Sciences Duke Kunshan University Kunshan China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China IBM T. J. Watson Research Center NY USA Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Department of Electrical Engineering Institute for Network Science Yale University USA

Incentive mechanism is crucial for federated learning (FL) when rational clients do not have the same interests in the global model as the server. However, due to system heterogeneity and limited budget, it is generally impractical for the server to incentivize all clients to participate in all training rounds (known as full participation). The existing FL incentive mechanisms are typically designed by stimulating a fixed subset of clients based on their data quantity or system resources. Hence, FL is performed only using this subset of clients throughout the entire training process, leading to a biased model because of data heterogeneity. This paper proposes a game-theoretic incentive mechanism for FL with randomized client participation, where the server adopts a customized pricing strategy that motivates different clients to join with different participation levels (probabilities) for obtaining an unbiased and high-performance model. Each client responds to the server's monetary incentive by choosing its best participation level, to maximize its profit based on not only the incurred local cost but also its intrinsic value for the global model. To effectively evaluate clients' contribution to the model performance, we derive a new convergence bound which analytically predicts how clients' arbitrary participation levels and their heterogeneous data affect the model performance. By solving a non-convex optimization problem, our analysis reveals that the intrinsic value leads to the interesting possibility of bi-directional payment between the server and clients. Experimental results using real datasets on a hardware prototype demonstrate the superiority of our mechanism in achieving higher model performance for the server as well as higher profits for the clients.

关键词：

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An Incentive Mechanism Considering Privacy Metric for Personalized Application

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IEEE Transactions on Consumer Electronics 2025年

作者： Xie, Yun Li, Peng Zhang, Jindan Attar, Razaz Waheeb Arya, Varsha Gupta, Brij B. Nanjing Vocational College of Information Technology Jiangsu Province Service Customization Network Application Engineering Research Center Jiangsu Province Service Customization Network Application Engineering Research Center Nanjing210023 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China Xianyang Polytechnic Institute China Princess Nourah bint Abdulrahman University College of Business Administration Management Department Riyadh11671 Saudi Arabia Hong Kong Metropolitan University Hong Kong Hong Kong Center for Interdisciplinary Research Dehradun India Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan China Medical University Hospital China Medical University Department of Medical Research Taichung Taiwan Pune India Korea University School of Cybersecurity Seoul Korea Republic of Kyung Hee University Kyungheedae-ro Seoul26 Korea Republic of

Edge-based federated learning (EFL) is a solution for personalized applications. It addresses the limitations of cloud-based consumer service systems, such as data privacy and network latency. To further enhance the EFL system’s ability to protect data privacy, differential privacy (DP) is introduced. It is crucial to motivate high-quality collaboration among participants for optimal performance of the DP-EFL system. Researchers primarily focus on efficient learning algorithms, optimized resource scheduling, and user-oriented incentive mechanisms. Existing research exhibits notable limitations in modeling and analyzing the heterogeneity of resource costs (e.g., communication costs), particularly failing to comprehensively account for the additional costs imposed by participants’ privacy protection mechanisms‌. To address this gap, we propose a novel market transaction model based on Stackelberg game theory. This framework integrates incentive-driven reward allocation mechanisms to rigorously investigate the collaborative behaviors and strategic interactions between edge nodes (ENs) and the cloud server (CS)‌. We incorporate privacy costs into the multidimensional costs of participants with privacy metric, and based on this, derived collaboration strategies between ENs and CS, thus solving the Stackelberg game problem by theoretical analysis. Furthermore, numerical experiments validated the rationality of our proposed model, the impact of strategies on the performance of collaborators, and the advantages over other incentive mechanisms. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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Time-Frequency Analysis-Based Transient Harmonic Feature Extraction for Load Monitoring

Time-Frequency Analysis-Based Transient Harmonic Feature Ext...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Peng Xia Hao Zhou Shenyao Jiang Fan Deng Zhi Liu Xiang-Yang Li LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou Zhejiang China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

Feature extraction is important for non-intrusive appliance load monitoring (NILM), because it includes crucial steps which altogether transform raw signals into distinct features of appliances. Although numerous studies have achieved good results using transient harmonic features, their performances degrade in practical scenarios. In this paper, a time-frequency analysis-based framework for transient harmonic feature extraction is proposed, which takes all factors that would eventually influence NILM system into account. More specffically, the framework proposes a new qualitative transient harmonic feature, along with current retrieval and data augmentation methods. The proposed framework is evaluated on two well-known datasets (i.e., Controlled On/Off Loads Library and Home Equipment Laboratory Dataset). As compared with the other state-of-art methods, the proposed framework achieves similar accuracy (i.e., more than 96%) on single-load data, and more than 33% accuracy improvement over multi-load data (e.g., from 59.4% to 93% when six devices are active at the same time).

关键词： Load monitoring Home appliances Time-frequency analysis Transforms Harmonic analysis Feature extraction Libraries

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License Detection and Accident Prevention System

License Detection and Accident Prevention System

引用

Advancements in Computing (ICAC), International Conference on

作者： P.D.R Dewmin P.S.H Yapa S.D. Lokuge M.G.N.M. Pemadasa Nelum Amarasena Department of Computer Systems and Network Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

According to census and statistics department of Sri Lanka, more than 30,000 accidents occurred from 2013 to 2019. More than two thousand are fatal in this time-period. When considering the top 10 causes of deaths in Sri Lanka, road accidents is at 10th place. Drink and drive, fatigue, drowsiness, distracted driving and driving without a valid license are root causes to these accidents. 20% of the accidents are caused by the drivers without a valid driving license. Currently there is no automated system built using IoT to verify driver’s license. A system to check drowsy driving, distracted driving and driver intoxication is also lacking in the society. By analyzing the data, smart license detection and accident prevention system was proposed to identify and validate driver’s license using RFID. The proposed system also facilitates sub-systems to check driver’s drowsiness, fatigue, alcohol level and driver distracted or not using Raspberry Pi camera module based on computer vision using TensorFlow Lite. An initialized sub-system detects the intensity of the brake pedal being engaged using pressure sensor. The system analyzes the pressure and indicate the intensity level accordingly using the brake light brightness. While these sub systems reduce the probability of occurring an accident, airbag detection sub system reduce the fatality rate of an accident by detecting the deployment of airbags and informing the nearest police station and hospital about an accident using GSM module and SMS Gateway API. The proposed system will reduce the number of accidents occurring throughout the year.

关键词： Pressure sensors Road accidents Licenses Logic gates Fatigue Brakes Monitoring

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