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IEEE Conference on Wireless Communications and Networking

作者： Xuefei Li Mingzhe Chen Ye Hu Zhilong Zhang Danpu Liu Shiwen Mao Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijina China Department-of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Industrial and System Engineering University of Miami Coral Gables FL USA Department of Electrical and Computer Engineering Auburn University Auburn AL USA

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based scheme can improve the number of successfully served vehicles by up to 17% compared to a GNN based algorithm with a fixed neural network model.

关键词： Network topology Simulation Heuristic algorithms Graph neural networks Sensors Data mining Vehicle dynamics

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Mechanical Design with Experimental Verification of a Lightweight Exoskeleton Chair

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Journal of Bionic engineering 2021年第2期18卷 319-332页

作者： Zihao Du Zefeng Yan Tiantian Huang Ou Bai Qin Huang Bin Han State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Human Cyber-Physical Systems Laboratory Department of Electrical and Computer EngineeringFlorida International UniversityMiami 33174USA Department of Rehabilitation Medicine Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China

In this study,a human-chair model was developed as the basis for a wearable-chair design.A prototype chair,HUST-EC,based on the model was fabricated and *** the optimization under the golden divisional method,an optimized simulation of the operating mode with the lowest chair height was implemented.A novel multi-link support structure has been established with parameters optimized using Matlab *** stress analysis of the solid models was conducted to ensure the adequate support from the designed chair for the *** subjects participated in the evaluation experiment,who performed both static tasks and dynamic *** experimental results consisted of subjective evaluation and objective *** experimental data demonstrate that(1)the HUST-EC can effectively reduce the activation level of related muscles at a variety of tasks;(2)the plantar pressure was reduced by 54%–67%;(3)the angle between the upper body and the vertical axis was reduced by 59%–77%;(4)the subjective scores for chair comfortability,portability,and stability were all higher than *** results further revealed that the designed chair can reduce the musculoskeletal burden and may improve work efficiency.

关键词： exoskeleton wearable chair bionic robot mechanism design ergonomics

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth IoanninaGR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory HeraklionGR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering HeraklionGR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep learning

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Optimizing Vehicle Positioning via Multi-Model Image and Radio Frequency Fusion

Optimizing Vehicle Positioning via Multi-Model Image and Rad...

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IEEE International Conference on Communications (ICC)

作者： Ouwen Huan Mingzhe Chen Tao Luo Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one base station (BS), and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design a hard expectation-maximization (EM) based deep learning (DL) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the log-likelihood function as a maximum matching problem. Subsequently, the model parameters are updated according to the maximum matching between unlabeled CSI and position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 60% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning.

关键词： Training Deep learning Base stations Simulation Fingerprint recognition Cameras Data models

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Road Signs Recognition by Using YOLOv8 Model

Road Signs Recognition by Using YOLOv8 Model

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International Conference on Intelligent Systems: Theories and Applications (SITA)

作者： Abdellah Nabou Atheer L. Salih Ibtissam Ben Rabbaa El-Hassan Abdelwahed Mouaad Ait Ahmed Ouhamou Computer Science Dept Computing Systems Engineering Laboratory (LISI) Cadi Ayyad University Marrakesh Morocco Electrical & Communications Engineering Department DHBW University - Campus Friedrichshafen Germany Computer Science Dept Cadi Ayyad University Marrakesh Morocco

Efficient Road sign recognition is key to improving road safety, navigation and to enhance driver assistance for an intelligent transportation system (ITS). However, achieving efficient road sign and traffic recognition is accompanied by many challenges, in our last few years, several deep learning algorithms have been deployed for responding to these limitations. With the exponential increase in the number of vehicles in Morocco, the need for accurate and real-time detection and classification of road signs has become essential. In this paper, we propose an advanced approach for recognizing Moroccan road signs utilizing the YOLOv8 (You Only Look Once) model that very known for its efficiency and effectiveness in object detection tasks. the model is trained and evaluated under a comprehensive dataset that comprising diverse Moroccan road signs. The test of YOLOv8 model give an encouraging result in different performance metrics such as accuracy, recall, precision and mAP which have respectively an average of 0.95, 0.94, 0.96 and 0.97.

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Joint Secure and Covert Communications for Active STAR-RIS Assisted ISAC Systems

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Liang Jia, Jie Mu, Xidong Liu, Yuanwei Chen, Jian Wang, Xingwei Northeastern University School of Computer Science and Engineering China Northeastern University Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China BelfastBT3 9DT United Kingdom Department of Electrical and Electronic Engineering China

This paper investigates the design of jointly supporting physical layer security (PLS) and covert communications (CCs) in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a-STAR-RIS) assisted integrated sensing and communication (ISAC) system. Due to the unified waveform design of ISAC signals, we consider a challenging scenario with two targets being suspicious attackers, where one warden target potentially detects the confidential transmission behavior of covert users and another eavesdropper target attempts to intercept the broadcasted confidential information of security users. We investigate the joint beamforming design at the base station (BS) and the a-STAR-RIS to achieve a high-quality sensing beampattern while meeting covertness and security communication requirements. (1) For the ideal scenario with perfect channel state information (CSI) and precise target locations, we propose an alternative optimization (AO) method to address the optimization problem involving highly coupled variables. Specifically, the optimal beamforming design at the BS is handled using the semi-definite relaxation (SDR) technique, while the beamforming design at the a-STAR-RIS is addressed through a penalty-based iterative algorithm. (2) A more practical case with uncertain target locations and imperfect CSI is considered to achieve a robust beamforming design, where the non-deterministic outage probability constraints are effectively transformed by employing the Bernstein-type inequality. Numerical results demonstrate the superiority of the a-STAR-RIS over the baseline cases and certify that the proposed algorithms can effectively balance the tradeoff among the sensing quality, covert and secure communication requirements. Besides, results also show that the proposed robust beamforming scheme can construct adequate sensing beampattern, even with imperfect CSI and uncertain target locations. © 2002-2012 IEEE.

关键词： Beamforming

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Contrastive Learning Based Human Activity Recognition Using Body Sensors 9

Contrastive Learning Based Human Activity Recognition Using ...

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9th International Conference on Big Data computing and Communications, BigCom 2023

作者： Xie, Chuanshi Zhou, Yu Yu, Dandan Sun, Shilong Zhang, Xiao Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The First Affiliated Hospital of Dalian Medical University Information Center Dalian China Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China South-Central Minzu University Department of Computer Science China South-Central Minzu University Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission Wuhan430074 China

ISBN: (纸本)9798350331240

Pervasive sensing and wearable sensor techniques have been increasingly employed to monitor and recognize human activities through body sensors in areas of smart healthcare and manufacturing. However, conventional machine learning or deep learning based human activity recognition (HAR) requires a large amount of labeled data, which is cost-expensive in real-world scenarios. To tackle this issue, we propose a contrastive learning framework for HAR (CL-HAR), which utilizes the generated unlabeled data as the input and explores the supervised features of the unlabeled data under the principle of self-supervised learning. A simple yet effective backbone network as a feature extractor for subsequent activity recognition is proposed. By using a small portion of the labeled samples as the training set which is fed into our learned feature extractor, we build a classifier and use the rest of the data to verify the feasibility and effectiveness of our CL method. Extensive experiments on three benchamark datasets and one real-world dataset demonstrate that CL-HAR can achieve better classification accuracy than compared supervised and semi-supervised methods with less labelled samples, which is of practical use. © 2023 IEEE.

关键词： Wearable sensors

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Analysis and Treatment of Missing Data in Happiness Score Using Machine Learning

Analysis and Treatment of Missing Data in Happiness Score Us...

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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Wassim Touati Yamen El Touati Nejib Ben Hadj-Alouane Abderrazek Jemai SERCOM Laboratory Polytechnic School of Tunisia Carthage University Tunis Tunisia Department of Computer Science Faculty of Computing and Information Technology Northern Border University Kingdom of Saudi Arabia OASIS Laboratory ENIT University of Tunis EI Manar Tunisia Electrical Engineering and Computer Engineering Department American University in Dubai Dubai UAE

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Machine learning, a vital part of artificial intelli-gence, improves our ability to make predictions from complex data. The success of these predictions relies heavily on the model's fit with its data and the data's consistency. Accurate predictions depend on closely matching the model to the data patterns. However, missing data can seriously disrupt the accuracy of predictions, causing errors. To address this, we recommend adopting a proposed framework that enhances predictions by effectively filling in missing data. Our objective is to refine the accuracy of our predictions, especially in scenarios where the data might be incomplete and features exhibit patterns of dependency. In the context of missing data, our approach concentrates on enhancing regression performance to minimize the effects of missing data as effectively as possible.

关键词： Accuracy Correlation Statistical analysis Smart cities Predictive models Data models Imputation Reliability Random forests Robots

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DDPS: Dynamic Differential Pricing-Based Edge Offloading System With Energy Harvesting Devices

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IEEE Transactions on Network science and engineering 2025年

作者： Xue, Hai Xia, Yun Xiong, Neal N. Zhang, Di Pei, Songwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Sul Ross State University Department of Computer Science and Mathematics AlpineTX79830 United States Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Zhengzhou University Henan International Joint Laboratory of Intelligent Health Information System Zhengzhou450001 China Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China

Mobile edge computing (MEC) mitigates the energy and computation burdens on mobile users (MUs) by offloading tasks to the network edge. To optimize MEC server utilization through effective resource allocation, a well-designed pricing strategy is indispensable. In this paper, we propose a dynamic differential pricing scheme (DDPS) for an edge offloading scenario with energy harvesting devices, which determines prices based on computing resource usage to enhance edge server (ES) utilization. First, an offloading decision algorithm is proposed to balance harvested and consumed energy, determining whether and how much data to offload. Second, a Stackelberg game-based differential pricing algorithm is proposed to optimize computing resource allocation for MUs and reallocate surplus resources to delay-sensitive devices. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Specifically, in comparison to the existing best-performing pricing scheme, for different task arrival rates, DDPS can achieve a 5.3% decrease in average execution delay, a 1.7% increase in ES utility (Userver, which represents the payment from MUs minus penalties for discarded tasks), and a 2.1% increase in the average ratio of service for MUs. In addition, DDPS also improves 2.8% Userver on average with different ES computation capacities. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Online Demand Peak Shaving with Machine-Learned Advice in Cyber-Physical Energy Systems

Online Demand Peak Shaving with Machine-Learned Advice in Cy...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Minxi Fengl Wei Li Boyu Qin Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Electrical Engineering State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University China

With the deep integration of cyber tools, control algorithms are increasingly employed in cyber-physical energy systems to enhance management, cost efficiency, and robustness. Effective demand load management is crucial in cyber-physical energy systems as it directly impacts operational costs. Failure to adequately manage spiky or seasonal demand loads can lead to significant expenses on monthly utility bills. In this study, we propose AMPAMOD, a randomized online algorithm with machine-learned advice, to optimize battery operations and mitigate highly dynamic peak loads. AMPAMOD utilizes limited advice from machine learning algorithms to guide our online algorithm and ensure cost-effective peak load management. The theoretical analysis shows that our solution has minimal advice complexity, a linear computational cost, and an improved competitive ratio. Finally, we conduct extensive trace-driven experiments on real-world datasets. AMPAMOD achieves a peak shaving success rate of over 90%, outperforming baselines by at least 50%. These experimental results confirm theoretical findings and demonstrate the potential of AMPAMOD.

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