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ACS/IEEE International Conference on computer Systems and Applications

作者： Wafaa Radi Hesham M. El Badawy Ahmed Mudassir Hesham Kamel Department of Communication and Electronics Engineering Canadian International College CIC Cairo Egypt Network Planning Department National Telecommunications Institute Cairo Egypt Department of Electrical and Computer Engineering Comsats University Islamabad Lahore Pakistan Department of Electronics and Communication Canadian International College Giza Egypt

Intelligent and sustainable vehicle networking (ISVN) is a new paradigm for transportation that makes use of developments in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication to encourage collaboration between vehicles and the infrastructure in order to enhance traffic flow, safety, and environmental effect. Traffic accidents are a major public safety concern, resulting in significant casualties and economic losses each year, as well as a significant contributor to air pollution and greenhouse gas emissions. In order to increase traffic accident management’s efficacy and efficiency and support a more environmentally friendly transportation system, this article suggests an integrated ISVN-ML traffic accident management system. The proposed system leverages ISVN sensors and cameras to collect data about traffic accidents in real time. This data is then transmitted to an ML-based traffic accident management system, which uses it to predict the number of killed and injured people involved in the accident and to immediately prioritize the dispatch of emergency responders. Additionally, the system provides real-time information to police stations and ambulance services to help them respond to accidents more quickly and efficiently. After the police station has completed its investigation of the accident, the details of the accident are sent back to the ML-based traffic accident management system to improve the accuracy of the system’s predictions and make the system more efficient over time. Overall, the proposed system is a promising approach to improving traffic safety, reducing the economic costs associated with traffic accidents, and contributing to a more sustainable transportation system.

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A Resource-Efficient Algorithm–Hardware Co-Design Toward Semi-Supervised Neurological Symptoms Prediction

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhao, Shiqi Li, Shanjin Wu, Di Tian, Fengshi Yang, Jie Sawan, Mohamad Northeastern University School of Computer and Communication Engineering Shenyang110819 China Northeastern University at Qinhuangdao Hebei Key Laboratory of Marine Perception Network and Data Processing Hebei Qinhuangdao066004 China School of Engineering Hangzhou310024 China Westlake Institute for Advanced Study Institute of Advanced Technology Hangzhou310024 China Department of Electronic and Computer Engineering Hong Kong

Recent advancements in neuroscience, coupled with the rise of deep neural networks (DNNs), have markedly enhanced the ability to predict physiological disorders. Typically, these models are trained on cloud servers using labeled datasets and subsequently deployed to devices for inference. However, this conventional cloud-based training and edge inference approach is not feasible for real-life scenarios owing to issues such as data privacy, the laborious data-labeling process, and variations in conditions resulting from electrode shifts and changes in physiological status. In response to these challenges, we present resource-efficient algorithm (REAL), a resource-efficient algorithm–hardware co-design training framework. This transformer-based framework incorporates direct feedback alignment (DFA) to optimize hardware and computational efficiency during the training process. Moreover, it utilizes semi-supervised learning (SSL) along with bespoke data augmentation techniques to reduce the dependency on extensive data labeling. Based on the proposed algorithmic model, we design and implement a dedicated hardware architecture. We tested REAL on both electroencephalogram (EEG)-based seizure prediction and electrocardiogram (ECG)-based arrhythmia classification tasks. For the EEG task, our framework enhances prediction sensitivity by an average of 7.24% and decreases the false-positive rate (FPR) by 0.04/h, demonstrating robust performance across varying levels of annotated data. With minimal labeled data, REAL reached an AUC of 0.91 and a sensitivity of 89.15%, outperforming all previous works with similar task settings. For the ECG task, REAL achieved an accuracy of 82.81% and a macro-F1-score of 0.827 under a 20% labeling rate, demonstrating strong generalization to a different biomedical modality. Additionally, REAL is the first hardware design targeting semi-supervised neurological symptom prediction, achieving an energy efficiency of 3.97 TOPS/W and an area efficien

关键词： Semi-supervised learning

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The effect of real-time pose recognition on badminton learning performance

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Interactive Learning Environments 2023年第8期31卷 4772-4786页

作者： Lin, Kuo-Chin Ko, Cheng-Wen Hung, Hui-Chun Chen, Nian-Shing Center for Physical and Health Education National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Graduate Institute of Network Learning Technology National Central University Chung-Li Taiwan Institute for Research Excellence in Learning Sciences and Program of Learning Sciences National Taiwan Normal University Taipei Taiwan

Badminton is a very popular subject in Physical Education (PE). Many students enroll badminton courses in every semester which pose a tremendous teaching load to the instructors. The one-on-one guiding/feedback time provided by the instructor to each student is also greatly reduced. To overcome this challenge, some studies have tried to adopt pose recognition technique in teaching badminton. However, the lack of mobility and recognition accuracy problems hinder its applicability. To address this issue, a new pose recognition technique, OpenPose, was employed to develop a real-time pose recognition badminton teaching APP in this study. The APP was then installed on a mobile device to enhance badminton smash skill learning performance. The scores distribution of the experimental group shows majority of students achieved satisfactory performance, this implies the badminton teaching APP is helpful to all students no matter what their initial skill levels are. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： badminton teaching motor skill learning physical education Pose recognition real-time feedback

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A Multi-Optimization Technique for Improvement of Hadoop Performance with a Dynamic Job Execution Method Based on Artificial Neural network

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SN computer Science 2020年第3期1卷 1-11页

作者： Alanazi, Rayan Alhazmi, Fawaz Chung, Haejin Nah, Yunmook Department of Computer Science and Information Technology Jouf University Al-Jawf Saudi Arabia Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Computer Engineering Department (Department of Data Base Engineering) Dakook University Yongin South Korea Applied Computer Engineering Department Dankook University Yongin South Korea

The improvement of Hadoop performance has received considerable attention from researchers in cloud computing fields. Most studies have focused on improving the performance of a Hadoop cluster. Notably, various parameters are required to configure Hadoop and must be adjusted to improve performance. This paper proposes a mechanism to improve Hadoop, schedule jobs, and allocate and utilize resources. Specifically, we present an improved ant colony optimization method to schedule jobs according to the job size and the time expected for execution. Priority is given to the job with the minimum data size and minimum response time. The resource usage and running jobs by data node are predicted using an artificial neural network, and job activity and resource usage are monitored using the resource manager. Moreover, we enhance the Hadoop Name node performance by adding an aggregator node to the default HDFS framework architecture. The changes involve four entities: the name node, secondary name node, aggregator nodes, and data nodes, where the aggregator node is responsible for assigning the jobs among the data node, and the Name node keeps tracking only the aggregator nodes. We test the overall scheme among Amazon EC2 and S3, and show the results of throughput and CPU response time for different data sizes. Finally, we show that the proposed approach shows significant improvement compare to native Hadoop and other approaches. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Artificial neural network Hadoop performance enhancement Improved ant colony optimization Job scheduling Map-shuffle-reduce

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Fused Fuzzy Deep Learning and Information Steganography for Privacy Preservation in Medical Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Jingxue Zhao, Pengbiao Deng, Erqiang Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Xiong, Hu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China the School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China the KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India the Division of Research and Development Lovely Professional University Phagwara India the University Centre of Research and Development Chandigarh University Mohali India the Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia the Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

—Medical Consumer Electronics (MCE) have greatly facilitated people’s lives, allowing patients to obtain personal medical information including medical images. These medical images obtained from MCE need to be processed using segmentation, diagnosis, recognition to obtain useful information. Deep learning plays an important role in medical image segmentation and recognition. However, other personal information about the patient, such as previous cases, is often required to determine the final diagnosis. However, sending personal information and medical images generated through MCE to the clinic directly exposes patient privacy. Therefore, in this paper, we propose a digital medical image privacy protection framework based on information steganography with fuzzy deep learning. The proposed framework transforms patient privacy information into invisible noise and hides it in the deep covert space of the image. This method plays a crucial role in ensuring patient privacy and data security. Furthermore, this method can ensure a high level of image segmentation accuracy that supports the development of personalized medical services. The experimental results show that the proposed framework can embed patient information in medical images without affecting the segmentation task, and effectively protect privacy. Finally, the proposed framework provides new possibilities and impetus for the popularization of MCE. © 2025 IEEE.

关键词： Medical imaging

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Study on the Development of an Underground Fiber Optic network Alongside Electrical Distribution Systems in Greece

Study on the Development of an Underground Fiber Optic Netwo...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Theofano Kollatou Alexandra Karamouza Dimitrios Stimoniaris Nikolaos Lettas Dimitrios Tsiamitros Evangelos Boutsiadis Stefanos Beis Department of Electrical & Computer Engineering University of Western Macedonia Kozani Greece INNORA P.C. Spin-off Company of the University of Western Macedonia Greece HEDNO (Hellenic Electricity Distribution Network Operator)

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

This study explores the strategic integration of a fiber optic network within the existing underground electrical distribution infrastructure in Greece, facilitated by HEDNO. It aims to enhance the telecommunications framework while maintaining harmony with the electrical systems, contributing to sustainable infrastructure goals. The research assesses international standards such as ELOT EN 50174.03 and IEEE2023 National Electrical Safety Code, along with Greek regulations, to develop an installation plan that minimizes risks like electrical interference and environmental impacts. The analysis includes determining optimal burial depths, implementing protective measures, and maintaining safe distances between fiber optic and power cables. The paper provides visual representations of the installation methods, emphasizing the practical implementation of these guidelines. This study offers insights that could guide similar integrations worldwide, promoting technological progress alongside sustainable infrastructure development.

关键词： Optical fibers Visualization Protocols Prevention and mitigation Regulation Telecommunications Security Proposals Standards Resilience

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A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G networks

A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G...

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IEEE Conference on Standards for Communications and networking (CSCN)

作者： Giuseppe Caso Mohammad Rajiullah Anna Brunstrom Luca De Nardis Özgü Alay Marco Neri Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy Department of Informatics University of Oslo Oslo Norway Rohde & Schwarz Mobile Network Testing Rome Italy

ISBN: (数字)9798331507428

ISBN: (纸本)9798331507435

5th Generation (5G) mobile systems are being deployed to address the Quality of Service and Experience (QoS/QoE) requirements of several use cases, including enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). While eMBB performance testing inherits well-established methodologies and Key Performance Indicators (KPIs), beyond-eMBB services (i.e., URLLC and eMBBURLLC real-time applications) are often tested by adopting simplistic or in-house methodologies, which do not help towards accurate assessment and comparison. In this paper, we fill this gap by providing a detailed analysis of a methodology, recently standardized by the International Telecommunication Union Telecommunication Standardization Sector (ITU-T), that targets systematic performance evaluations of beyond-eMBB services. The methodology relies on the definition of a QoE KPI, i.e., the interactivity score (i-score), on top of three QoS KPIs measuring service latency, stability, and continuity. To this aim, we perform a multi-service measurement campaign on two 5G networks across two cities in Sweden, during which we run a large number of tests compliant with the ITU-T methodology, and analyze the collected data. Our results empirically validate the methodology, showcasing its ability of capturing heterogeneous service characteristics and requirements, as well as the interdependencies between $\boldsymbol{i}$-score and QoS KPIs, and the impact of different factors on QoS/QoE performance, including user mobility, connection capability, and server location.

关键词： Accuracy Key performance indicator Urban areas Quality of service Ultra reliable low latency communication Traffic control Servers Quality of experience Vehicle-to-everything Testing

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Deep Learning in Early Alzheimer’s Disease’s Detection: A Comprehensive Survey of Classification, Segmentation and Feature Extraction Methods

arXiv

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arXiv 2025年

作者： Hafeez, Rubab Waheed, Sadia Naqvi, Syeda Aleena Maqbool, Fahad Sarwar, Amna Saleem, Sajjad Sharif, Muhammad Imran Siddique, Kamran Akhtar, Zahid Department of Computer Science University of Wah Wah Cantt Pakistan NUST Pakistan Department of Information and Technology Washington University of Science and Technology VirginiaVA United States Department of Computer Science Kansas State University ManhattanKS United States Department of Computer Science and Engineering University of Alaska Anchorage Anchorage United States Department of Network and Computer Security State University of New York Polytechnic Institute United States Department of Computer Science Air University Aerospace and Aviation Campus Kamra Pakistan

Alzheimer’s disease is a deadly neurological condition, impairing important memory and brain functions. Alzheimer’s disease promotes brain shrinkage, ultimately leading to dementia. Dementia diagnosis typically takes 2.8-4.4 years after the first clinical indication. Advancements in computing and information technology have led to many techniques for studying Alzheimer's disease. Early identification and therapy are crucial for preventing Alzheimer's disease, as early-onset dementia hits people before the age of 65, while late-onset dementia occurs after this age. According to the 2015 World Alzheimer's Disease Report, there are 46.8 million individuals worldwide suffering from dementia, with an anticipated 74.7 million more by 2030 and 131.5 million by 2050. Deep Learning has outperformed conventional machine learning techniques by identifying intricate structures in high-dimensional data. Convolutional Neural network (CNN) and Recurrent Neural network (RNN) have achieved an accuracy of up to 96.0% for Alzheimer’s disease classification and 84.2% for Mild Cognitive Impairment (MCI) conversion prediction. There have been few literature surveys available on applying ML to predict dementia, lacking in congenital observations. However, this survey has focused on a specific data channel for dementia detection. This study evaluated deep learning algorithms for early Alzheimer's disease detection using openly accessible datasets, feature segmentation, and classification methods. This article also has identified research gaps and limits in detecting Alzheimer's disease, which can inform future research. © 2025, CC BY.

关键词： Image segmentation

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GRDNet: A Lightweight Railway Foreign Object Intrusion Detection Algorithm

GRDNet: A Lightweight Railway Foreign Object Intrusion Detec...

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Advanced Algorithms and Control engineering (ICAACE), International Conference on

作者： Zhaonan Wang Lin Wei Caixia Meng Lei Shi Yufei Gao Qingxian Wang School of Cyber Science and Engineering Zhengzhou University Zhengzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Department of Image and Network Investigation Technology Zhengzhou Police College Zhengzhou China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

Railway intrusion detection using convolutional neural networks has significant results, but it requires a large amount of data and arithmetic power to train and deploy neural network models, and it is still a challenge to improve detection accuracy under limited computational resources. Based on this problem, this paper proposes a lightweight railway foreign object intrusion detection algorithm: GRDNet, which firstly adopts a lightweight backbone network GhostNetV2, and makes use of the decoupled fully connected attention mechanism to aggregate local and long-distance information, which significantly reduces the number of parameters and computation while maintaining the detection performance. Next, a deep feature extractor, RSPPFCSPC, is proposed to reduce the computational effort by decreasing the number of channels in the feature map while maintaining the diversity and effectiveness of the features. Finally, a knowledge distillation algorithm is used to improve the accuracy of foreign object localization and classification and reduce the leakage rate of the model. Experiments show that the proposed method is superior to the state-of-the-art work of each comparison.

关键词： Location awareness Computational modeling Neural networks Intrusion detection Object detection Feature extraction Rail transportation

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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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