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IEEE Transactions on Radar Systems 2025年 3卷 360-378页

作者： Peng, Xuanyu Hu, Yaokun Liu, Ting Wu, Ying Saito, Tatsunori Toda, Takeshi Nihon University Graduate School of Science and Technology Tokyo101-0062 Japan Fujitsu Ltd. Kanagawa211-8588 Japan Nihon University College of Science and Technology Tokyo101-0062 Japan National University of Defense Technology College of Electronic Science Changsha410073 China University of Leeds School of Computing LeedsLS2 9JT United Kingdom Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

This study explores the application of a 77 GHz mm-wave frequency-modulated continuous wave (FMCW) radar system for human activity recognition (HAR). We propose a novel density-aware convex hull (DACH) algorithm specifically designed to address the challenge of point cloud sparsity, which is particularly evident when using few-channel radar systems. This algorithm combines a triple-view convolutional neural network (CNN) and long short-term memory (LSTM) models for classification. Unlike traditional methods that often overlook the impact of sparse point cloud data, our approach emphasizes the importance of maintaining robust and dense data for precise activity recognition. Our experiments, which involved classifying nine human activities—standing, sitting, squatting, lying on the floor, transitions between these postures, and walking—demonstrate the method’s effectiveness. By using a compact 3Tx4Rx few-channel radar, we achieve a balance among cost, size, and performance, making it suitable for practical applications like indoor health monitoring and elderly care. The proposed method achieves an average classification accuracy of approximately 99.63% across all four scenarios, marking a significant improvement over existing approaches, and shows promise for real-time applications in various fields. © 2025 IEEE. All rights reserved,

关键词： Convolutional neural networks

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Investigation of AI with OpenCV-Python for Detecting Diabetes

Investigation of AI with OpenCV-Python for Detecting Diabete...

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Conference of Recent Trends and Applications of Soft Computing in engineering, RTASCE 2024

作者： Mutar, Hussein A. Yalwi, Ilyas Khudhair Mezaal, Yaqeen S. Th. Salim ALRikabi, Haider Alaidi, Abdul Hadi M. Niama ALRubeei, Ibtihal R. Magdy, Ahmed Electrical Engineering Department College of Engineering Wasit University Wasit Iraq College of Computer Science and Information Technology Wasit University Wasit Iraq College of Engineering University of Information Technology and Communications Baghdad Iraq Electrical Engineering Department Faculty of Engineering Suez Canal University Ismailia Egypt

ISBN: (纸本)9783031828805

Plenty of different diagnosing methods have been extensively utilized to identify diabetes accurately;however, an absolutely precise and definitive diagnosis has not yet been attained. Within the context of this research, our primary objective is to leverage the cutting-edge capabilities of Artificial Intelligence (AI) coupled with OpenCV to assist medical professionals, thereby minimizing the rate of misdiagnosis. Specifically, we harness the power of AI to effectively classify diverse images portraying patients afflicted with Non-Proliferative Diabetic Retinopathy (NPDR), with the ultimate goal of determining the severity level at which they are situated. In conjunction with this, Python, with OpenCV, has a crucial role in extracting pertinent features that may be discernible within the given images. Our methodology involves the collection and preprocessing of the Eye PACS Dataset on Kaggle, followed by feature extraction and model training using some machine learning algorithms, including convolutional Neural Network CNN, decision trees, support vector machines SVM, and neural networks. OpenCV is utilized for image processing tasks, enhancing the feature extraction process, certain individual features present within the images are precluded from being considered as contributing factors in the classification process. Some of these features include but not limited to, the measurement of the luminous blobs which are present in the image, the specific area of existence of red lesion. The evaluation of the models includes the analysis of their performance based on the goal of the prediction task, specifically decimal-based accuracy, precision, recall, and F1-score. This research employs a wide-ranging dataset embracing low, medium and high level of image severity. At last, after lots of simulation, it came to a conclusion that the CNN increases its level of classification accuracy up to 98%. These findings show that the proposed application of AI improves the accuracy

关键词： Convolutional neural networks

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Investigation of the Internet of Things to Track the Distribution Temperature of Transformers with Accounting Residential Loads

Investigation of the Internet of Things to Track the Distrib...

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Conference of Recent Trends and Applications of Soft Computing in engineering, RTASCE 2024

作者： Mezaal, Yaqeen S. Abed, Faisal Theyab ALRubeei, Ibtihal R. Niama Alrikabi, Aday R. H. Salim ALRikabi, Haider Th. Mutar, Hussein A. Alaidi, Abdul Hadi M. Farhan, Mansour S. Obod, Ivan College of Engineering University of Information Technology and Communications Baghdad Iraq Electrical Engineering Department College of Engineering Wasit University Wasit Iraq Department of Physics College of Science Wasit University Wasit Iraq College of Computer Science and Information Technology Wasit University Wasit Iraq Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (纸本)9783031828805

Commercial and industrial loads are relatively lighter than residential loads and have become lighter over the years as distribution transformer loads are affected by the rate of urbanization. When geared to provide distribution infrastructure that will supply electrical power in growing cities, it often struggles to meet the demand for power due to urbanization. This has compounded the issue of distribution transformers and their tendency to overheat. The impact between the residential load and the distribution transformer temperature in monitoring is approaching through the lens of the Internet of Things (IoT), which is the focus of this study. The distribution transformers’ optimum performance is essential for maximizing distribution network function as well as ensuring electric supply to residential areas. Fluctuations in residential loads also have an impact in changing the temperature of the transformers and this may, in the process, pose operational challenges as well as cause equipment failures. When many distribution transformers mounted with IoT temperature sensors are connected to a centralized management platform, temperature information can be captured and processed in real time. Today, it is possible to identify temperature anomalies that can speak about the presence of problems in the form of, for instance, overload or insulation damage in advance. Besides the understanding of the consumer demand profile, the utility can use the temperature data alongside the domestic load profile for the anticipation of the peak demand periods. Some of the outcomes observed when IoT technology is utilized for temperature monitoring include enhanced operating efficiency, reduced downtime, and enhanced product reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information management

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Efficient Hierarchical Feature Collaboration Transformer for Image Inpainting

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Dengyong Fu, Nuo Liao, Xin Chen, Jiaxin Yang, Hengfu Yang, Gaobo Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410004 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan First Normal University School of Computer Science Changsha410205 China

Existing image inpainting methods face limitations in detail restoration. Although transformer-based models have made certain progress recently, the lack of hierarchical feature interaction and insufficient consideration of the importance of features at different network levels lead to semantic ambiguity in image reconstruction. To enhance the visual quality and accuracy of image inpainting, we adopt a multi-level feature fusion approach and propose a novel, efficient hierarchical feature collaboration transformer (HFCT). Our approach comprises two modules: dual stream gated feature fusion (DSGF) and region-separated attention module (RSAM), effectively capturing features at different levels of the network and enhancing inter-level information exchange. The DSGF module uses soft gating to fuse primary and advanced features, strengthening the connection from local to global consistency and reducing artifacts. The RSAM module resolves attention isolation issues in feature fusion through region-separated attention, strengthening the understanding of feature relationships, capturing more image semantics, and improving restoration accuracy. Extensive experiments on the Paris StreetView, CelebA-HQ, and Places2 benchmark datasets demonstrate that our proposed method achieves superior image inpainting quality compared to several state-of-the-art inpainting algorithms. Please refer to the project page: https://***/csfunuo/HFCT. © 2024 IEEE.

关键词： Restoration

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Efficient Intrusion Detection in AMI Systems Based on Federated Semi-supervised Learning

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IEEE Transactions on Network science and engineering 2025年

作者： Xia, Zhuoqun Tang, Haidong Hu, Zhenzhen Zhou, Hongmei Changsha University of Science and Technology School of Computer and Communication Engineering China University of South China College of Computer Science China

The advanced metering infrastructure (AMI) network is the most critical part of a smart grid, and it faces serious security challenges from network attacks. Federated learning (FL) is a common method for addressing network security problems;however, it has several shortcomings, such as difficulty in obtaining labeled data and high communication costs. Therefore, in this paper, an efficient intrusion detection method based on federated semi-supervised learning is proposed to improve the efficiency of AMI network intrusion detection and reduce communication overhead. First, an intrusion detection framework based on federated distillation (FD) is established to address intense assumption dependency problems and reduce communication overhead. Then, an efficient intrusion detection algorithm is used to improve the classification performance. Finally, the designed deep convolutional generative adversarial network (DCGAN) model is used to obtain high-quality sample data. The experimental results show that the scheme achieves an accuracy of 99.58%, a communication overhead of 75 MB, and a reduction in the false-positive rate of 6%. © 2013 IEEE.

关键词： Federated learning

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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Radio resource management and path planning in intelligent transportation systems via reinforcement learning for environmental sustainability

Radio resource management and path planning in intelligent t...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Norouzi, S. Azarasa, N. Abedi, M.R. Mokari, N. Seyedabrishami, S.E. Saeedi, H. Jorswieck, E.A. Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Civil and Environmental Engineering Tarbiat Modares University Tehran Iran College of Engineering Doha University for Science and Technology Doha Qatar Institute of Communications Technology TU Braunschweig Lower Saxony Germany

ISBN: (纸本)9781032803722

Efficient and dynamic path planning has become an important topic for urban areas with larger density of connected vehicles (CV) which results in reduction of travel time and directly contributes to environmental sustainability through reducing energy consumption. CVs exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the vehicle-to-infrastructure (V2I) messages to the Base-station (BS) to improve situation awareness on urban roads. In this paper, we investigate radio resource management (RRM) in such a framework to minimize the age of information (AoI) so as to enhance path planning results. We use the fact that V2I messages with lower AoI value result in less error in estimating the road capacity and more accurate path planning. Through simulations, we compare road travel times and volume over capacity (V/C) against different levels of AoI and demonstrate the promising performance of the proposed framework. © 2025 The Author(s).

关键词： Resource allocation

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EBCFL: An Efficient BlockChain-based Federated Learning Framework for Internet of Vehicles

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IEEE Network 2025年

作者： Cao, Dun Liao, Yonghui Alqahtani, Fayez Wang, Jin School of Computer and Communication Engineering Changsha University of Science and Technology China Changsha University of Science and Technology China Software Engineering Department College of Computer and Information Sciences King Saud University Saudi Arabia Hunan University of Science and Technology China

Federated Learning (FL) can train a shared model while protecting vehicle data privacy, which has significant advantages in improving driving safety and efficiency. However, there are still many challenges in applying FL to Internet of Vehicles (IoV). First, the varying computing capabilities of moving vehicles result in slow convergence speed due to underperforming vehicles. Second, malicious vehicles may tamper with model parameters before uploading them to the central server, which is itself vulnerable to single-point failure. To solve the above challenges, we design an efficient and decentralized blockchain FL framework (Efficient BlockChain-based Federated Learning Framework, EBCFL). This framework effectively prevents possible malicious behaviors in the vehicular network nodes and improves the efficiency of FL through a well-designed security verification mechanism. The experimental results show that EBCFL outperforms other schemes in terms of average test accuracy and average total time cost, indicating that EBCFL significantly improves the learning efficiency while bearing an acceptable additional time cost for blockchain communication, demonstrating its strong ability to guard against malicious behaviours and ensure robustness. © 1986-2012 IEEE.

关键词： Blockchain Federated learning IoV Security verification

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A Contend Node-Restricted Receiver-Initiated MAC Protocol With Consecutive Packet Transmission

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green communications and Networking 2025年

作者： Liu, Yuxin Ota, Kaoru Dong, Mianxiong Wang, Ruoyu Changsha University College of Computer Science and Engineering Changsha410022 China Muroran Institute of Technology Department of Sciences and Informatics Japan Central South University School of Computer Science and Engineering Changsha410083 China

The medium access control (MAC) protocol plays a crucial role in wireless communications, influencing key performance metrics, such as energy consumption, delay, collision, and throughput. This paper introduces the Contend Node Restricted Joint Consecutive Packet Transmissions Receiver-Initiated (CNR-CPT-RI) protocol, specifically designed to minimize collisions and reduce energy consumption in wake-up radio-enabled wireless sensor networks (WSNs). The CNR-CPT-RI protocol is an effective communication protocol that is mainly applied with nodes in the one-hop range of the sink. Since the sink has the ability to control each node, it becomes feasible to manage the nodes in a way that effectively reduces collisions and enhances network lifetime as well as throughput. Moreover, the CNR-CPT-RI protocol can adaptively adjust the number of nodes participating in channel contention, maintaining it within an optimal range to balance reduced collisions, decreased energy consumption, and sustained high throughput. Furthermore, the winning node in channel contention employs Consecutive Packet Transmissions (CPTs) method to further diminish competition. Comprehensive theoretical and experimental results demonstrate that the number of collisions of the CNR-CPT-RI protocol is only 42.4%, 35.9%, and 32.5% of the baseline protocol when the number of nodes is 10, 20, and 30, respectively. The energy consumption of the CNR-CPT-RI protocol is reduced by 5.7%, 15%, and 22.6%, and the delay is decreased by 6.7%, 16%, and 32%, respectively, compared with that of the existing protocol. © 2017 IEEE.

关键词： Internet protocols

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Semi-Blind Channel Estimation and Achievable Rate Analysis for Uplink RIS-Enhanced Multi-User Networks

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular technology 2025年 6卷 1108-1139页

作者： Farghaly, Samar I. Ismail, Mohamed I. Fouda, Mostafa M. Alwakeel, Ahmed S. Tanta University Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta31111 Egypt Idaho State University Department of Electrical and Computer Engineering College of Science and Engineering PocatelloID83209 United States Modern Academy for Engineering Department of Electronic Engineering and Communication Technology Cairo Governorate 4411602 Egypt Center for Advanced Energy Studies Idaho FallsID83401 United States Suez University Department of Electrical Engineering Faculty of Engineering Suez43221 Egypt

Future wireless networks could benefit from the energy-efficient, low-latency, and scalable deployments that Reconfigurable Intelligent Surfaces (RISs) offer. However, the creation of an effective low overhead channel estimate technique is a major obstacle in RIS-assisted systems, especially given the high number of RIS components and intrinsic hardware constraints. This research examines the uplink of a RIS-empowered multi-user MIMO communication system and presents a novel semi-blind channel estimate approach. Unlike current approaches, which rely on pilot-based channel estimation, our methodology uses data to estimate channels, considerably enhancing the achievable rate. We provide a closed-form deterministic expression for the uplink achievable rate in actual settings where the channel state information (CSI) must be estimated rather than assumed perfect. The results of the simulations show that the formula obtained is accurate, with a close alignment between the deterministic and actual achievable rates (generally between 2 5% deviations). The proposed approach outperforms traditional approaches, resulting in rate increases of up to 35–40%, especially in instances with more RIS elements. These findings illustrate RIS technology's tremendous potential to improve system capacity and coverage, providing useful insights for optimizing RIS adoption in future wireless networks. © 2020 IEEE.

关键词： Channel state information

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