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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Chejarla Raja S D. Arivazhagan S Lavanya Rajeswari C Banu S Rahmaan K Faculty of Information and Communication Engineering Anna University Chennai Tamil Nadu India AMET Business School Academy of Maritime Education and Training Deemed to be University Chennai India Department of Computer Science and Engineering KCG College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

This research presents a dual-pathway SlowFast network in order to provide a novel approach for the recognition of human actions. The method has the ability to capture low-intensity as well as high-intensity activities efficiently. The model is trained on two different datasets: one on the routine activities, such as sitting and walking, and the other on complex sports and leisure activities. The SlowFast network increases the accuracy in a wide variety of scenarios by incorporating high-level spatial context along with fast motion characteristics using a dual-path architecture. The proposed model outperforms the conventional HAR methods on different grounds such as adaptability and accuracy, and thus aptly useful for applications such as security surveillance, healthcare, and sports analysis. This approach brings forth a robust system able to identify a range of human actions in static as well as dynamic environments by removing some of the limitations from existing HAR models.

关键词： Adaptation models Analytical models Accuracy Medical services Video surveillance Real-time systems Motion capture Human activity recognition Security Sports

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Target Localization for Distributed Hybrid Active-Passive Radars

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhou, Qiyu Yuan, Ye Li, Hongbin Lai, Yangming Greco, Maria Sabrina Gini, Fulvio Yi, Wei University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States Fuzhou University School of Advanced Manufacturing Fuzhou350108 China University of Pisa Department of Information Engineering Pisa56122 Italy

Hybrid active-passive radar (HAPR) can effectively reduce the radiation power of active nodes, thus improving the radar system's electromagnetic environmental friendliness and anti-interception capability. Meanwhile, active nodes in HAPR can provide cooperative transmitters with known signals for passive nodes, overcoming performance losses caused by non-cooperative illuminators of opportunity (IOs). Although the advantages of HAPR have been gradually recognized in recent years, there is currently almost no systematic solution to the joint localization problem involving active and passive nodes. To address this problem, we propose two target localization algorithms for distributed HAPRs based on two-step and, respectively, direct localization. In the two-step localization, we establish two geometric equation sets with consistent structures to handle different geometric relationships among the target, transmitters/IOs, and receivers associated with the active and passive measurements. In direct localization, we reduce the high-dimensional joint likelihood function constructed from active and passive received signals into a low-dimensional one through two dimensionality reductions, significantly reducing the complexity. These algorithms combine active and passive received signals, enhancing localization performance. We also derive the Cramér-Rao lower bound (CRLB), which provides a useful performance indicator. The results indicate that the two-step localization algorithm yields suboptimal performance, with localization errors approaching the CRLB only under specific conditions. The direct localization algorithm approaches the CRLB and outperforms systems using only active or passive nodes, thereby confirming its effectiveness and robustness. © 1965-2011 IEEE.

关键词： Transmitters

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Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach

Breast Cancer Detection using Domain-Adversarial Training (D...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Akash K V Esther Daniel S. Seetha S. Durga Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Information Science & Engineering CMR Institute of Technology India TIFAC CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older *** that this model act as an effective tool for detecting the breast cancer in the early stages.

关键词： Training Accuracy Risk minimization Neural networks Transfer learning Medical services Breast cancer Data models Reliability Tumors

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Fault-tolerant Mobile Service Offloading in Mobile Edge Computing

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IEEE Transactions on Automation Science and engineering 2025年

作者： Long, Tingyan Xia, Yunni Zhou, Mengchu Li, Jianqi Ma, Yong Ai-Turki, Yusuf Guizhou University School of Computer Science and Technology Guiyang550025 China Chongqing University College of Computer Science Chongqing400030 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Global energy interconnection research institute Co. Ltd Beijing102209 China Jiangxi Normal University School of Computer and Information Engineering Nanchang China King Abdulaziz University K. A. Care Energy Research and Innovation Center The Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering Jeddah21589 Saudi Arabia

Mobile edge computing (MEC) is an evolving paradigm for rendering services through network-accessible resources deployed over Internet of Things (IoT) nodes at the edge. Nevertheless, an MEC environment usually employs thousands of physical machines connected via hundreds of switches/routers that communicate and coordinate to deliver computing service. In such complicated systems, faults caused by software, human errors, and hardware are often unavoidable. The edge of network presents a dynamic environment with great quantities of terminals, high mobility of mobile devices, heterogeneous applications, and intermittent traffic. In such an environment, MEC can suffer from unbalanced resource provisioning and interruptions of faults occurring at different levels, which further causes task faults and affects service quality. To address this challenge, this work proposes a novel fault-tolerant offloading method for handling faults by leveraging a reinforcement-learning-based service offloading decision model. The model synthesizes a Dueling Deep Q Network (DQN)-based algorithm for deciding user offloading behaviors and an adaptive checkpointing method for improving task execution reliability. For the purpose of model validation and comparison, extensive simulations are conducted. Numerical results clearly demonstrate that the proposed method is highly effective and outperforms existing methods. © 2004-2012 IEEE.

关键词： Mobile applications

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computer vision algorithms in healthcare: Recent advancements and future challenges

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computers in Biology and Medicine 2025年 185卷 109531-109531页

作者： Kabir, Md Mohsin Rahman, Ashifur Hasan, Md Nahid Mridha, M.F. School of Innovation Design and Engineering Mälardalens University Västerås722 20 Sweden Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur-2 Dhaka1216 Bangladesh Department of Computer Science University of Wisconsin–Milwaukee MilwaukeeWI53211 United States Department of Computer Science American International University-Bangladesh Dhaka Dhaka1229 Bangladesh

computer vision has emerged as a promising technology with numerous applications in healthcare. This systematic review provides an overview of advancements and challenges associated with computer vision in healthcare. The review highlights the application areas where computer vision has made significant strides, including medical imaging, surgical assistance, remote patient monitoring, and telehealth. Additionally, it addresses the challenges related to data quality, privacy, model interpretability, and integration with existing healthcare systems. Ethical and legal considerations, such as patient consent and algorithmic bias, are also discussed. The review concludes by identifying future directions and opportunities for research, emphasizing the potential impact of computer vision on healthcare delivery and outcomes. Overall, this systematic review underscores the importance of understanding both the advancements and challenges in computer vision to facilitate its responsible implementation in healthcare. © 2024

关键词： Artificial intelligence computer vision Medical imaging computer-aided diagnosis Healthcare Advancements Challenges

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A Real-time Approach for Multi-physics Field Reconstruction of Active Phased Array Antenna

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IEEE Transactions on Antennas and Propagation 2025年

作者： Feng, Shizhe Chen, Qihang Li, Zhixiong Sotelo, Miguel Angel The State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China The Faculty of Mechanical Engineering Opole University of Technology Opole45-758 Poland The Department of Computer Engineering University of Alcalá Madrid28801 Spain

This study proposes an effective approach to reconstruct the multi-physics field of the active phased array antenna (APAA) through limited information of temperature and strain. First, the multi-physics coupling model of the APAA is constructed to analyze the thermal-mechanical-electromagnetic responses;the model calculation accuracy is verified by experimental testing. Then, a large number of samples containing the temperature and deformation information are generated through the coupling model. Subsequently, the multi-physics field reconstruction model is established based on the extreme gradient boosting (XGBoost) method, in which the parallelization computing is implemented to fully improve the prediction efficiency. Lastly, the testing samples are used to evaluate the performance of the reconstruction model. The obtained results indicate that the developed model is able to reconstruct the multi-physics field of the APAA in real time more precisely through comparing with existing machine learning methods. This approach can provide reliable temperature and displacement reconstruction information for the compensation of the APAA electromagnetic performance. © 1963-2012 IEEE.

关键词： Antenna phased arrays

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A new locally t -diagnosable structure under the PMC model with an application to matching composition networks

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Discrete Applied Mathematics 2025年 374卷 1-15页

作者： Meirun Chen Cheng-Kuan Lin Kung-Jui Pai School of Mathematics and Statistics Xiamen University of Technology Xiamen Fujian China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City Taiwan

The PMC model is the test-based diagnosis in which a node performs the diagnosis by testing the neighbor nodes via the links between them. If we concentrate on the status of some nodes then instead of doing the global test, Hsu and Tan proposed the concept of local diagnosis and two structures to diagnose a node under the PMC model. To better evaluate the local diagnosability of a node, we propose a new structure and the related algorithm to diagnose a node under the PMC model in this paper. Applying the two structures proposed by Hsu and Tan, and the new structure we propose in this paper, we determine the accurate value of the local diagnosability of each node in matching composition networks. Simulation results are presented, showing the performance of our algorithm. It shows that even if the failure probability of a node is 0.4, our algorithm can still determine the state of a node with the accuracy above 0.9.

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UNETVL: Enhancing 3D Medical Image Segmentation with Chebyshev KAN Powered Vision-LSTM

UNETVL: Enhancing 3D Medical Image Segmentation with Chebysh...

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IEEE International Symposium on Biomedical Imaging

作者： Xuhui Guo Tanmoy Dam Rohan Dhamdhere Gourav Modanwal Anant Madabhushi Electrical Engineering and Computer Science Department University of Michigan Ann Arbor USA Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

3D medical image segmentation has progressed considerably due to Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), yet these methods struggle to balance long-range dependency acquisition with computational efficiency. To address this challenge, we propose UNETVL (U-Net Vision-LSTM), a novel architecture that leverages recent advancements in temporal information processing. UNETVL incorporates Vision-LSTM (ViL) for improved scalability and memory functions, alongside an efficient Chebyshev Kolmogorov-Arnold Networks (KAN) to handle complex and long-range dependency patterns more effectively. We validated our method on the ACDC and AMOS2022 (post challenge Task 2) benchmark datasets, showing a significant improvement in mean Dice score compared to recent state-of-the-art approaches, especially over its predecessor, UNETR, with increases of 7.3% on ACDC and 15.6% on AMOS, respectively. Extensive ablation studies were conducted to demonstrate the impact of each component in UNETVL, providing a comprehensive understanding of its architecture. Our code is available at https://***/tgrex6/UNETVL, facilitating further research and applications in this domain.

关键词： computer vision Three-dimensional displays Semantic segmentation Scalability Memory management Information processing Chebyshev approximation Transformers Convolutional neural networks Biomedical imaging

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Standardization aspects of Authentication Relay Attack using False Base Station

Standardization aspects of Authentication Relay Attack using...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： K Sowjanya Soumen Jana Vipin Kumar Dhiman Saha Brejesh Lall Bharti School TTM Indian Institute of Technology Delhi New Delhi India Computer Science and Engineering Indian Institute of Technology Bhilai Bhilai India Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

False Base Stations (FBSs) are nowadays proven to be a serious threat to location privacy. Apart from the location privacy threats, FBS causes several threats like Manin-the-Middle (MitM) attacks, eavesdropping, identity tracking, phishing attacks, etc. FBS-based two threats are presented in the 3GPP TR 33.809, namely, Authentication Relay Attack (ARA) and MitM attack. The corresponding solution mentioned for the ARA (which occurs because the user is connected with FBS) in TR 33.809 uses the real location of the user causing the location privacy issue. i.e., the user has to share its GPS location with the legitimate base station, even though it was not connected with the legitimate base station. In this regard, this work proposed an Elliptic Curve Cryptography (ECC) based proximity testing protocol termed as ECC based Private Equality Testing (EPET) to preserve the location privacy of the user under FBS.

关键词： Base stations Protocols Phishing Authentication Standardization Elliptic curve cryptography Relays Testing Global Positioning System Eavesdropping

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Real-Time Detection of Rice Bacterial Blight using Remote Sensing and Deep Learning

Real-Time Detection of Rice Bacterial Blight using Remote Se...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Nitin Rakesh Thirumurugan R N. Mohankumar Prabakaran Kasinathan S. N. Sheela Evangelin Prasad C. Srinivasan Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Electronics and Communication Engineering Adhi College of Engineering and Technology Kanchipuram Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Krishna Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This research introduces an innovative method for the real-time identification of bacterial blight in rice crops via remote sensing and deep learning (DL) technologies. Bacterial blight represents a significant threat to rice production, requiring quick detection for efficient control. To address this challenge, remote sensing data from drones and satellites is analyzed to detect visual signs connected to the diseases. A convolutional neural network (CNN) model is used to classify and identify sick rice plants using high-resolution images. The results demonstrate that the model attained an accurate rate of 98.2%, significantly enhancing the speed and precision of illness identification relative to conventional approaches. The combination of advanced DL algorithms with remote sensing technologies enables quick responses and improves sustainable agriculture practices. It illustrates the capability of using technology for crop monitoring, enhancing food security, and optimizing resource management in agriculture.

关键词： Deep learning Microorganisms Satellites Crops Production Real-time systems Loss measurement Convolutional neural networks Remote sensing Diseases

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