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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Sanila, Kunnathully Sadanandan Nilsson, Rickard Ibrahim, Emad Rajamohan, Neelakandan Luleå University of Technology Department of Computer Science Electrical and Space Engineering Sweden Ericsson Research Sweden School of Electrical Sciences Indian Institute of Technology Goa India

ISBN: (纸本)9798350368369

We propose a low-power mobile low earth orbit (LEO) satellite communication architecture, employing double reconfigurable intelligent surfaces (RIS) to enhance energy efficiency and signal performance. With a distance between RISs that satisfies the far-field requirement, this architecture positions one small RIS each in the near-field of the satellite's antenna and the user on the ground. Moreover, we develop a path loss model for the double- Ris communication link, considering the near-field and far-field effects. Further, with the help of dual-stage beamforming, the proposed system maximizes the signal power and minimizes power consumption. Simulation results show that the proposed architecture can reduce the power consumption with 40 dB in the uplink, with a small 0.252m2 RIS near the user, to communicate in energy-constrained LEO satellite communication circumstances. © 2025 IEEE.

关键词： Satellite communication systems

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ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN

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Journal of Biomedical Physics and engineering 2025年第1期15卷 77-92页

作者： Rahman, Tawfikur Ahommed, Rasel Deb, Nibedita Das, Utpal Kanti Moniruzzaman, Md. Bhuiyan, Md. Alamgir Sultana, Farzana Kausar, Md. Kamruzzaman Department of Electrical and Electronic Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Agriculture International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Computer Science and Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh

Background: Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency. Objective: This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities along-side normal signals. Material and Methods: This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on di-verse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score. Results: The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting ***: The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analy-sis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection. © Journal of Biomedical Physics and engineering.

关键词： Wavelet transforms

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Surface flashover in 50 years:*** modification,structureoptimisation,and characteristics enhancement

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High Voltage 2025年第2期10卷 243-278页

作者： Zhen Li Ji Liu Yoshimichi Ohki George Chen Shengtao Li Key Laboratory of Engineering Dielectrics and Its Application Ministry of EducationHarbin University of Science and TechnologyHarbinHeilongjiangChina State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong UniversityXi'anShaanxiChina Research Institute for Materials Science and Technology Waseda UniversityShinjukuJapan School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Surface flashover is a gas-solid interface insulation failure that significantly jeopardises the secure operation of advanced electronic,electrical,and spacecraft *** the widespread application of numerous material modification and structure optimisation technologies aimed at enhancing surface flashover performance,the influence mechanisms of the present technologies have yet to be systematically discussed and *** review aims to introduce various material modification technologies while demonstrating their influence mechanisms on flashover performances by establishing relationships among‘microscopic structure-mesoscopic charge transport-macroscopic insulation failure’.Moreover,it elucidates the effects of chemical structure on surface trap parameters and surface charge transport concerning flashover *** review categorises and presents structure optimisation technologies that govern electric field *** identified technologies highlight that achieving a uniform tangential electric field and reducing the normal electric field can effectively enhance flashover ***,this review proposes recommendations encompassing mathematical,chemical,evaluation,and manufacturing *** systematic summary of current technologies,their influence mechanisms,and associated advantages and disadvantages in improving surface insulation performance is anticipated to be a pivotal component in flashover and future dielectric theory.

关键词： material modification structure optimization surface flashover structure optimisation technologies chemical structure charge transport insulation failure

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Joint trust-based detection and signature-based authentication technique for secure localization in underwater wireless sensor network

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Multimedia Tools and Applications 2025年 1-20页

作者： Kumar, Manni Goyal, Nitin Qaisi, Ramy Mohammed Aiesh Najim, Mohd Gupta, Sachin Kumar Department of Computer Science & Engineering Chandigarh University Gharuan Punjab India Department of Computer Science Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Electrical and Electronic Engineering College of Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Electronics and Communication Engineering Central University of Jammu UT of J&K Jammu181143 India

Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interference. This research addresses this issue by proposing a Joint Trust-based Detection and Signature-based Authentication (JTDSA) technique, aimed at improving both the security and accuracy of node localization in UWSNs. The technique utilizes Intrusion Detection System (IDS) methods to calculate the trust values of secondary-anchor nodes (SNs), ensuring only the most trusted SNs are selected. Furthermore, a digital signature algorithm secures the Received Signal Strength (RSS) data from tampering. The method incorporates a hybrid approach combining Angle of Arrival (AoA) and RSS ranging techniques to achieve robust localization. Simulation results demonstrate that JTDSA improves localization accuracy by 4% compared to existing methods, achieving an overall accuracy of 99.2%. The technique also reduces packet drop by 35% and enhances attack detection accuracy by 3%. These results indicate that the proposed JTDSA technique offers an effective solution for secure and reliable localization in UWSNs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sensor nodes

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Pandemic Analysis and Prediction of COVID-19 Using Gaussian Doubling Times

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computers, Materials & Continua 2022年第7期72卷 833-849页

作者： Saleh Albahli Farman Hassan Ali Javed Aun Irtaza Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of Computer Science and Engineering Oakland UniversityMIUSA Department of Computer and Electrical Engineering University of MichiganDearbornUSA

COVID-19 has become a pandemic,with cases all over the world,with widespread disruption in some countries,such as Italy,US,India,South Korea,and *** and reliable detection of COVID-19 is mandatory to control the spread of ***,prediction of COVID-19 spread in near future is also crucial to better plan for the disease *** this purpose,we proposed a robust framework for the analysis,prediction,and detection of *** make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the *** estimates,analysis and predictions are based on the data gathered fromJohns Hopkins Center during the time span of April 21 to June 27,*** use the normal distribution for simple and quick predictions of the coronavirus pandemic model and estimate the parameters of Gaussian curves using the least square parameter curve fitting for several countries in different *** predictions rely on the possible outcomes of Gaussian time evolution with the central limit theorem of statistics the predictions to be well *** parameters of Gaussian distribution,i.e.,maximumtime and width,are determined through a statisticalχ^(2)-fit for the purpose of doubling times after April 21,*** COVID-19 detection,we proposed a novel method based on the Histogram of Oriented Gradients(HOG)and CNN in multi-class classification scenario i.e.,Normal,COVID-19,viral pneumonia *** results show the effectiveness of our framework for reliable prediction and detection of COVID-19.

关键词： Prediction coronavirus time-series prediction gaussian mathematical model pandemic spreading

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Finite-time synchronization control for a class of delayed neural networks:an improved two-step control method

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science China(Information sciences) 2025年

作者： Yue CHEN Song ZHU Mouquan SHEN Xiaoyang LIU Shiping WEN School of Mathematics China University of Mining and Technology College of Electrical Engineering and Control Science Nanjing Technology University School of Computer Science and Technology Jiangsu Normal University Faculty of Engineering and Information Technology University of Technology Sydney

In this paper,a two-step control method is proposed,leveraging the generalized Halanay inequality and existing finite-time stability theorems,to achieve finite-time synchronization for a class of neural networks with bounded time-varying *** the first step,the system state is attenuated from V （t0） to γV （t0） using the generalized Halanay inequality,where0<γ 1 is a free *** the second step,by applying existing finite-time stability theorems,the system state further decays from γV （t0） to *** on the above ideas,two novel finite-time stability lemmas for the error system are presented,and the convergence rate as well as the settling time is ***,the value of γ that results in the shortest settling time for the error system is also *** the help of the derived lemmas,several sufficient algebraic criteria are established to achieve finite-time synchronization between the considered delayed neural *** results of this paper not only improve the existing two-step control method but also overcome the limitations of certain one-step finite-time control ***,the validity and practical applicability of the obtained theoretical results are demonstrated through two numerical examples and an image protection experiment.

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Bio-Signal Acquisition System for Psychological Stress Classification 15

Bio-Signal Acquisition System for Psychological Stress Class...

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15th International Conference on Information and Communication technology Convergence, ICTC 2024

作者： Rahman, Daris Muhammad Permana, Arvanida Feizal Sakanti, Maria Mahardini Adristi, Cornelia Luo, Win-Jet Kuncoro, C. Bambang Dwi National Chin-Yi University of Technology Department of Refrigeration Air Conditioning and Energy Engineering Taichung41170 Taiwan Graduate Institute of Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung41170 Taiwan Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung41170 Taiwan Universitas Indonesia Faculty of Engineering Department of Electrical Engineering Depok16424 Indonesia

ISBN: (纸本)9798350364637

The increasing prevalence of psychological stress in modern society necessitates the development of effective monitoring and classification systems. This paper presents the design and implementation of the acquisition system for the bio-signal measurement. The proposed system utilizes a combination of sensors and a microcontroller to collect and analyze physiological data indicative of stress levels. The bio-signals data monitored include heart rate, pulse rate, galvanic skin response, body temperature, and acceleration. The data acquisition process is managed by a microcontroller, which processes the raw signals and transmits them to a central processing unit for further analysis. The built system captures the acquired data in real time, offering immediate feedback and long-term monitoring capabilities. The system operates on a small power supply of 5V and 250mA, featuring a modular and portable design for ease of use. This approach aims to offer a cost-effective, non-invasive solution for early detection and management of psychological stress, contributing to improved mental health outcomes. Experimental results demonstrate the system's accuracy and reliability in acquiring and processing bio-signal data. These results represent preliminary research, with future work focusing on integrating advanced data analysis techniques and machine learning algorithms to enhance stress classification and prediction, expanding the system's applicability in clinical and everyday settings. © 2024 IEEE.

关键词： Microcontrollers

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Fast Hybrid Far/Near-Field Beam Training For Extremely Large-Scale Millimeter Wave/Terahertz Systems

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IEEE Transactions on Communications 2024年第6期73卷 3956-3970页

作者： Wang, Hongwei Fang, Jun Duan, Huiping Li, Hongbin University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China University of Electronic Science and Technology of China School of Information and Communications Engineering Chengdu611731 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

In this paper, we consider the problem of downlink beam training for extremely large-scale millimeter wave (mmWave)/Terahertz (THz) systems, where the far-field assumption which treats wavefronts as planar waves may not hold valid. For such hybrid far/near-field channels, beam training needs to identify the best beam alignment on a two-dimensional angle-range domain. An exhaustive search scheme sequentially scanning the entire angle-range space incurs a high training overhead. To address this issue, in this paper, we propose an efficient hybrid far/near-field beam training method. By utilizing the approximate orthogonality of near-field steering vectors of the same effective distance, we devise a multi-directional beam training sequence which can more efficiently scan the entire angle-range space. Based on the devised beam training sequence, we develop a simple estimation method at the receiver that can simultaneously identify the angle and the range associated with the dominant path. Simulation results show that the proposed method achieves better performance than the exhaustive search scheme, while with a much lower overhead cost. The proposed method also presents a clear advantage over other existing state-of-the-art hybrid far/near-field beam training methods in terms of performance and generality. © 1972-2012 IEEE.

关键词： Wavefronts

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MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 IEEE.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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Computational Optimization of RIS-Enhanced Backscatter and Direct Communication for 6G IoT:A DDPG-Based Approach with Physical Layer Security

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computer Modeling in engineering & sciences 2025年第3期142卷 2191-2210页

作者： Syed Zain Ul Abideen Mian Muhammad Kamal Eaman Alharbi Ashfaq Ahmad Malik Wadee Alhalabi Muhammad Shahid Anwar Liaqat Ali College of Computer Science and Technology Qingdao UniversityQingdao266071China School of Electronic Science and Engineering Southeast UniversityNanjing210018China Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah80200Saudi Arabia Department of Quality Assurance Al-Kawthar UniversityKarachi75300Pakistan Department of Computer Science Immersive Virtual Reality Research GroupKing Abdulaziz UniversityJeddah80200Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea Department of Electrical Engineering University of Science and TechnologyBannu28100Pakistan

The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient *** study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication *** traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter *** propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase *** optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy ***,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS *** results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G *** work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.

关键词： Computational optimization reconfigurable intelligent surfaces(RIS) 6G networks IoT and DDPG physical layer security(PLS) backscatter communication

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