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2024 Advances in Science and engineering Technology International Conferences, ASET 2024

作者： Vasileiadis, Nikolaos Bilal, Asif Iezekiel, Stavros Voudouris, Konstantinos University of West Attica Department of Electrical and Electronics Engineering Athens Greece University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (纸本)9798350344134

This paper presents the results of a wireless power transfer (WPT) rectifier connected to an antenna (rectenna), suitable to bias the front-end of a 5G receiver at FR1 frequency band. A patch antenna has been designed to receive the RF power at 3.45 GHz (Sub 6 GHz band), while the rectification process is based on a full wave voltage doubler rectifier. The matching circuit between the patch antenna and the rectifier is tuned for maximum power transfer with a varying load between 1kOhm to 5kOhm. When the antenna receives a power of - 10dBm, the rectifier produces a corresponding DC voltage of one Volt with an RF to DC power conversion efficiency of 38 The rectenna circuit is studied and designed using the PathWave Advanced Design System (ADS) tool. The proposed design fully covers the power needs of an RF front-end receiver at FR1 and paves the way for other practical implementations in various wireless energy harvesting applications. © 2024 IEEE.

关键词： Rectennas

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A Review of Genome to Chaos: Exploring DNA Dynamics in Security

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Multimedia Tools and Applications 2024年 1-28页

作者： Aashiq Banu, S. Rao, L. Koteswara Priya, P. Shanmuga Thanikaiselvan Hemalatha, M. Dhivya, R. Rengarajan, Amirtharajan Department of Systems Engineering Military Technological College Muscat Oman Department of ECE Koneru Lakshmaiah Education Foundation Telangana Hyderabad500043 India Department of Electronics and Communication Engineering Rajalakshmi Engineering College ThandalamChennai602105 India Vellore632 014 India Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi626005 India School of Electrical & amp Electronics Engineering SASTRA Deemed University Thanjavur613401 India

One of the largest and fastest-growing industries globally is e-healthcare. Digital medical imaging constantly evolves, advancing from standard computed tomography to powerful multi-dimensional imaging. This medical imaging forms patients’ real-time electronic health records (EHR), making it a prime target for hackers. To counter cipher-attacks, it is necessary to secure medical imaging. This research thoroughly analyses the properties of DNA chaos-based digital medical image encryption and reviews various algorithms. Researchers have performed several statistical and differential metrics, conducting simulation experiments to determine which methodology provides the best security outcome. The paper concludes by analysing significant advances in medical image encryption and emphasising future challenges. This paper discusses the importance of securing medical images, the potential breaches, and the security needs, providing a comprehensive review of DNA-based chaos cryptography techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

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An Optimal Speed Control Stratergy for Brushless DC Motor Drive in EV Applications 18

An Optimal Speed Control Stratergy for Brushless DC Motor Dr...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Devi Vighneshwari, B. Raichel Ruby, M. Sumitha, T.L. Resna, S.R. Jayakumar, N. Kumar, Saravana The Oxford College Engineering Department of Electrical and Electronics Engineering Bengaluru India The Oxford College Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9789380544519

Because of their high torque, high power density, and appropriate speed range, brushless DC (BLDC) motors are the top choice for lightweight electric vehicles. The application's goal is to use a Hall position sensor to build a speed-closed loop BLDC driver. The Enhanced Exploration of Aquila optimized control for speed in brushless DC motor is presented in this study as a new conceptual upgrade of Aquila optimization. In this study we employ and optimization - driven methodology to fine tune both the PI speed and voltage controllers, where the objective function is centered on minimizing errors. We evaluate the performance of our approach in contrast to a system utilizing AO optimization. When compared to the existing optimization of the PI controller, MATLAB simulation results reveal that the suggested optimized controller is more effective in the time domain performance. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Aquila Optimization(AO) Brushless DC motor Electric Vehicle Enhanced Exploration of Aquilla Optimization (EEAO) PI controller Speed controller Voltage controller

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Physical Beam Sharing for Communications With Multiple Low Earth Orbit Satellites

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IEEE Transactions on Signal Processing 2024年 72卷 2783-2798页

作者： He, Yan-Yin Tsai, Shang-Ho Poor, H. Vincent Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Physical (analog) beamforming is expected to become an important technique in Low Earth Orbit (LEO) satellite transmission in upcoming 6G communications. To build dense networks via LEO satellites and decrease deployment expenses, the corresponding satellites should have minimal hardware, low computational complexity, and limited power consumption. Additionally, issues such as different propagation delays, Doppler effects, long feedback delays, and serious attenuation caused by bad weather should also be addressed in LEO satellite communications. This work proposes a novel physical beam sharing scheme for multiple-satellite systems that enables a satellite to simultaneously serve multiple users using only one Radio-Frequency (RF) chain, consequently leading to a significant reduction in hardware complexity and power consumption. In this case, all users can achieve full multiplexing gain. Furthermore, we propose two ways of optimizing for this novel scheme. One is maximizing the throughput of all users, and the other is minimizing the LEO satellite system's power consumption. For the problems, we propose both solutions using instantaneous Channel State Information (iCSI) and long-term statistical Channel State Information (sCSI). Simulation results demonstrated that the proposals significantly outperform conventional schemes in terms of throughput and power consumption, and the performance of the proposed sCSI solutions is close to that of iCSI ones. © 1991-2012 IEEE.

关键词： Electric power utilization

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Overview of Versatile Video Coding (H.266/VVC) and Its Coding Performance Analysis

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IEIE Transactions on Smart Processing and Computing 2023年第2期12卷 122-154页

作者： Lee, Minhun Song, HyeonJu Park, Jeeyoon Jeon, Byeungwoo Kang, Jungwon Kim, Jae-Gon Lee, Yung-Lyul Kang, Je-Won Sim, Donggyu Department of Computer Engineering Kwangwoon University Seoul Korea Republic of Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Kyunggi-do Korea Republic of Communication and Media Research Laboratory Electronics and Telecommunications Research Institute Deajeon Korea Republic of School of Electronics and Information Engineering Korea Aerospace University Kyunggi-do Korea Republic of Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

Versatile Video Coding (H.266/VVC) is the newest video coding standard jointly developed by the Joint Video Experts Team (JVET), which is organized by the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). H.266/VVC provides about 40% bitrate reduction compared with High Efficiency Video Coding (H.265/HEVC) for the same visual quality. This paper introduces in detail the core structure of H.266/VVC by highlighting its features within block partitioning structure, intra/inter prediction, transform, quantization, and in-loop filtering, compared to its predecessor (H.265/HEVC). H.266/VVC yields significantly improved the coding performance, but it increased the computational complexity, particularly for the encoder side, which remains a problem to be tackled for successful commercialization in the future. This paper examined the statistical performance of H.266/VVC coding tools from the bitstreams encoded by the VVC test model (VTM12.0) encoder through rate-distortion optimization under the JVET common test conditions. In addition, the complexity and performance analyses are conducted on the block partitioning structure and group of picture structure. It is expected that an optimized H.266/VVC encoder can be designed and developed by minimizing the coding loss based on the analysis data. © 2023 Institute of electronics and Information Engineers. All rights reserved.

关键词： Image compression

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Hybrid Reptile-Snake Optimizer Based Channel Selection for Enhancing Alzheimer’s Disease Detection

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Journal of Bionic engineering 2025年第2期22卷 884-900页

作者： Digambar Puri Pramod Kachare Smith Khare Ibrahim Al-Shourbaji Abdoh Jabbari Abdalla Alameen Department of Electronics and Telecommunication Ramrao Adik Institute of TechnologyD.Y.Patil CampusNavi-MumbaiMaharashtra400706India Maersk Mc-Kinney Moller Institute Faculty of EngineeringUniversity of Southern DenmarkOdenseDenmark Department of Electrical and Electronics Engineering Jazan UniversityJazan45142Saudi Arabia Department of Computer Science University of HertfordshireHatfieldUK Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam Bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia

The global incidence of Alzheimer's Disease(AD)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning *** of AD using EEG involves multi-channel ***,the use of multiple channels may impact the classification performance due to data redundancy and *** this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition *** Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG *** extracted thirty-four features from each subband of EEG ***,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel *** effectiveness of this model is assessed by two publicly accessible AD EEG *** accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG ***,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)*** performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.

关键词： Alzheimer's Disease Brain disorder Electroencephalogram Reptile Search Algorithm Snake Optimizer Optimization

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An Ant-colony Based Model for Load Balancing in Fog Environments

Supercomputing Frontiers and Innovations

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Supercomputing Frontiers and Innovations 2023年第1期10卷 4-20页

作者： Mirtaheri, Seyedeh Leili Azari, Mahya Greco, Sergio Arianian, Ehsan Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Faculty of Mathematics and Computer Science Kharazmi University Tehran Iran Department of Computer Engineering Modeling Electronics and Systems University of Calabria Italy Tehran Iran

Delay-sensitive applications are becoming more and more in demand as a result of the development of information systems and the expansion of communication in cloud computing technologies. Some of these requests will be overlooked in cloud environments due to the communication delay between the processing center and the client’s request. The ‘fog-based computing paradigm’, a novel processing model, can be added to cloud computing to help with the aforementioned issues. The performance of computing systems is always influenced by latency. In this paper, we focus on balancing the load on the fog nodes to lower the latency. It is also a crucial component of fog computing devices. It necessitates the use of load-balancing algorithms to select the optimal hosts, resulting in an even distribution of the load on the available *** provide a load-balancing approach based on the Ant-colony optimization algorithm’s latency rate for responding to tasks. A random data set evaluation of this model reveals shorter response times than those of earlier strategies suggested in this field. © The Authors 2023. This paper is published with open access at ***

关键词： Fog

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance

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computers, Materials & Continua 2024年第9期80卷 4085-4110页

作者： Syed Kamran Haider Abbas Ahmed Noman Mujeeb Khan Ali Nauman Sung Won Kim College of Internet of Things(IoT)Engineering Hohai UniversityChangzhou213001China Department of Electrical and Electronics Engineering Beaconhouse International CollegeIslamabad44000Pakistan School of Computer Science and Engineering Yeungnam UniversityGyeongsan38428Republic of Korea

In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network *** research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in *** proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path *** a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication *** comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced ***,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.

关键词： UAV trajectory clustering next-generation wireless sensor network(NGWSN) energy efficiency mobile sink

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Multi-Scale Network with Integrated Attention Unit for Crowd Counting

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computers, Materials & Continua 2022年第11期73卷 3879-3903页

作者： Adel Hafeezallah Ahlam Al-Dhamari Syed Abd Rahman Abu-Bakar Department of Electrical Engineering Taibah UniversityMadinahSaudi Arabia Department of Electronics and Computer Engineering School of Electrical EngineeringFaculty of EngineeringUniversiti Teknologi Malaysia81310JohorMalaysia Department of Computer Engineering Hodeidah UniversityHodeidahYemen

Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large ***,since the crowd images in this case can range from low density to high density,detection-based approaches are hard to apply for crowd ***,deep learning-based regression has become the prominent approach for crowd counting problems,where a density-map is estimated,and its integral is further computed to acquire the final count *** this paper,we put forward a novel multi-scale network(named 2U-Net)for crowd counting in sparse and dense *** proposed framework,which employs the U-Net architecture,is straightforward to implement,computationally efficient,and has single-step *** layers are used to retrieve the pooling layers’erased information and learn hierarchically pixelwise spatial *** helps in obtaining feature values,retaining spatial locations,and maximizing data integrity to avoid data *** addition,a modified attention unit is introduced and integrated into the proposed 2UNet model to focus on specific crowd *** proposed model concentrates on balancing the number of model parameters,model size,computational cost,and counting accuracy compared with other works,which may involve acquiring one criterion at the expense of other *** on five challenging datasets for density estimation and crowd counting have shown that the proposed model is very effective and outperforms comparable mainstream ***,it counts very well in both sparse and congested crowd *** 2U-Net model has the lowest MAE in both parts(Part A and Part B)of the ShanghaiTech,UCSD,and Mall benchmarks,with 63.3,7.4,1.5,and 1.6,***,it obtains the lowest MSE in the ShanghaiTech-Part B,UCSD,and Mall benchmarks with 12.0,1.9,and 2.1,respectively.

关键词： computer vision crowd analysis crowd counting U-Net max-pooling index unpooling attention units

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