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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Saad, Rasha A. Abd El-Samie, Fathi E. Abdel-Azim, Mohammed El-Hoseny, Heba M. Obour Higher Institute for Engineering and Technology Department of Electronics and Electrical Communications Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Egypt Mansoura University Faculty of Engineering Department of Electronics and Electrical Communications Egypt The Higher Future Institute for Specialized Technological Studies Department of Computer Science Egypt

ISBN: (纸本)9798350323511

A count of crowds tries to determine the total number of persons in crowded places in order to avoid disruption of the safety system and maintain crowd safety. Accurate estimation of the size of crowds is a challenging process because of numerous challenges like occlusion, complicated backgrounds, varying sizes, uneven distribution, alteration of lighting, and adaptable weather. In this study, we propose a solution to two crowd counting challenges: poor weather such as rain and image scaling. We investigate crowd counting in the rainy Shanghai-Tec images. We adopt the GAN-U-Net de-raining model and the SAS-Net [1] and SGA-Net [2] crowd-counting models in our study. © 2023 IEEE.

关键词： computer vision

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Through-Wall Human Sensing Using mmWave FMCW Radar 4

Through-Wall Human Sensing Using mmWave FMCW Radar

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4th Research, Invention, and Innovation Congress: Innovative Electricals and electronics, RI2C 2023

作者： Phaisiri, Kritthee Boonpoonja, Akkarat Kittiyanpunya, Chainarong Kaemarungsi, Kamol Chudpooti, Nonchanutt Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mechatronics Engineering Faculty of Engineering Rajamangala University of Technology Rattanakosin Nakhon Pathom Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Department of Industrail Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798350330618

This paper experimentally demonstrates the through wall sensing to detect human movement using a mmwave FMCW radar operating in the 60 GHz to 64 GHz band. The mmWave radar data processing framework focuses on the generation of point cloud data. Each human movement is illustrated with point cloud data, whose represents the position and orientation of pose, usually in 3D-cartesian coordinate system. In experiments, point cloud data from sensing through two different walls were collected for three activities into a 3 m by 3 m room. The data was analyzed through a comparison between the position of point cloud data and the position of the actual posture movement. The results prove that a mmWave radar placed behind the wall of the room is suitable for human detection and tracking in indoor environments, in terms of consistency with the actual posture. . ©2023 IEEE.

关键词： Frequency modulation

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Differential coding scheme based FSO channel for optical coherent DP-16 QAM transceiver systems

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Journal of Optical Communications 2024年第2期45卷 331-345页

作者： Urooj, Shabana Alwadai, Norah Muhammad Sorathiya, Vishal Lavadiya, Sunil Parmar, Juveriya Patel, Shobhit K. Abd El-Aziz, Ismail A. Rashed, Ahmed Nabih Zaki Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf 32951 Egypt Department of Electrical Engineering College of Engineering Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Department of Physics College of Sciences Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Department of Information and Communication Technology Marwadi University Rajkot India Department of Physics Marwadi University Rajkot360003 India Department of Electronics and Communication Engineering Marwadi University Rajkot360003 India Department of Computer Engineering Marwadi University Rajkot360003 India Benha Faculty of Engineering Benha University Benha Egypt

This article has indicated optical coherent differential polarization (DP) 16 quadrature amplitude modulation (QAM) transceiver systems with free-space optical (FSO) channel in the presence of differential coding scheme. The optical coherent DP 16-QAM receiver executes the reverse process conversion of the optical signal into an electrical one that is detected to the users. The proposed optical coherent DP-16 QAM transceiver systems based FSO channel model with differential coding has been presented and compared with the previous model. However, the simulation results have confidence realization about the superiority of the proposed simulation model. Hence the proposed optical coherent DP-16 QAM transceiver systems simulation model with differential coding is verified and validated the enhancement performance based on simulation performance parameters. © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Quadrature amplitude modulation

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A novel end-to-end approach for epileptic seizure classification from scalp EEG data using deep learning technique

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International Journal of Information technology (Singapore) 2023年第8期15卷 4223-4231页

作者： Kumar, Puranam Revanth Shilpa, B. Jha, Rajesh Kumar Mohanty, Sachi Nandan Department of Electronics and Communication Engineering Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education Hyderabad India School of Computer Science and Engineering (SCOPE) VIT-AP University Andhra Pradesh Amaravathi India

Early detection and proper treatment of epilepsy seizure is essential and meaningful to those who suffer from this disease. Symptoms of seizures are confusion, abnormal gazing, and obsessive hand movements. Neurological exams, blood testing, cognitive tests, and neuroimaging modalities are some of the techniques used to detect epileptic seizure activity in patients. Among them, neuroimaging techniques have attracted a lot of interest from doctors in the field. The use of computer-aided diagnostic systems (CADS) based on deep learning (DL) and neuroimaging modalities can help quickly and accurately diagnose epileptic seizures. This research introduces a novel seizure detection approach based on a bidirectional long short-term memory (Bi-LSTM) network. As a result, Bi-LSTM preserves the nonstationary nature of electroencephalogram (EEG) data while decreasing processing costs by employing local mean decomposition (LMD) and statistical feature extraction algorithms. Two distinct LSTM networks with opposing propagation directions are combined in the deep architecture. So, the deep model can use information from both before and after the current time of analysis to decide the output state. On a long-term scalp EEG database, an average accuracy of 97%, sensitivity of 95.70%, specificity of 93.90%, and G-Mean of 94.80% were achieved, with an anticipation time of 10 min. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Automatic seizure detection Bi-LSTM Deep learning EEG Data Epilepsy Local Mean Decomposition (LMD)

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Fingerprint Matching for Noisy and Distorted Patterns using a Siamese Network with ResNet50 and Multihead Attention

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IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Lehana, Parveen Kumar Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii AlZubi, Ahmad Ali University of Jammu DSP Lab Department of Electronics India University of Jammu Department of Computer Science and IT India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Manipal University Jaipur Jaipur India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa Poland National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Faculty of Informatics and Software Engineering Kyiv Ukraine King Saud University Community College Department of Computer Science Riyadh Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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Guest Editorial: Special Issue on Direct Papers to the IEEE International Flexible electronics technology Conference (IFETC) 2024

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible electronics 2024年第7期3卷 290-291页

作者： Benson, Niels Colella, Riccardo Petti, Luisa Rivadeneyra, Almudena Whiting, Gregory L. Institute of Technology for Nanostructures University of Duisburg-Essen Duisburg Germany National Research Council Rome Italy Free University of Bozen-Bolzano Faculty of Engineering Bozen-Bolzano39100 Italy University of Granada Department of Electronics and Computer Technology Granada Spain University of Colorado Boulder Paul M Rady Department of Mechanical Engineering BoulderCO80309 United States

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Multifunctional control system to enhance the operational capability of the unmanned multirotor platform dedicated to the diagnostics of building structures

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Bulletin of the Polish Academy of Sciences: Technical Sciences 2025年第3期73卷

作者： Czyba, Roman Domin, Jaroslaw Górski, Marcin Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Silesian University of Technology Faculty of Electrical Engineering Gliwice Poland Silesian University of Technology Faculty of Civil Engineering Gliwice Poland

Drones have become common devices that are increasingly used, from toy mini drones to very advanced autonomous flying platforms used in various industries. The safety of using engineering structures and the need to shorten the time of carrying out diagnostic activities, and thus reduce costs, force the use of innovative tools to carry out an automated and unmanned process of assessing the technical condition of existing engineering structures, especially large-size ones, including bridges strengthened with external FRP (fibre reinforced polymer) composite overlays. There are conventional methods for assessing the technical condition of existing building structures conducted by inspectors and experts, involving the use of typical inspection and diagnostic tools. During the research, it was found that it is possible to conduct an automated and unmanned process of assessing the technical condition of engineering structures using an unmanned flying platform equipped with a thermal imaging diagnostic device. The uniqueness of the presented solution is that the drone is equipped with a docking module that allows the drone to be temporarily attached to the structure being diagnosed for the duration of diagnostic activities. To ensure appropriate conditions for structural diagnostics, a unique system of automated diagnostic processes and a multi-stage structure of the control system in all phases of operation were developed. The article presents a solution for the control system based on control consistent with the reference model. In particular, the focus was on the difficult and dangerous issue of altitude control during the docking phase. Simulation tests conducted in the MATLAB/Simulink environment, as well as laboratory and field tests on a real drone proved the correctness of the proposed solutions, which facilitated the undisturbed operation of the diagnostic module. © 2025 The Author(s).

关键词： Drones

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Analysis and Assessment of Wind Energy Potential of Socotra Archipelago in Yemen

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computers, Materials & Continua 2022年第1期70卷 1177-1193页

作者： Murad A.Almekhlafi Fahd N.Al-Wesabi Imran Khan Nadhem Nemri Khalid Mahmood Hany Mahgoub Noha Negm Amin M.El-Kustaban Ammar Zahary Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarP.O.B 814Pakistan Department of Information Systems King Khalid UniversityMayahel AseerKSA Computer Science Department King Khaled UniversityKSA&Faculty of Computers and InformationComputer Science DepartmentMenoufia UniversityEgypt Computer Science Department King Khaled UniversityKSA&Faculty of ScienceDepartment of Mathematics and Computer ScienceMenoufia UniversityEgypt Electronics Department Faculty of EngineeringUSTSana’aYemen Faculty of Computer and IT Sana’a UniversityYemen

The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of *** the high potential of renewable energy sources inYemen and the absence of similar studies in the region,this study aims to examine the potential of wind energy in Socotra *** was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a number of wind turbines and determining the *** average wind speed in Socotra Island was obtained from the Civil Aviation and Meteorology Authority data,only for the five-year data currently *** results showed high wind speeds from June to September(9.85-14.88 m/s)while the wind speed decreased for the rest of the *** average wind speed in the five years was 7.95 m/*** average annual wind speed,wind energy density,and annual energy density were calculated at different altitudes(10,30,and 50 m).According to the International Wind Energy Rating criteria,the region of Socotra Island falls under Category 7 and is classified as‘Superb’for most of the *** study provides useful information for developing wind energy and an efficient wind approach.

关键词： Energy potential wind characteristics Weibull distribution wind power density Socotra island

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An Ensemble MLP-RF Model for the Prediction of DG-MOSFETs: Addressing Fabrication Process Variations 38

An Ensemble MLP-RF Model for the Prediction of DG-MOSFETs: A...

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38th International Conference on VLSI Design, VLSID 2025

作者： Muthuraman, Vaikunth Sehra, Khushwant Kumari, Vandana Saxena, Manoj College of Engineering Anna University Department of Computer Science and Engineering Guindy Chennai India University of Delhi Faculty of Technology Department of Electronics and Communication Engineering New Delhi110007 India Maharaja Agrasen College University of Delhi Department of Electronics New Delhi India Deen Dayal Upadhyaya College University of Delhi Department of Electronics New Delhi India

ISBN: (纸本)9798331522445

To improve the efficacy of semiconductor device characteristics prediction, this work proposes a machine learning (ML) model that can predict drain current, gate-source capacitance, and drain-gate capacitance. The model displays outstanding accuracy, thus, allowing for the prognosis of device characteristics with reliability. Compared to the simulations in technology computer-Aided Design (TCAD), this ML approach can be considered a speedy alternative. To the best of the authors' knowledge, this manuscript specifically proposes a novel approach to utilize the MLP-RF (ensemble of multilayer perceptron (MLP) regressor and random forest (RF) regressor) model for Double-Gate MOSFET. The accuracy of the ensemble model is verified by calculating the mean squared error, root mean squared error, and mean coefficients of determination values. In addition, an MLP-RF classification model was implemented that would take, as input, characteristics of devices whose channel lengths are unknown and classify them into the most probable devices from a set of devices whose channel lengths are known thereby modeling the process variations during the fabrication process. The results thus encourage further interdisciplinary endeavors to benefit the semiconductor industry. © 2025 IEEE.

关键词： MOSFET devices

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