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13th International Electrical engineering Congress, iEECON 2025

作者： Sitjongsataporn, Suchada Du, Wenzhang Sakarin, Pipat Faculty of Engineering and Technology Dept. of Electronic Engineering Bangkok Thailand Faculty of Engineering and Technology Mahanakorn University of Technology Dept. of Computer Engineering Thailand

ISBN: (纸本)9798331543952

This experiment investigates the impact of reducing GRU units and dense layer neurons in a lightweight GRU architecture (LW-GRU-RU) compared to a baseline GRU model for electroencephalogram (EEG) emotion classification. A baseline GRU model is used as a reference, with an optimized GRU variant utilizing feature selection through a Random Forest-based algorithm. Experiments are conducted on a labeled emotion dataset, comparing accuracy and training efficiency across five trials. Results highlight the trade-off between model complexity, accuracy, and computational efficiency, providing insights for practical applications. Both models are evaluated on an emotion dataset for accuracy and training efficiency. The lightweight model achieves a competitive accuracy of 97.486% while reducing the average training time to 0.19 seconds per epoch, showcasing its potential for efficient real-world applications. © 2025 IEEE.

关键词： Baseline GRU model Electroencephalogram (EEG) signal GRU with Feature selection

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EMI Performance Analysis in IRS-Aided Multi-User Wireless Communication Systems

EMI Performance Analysis in IRS-Aided Multi-User Wireless Co...

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2023 IEEE Virtual Conference on Communications, VCC 2023

作者： Elshishtawy, Rhana M. Ali, Shimaa S. Tag Eldien, Adly S. Fouda, Mostafa M. Ismail, Mohamed I. Eid, Esraa M. Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Egypt Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Idaho FallsID United States Department of Electronic Engineering & Communication Technology Modern Academy for Engineering Egypt

ISBN: (纸本)9798350318807

This paper presents a comprehensive investigation into the effect of Electromagnetic Interference (EMI) on wireless systems employing single and distributed Intelligent Reflecting Surfaces (IRSs) in three distinct conditions by varying the number of antennas, elements, and users. Through extensive numerical simulations, the performance of the wireless systems is rigorously evaluated under different EMI scenarios. Firstly, the influence of varying the number of antennas on the system performance is thoroughly analyzed. The results demonstrate that an increased number of antennas contributes to improved signal reception and reduced interference levels. Moreover, the enhanced beamforming capabilities offered by additional antennas lead to higher signal quality and increased system Achievable Rate (AR). Secondly, the impact of changing the number of elements within the IRS under EMI conditions is investigated in detail. The simulations reveal that an augmented number of elements facilitates efficient EMI mitigation, enabling advanced signal manipulation and more precise signal focusing. As a result, the system exhibits improved performance in terms of signal strength, coverage, and AR. Lastly, the effect of varying the number of users on system performance in the presence of EMI is thoroughly assessed. The findings unveil that an increased number of users under EMI conditions leads to elevated interference levels, degraded signal quality, and diminished system AR. Thus, it is crucial to develop effective EMI mitigation strategies and resource allocation schemes to ensure reliable and efficient communication in IRS-enabled wireless systems. © 2023 IEEE.

关键词： MIMO systems

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Efficient Deep Learning Modalities for Object Detection from Infrared Images

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computers, Materials & Continua 2022年第8期72卷 2545-2563页

作者： Naglaa F.Soliman E.A.Alabdulkreem Abeer D.Algarni Ghada M.El Banby Fathi E.Abd El-Samie Ahmed Sedik Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafr el-sheikhEgypt

For military warfare purposes,it is necessary to identify the type of a certain weapon through video stream tracking based on infrared(IR)video *** vision is a visual search trend that is used to identify objects in images or video *** military applications,drones take a main role in surveillance tasks,but they cannot be confident for longtime ***,there is a need for such a system,which provides a continuous surveillance task to support the drone *** a system can be called a Hybrid Surveillance System(HSS).This system is based on a distributed network of wireless sensors for continuous *** addition,it includes one or more drones to make short-time missions,if the sensors detect a suspicious *** paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks(CNNs)and Convolutional Long Short-Term Memory(ConvLSTM).Based on initial results,the importance of video frame enhancement is obvious to improve the visibility of objects in video *** accuracy of the proposed methods reach 99%,which reflects the effectiveness of the presented *** addition,the experimental results prove that the proposed methods provide superior performance compared to traditional ones.

关键词： Deep learning object detection military applications OFDM SPIHT IoT

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Research and Analysis on the Labor Market in the Field of Applied AI 16

Research and Analysis on the Labor Market in the Field of Ap...

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16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023

作者： Plamenac, Aleksandar Jovancevic, Igor Dimitrov, Georgi Marstenyuk, Vasyl Rancic, Dejan Dirgova-Luptakova, Iveta Faculty of Natural Sciences and Mathematics Podgorica81000 Montenegro Faculty of Information Science Sofia Bulgaria Department of Computer Science and Automatics Poland Faculty of Electronic Engineering Serbia Faculty of Natural Sciences Slovakia

ISBN: (纸本)9798350347029

This article is fulfilled within the framework of Erasmus+ project "The Future is in Applied Artificial Intelligence (FAAI). It gives overview of current job market related to the field of Applied Artificial Intelligence. The data is obtained from online survey, and it gives highlights of several aspects of labor market divided into research and analysis of the market, and specific requirements necessary. Regarding research and analysis, the data provided deals with:-positions offered in the market.-machine learning problems occurring.-models being developed while resolving the real-world problem.-machine learning tasks to be *** collected data in the domain of job market requirements gives highlight about:-required programming languages.-educational requirements.-required *** given can serve as a guide to which competencies are necessary in the field of AAI and provide information for both professionals and curriculum creators. © 2023 IEEE.

关键词： Curricula

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All-optically linearized silicon modulator with ultrahigh SFDR of 131 dB·Hz^(6/7)

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Photonics Research 2025年第2期13卷 433-441页

作者： QIANG ZHANG QIKAI HUANG PENGHUI XIA YAN LI XINGYI JIANG SHUYUE ZHANG SHENGYU FANG JIANYI YANG HUI YU Zhejiang Lab Hangzhou 311100China Institute of Integrated Microelectronic Systems College of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Integrated high-linearity modulators are crucial for high dynamic-range microwave photonic(MWP)*** linearization schemes usually involve the fine tuning of radio-frequency(RF)power distribution,which is rather inconvenient for practical applications and can hardly be implemented on the integrated photonics *** this paper,we propose an elegant scheme to linearize a silicon-based modulator in which the active tuning of RF power is *** device consists of two carrier-depletion-based Mach-Zehnder modulators(MZMs),which are connected in series by a 1×2 thermal optical switch(OS).The OS is used to adjust the ratio between the modulation depths of the two *** a proper ratio,the complementary third-order intermodulation distortion(IMD3)of the two sub-MZMs can effectively cancel each other *** measured spurious-free dynamic ranges for IMD3 are 131,127,118,110,and 109 d B·Hz^(6∕7)at frequencies of 1,10,20,30,and 40 GHz,respectively,which represent the highest linearities ever reached by the integrated modulator chips on all available material platforms.

关键词： microwave photonic systems ultrahigh SFDR all optical linearization silicon modulator active tuning rf power high linearity modulators third order intermodulation distortion integrated photonics

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SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

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IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 5122-5132页

作者： Huang, Shih-Chia Hoang, Quoc-Viet Le, Trung-Hieu National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology International Graduate Program in Electrical Engineering and Computer Science Taipei10608 Taiwan Hung Yen University of Technology and Education Faculty of Information Technology Hungyen 17000 Viet Nam

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed. © 2012 IEEE.

关键词： Feature extraction

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NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication 28

NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulat...

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28th Conference on Computational Natural Language Learning, CoNLL 2024

作者： Lian, Yuchen Verhoef, Tessa Bisazza, Arianna Faculty of Electronic and Information Engineering Xi’an Jiaotong University China Leiden Institute of Advanced Computer Science Leiden University Netherlands Center for Language and Cognition University of Groningen Netherlands

ISBN: (纸本)9798891761780

Recent advances in computational linguistics include simulating the emergence of human-like languages with interacting neural network agents, starting from sets of random symbols. The recently introduced NeLLCom framework (Lian et al., 2023) allows agents to first learn an artificial language and then use it to communicate, with the aim of studying the emergence of specific linguistics properties. We extend this framework (NeLLCom-X) by introducing more realistic role-alternating agents and group communication in order to investigate the interplay between language learnability, communication pressures, and group size effects. We validate NeLLCom-X by replicating key findings from prior research simulating the emergence of a word-order/case-marking trade-off. Next, we investigate how interaction affects linguistic convergence and emergence of the trade-off. The novel framework facilitates future simulations of diverse linguistic aspects, emphasizing the importance of interaction and group dynamics in language evolution. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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EOG acquisition system based on ATmega AVR microcontroller

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16589-16605页

作者： Abdel-Samei, Abdel-Gawad A. Shaaban, Abdel-Salam Brisha, Ayman M. El-Samie, Fathi E. Abd Ali, Ahmed S. Department of Electronics Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Department of Process of Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Department of Mechanical Engineering Mechatronics Division Faculty of Engineering Assiut University Assiut Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

This paper presents a hardware and software of an electrooculogram (EOG) acquisition system based on ATmega AVR microcontroller for the acquisition of vertical and horizontal eye movements. The presented system is used to acquire a dataset of eye movements for volunteers. This system gives two channels representing vertical and horizontal EOG signals. The frequency range of the EOG signal is known to be 0.1 to 10 Hz, and hence this frequency range is isolated with a High-Pass Filter (HPF) with a cutoff frequency of 0.1 Hz followed by a Low-Pass Filter (LPF) with a cutoff frequency of 10 Hz. The EOG acquisition system is interfaced with an ATmega AVR microcontroller to acquire a dataset that can be used for controlling hardware such as Light Emitting Diodes (LEDs), wheelchair, and robot arm. The presented system is composed of EOG signal acquisition, Ag/AgCl electrodes, analog-to-digital converter through Arduino Mega 2560 board microcontroller unit, trainer board, laptop, keypad, and Liquid Crystal Display (LCD). The eye movement is detected by measuring the potential difference between cornea and retina using five Ag–Agcl disposable electrodes. Different volunteers of different ages at different times have been treated with the presented system to obtain data. Classified vertical and horizontal EOG signals and the basic eye movements e.g., open eye, left, right, up, and down can be used to control robots and wheelchairs for rehabilitation purposes. © 2023, The Author(s).

关键词： Electrodes

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Machine Learning in Medical Image Processing

Machine Learning in Medical Image Processing

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International Conference on Wireless Communications, Networking and Applications, WCNA 2021

作者： Elmahalawy, Ahmed Abdel-Aziz, Ghada Computer Science and Engineering Department Faculty of Electronic Engineering Menofia University Al Minufiyah Egypt Higher Institute of Engineering El Shorouk Academy Cairo Egypt Department of Communications and Computer Engineering Electrical Engineering Department is Part of the Faculty of Engineering Benha University Banha Egypt

ISBN: (纸本)9789811924552

Medical images provide information that can be used to detect and diagnose a variety of diseases and abnormalities. Because cardiovascular disorders are the primary cause of death and cancer is the second, good early identification can aid in the reduction of cancer mortality rates. There are different medical imaging modalities that the radiologists use in order to study the organ or tissue structure. The significance of each imaging modality is changing depending on the medical field. The goal of this research is to give a review that shows new machine learning applications for medical image processing and gives a review of the field’s progress. The classification of medical photographs of various sections of the human body is the focus of this review. Additional information on methodology developed using various machine learning algorithms to aid in the classification of tumors, non-tumors, and other dense masses is available. It begins with an introduction of several medical imaging modalities, followed by a discussion of various machine learning algorithms to segmentation and feature extraction. © 2022, The Author(s).

关键词： Feature extraction

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Full-custom Design of Improved Carry Adder Circuit for CLBs 15

Full-custom Design of Improved Carry Adder Circuit for CLBs

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15th IEEE International Conference on ASIC, ASICON 2023

作者： Xu, Mengfan Zhang, Yuejun Zhang, Huihong Wen, Liang Yuan, Tengfei Wang, Pengjun Li, Zhiyi Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China China Coast Guard Academy Department of Electronic Technology Ningbo315211 China Wenzhou University College of Electrical and Electronic Engineering Wenzhou325035 China

ISBN: (纸本)9798350312980

As a highly repetitive logic operation unit in programmable logic modules, the operating speed of a carry adder circuit is crucial to the overall performance of the programmable logic circuit. In order to optimize circuit propagation delay and area overhead, this paper proposes an improved design scheme for carry adder circuits. Firstly, several basic adder structure types and their performance characteristics are compared to determine the most suitable architecture for carry adder circuits in programmable modules. Then, based on the carry-propagate principle of the carry lookahead adder, a novel single MOS transmission gate structure is proposed to optimize the underlying carry logic circuit. Finally, the carry adder circuit is implemented using a full-custom design flow in TSMC 28nm technology. Experimental results show that compared to existing logic designs, the proposed carry adder circuit in this paper reduces propagation delay and area overhead by 14% and 26.5% respectively. © 2023 IEEE.

关键词： carry adder full custom low overhead single MOS transmission gate

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