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4th International Youth Conference on Radio electronics, Electrical and Power engineering, REEPE 2022

作者： Ebied, Mostafa A. Azim, Mohamed A. Emad-Eldeen, Ahmed Beni-Suef University Faculty of Postgraduate Studies for Advanced Sciences ' Psas ' Renewable Energy Science and Engineering Department Beni-Suef62511 Egypt Beni-Suef University Faculty of Technology and Education ' Te ' Electronics Technology Department Renewable Energy Science and Engineering Department Beni-Suef62511 Egypt Electronics Technology Department Department of Computer Science Faculty of Technology and Education 'TE' College of Computer and Cyber Sciences 'CCCS' University of Prince Mugrin Saudi Arabia

ISBN: (纸本)9781665414340

This paper presents the fabrication of binder-free nanostructured Zinc oxide (ZnO) electrode for supercapacitor by the chemical bath deposition method. Further, cyclic voltammetry was modeled using an artificial neural network. The prepared material resulted in increasing the capacitance value;acting as a binder-free electrode. Results of the electrochemical study revealed that the medium current is 1.335mA. Using artificial neural network modeling primarily based totally on the idea of multilayer Perception. The cyclic behavior of the organized fabric is examined. From the evaluation of the artificial neural network, the outcomes have been obtained to be much like the experimental values with an error rate of 0.1. © 2022 IEEE.

关键词： Zinc oxide

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Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah

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computers, Materials & Continua 2022年第6期71卷 6275-6291页

作者： Riad Alharbey Ameen Banjar Yahia Said Mohamed Atri Abdulrahman Alshdadi Mohamed Abid Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Electrical Engineering Department College of EngineeringNorthern Border UniversityArarSaudi Arabia Laboratory of Electronics and Microelectronics(LR99ES30) Faculty of Sciences of MonastirUniversity of MonastirTunisia College of Computer Sciences King Khalid UniversityAbhaSaudi Arabia CES Laboratory ENISUniversity of SfaxTunisia

Hajj and Umrah are two main religious duties for *** help faithfuls to perform their religious duties comfortably in overcrowded areas,a crowd management system is a must to control the entering and exiting for each *** the number of people is very high,an intelligent crowd management system can be developed to reduce human effort and accelerate the management *** this work,we propose a crowd management process based on detecting,tracking,and counting human faces using Artificial Intelligence *** detection and counting will be performed to calculate the number of existing visitors and face detection and tracking will be used to identify all the humans for security *** proposed crowd management system is composed form three main parts which are:(1)detecting human faces,(2)assigning each detected face with a numerical identifier,(3)storing the identity of each face in a database for further identification and *** main contribution of this work focuses on the detection and tracking model which is based on an improved object detection *** improved Yolo v4 was used for face detection and *** has been very effective in detecting small objects in highresolution *** novelty contained in thismethod was the integration of the adaptive attention mechanism to improve the performance of the model for the desired *** wise attention mechanism was applied to the output layers while both channel wise and spatial attention was integrated in the building *** main idea from the adaptive attention mechanisms is to make themodel focus more on the target and ignore false positive *** demonstrated the efficiency of the proposed method through expensive experimentation on a publicly available *** wider faces dataset was used for the train and the evaluation of the proposed detection and tracking *** proposed model has achieved good results with 91.2%of mAP and a processing speed

关键词： Crowdmanagement Hajj and Umrah face detection object tracking convolutional neural networks(CNN) adaptive attention mechanisms

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An Effective Customer Segmentation Approach to Improve E-Commerce Platform Quality Based on Intensive Learning Methodology

An Effective Customer Segmentation Approach to Improve E-Com...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Suresh, G.R. Vinoparkavi, D. Govindasamy, Suresh Rohini, S. Giri, Jayant Sabri, M. Omar Dhanalakshmi Srinivasan College of Engineering and Technology Department of Ece Mamallapuram Chennai India Nandha Engineering College Department of Cse Erode India Sona College of Technology Department of Electrical and Electronics Engineering Salem India R.M.K Engineering College Department of Computer Science Engineering Kavaraipettai Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Economic Department of Business Information Technology Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

To accomplish company goals and reap advantages while satisfying and meeting consumer expectations, segmentation is an essential facilitator. Identifying subgroups of the target market that share common traits, requirements, and priorities is customer segmentation's primary goal. Various dataset dimensionalities are also considered, as are changes across time. In this study, we use time-based segmentation and clustering approaches to the e-commerce industry to conduct predictive neural network analysis using factors such as product reviews, goods, buying trend, watching pattern, and more. In this research, Intensive Neural Classification Learning (INCL) is presented as a novel deep learning technique. To assess its efficacy, it is cross-validated with the traditional Neural Network (NN) learning algorithm. We have evaluated unigrams, bigrams, and trigrams as part of the feature extraction process and identified the unique brands per consumer. In addition, the customer's chosen brands may be determined using a neural network, and the projected data can then be utilized for statistical analysis. We all know that sentiment analysis and categorization are huge help when trying to guess how customers feel about certain companies and items. Lastly, using the provided set of input data, we will choose the top brands by assessing the accuracy of the forecasts. © 2024 IEEE.

关键词： Sales

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A Multifunctional Reflectarray Antenna for Sub-THz Applications 6

A Multifunctional Reflectarray Antenna for Sub-THz Applicati...

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6th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2023

作者： Yang, Xingxing Feng, Botao Ding, Xiao Gu, Wenzhe Sun, Sanshan Chung, Kwok L. College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts 999078 China School of Computer Science and Engineering Huizhou University Huizhou516007 China College of Physics and Electronic Engineering Sichuan Normal University Sichuan China

ISBN: (纸本)9798350399059

In this paper, a reflection element sensitive to polarization direction is proposed, which can independently control the phase of incident wave in dual polarized directions. Then, a reflectarray including the proposed 7×7 elements is designed to offer dual-polarized deflection. The results show that the reflectarray achieves a -15° beam deflection with an X-axis polarized incident wave. In contrast, it realizes a +15° beam deflection with a Y - axis polarized wave. In addition, the cross polarization (X-pol) level is less than -20 dB, and the gain of the array is higher than that of the waveguide feed by 8 dBi. Therefore, significant gain enhancement can be successfully obtained. © 2023 IEEE.

关键词： Polarization

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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IEEE International Conference on Big Data

作者： Krzysztof Hanzel Damian Grzechca Adam Ziebinski Lukas Chruszczyk Artur Janus Department of Telecommunications and Teleinformatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Distributed Systems and Informatics Devices Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Student of Electrical Engineering Faculty of Electrical Engineering Silesian University of Technology Gliwice Poland

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested; however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV – so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime.

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Ultra-low-thickness, highly sensitive, and perfect-absorption THz refractive index and temperature sensors based on Tamm plasmon polaritons and Fabry-Perot resonators

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Optik 2024年 315卷

作者： Mostafavi Ghahfarokhi, Zahrasadat Pakray, Farzaneh Rezaei, Mir Hamid Singh, Hari Shankar School of Electrical and Computer Engineering University of Missouri Columbia United States Department of Electrical Engineering Faculty of Engineering Shahid Chamran University of Ahvaz Ahvaz Iran Department of Communications and Electronics School of Electrical and Computer Engineering Shiraz University Shiraz Iran Department of Electronics and Communication Engineering Thapar Institute of Engineering and Technology Punjab Patiala147004 India

In this paper, an ultra-low thickness with high sensitivity and perfect absorption based on the excitation of Tamm plasmon polaritons and Fabry-Perot resonance for sensing applications is presented. The sensor comprises a graphene sheet, a spacer layer, an analyte region, and a one-dimensional periodic stack of the dielectric and metal layers. The sensor's performance is evaluated using the transfer matrix method under different conditions, including the presence of the graphene sheet and metallic layers of the periodic stack, a change in the chemical potential of the graphene sheet, the thickness of the layers, as well as the incident polarization and angle. The simulation results show that the presence of the graphene sheet causes stimulation of Tamm plasmon polaritons, and the presence of metal layers simultaneously increases the absorption and decreases the thickness of the sensor. The sensitivity of the sensor is 0.9667 THz/RIU (equivalent to 305 μm/RIU) with an absorption peak of 99.99 %. The use of silicon as the analyte allows the proposed structure to perform as a temperature sensor in the range of 1 THz, which results in a temperature sensitivity of 0.055 THz/°C (equivalent to 16.45 nm/°C). The proposed sensor has the advantages of extremely thin thickness, perfect absorption, high sensitivity, tunability, and fabrication-friendly structure. The proposed sensor has high potential for use in various sensing applications. © 2024 Elsevier GmbH

关键词： Fabry-Perot interferometers

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A Wideband Rotary Antenna Based on Gap Wavegudie technology for mmWave Applications

A Wideband Rotary Antenna Based on Gap Wavegudie Technology ...

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European Conference on Antennas and Propagation, EuCAP

作者： Ali Farahbakhsh Davood Zarifi Michal Mrozowski Department of Microwave and Antenna Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland Department of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman Iran School of Electrical and Computer Engineering University of Kashan Kashan Iran

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This study introduces the design of a gap waveguide-based rotary antenna capable of 360° mechanical beam-steering for wideband mmWave applications. This innovative design overcomes the typical limitations of electronically steerable antennas, such as narrow bandwidth, restricted steering range, gain reduction, and high cost and complexity. The designed rotary antenna achieves a gain of 13.8 dBi without any scan loss across the entire 360° steering range from 45 to 60 GHz. The proposed rotary antenna is a promising candidate for a variety of mmWave mechanical beam scanning systems.

关键词： Steerable antennas Costs Electromagnetic scattering Gap waveguide Europe Broadband antennas Impedance Millimeter wave communication Electromagnetics Wideband

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Parkinson's Disease Early Detection and Classification based on EMG Signal using Spherical Convolutional Neural Network 8

Parkinson's Disease Early Detection and Classification based...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Nandankar, Praful V. Kothiyal, Arnav Kiran Kumar, D. Patil, Anuradha Patil, Harshal Maranan, Ramya Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Bgs Institute of Technology Department of Information Science and Engineering Karnataka B G Nagar571448 India Sharnbasva University Faculty of Engineering and Technology Exclusively for Women Department of Electronics and Communication Engineering Karnataka Kalaburagi585103 India Iilm University School of Computer Science & Engineering Uttar Pradesh Greater Noida201 306 India Simats Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350376425

Parkinson's Disease (PD) is a degenerative neurological condition that impairs motor function. Tremors, stiffness, sluggishness, and trouble balancing are its hallmarks. Degeneration of dopamine-producing neurons in the brain is the disease's primary cause, resulting in progressive motor and non-motor symptoms. But the existing methods have lot of disadvantages such as over fitting, quality control and limited data availability. To overcome the aforementioned problem, A Spherical Convolutional Neural Network with Mantis Search Algorithm (SCNN-MSA) is proposed for accurately classifying PD. In this input image is taken from two datasets such as ADL dataset and REMPARK dataset. The Wavelet Decomposition Threshold Anisotropic Filter is used for the pre-processing (WDTAF) is proposed, for the purpose of removing noises. Following that segmentation are done using Spike-Driven Transformer-based U-Net (SDT-Unet). After that classification and feature extraction are done using Spherical Convolutional Neural Network (SCNN) and optimization are done using Mantis Search Algorithm (MSA) for classifying the Parkinson's Disease. The efficiency of the proposed SCNN-MSA is analyzed using a dataset and attains 99.93% accuracy, 98.76% recall and attains better results compared with the existing methods. This demonstrates the approach's increased efficiency and potential advancement in the industry. The model's accuracy at identifying PD in EMG signals might allow doctors to treat patients considerably faster. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Evaluation and Analysis of Deep Learning Autoencoder-Based Wireless Communication System

Performance Evaluation and Analysis of Deep Learning Autoenc...

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Electronic engineering (ICEEM), International Conference on

作者： Eman Ismail Abdelhay Ali Omar A. M. Aly Mohammed Abo-Zahhad Department of Electrical Engineering Faculty of Engineering Assuit University Assuit Egypt Department of Electronics and Communications Engineering Faculty of Electronics Communications and Computer Engineering Egypt-Japan University of Science and Technology Alexandra Egypt

Recent advancements in deep learning have led to the emergence of autoencoder-based (AE) wireless communication systems, presenting a promising approach to tackle the challenges posed by conventional mathematical models. In this research, a thorough evaluation and analysis of the performance of deep learning AE-based wireless communication systems is conducted. Specifically, our investigation focuses on employing the additive white Gaussian noise (AWGN) channel and explores the impact of varying signal-to-noise ratio (SNR) conditions during the AE training process on system performance. The results show that training the autoencoder under diverse SNR conditions, particularly with an extended number of epochs, surpasses the performance of a fixed-trained autoencoder regarding block error rate (BLER). Additionally, a comparison of BLER between multiple AE models and traditional mathematical representations used in communication systems is conducted. The obtained findings indicate that deep learning AE-based wireless communication systems exhibit promising performance compared to conventional models, underscoring their potential as an effective solution for wireless communication systems.

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Brain computer Interaction Framework for Speech and Motor Impairment Using Deep Learning

Brain Computer Interaction Framework for Speech and Motor Im...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Somnathe, Abhijit T. Tayubi, Iftikhar Aslam Reddy, Pundru Chandra Shaker Sharma, Nipun Sharma, Vikas Yesubabu, Mannava Electronics and Telecommunication Engineering Smt. Kashibai Navale College of Engineering Pune India King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia School of Computer Sicence and Engineering Manipal University Jaipur Rajasthan Jaipur303007 India Electronics and Communication Engineering Presidency University Karnataka Bangalore India School of Computer Science & Applications Iimt University Uttar Pradesh Meerut India Computing Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India

ISBN: (纸本)9798350357769

Some people may have trouble communicating with others because of motor and speech difficulties brought on by accidents, strokes, or diseases. Paralyzed people often struggle to communicate their demands, which can make even the simplest tasks challenging. People with disorders like dysarthria and amyotrophic lateral sclerosis may have trouble following conversations. The proposed method for automatic detection of everyday basic desires will help those with dysarthria and quadriplegic paralysis has more fulfilling lives. The device accomplishes this by monitoring brain impulses, which it then converts into either perceptible voice commands or messages that can be delivered to a healthcare provider's smart devices, depending on the user's preferences. The suggested method randomly displays an image of one of six basic requirements while using event-related-potentials(ERPs) detected from the Electroencephalogram (EEG) data to determine which need to fulfill. The dataset utilized for train, test, & validation was built using input from 10 participants. The proposed method had a 96.3|% success rate. © 2023 IEEE.

关键词： Brain-computer Interface CNN Deep Learning EEG ERP

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