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Electrical, computer, Telecommunication and Energy Technologies (ECTE-Tech), International Conference on

作者： Nafaa Mohamad Shebani Alyani Ismail Electrical and Electronic Engineering Faculty of Engineering Sabratha University Sabratha Libya Department of Computer and Communication Systems Engineering Wireless and Photonics Networks Research Centre Faculty of Engineering Universiti Putra Malaysia Serdang Malaysia

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

Affectively 5G antennas operating at mm-Wave bands with small size, high gain and large bandwidth are required to meet the desired high data rate and overcome of attenuation effects at mm-Wave bands. In this paper, single element and two elements are designed and analyzed at 38 GHz, based on the optimal configuration of two elements antenna with good propagation properties, a four-element MIMO microstrip antenna is proposed and modeled by a computer Simulation Technology (CST) software to operate at 38 GHz. The antenna is designed on FR-4 substrate has a thickness of 0.76 mm, a dielectric constant of 4.3, and a loss tangent of 0.025. The overall gain, diversity gain (DG), and envelope correlation coefficient (ECC) are used in evaluating the performance of the suggested four-element MIMO antenna. Moreover, the effects of changing of phase ports are done in order to make more improvement of overall realized gain of four-element microstrip antenna. Additionally, the optimal design of a 4-element antenna is compared with antennas that have been published in the literature to demonstrate the reliability of the design.

关键词： Correlation coefficient Dielectric constant 5G mobile communication Dielectric losses Microstrip antennas Bandwidth Attenuation Envelope detectors Microstrip Substrates

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Vibrating Particles System Algorithm for Solving Classification Problems

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computer systems Science & engineering 2022年第12期43卷 1189-1206页

作者： Mohammad Wedyan Omar Elshaweesh Enas Ramadan Ryan Alturki Department of Autonomous Systems Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Software Engineering Information Technology CollegeAL-Hussein bin Talal UniversityMa’an71111Jordan Department of Computer Science Prince Abdullah Bin Ghazi Faculty of Communication and Information TechnologyAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21961Saudi Arabia

Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of *** immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of *** is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial *** probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition *** this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more *** data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was *** suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.

关键词： Vibrating particles system(VPS) probabilistic neural network(PNN) classification problem data mining

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Sustainable electric vehicles fault detection based on monitoring by deep Learning Architectures in feature extraction and classification

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2023年 57卷

作者： Wongchai, Anupong Aoudni, Yassine Yesubabu, Mannava Reegu, Faheem Ahmad Gowri, N. Vasantha Vijayakumar, P. [a]The Independent researcher and Social Worker Thailand [b]Department of Computers and Information Technology Faculty of Sciences and Arts Turaif Northern Border University Arar 91431 Saudi Arabia [c]Computer & Embedded Systems Laboratory National School of Engineers of Sfax (ENIS) University of Sfax BP 1173 3038 Sfax Tunisia [d]Department Of Computer Science & Engineering Vardhaman College Of Engineering Shamshabad Hyderabad 501218 India [e]College of Computer Science And Information Technology Jazan University Jazan 45142 Saudi Arabia [f]Department of EEE Chaitanya Bharathi Institute of Technology (A) Hyderabad India [g]School of Electronics Engineering Vellore Institute of Technology (VIT) Chennai Tamil Nadu India

Numerous industrial sector paradigms have been altered by the necessity to produce more competitive ma-chinery and the introduction of digital technologies from so-called Industry 4.0. This research proposes novel technique in electric vehicle fault detection based on monitoring data classification and feature extraction using deep learning architectures. The real Proton exchange membrane fuel cell (PEMFC) experiment dataset has been collected as sustainable electric vehicles data using multi-cell parallel electric vehicle and it has been processed for noise removal, dimensionality reduction and extraction using deep stacking auto gradient descent for-classifyingthrough monte Carlo regressive Gaussian naive bayes *** of experiments demonstrate that suggested model achieves over 99% accuracy in identifying flooding fault of fuel cell under load-varying situations. The experimental analysis has been carried out in terms of accuracy, robustness, reliability, preci-sion, recall. The proposed technique attained 99% of accuracy, 89% of robustness, 85% of Reliability, 95% of precision and 81% of recall.

关键词： Electric vehicles Fault detection Monitoring Data classification Feature extraction Deep learning

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Implementing and Measuring the Performance of PB, RR and PBRR Scheduling Algorithms on ATMega32A using FreeRTOS

Implementing and Measuring the Performance of PB, RR and PBR...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Abdelrahman M. H. Abdelhamid Amr A. Zamel ECE Department Faculty of Engineering The British University in Egypt (BUE) Egypt Computer and Systems Department Faculty of Engineering Zagazig University Egypt

This research paper discusses the Real Time Operating systems; their types, software, benefits, and drawbacks, as well as how they work, including task states and control blocks. This research goes into great detail to explain many scheduling algorithms, including Round Robin, Priority Based, Earliest Deadline First, First Come First Serve, and Non-preemptive Deadline First. It also covers the performance monitoring tools and the parameters to be monitored while measuring the performance. The problem that we shed light on is the increment of death and incidents of Liquified Petroleum Gas (LPG) burns. In 2018, The researchers found that almost 81.03% of the total burns’ percentage were related to gas leaks and 2% to negligence in the kitchen. To solve this problem, we made an application using FreeRTOS. We used different scheduling algorithms such as PB and RR. Furthermore, we utilized three different time slicing intervals: (0.1 sec, 10m sec, 100μ sec) . Our application was implemented on ATMega32A with internal frequency 1 MHz. The application includes two sensors and an LCD. Additionally, We added a DC motor that functions as a ventilating fan, allowing us to take appropriate actions based on the sensor readings. The results show that the best scheduling algorithm with respect to time execution was PBRR with time = 0.0656 secs, while the worst was without RTOS taking 4.581 secs. However, the memory consumption of the application without RTOS was RAM of 8%, and ROM of 18.7%, while the PBRR memory consumption was RAM of 89.9%, ROM of 31.5%. Also, We simulated the execution of the tasks using SimSo for PB scheduling algorithm.

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LIMIT THEOREMS FOR β-LAGUERRE AND β-JACOBI ENSEMBLES

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Acta Mathematica Scientia 2022年第5期42卷 2025-2039页

作者： Naqi HUANG Yutao MA Faculty of Electrical Engineering Mathematics and Computer ScienceDelft University of TechnologyMekelweg 42628 CD DelftThe Netherlands School of Mathematical Sciences&Laboratory of Mathematics and Complea Systems of Ministry of Education Beijing Normal UniversityBeijing 100875China

We use tridiagonal models to study the limiting behavior of β-Laguerre and β-Jacobi ensembles,focusing on the limiting behavior of the extremal eigenvalues and the central limit theorem for the two *** the central limit theorem of β-Laguerre ensembles,we follow the idea in[1]while giving a modified version for the generalized *** we use the total variation distance between the two sorts of ensembles to obtain the limiting behavior of β-Jacobi ensembles.

关键词： beta-ensembles largest and smallest eigenvalues central limit theorem total variationdistance

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Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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Enhancing Student Performance Prediction with Greylag Goose Optimization Algorithm

Enhancing Student Performance Prediction with Greylag Goose ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Rizk, Faris H. Mohamed, Mahmoud Elshabrawy Sameh, Basant Zaki, Ahmed Mohamed Eid, Marwa M. El-Kenawy, El-Sayed M. Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt

ISBN: (纸本)9798350351408

The current paper addresses the central role of hyperparameter optimization in improving the predictive power of the MLP Regressor for forecasting student performance in Portuguese secondary schools. The uniqueness of this research lies in its exploration of metaheuristic optimization algorithms, specifically highlighting GGO (Greylag Goose Optimization) for enhancement. The study utilized a dataset crucial for understanding and predicting student performance, with a special focus on its distinctive features. By comprehensively tuning the MLP Regressor, the paper demonstrates remarkable improvements in various performance measures, as evident in the enclosed tables. Specifically, the MSE values calculated for the MLP Regressor both before and after GGO optimization are compared. Without optimization, the MLP Regressor had an MSE of 0.0103. After GGO optimization, the MSE significantly improved to 0.0060, indicating enhanced accuracy with GGO in the model. These findings emphasize that hyperparameter optimization, particularly the GGO technique, is crucial for refining the MLP Regressor in predicting student performance. The paper not only delves into the technical aspects but also concludes by highlighting the broader consequences of these outcomes. The potential educational applications are substantial, as improved models can provide more accurate predictions, empowering educators and policymakers to make informed decisions in education. This paper establishes a foundation for future research directions, contributing to the pool of ideas for educational predictive modeling. © 2024 IEEE.

关键词： Optimization algorithms

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Diverse experiences by active travel for carbon neutrality:A longitudinal study of residential context,daily travel and experience types

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Geography and Sustainability 2024年第3期5卷 459-469页

作者： Karl Samuelsson S.Anders Brandt Stephan Barthel Noah Linder Nancy Joy Lim David Hallman Matteo Giusti Department of Building Engineering Energy Systems and Sustainability ScienceFaculty of Engineering and Sustainable DevelopmentUniversity of Gävle 80176 GävleSweden ISGlobal Barcelona Biomedical Research ParkDoctor Aiguader 8808003 BarcelonaSpain Department of Computer and Geospatial Sciences Faculty of Engineering and Sustainable DevelopmentUniversity of Gävle 80176 GävleSweden Stockholm Resilience Centre Stockholm University 10691 StockholmSweden Global Economic Dynamics and the Biosphere Royal Swedish Academy of ScienceLilla Frescativägen 4A 11418 StockholmSweden Department of Occupational Health Science and Psychology Faculty of Health and Occupational StudiesUniversity of Gävle 80176 GävleSweden School of Sustainability Civil and Environmental EngineeringFaculty of Engineering and Physical SciencesUniversity of SurreyGuildfordSurrey GU27XHUK

Two key goals for sustainable spatial planning are to promote low-carbon travel in daily life and to enhance human wellbeing through diverse human-environment ***,the integration of these goals has been *** study investigates the potential for experiential diversity via active travel in different resi-dential contexts within the Gävle city-region,*** 15 months,we collected spatiotemporal data from 165 participants,analyzing 4,362 reported experiences and 13,192 GPS-derived travel *** analysis uncovered a significant spatial discrepancy:while the travelled distances to locations of positive experiences typ-ically ranged from 1.5 km to 5 km,active travel predominated only within 1.5 *** discrepancy persisted across urban,suburban,and peripheral *** residents in different contexts reported the same types of experiences,urban dwellers travelled about 50%farther for nature experiences compared with other positive experiences,whereas peripheral dwellers travelled twice the distance for urbanicity experiences compared with other positive ***,urban residents mostly relied on active travel for urbanicity experiences and motorised travel for nature experiences,with the reverse trend observed among peripheral *** results illustrate the importance of spatial scale for promoting diverse positive experiences via active travel,re-gardless of residential *** planning strategies may include enhancing environmental diversity near homes and developing infrastructure that favours active over motorised travel for short to moderate distances.

关键词： Walking Biking Experiential diversity GPS data Smartphone app Topodiversity

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Real-Time Wide Area Event Identification and Analysis in Power Grid Based on EWAMS

Real-Time Wide Area Event Identification and Analysis in Pow...

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2022 IEEE Industry Applications Society Annual Meeting, IAS 2022

作者： Abdelrahman, Mahmoud S. Kassem, A. Saad, Ahmed A. Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States Helwan University Faculty of Engineering Department of Electrical Engineering Cairo Egypt

ISBN: (数字)9781665478151

ISBN: (纸本)9781665478151

Event detection and classification are crucial to power system stability. The Wide Area Measurement System (WAMS) technology helps in enhancing wide area situational awareness by providing useful synchronized information to the grid control center in order to accurately identify various power system events. This paper demonstrates the viability of using EWAMS (Egyptian Wide Area Measurement System) data as one of the evolving technologies of smart grid to identify extreme events within the Egyptian power grid. The proposed scheme is based on online synchronized measurements of wide-area monitoring devices known as Frequency Disturbance Recorders (FDRs) deployed at selected substations within the grid. The FDR measures the voltage, voltage angle, and frequency at the substation and streams the processed results to the Helwan University Host Server (HUHS). Each FDR is associated with a timestamp reference to the Global Positioning System (GPS) base. An EWAMS-based frequency disturbance detection algorithm based on the rate of frequency deviation is developed to identify varies types of events such as generator trip and load shedding. Based on proper thresholding on the frequency and rate of change of frequency of the Egyptian grid, different types of events have been captured in many locations during the supervision and monitoring the operation of the grid. EWAMS historical data is used to analyze a wide range of data pre-event, during and post-event for future enhancement of situational awareness as well as decision making. © 2022 IEEE.

关键词： Electric power transmission networks

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