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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Raju, Leo Thirunavukkarasu, K. SSN College of Engineering Department of Electrical and Electronics Engineering Chennai India Thanthai Periyar Govt Institute of Technology Department of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331300579

Over the past few decades, energy has been used mindlessly leading to many problems like reduction in supply, high power rates, increased carbon footprint, etc. Smart campuses, home automation, smart grid, etc. make living comfortable and economical. It optimizes energy consumption and is quite simple to operate too. This project proposes Internet of Things (IoT) based automation for smart car parking inside the campus, thus converting existing campus into smart campus. In this work, we have developed a prototype for smart campus using STM32 microcontroller. This microcontroller has better features compared to other contemporary microcontrollers. This prototype includes a smart car parking system which reduces traffic congestion and facilitates car parking inside the campus. © Grenze Scientific Society, 2024.

关键词： Traffic congestion

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The design and analysis of an advanced hybrid renewable energy microgrid for Cyangugu Remote rural community in Rwanda using Homer Pro

The design and analysis of an advanced hybrid renewable ener...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Asiegbu, Adimchinobi Almaktoof, Ali Adonis, Marco Electrical Electronics and Computer Engineering Department Cape Peninsula University of Technology Cape Town South Africa

ISBN: (纸本)9781032803722

In Rwanda, and in Cyangugu Remote community near the Kivu Lake, there is high solar sunshine energy, significant wind energy, abundant methane gas, appreciable geothermal energy, peat(biomass), hydro renewable energy sources, and grid power access. To ensure feasibility, accessibility, and sustainability, a balance need to be maintained to optimize this energy mix options, in terms of cost (NPV, COE etc.), maintenance, sensitivity, optimization, and other system parameters. The best combination of these Hybrid Renewable Energy Sources (HRES) considering the community’s energy needs, is modelled using the Hybrid Optimization of Multiple Electric Renewables – Professional (HOMER Pro), innovatively, to design and find out the proposed optimum system requirements with environmental advantages or benefits, best payback duration, lower or zero net GHGs and radiation emissions, performance optimization with lest value of Net Present Cost (NPC) and Cost of Energy (COE), sensitivity analysis involving components supply changes like wind speed change based atmospheric conditions, variable sunshine frequencies based on time of the day, seasons, fuel price changes etc. These seven energy supply components is used to provide access to modern, clean and reliable energy to Cyangugu. © 2025 The Author(s).

关键词： Clean energy

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Intrusion Detection System Through Deep Learning in Routing MANET Networks

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Intelligent Automation & Soft Computing 2023年第7期37卷 269-281页

作者： Zainab Ali Abbood DoguÇagdaşAtilla Çagatay Aydin Department Electrical and Computer Engineering Altinbas UniversityIstanbulTurkey Department Computer Technology Engineering Al-Esraa UniversityBaghdadIraq Electric Autonomous and Unmanned Vehicles Application and Research CentreAltinbas UniversityIstanbulTurkey Faculty of Engineering and Architecture Altinbas UniversityIstanbulTurkey Department Electrical and Electronics Engineering Ege UniversityIzmirTurkey

Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in *** reasons including node mobility,due to MANET’s potential to provide small-cost solutions for real-world contact challenges,decentralized management,and restricted bandwidth,MANETs are more vulnerable to security *** protecting MANETs from attack,encryption and authentication schemes have their ***,deep learning(DL)approaches in intrusion detection sys-tems(IDS)can adapt to the changing environment of MANETs and allow a sys-tem to make intrusion decisions while learning about its mobility in the *** are a secondary deﬁance system for mobile ad-hoc networks *** since they monitor network trafﬁc and report anything ***,many scientists have employed deep neural networks(DNNs)to address intrusion detection *** paper used MANET to recognize com-plex patterns by focusing on security standards through efﬁciency determination and identifying malicious nodes,and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network(CBPNN),Feedforward-Neural-Network(FNN),and Cascading-Back-Propagation-Neural-Network(CBPNN)(FFNN).In addition to Convolutional-Neural-Network(CNN),these primary forms of deep neural network(DNN)building designs are widely used to improve the performance of intrusion detection systems(IDS)and the use of IDS in conjunction with machine learning(ML).Further-more,machine learning(ML)techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to differ-ent *** with another current model,The proposed model has better average receiving packet(ARP)and end-to-end(E2E)*** results have been obtained from CBP,FFNN and CNN 74%,82%and 85%,respectively,by the ti

关键词： ARP CBPNN CNN DNN DL E2E FFNN IDS ML MANET security

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Machine Learning for Wireless Network Optimization: Algorithms and Applications for Adaptive Communication 1

Machine Learning for Wireless Network Optimization: Algorith...

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1st International Conference on Advances in computer Science, electrical, electronics, and Communication Technologies, CE2CT 2025

作者： Srinivas, Madhira Surya, S. Thakar, Prachi Deepak Jain, Amit Kumar Department of Computer Science & Engineering Hyderabad501301 India Chennai600116 India KJ college of Engineering & Management Research Department of Computer Engineering Pune India Poornima University Department of Electrical & Electronics Engineering Rajasthan Jaipur303905 India

ISBN: (纸本)9798331518578

This project looks into the possibility of applying machine learning to optimize wireless networks for adaptive communication. Using 5G resource data, it applies preprocessing, exploratory analysis, and visualization to identify key trends: signal strength, latency, and bandwidth utilization. Machine learning algorithms predict resource allocation based on latency-sensitive and bandwidth-demanding applications. The approach emphasizes energy efficiency, scalability, and service quality, and dynamically adjusts communication strategies to meet diverse network demands. The findings indicate the machine learning importance in enhancing elasticity and robustness in a wireless network and its general function. © 2025 IEEE.

关键词： 5G Optimization Adaptive Communication Bandwidth Data Pre-processing EDA Energy Efficiency Latency Machine Learning Network Elasticity Predictive Models Resource Allocation Scalability Signal Strength Wireless Networks

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Cross-Layer Optimized HARQ Protocol for Underwater Acoustic Wireless Sensor Networks Using Stochastic Network Calculus 2

Cross-Layer Optimized HARQ Protocol for Underwater Acoustic ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Sivajayaprakash, A. Sukumaran, Rajeev Kumar, C. SRM Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore641032 India

ISBN: (纸本)9798331534950

UAWSNs face challenges such as long propagation delays, limited bandwidth, and varying channel conditions. To solve these problems, we developed a new protocol called Multi- Hop Cross-Layer Optimized Hybrid Automatic Repeat Request (CLO-HARQ) suitable for UAWSNs. This protocol automatically changes the retransmission strategy and error correction redundancy based on real-time feedback from the physical and network layers. A unique feature of this protocol is the inclusion of environmental factors such as water salinity and temperature in the decision-making process. Also, using Stochastic Network Calculus (SNC), stochastic traffic characteristics predict single-hop and multi-hop communication delay (delay), energy usage (energy usage), and throughput. In the performance evaluation of CLO-HARQ, it has been shown to provide significant improvement in efficiency, throughput, and delay over existing HARQ methods. It is well-suited for long-term underwater monitoring and is a sustainable solution for energy-constrained sensor networks. © 2024 IEEE.

关键词： Network layers

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Trade Ease with Machine Learning and AWS

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IEEE Access 2023年 11卷 25893-25905页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research. © 2013 IEEE.

关键词： Deep neural networks

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Design of PID Droop Control for Fast Power Sharing in Inverter-Based Microgrid

Design of PID Droop Control for Fast Power Sharing in Invert...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Nazeri, Ahmad Ali Friebe, Jens Zacharias, Peter Faculty of Electrical Engineering Computer Science University of Kassel Department of Power Electronics Kassel Germany

ISBN: (纸本)9798350364446

Microgrids (MGs) serve as central interfaces for distributed generation, predominantly employing voltage source inverters (VSIs). These MGs operate in either autonomous or grid-connected modes, facilitating local power generation and storage. However, autonomous MGs, lacking grid support, are susceptible to instability, particularly with multiple generators sharing power. This paper addresses instability challenges in VSI-based MGs through comprehensive system modeling, analysis, and design. A novel approach is proposed, employing a modified virtual inertia PID droop control with virtual impedance, facilitates swift response to frequency and voltage fluctuations, ensuring precise power allocation among paralleled VSIs. Moreover, this paper presents the small signal analysis of the PID droop control and outlines the secondary control design, encompassing DDSRF-PLL, frequency, and voltage restoration loops. System validation is conducted through MATLAB/Simulink modeling and experimental verification on a hardware-in-the-loop (HiL) testbench, confirming the efficacy of the proposed methodologies in low inertia MGs. © 2024 IEEE.

关键词： MATLAB

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Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

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computers, Materials & Continua 2023年第6期75卷 5271-5286页

作者： Saeed Mohsen Sherif S.M.Ghoneim Mohammed S.Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications Engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)***,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic *** this perspective,an automated AI technique with a digital processing method can be used to improve these *** paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG *** classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and *** addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the ***,Hadamard coefficients of the EEG signals are obtained via the ***,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG ***,a k-fold cross-validation technique is applied to validate the performance of the proposed *** LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,*** computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,*** results show that the LSTM classifier provides better performance than SVM in the classification of EEG ***,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTM SVM fast Walsh-Hadamard transform seizure accuracy sensitivity specificity

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Traffic Sign Categorization Using YOLO Algorithm: Leveraging Real-Time Object Detection for Improved Road Safety 9

Traffic Sign Categorization Using YOLO Algorithm: Leveraging...

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9th International Conference on Communication and electronics Systems, ICCES 2024

作者： Santha Sheela, K. Jason Kingsly, R. Bhalaji, C.K. Selvalakshmi, C.B. Radhika, A. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Velammal college of engineering and Technology Dept of Electrical and Electronics Engineering Madurai India

ISBN: (纸本)9798350377972

Traffic signs, and navigation provide drivers with crucial information about speed limits, hazards and are essential for maintaining road safety. Detecting these signs and classifying in real-time plays a vital for autonomous driving systems, as timely information can prevent accidents and ensure compliance with traffic regulations. This paper presents a traffic sign detection system based on the YOLOv8 algorithm, known for its high-speed object detection capabilities, addressing challenges such as occlusion, small object detection, and adverse lighting conditions. The system is trained using a dataset of traffic signs sourced from Roboflow, capturing various types of traffic signs under different environmental conditions, such as lighting, weather, and occlusion scenarios. By using advanced feature extraction techniques, we have visualize the critical features that proposed for the model's classification decisions. The results confirm the efficacy and real-time applications in autonomous vehicles and smart traffic management systems of YOLOv8, indicating the intelligence potential for widespread adoption in the field of vehicles transportation systems. © 2024 IEEE.

关键词： Advanced traffic management systems

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Effective Posture Classification Using Statistically Significant Data From Flexible Pressure Sensors

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible electronics 2024年第5期3卷 173-180页

作者： Yoon, Jungeun Moon, Aekyeung Son, Seung Woo Electronics and Telecommunication Research Institute Daejeon34129 Korea Republic of University of Massachusetts Lowell Electrical and Computer Engineering Department LowellMA01854 United States

Advancements in flexible and printable sensor technologies to overcome the limitations of conventional rigid counterparts offer an excellent opportunity to design various healthcare applications for humans, and their potential flexibility can be used in real-time health monitoring and personalized physical conditions with minimal or no inconvenience. However, managing a large volume of obtained sensor datasets and ensuring accurate predictions can take time and effort. While statistical analysis and the Pearson correlation coefficient can reduce data volume, whether this would lead to losing important information and affect downstream application performance is still being determined. In this article, we use posture classification as an exemplar of timely services in digital healthcare, especially for bedsores or decubitus ulcers. Our sensors, placed under hospital beds, have a thickness of just 0.4 mm and collect pressure data from 28 sensors (7 \times 4 ) at an 8-Hz cycle, categorizing postures into four types from five patients. We then collected sensor data to explore the possibility of using a small number of pressure sensors for patient posture classification. Next, we apply a statistical analysis to the datasets obtained to select the featured sensor data cells and evaluate the performance of posture classification models on various groups of sensors. Our evaluation involves the analysis of reduced datasets through statistical methods and the Pearson correlation coefficient. The classification performance using datasets comprising five featured and 28 sensors are 0.93 and 0.99, respectively. These results suggest comparable performance and the viability of useful classifiers for both the cases. Consequently, comparable posture classification performance can be achieved using only 17.9% of the entire dataset. © 2022 IEEE.

关键词： Classification (of information)

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