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Meta-heuristic computing knacks for target angle estimation in monostatic radar system with coprime arrays

Ain Shams engineering Journal 2024年第5期15卷

作者： Akbar, Sadiq Sohail, Muhammad Asif Zahoor Raja, Muhammad Zaman, Fawad Ullah, Rizwan Abdul Rehman Khan, Muhammad Ajavakom, Nopdanai Phanomchoeng, Gridsada Department of Mechanical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Hefei230026 China Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Electrical & Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Department of Electrical & Computer Engineering COMSATS University Islamabad Attock Campus 43600 Pakistan Human-Robot Collaboration and Systems Integration Research Unit Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

Angle estimation of signals received by antenna arrays is a key step in adaptive beamforming, used to detect and locate targets in space. It is desirable to reduce costs by employing a smaller number of antennas while maintaining efficiency and degrees of freedom (DOF) of the system at the same time. Co-prime antenna arrays not only circumvent the operational constraints of cost and hardware complexity, but also boost the DOF by utilizing far fewer antennas as compared to traditional uniform linear arrays. In this paper, meta-heuristic computing paradigm for estimating arrival angle of targets with co-prime arrays incorporated in monostatic radar is proposed. The system's autocorrelation and Mean Square Error (MSE) are used for fitness function optimized by global/local search with Genetic Algorithm (GA) and Pattern Search (PS). Monte Carlo simulations are performed to evaluate the performance via estimation accuracy, MSE, and computational cost for varying numbers of impinging waves. © 2024 THE AUTHORS

关键词： Genetic algorithms

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A Cheap and Practical IoT Solution for Agricultural Data Monitoring

A Cheap and Practical IoT Solution for Agricultural Data Mon...

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Electric And Power engineering (EPEi), IEEE International Conference And Exposition On

作者： Doru Cornei Cristian Foșalău Laura Cornei Faculty of Electrical Engineering ”Gheorghe Asachi” Technical University of Ia.i Romania Faculty of Computer Science ”Alexandru Ioan Cuza” University of Ia.i Romania

ISBN: (数字)9798350356199

ISBN: (纸本)9798350356205

In this study we propose an agricultural data monitoring solution which uses widely available and affordable IoT components, being integrated in the local edge infrastructure. Data related to several important agricultural parameters (air and soil temperature, air and soil humidity, luminosity) is collected and stored with the use of a local server, which also performs the distribution of the information to the end-user (usually the farmer). The presented application uses open-source software solutions. It is also complete, encompassing all aspects from the used sensors to the monitoring and control application. The sensors communicate using the LoRaWAN protocol and are independent, being powered with the use of solar panels. The proposed system has been tested in real conditions for over a year, proving to be reliable and requiring no additional maintenance costs.

关键词： Temperature sensors Temperature distribution Crops Humidity Watermarking Soil Sensors Solar panels Surface cracks Monitoring

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A Comparative Analysis of Rainfall Forecasting in Cox’s Bazar using SARIMA, LSTM and GRU

A Comparative Analysis of Rainfall Forecasting in Cox’s Baz...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Eyakub Ali Panna Shil Md. Shadman Shakib Anirban Chanda Robin Khisa Md. Azizul Baten Ferdousi Mayoa Department of Electrical and Electronic Engineering Noakhali Science & Technology University Noakhali Bangladesh Department of Statistics Shahjalal University of Science And Technology Sylhet Bangladesh Faculty of Computer Science and Engineering Sylhet International University Sylhet Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Evaluating climate change, particularly rainfall fluctuations, is critical for regions prone to extreme weather, like Cox's Bazar, Bangladesh. This study employs both traditional time-series models and deep learning techniques to forecast rainfall patterns. An Exploratory Data Analysis (EDA) was performed on the monthly average rainfall data (1981-2022) from the Bangladesh Meteorological Department, revealing significant trends and seasonal patterns that guided model selection. ARIMA (0,0,1)(2,0,1)12, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models were developed to predict future rainfall. Among them, the GRU model achieved the lowest training errors (RMSE: 5.61, MAE: 3.48), while LSTM excelled in test data performance, producing lower errors (RMSE: 7.79, MAE: 4.68). Additionally, LSTM demonstrated the smallest percentage difference between training and testing errors, highlighting its superior generalization capabilities compared to SARIMA and GRU. These findings underscore the effectiveness of LSTM in capturing complex rainfall patterns, contributing to improved flood risk assessment and water resource management strategies.

关键词： Training Rain Predictive models Market research Data models Floods Risk management Forecasting Long short term memory Water resources

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Metaheuristic Approach for Optimizing Neural Networks Parameters in Battery State of Charge Estimation

Metaheuristic Approach for Optimizing Neural Networks Parame...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Zuriani Mustaffa Mohd Herwan Sulaiman Azlan Abdul Aziz Faculty of Computing Universiti Malaysia Pahang Pekan Pahang Malaysia Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Pekan Pahang Malaysia Faculty of Computer Science and Mathematics Universiti Teknologi MARA Arau Perlis Malaysia

To accurately estimate the battery state of charge (SOC), it is vital to improve the performance of a battery-powered system. This paper employs the recent proposed Evolutionary Mating Algorithm (EMA) for optimizing the weights and biases of Feed-Forward Neural Network (FNN) in estimating the state of charge (SOC) of Lithium-ion batteries. SOC estimation is the critical aspect in battery management system (BMS) to ensure the reliable operation of electric vehicles (EV) since there are no direct way to measure it. In addition, it is very nonlinear due to variation of charge/discharge currents and temperature. EMA is the recent evolutionary algorithm based on mating theory and environmental factor will be used in this paper to optimize the weights and biases of FNN on a common Li-ion battery, multiple data measurements, drive cycles and training repetitions. The performance of EMA will be compared with other algorithms to show the effectiveness of EMA in solving the SOC estimation problem. Findings of the study demonstrate the superiority of EMA in estimating the SOC of the batteries in terms of Root Mean Square Error (RMSE), mean Absolute Error (MAE) and Standard Deviation.

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Adaptive Video Streaming: An AI-Driven Approach Leveraging Cloud and Edge Computing

Adaptive Video Streaming: An AI-Driven Approach Leveraging C...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Mahmoud Darwich Kasem Khalil Yasser Ismail Magdy Bayoumi Department of Mathematics and Computer Science University of Mount Union Alliance OH Department of Electrical and Computer Engineering University of Mississippi University MS Electrical Engineering Department Southern University and A&M College Baton Rouge LA Department of Electrical and Computer Engineering University of Louisiana at Lafayette LA

Video streaming efficiency remains a major challenge, with increasing demands for high-resolution content and minimal buffering times. We propose a novel solution to enhance user experiences. Our approach combines adaptive streaming, AI algorithms predicting network conditions, and edge computing to optimize task allocation. These technologies aim to reduce latency, lower network traffic, and facilitate seamless transitions in video quality. Our results improve the streaming services and enable real-time video applications in remote or mobile environments, presenting substantial implications for the future of video streaming.

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Faster Greedy Optimization of Resistance-based Graph Robustness

Faster Greedy Optimization of Resistance-based Graph Robustn...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Maria Predari Robert Kooij Henning Meyerhenke Department of Computer Science Humboldt-Universität zu Berlin Berlin Germany Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands

The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph $G$ . We consider the optimization problem of adding $k$ new edges to $G$ such that the resulting graph has minimal total effective resistance (i. e., is most robust). The total effective resistance and effective resistances between nodes can be computed using the pseudoinverse of the graph Laplacian. The pseudoinverse may be computed explicitly via pseudoinversion; yet, this takes cubic time in practice and quadratic space. We instead exploit combinatorial and algebraic connections to speed up gain computations in established generic greedy heuristics. Moreover, we leverage existing randomized techniques to boost the performance of our approaches by introducing a sub-sampling step. Our different graph- and matrix-based approaches are indeed significantly faster than the state-of-the-art greedy algorithm, while their quality remains reasonably high and is often quite close. Our experiments show that we can now process large graphs for which the application of the state-of-the-art greedy approach was infeasible before. As far as we know, we are the first to be able to process graphs with $100K+$ nodes in the order of minutes.

关键词： Resistance Greedy algorithms Laplace equations Social networking (online) Robustness Indexes electrical resistance measurement

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An Experimental Comparison of RISC-V Processors: Performance, Power, Area and Security - Special Session Paper-

An Experimental Comparison of RISC-V Processors: Performance...

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IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems

作者： Elia Lazzeri Bruno Endres Forlin Gianluca Furano Marco Ottavi Luca Cassano Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Italy Faculty of Electrical Engineering Mathematics and Computer Science University of Twente The Netherlands European Space Research and Technology Centre Europeran Space Agency The Netherlands

ISBN: (数字)9798350366884

ISBN: (纸本)9798350366891

The RISC-V instruction set architecture (ISA) has garnered significant interest from both industry and academia because of its open source nature. Recent years have witnessed a surge in published implementations of the RISC-V ISA, with various companies actively pursuing its adoption in their products. However, a comprehensive analysis comparing these emerging RISC-V processors with each other and against established embedded computing platforms has not yet been published. This paper presents an experimental evaluation of three high-performance RISC-V processors: BOOM, NOEL-V, and CVA6 (formerly Ariane). The investigation is conducted on a Xilinx Kintex-7 Field-Programmable Gate Array (FPGA) platform. We perform a detailed analysis of critical performance metrics, covering area footprint, power consumption, and performance efficiency. Additionally, we assess the security posture of these cores against transient execution attacks, a prominent contemporary security threat. Finally, a comparative evaluation is undertaken between the RISC-V processors and two conventional application-level ARM processors to elucidate technological discrepancies and application suitability. This analysis aims to provide valuable insights into the current state and potential of RISC-V processors within the embedded computing domain.

关键词： Embedded computing Program processors Reduced instruction set computing Power demand Very large scale integration Security Transient analysis Surges Field programmable gate arrays Nanotechnology

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A DFT Study of Polyethylene Chain Deformation and Copper Surface Oxidation Effects on Charge Injection Barriers at Cu-PE Interfaces

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IEEE Transactions on Dielectrics and electrical Insulation 2025年

作者： Elsayed, Y.H.K. Huzayyin, A. Elkaramany, E. Cairo University Faculty of Engineering Engineering Mathematics and Physics Department Gamaa Street Giza12613 Egypt University of Toronto Edward S. Rogers Sr. Department of Electrical and Computer Engineering 10 King's College Road TorontoONM5S 3G4 Canada Cairo University Faculty of Engineering Electrical Power Engineering Department Gamaa Street Giza12613 Egypt

Chemical and physical defects (impurities and deformations, respectively) at the metal/polymer hetero-structures play a vital role in the charge injection process at the metal/polymer interface. The objective of the present work is to study the effect of polyethylene chain deformation and copper surface oxidation on the barriers to charge injection at the Cu(111)/PE(001) interface. The chain deformation in polyethylene is represented by lamella structure, in addition, copper surface oxidation appears using oxygen adatom impurities. The electronic properties of these structures are studied using density functional theory as a computational quantum mechanical method. The computations concluded that lamella and oxygen adatom defects produce the deepest trap states for holes 0.6 eV and electrons 0.77 eV, respectively. Additionally, this work concludes that the studied defects cannot explain the barrier to injection of 1 eV at the copper/polyethylene interfaces alone. © 1994-2012 IEEE.

关键词： Adatoms

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Time and frequency domain low order, low frequency approximation of mechanical systems

arXiv

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arXiv 2024年

作者： Zwart, Hans Veldman, Daniël W.M. Sharifi, Sahar F. the Department of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science University of Twente P.O. Box 217 Enschede7500 AE Netherlands The Netherlands the Department of Mechanical Engineering Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands the Department of Data Science Friedrich-Alexander Universität Erlangen-Nürnberg Bavaria Erlangen91052 Germany the Department of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science University of Twente P.O. Box 217 Enschede7500 AE Netherlands The Netherlands. She is now with TEMPRESS Radeweg 31 MD Vaassen8171 Netherlands

Control design for linear, time-invariant mechanical systems typically requires an accurate low-order approximation in the low frequency range. For example a series expansion of the transfer function around zero consisting of a mass, velocity, and compliance term. Because computing such a series expansion of the transfer function can be cumbersome, a new method to compute low-order approximations of mechanical systems is developed in this paper. The method does not require an explicit expression for the transfer function, which is not always available for infinite-dimensional systems. The advantages of the proposed method is demonstrated in three *** Codes 93B11, 93C20, 93C05, 93C80, 93C95 © 2024, CC BY.

关键词： Transfer functions

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Performance evaluation of artificial neural network and hybrid artificial neural network based genetic algorithm models for global horizontal irradiance forecasting

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Solar Energy Advances 2024年 4卷

作者： Wahidna, A. Sookia, N. Ramgolam, Y.K. Department of Mathematics Faculty of Science University of Mauritius Moka Reduit 80837 Mauritius Department of Electrical and Electronic Engineering Faculty of Engineering University of Mauritius Moka Reduit 80837 Mauritius

The output of photovoltaic (PV) systems is highly dependent on Global Horizontal Irradiance (GHI). Thus, accurate prediction of GHI is essential to meet increasing energy demands, stabilise the electric grid system and mitigate climate change. The main objective of this study is to accurately model and forecast GHI at Albion, Mauritius for a time step of every 15 min using the Artificial Neural Network (ANN) and hybrid Artificial Neural Network based Genetic Algorithm (ANN-GA) techniques. Ground-based measurement (GBM) data, collected every 15 min for a winter month was checked for stationarity and normalised to enhance its quality. Only strongly correlated input variables were selected to minimise uncertainties in forecasts. Special emphasis is given to short-term forecasting with a relatively small dataset size. This work is repeated for 30 min and 1 h time scales. The study is further validated using satellite data for a different location (Curepipe) in Mauritius. The performance evaluation over different statistical metrics indicated that the ANN model has the best capabilities for GHI forecasting, regardless of the location. The highest quality forecasts from the ANN technique resulted in values of 0.9999 for correlation coefficient (r), 0.9999 for coefficient of determination (R²), 0.1537 W/m² for Mean Absolute Error (MAE), 0.0641 W/m² for Mean Square Error (MSE) and 0.2532 W/m² for Root Mean Square Error (RMSE). The best ANN technique outperformed the strongest hybrid ANN-GA technique for every measured performance indicator. © 2024 The Author(s)

关键词： Artificial neural network Forecast Genetic algorithm Global horizontal irradiance Solar energy

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