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2023 31st National Conference with International Participation, TELECOM 2023

作者： Sofotasios, Paschalis C. Yoo, Seong Ki Simmons, Nidhi Cotton, Simon L. Valkama, Mikko Khalifa University Center for Cyber Physical Systems Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates Tampere University Department of Electrical Engineering TampereFI-33101 Finland Coventry University Centre for Future Transport and Cities Faculty of Engineering Environment and Computing United Kingdom Queen's University Belfast Institute of Electronics Communications and Information Technology BelfastBT3 9DT United Kingdom

ISBN: (纸本)9798350303292

The μ / inverse gamma and μ / inverse gamma distributions constitute two of the most distinct composite fading models. The present work offers novel results on the achievable ergodic capacity over these composite multipath/shadowing conditions. The derived expressions are corroborated with respective simulation results and are shown to be useful in practical wireless scenarios in the context of device to device communications, such as off-body, and vehicle-to-vehicle communications. © 2023 IEEE.

关键词： Vehicle to vehicle communications

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Hybrid Moving Object Detection Algorithm with Controlled Temporal Scale Frame Difference 23

Hybrid Moving Object Detection Algorithm with Controlled Tem...

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23rd International Conference on computer Theory and Applications, ICCTA 2013

作者： Awney, Mohammed Sayed, Mohammed S. Abd El-Samie, Fathi E. Electrical and Computer Engineering Higher Technological Institute Ramadan Egypt Electronics and Communications Engineering Egypt-Japan University of Science and Technology New Borg El Arab Egypt Electronics and Communications Engineering Zagazig University Zagazig Egypt Department of Electronics and Electrical Communications Faculty of Electronic Engineering Menoufia University Menouf Egypt

ISBN: (纸本)9781479924158

Moving object detection is the first stage in any video surveillance system. It is followed by two other stages;tracking and classification. The accuracy of the later two stages strongly depends on the first one. This paper proposes a hybrid moving object detection algorithm, which combines controlled temporal scale frame difference and selective running average as a method of background subtraction. In order to overcome the main drawbacks of traditional temporal difference methods that depend strongly on the speed of the moving object, we propose a new algorithm that controls the temporal scale between successive frames to solve the problem of emptiness phenomenon, whenan object moves slowly. Experimental results show that the proposed algorithm outperforms other commonly used object detection algorithms. © 2013 IEEE.

关键词： Object detection

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A Gradient Boosting Algorithm to Predict Energy Consumption for Home Applications

A Gradient Boosting Algorithm to Predict Energy Consumption ...

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computer, Communication and Control (IC4), International Conference on

作者： V G Sivakumar N. Arunfred N Anusha C. Balakrishnan B. Meenakshi S. Sujatha Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad Telangana India Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamil Nadu India Department of IoT School School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering S.A.Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Energy-efficient solutions are in high demand in today's fast-developing technological environment, necessitating the use of sophisticated predictive modeling methods. Conventional forecasting systems have problems given the particular aspects related to home demand for energy. As a consequence, new strategies are constantly being created for more accurately foreseeing future occurrences. Weather, user habits, and advancements in technology interfere with precise forecasts even more. This study provides a novel application of the Gradient Boosting approach to solve such obstacles and enhance energy use prediction for residential applications. Through utilising the electrical energy of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), the suggested method takes into account prior use patterns, appliance-specific data, and external factors like as climate. The model's response to changes in the actual world provides exact estimates, providing homeowners with the information they require to make energy-saving decisions. This method also investigates the comprehending of Gradient Boosting models, showing key characteristics that affect energy usage patterns. Stakeholders focus heavily on interpretability to make informed decisions with regard to ways to save energy and resource allocation.

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Fusion of Panchromatic and Low-Resolution Multispectral Images Using Combination of PCA and Morphological Hat Transformation 7th

Fusion of Panchromatic and Low-Resolution Multispectral Imag...

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7th International Conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2022

作者： Banerjee, Rupa Bhateja, Vikrant Mishra, Reetika Lin, Jerry Chun-Wei Travieso-Gonzalez, Carlos M. Faizabad Road UP Lucknow226028 India Department of Electronics Engineering Faculty of Engineering and Technology Veer Bahadur Singh Purvanchal University Shahganj Road Uttar Pradesh Jaunpur India Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applies Sciences Bergen Norway Campus Univeritario de Tafira sn. Telecommunicacion. Pabellon B. D-111 Las Palmas de Gran Canaria35017 Spain

ISBN: (纸本)9789811988646

In past few years, satellite imagery has made significant advances in the sectors of climate, farming, and defense. As seen in the Low-Resolution Multi-Spectral (LRMS) and Panchromatic (PAN) images, satellites acquire data in two domains: spectral resolution and spatial resolution. The goal of remote sensing image fusion is to merge the complimentary information from the PAN and LRMS images. The same has been accomplished in this study using Morphological Hat Transformation and Discrete Wavelet Transformation. Decomposition of LRMS and Pan images into approximation (cA) and detail (cD) coefficients is used in the proposed fusion method. To extract the first principal component, PCA is applied to the cA coefficients of both images, and MHT is applied to both cD coefficients at the same time. The end outcome is a fusion of the results after applying Inverse Wavelet Transformation (IDWT). The performance evaluation of the final-fused image is presented in the later sections. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image fusion

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Smart Processing of Exceptional Values in Electrical Networks Using Neuro-Fuzzy Algorithms

Smart Processing of Exceptional Values in Electrical Network...

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IEEE Students' Conference on Electrical, electronics and computer Science (SCEECS)

作者： Mahammad Anwar Arnav Kothiyal Praful V. Nandankar Amit Barve Harshal Patil Ramya Maranan Department of Electrical and Electronics Engineering Ballari Institute of Technology and Management Ballari Visvesvaraya Technological University Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Electrical Engineering Government College of Nagpur Maharashtra India Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University P.O. Limda Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

This research introduces a novel smart meter topology within the context of an integrated PV-Wind hybrid system for DC electric micro-networks. The proposed topology incorporates advanced techniques such as Linear Interpolation, Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for identifying and correcting outliers in the collected data. The integration of the smart meter with outlier detection and correction modules enhances the accuracy and reliability of the measurements, contributing to the overall efficiency of the micro-network. Linear Interpolation is employed to estimate and fill in missing data points, ensuring a continuous and consistent dataset. The ANN module is designed to identify outliers by learning patterns from historical data, allowing for real-time anomaly detection. Furthermore, the ANFIS module enhances the system's adaptability by providing a fuzzy logicbased approach to correct outliers, taking into account the dynamic nature of renewable energy sources in the hybrid system. The proposed smart meter topology offers a comprehensive solution for ensuring the integrity of data collected in DC electric micronetworks. By leveraging the synergies of Linear Interpolation, ANN, and ANFIS, the system demonstrates improved robustness against data anomalies, thereby enabling more accurate monitoring, control, and optimization of the PV-Wind hybrid system. This research contributes to the advancement of smart grid technologies, particularly in the realm of renewable energy micro-networks, by addressing the challenges associated with data quality and reliability.

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Smart Shelve System with Application

Smart Shelve System with Application

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Research, Invention, and Innovation Congress (RI2C)

作者： Suwaluck Meesomklin Tanayuk Bamrungpong Ekkapol Panyoo Prayoot Akkaraekthalin Department of Electronics Engineering Technology College of Industrial Technology King Mongkut's University of Technology North Bangkok Bangsue Bangkok Thailand Department of Electronics Telecommunications Technology College of Industrial Technology King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineer King Mongkut's University of Technology North Bangkok Bangkok Thailand

The growth of technology today is a fast leap, causing development in many areas including shops, products, businesses, and services. However, the development of shops is relatively expensive, especially modern shelves with intelligent systems that makes it less popular. For this reason, the researcher therefore foresees that there should be shelves that are not very expensive, easy to use, and have a system that can use applications and databases. By using the Arduino IDE, MIT App Inventor, and Firebase program to help create, it results in the development of stores that are applicable.

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Design and Implementation of Blockchain-Based Smart Contracts for Enhancing Ancillary Services Management in Electricity Markets

Design and Implementation of Blockchain-Based Smart Contract...

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National Proceedings Power and Energy Conference (PECon)

作者： Nazreen Bt Junaidi Md Pauzi Abdullah Mohamed Shaaban Nurul 'Izzati bt. Hashim Johari B. Abdullah Atiq Firdaus bin Fazari Department of Electrical and Electronics Engineering University of Malaysia Sarawak Kota Samarahan Sarawak Malaysia Faculty of Electrical Engineering University Teknologi Malaysia Johor Bharu Johor Malaysia Department of Electrical Engineering National Chung Cheng University Chiyayi Taiwan Faculty of Computer Science and Information Technology University of Malaysia Sarawak Kota Samarahan Sarawak Malaysia

ISBN: (数字)9798350361971

ISBN: (纸本)9798350361988

This paper presents a possible solution to the challenges of managing ancillary services on power systems, focusing on the development and execution of robust smart contracts on the Ethereum blockchain. These contracts automate essential procedures like bid submission, Market Clearing Price (MCP) calculation, and settlement, reducing manual involvement and improving operational efficiency. They ensure transparency and accountability through security mechanisms like role-based access restriction and event monitoring. The contracts were created using Solidity programming language and implemented on the Ethereum Sepolia test network. Unit tests using Foundry confirmed the functionality of each contract component, indicating their accuracy and effectiveness. The study highlights the novel features of the smart contract architecture and its potential impact on ancillary service administration.

关键词： Smart contracts Power system stability Electricity supply industry Throughput Stability analysis Blockchains Power system reliability Security Reliability Testing

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Numerical Analysis of Ultrathin TiO2Photoanode Layer of Dye Sensitized Solar Cell by Using SCAPS-1D 13

Numerical Analysis of Ultrathin TiO2Photoanode Layer of Dye ...

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13th IEEE Regional Symposium on Micro and Nanoelectronics, RSM 2021

作者： Noorasid, Nur Syamimi Arith, Faiz Mustafa, Ahmad Nizamuddin Muhammad Suhaimy, Syazwan Hanani Meriam Mohd Shah, Ahmad Syahiman Mohd Abid, Mohd Asyadi 'Azam Universiti Teknikal Malaysia Melaka Faculty of Electronic and Computer Engineering Melaka Malaysia Universiti Teknikal Malaysia Melaka Faculty of Electrical and Electronic Engineering Melaka Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Applied Sciences and Technology Johor Malaysia Universiti Malaysia Pahang Faculty of Electrical and Electronics Engineering Technology Pahang Malaysia Universiti Teknikal Malaysia Melaka Faculty of Manufacturing Engineering Melaka Malaysia

ISBN: (纸本)9781665412315

In this paper, the performance of dye-sensitized solar cells (DSSCs) was analyzed using SCAPS-1D simulation. This research focuses on the thickness variation of semiconductor layers between 1 to 50k nm. The complete DSSC structure consists of ITO / TiO2 / N719 / REDOX / Pt was used in this works. Four factors influencing the charge transfer between the DSSC layer are semiconductor thickness, operating temperature, doping concentration as well as the effect of neutral, acceptor, and donor defect were also analyzed. Next, the Voc, Jsc, FF, and PCE parameters were examined in each case based on the four factors mentioned above. The simulation results showed that the four factors analyzed are highly influential in improving the efficiency of DSSC. Based on this works, an efficiency of up to 8.14% can be achieved by applying a 50 nm ultra-thin layer of TiO2 with a doping concentration of 1 × 1018 cm-3. © 2021 IEEE.

关键词： Titanium dioxide

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Leveraging Randomization-Inspired Hybrid Deep Learning for Enhanced Diabetic Retinopathy Detection Via Multi-Scale Image Analysis

SSRN

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SSRN 2024年

作者： Mutawa, A.M. Hemalakshmi, G.R. Prakash, N.B. Murugappan, M. Computer Engineering Department College of Engineering and Petroleum Kuwait University Safat13060 Kuwait School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electrical and Electronics Engineering National Engineering College Tamilnadu Kovilpatti India Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha13133 Kuwait Department of Electronics and Communication Engineering Faculty of Engineering Vels Institute of Sciences Technology and Advanced Studies Tamilnadu Chennai600117 India Universiti Malaysia Perlis Perlis Kangar02600 Malaysia

Diabetic retinopathy (DR), a critical complication of diabetes, significantly increases the risk of vision loss. Prompt detection is essential for effective intervention, yet existing diagnostic practices are resource-intensive, inconsistent, and often inaccessible. This study pioneers an innovative framework, leveraging a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, synergized with Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, to fortify the detection of DR. Inspired by principles of randomization-based learning, our approach incorporates elements of stochastic modeling within the CNN-RBF architecture to optimize feature extraction and classification, mirroring the efficiency of non-iterative training processes. Through sophisticated image preprocessing, including advanced noise reduction and morphological techniques, complemented by Otsu’s thresholding for precise blood vessel segmentation, we enhance the model's diagnostic capability. Our methodology exhibits superior performance in DR screening, achieving benchmarks of 96.10% precision, 95.35% sensitivity, 97.06% specificity, and 96.10% accuracy, markedly surpassing conventional diagnostic methods. Applied across various publicly available datasets, this research not only advances the precision of DR diagnostics but also contributes to the broader discourse on the potential of hybrid, randomization-inspired neural networks in medical imaging. This fusion of deep learning innovation with the principles of randomization-based algorithms opens new avenues for developing accessible, accurate diagnostic tools, potentially alleviating the global impact of diabetic vision loss.. © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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A Novel Gooseneck Barnacle Optimizer Based Machine Learning Approach for Rainfall Prediction

A Novel Gooseneck Barnacle Optimizer Based Machine Learning ...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Marzia Ahmed Mohd Herwan Sulaiman Mostafijur Rahman Ahmad Johari Mohamad Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pahang Malaysia Department of Software Engineering Daffodil International university Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh

In dynamic meteorological prediction, accurate rainfall forecasting is a mystery. In a complex and dynamic natural environment with unpredictable sky movements, we propose an innovative methodology that forecasts weekly average rainfall patterns using the Gooseneck Barnacle Optimizer and the Least Squares Support Vector Machine (LSSVM). The significance of rainfall prediction is shown by its widespread implications, including public health. However, conventional single-model techniques and machine-learning methods must represent rainfall pattern changes, limiting our preparation. With a 2014–2018 dataset from reliable meteorological libraries, our research explores the possibility of this novel combination. The Gooseneck Barnacle Optimizer (GBO) serves as the bedrock of our methodology, introducing a novel evolutionary algorithm inspired by the intricate mating behaviors of gooseneck barnacles. GBO aptly captures the dynamic interplay of factors such as navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement during mating, culminating in two crucial optimization stages: exploration and exploitation. In contrast to previous algorithms like the Barnacle Mating Optimizer (BMO), GBO stands out by more accurately emulating the unique mating behaviors of gooseneck barnacles. The prediction job is performed by integrating the Least Squares Support Vector Machines (LSSVM) algorithm with GBO as an objective function, using the optimized hyper-parameter values. The results suggest that the GBO algorithm exhibits superior performance compared to existing methodologies. This is accomplished by efficiently augmenting the original random population for a particular problem, resulting in a convergence towards the global optimum and producing significantly enhanced optimization outcomes.

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