A Survey on Big Marine Wireless Optical Data:Key Factors and Recent Trends Asha K, K Muthuvel Proceedings 2025 5th International Conference on Expert Clouds and Applications Icoeca 2025, 2025 The Big Marine Data is a new concept derived from the underwater optical communication scheme. For BMD applications, the underwater wireless optical communication (UWOC) approach leverages its large bandwidth and minimal attenuation effect in the visible region, which sets it apart from other existing communication systems such underwater acoustic and radio frequency communications. The enormous amount of data collected by sensors, cameras, AUVs etc. from the ocean give rise to the analytics of marine data. An emerging highly efficient communication is needed for the collection of these large amount of data. The underwater replacement of IoT may be a solution; Internet of Underwater Things (IoUT).In order to handle the huge amount of data the conventional data analysis methods are not useful. Hence a machine learning approach is developed for extracting the detailed behavior, information etc. of BMD. The purpose of this paper is to synthesize the optical wireless ocean atmosphere and hence get a huge amount of data named BMD. Different modulation and machine learning approaches may be used depending on the undersea channel's characteristics. This review also covers the key factors that could influence BMD analysis as well as the different machine learning techniques that are employed in the synthesis and analysis of marine data.
Design and Performance Analysis of a High-Gain DC-DC Converter Employing an Active Switched Network Kcanandhan, V. Gowri Shankar, K. Muthuvel, A. Pradeep, P. Karthikeyan, P. Karputha Pandi Proceedings 2025 2nd International Conference on Electronic Circuits and Signaling Technologies Icecst 2025, 2025 High step-up DC-DC conversion is a critical requirement in applications such as renewable energy systems, electric vehicles, and DC microgrids, where low-voltage energy sources including photovoltaic arrays and fuel cells must be efficiently elevated to higher voltage levels. Conventional boost converter topologies, however, encounter limitations associated with extreme duty-cycle operation, elevated voltage stress on semiconductor devices, and deteriorated efficiency at higher gain conditions. To mitigate these challenges, this study proposes a high-gain DC-DC converter architecture founded on an active switched network (ASN). The configuration integrates coupled switched-inductor and switched-capacitor cells, enabling substantial voltage amplification under moderate duty ratios, thereby alleviating device stress and ensuring continuous input current characteristics. A comprehensive steady-state formulation in continuous conduction mode (CCM) is developed, followed by a systematic methodology for sizing both active and passive components. The dynamic performance is further assessed through the implementation of open-loop and closed-loop control schemes, validated via MATLAB/Simulink simulations, and subsequently verified on a 100 W hardware prototype. Comparative assessment against conventional and state-of-the-art converter structures demonstrates that the proposed ASN-based topology achieves a superior gain-to-component ratio along with enhanced efficiency, recording 86% under rated loading conditions. The outcomes establish the proposed converter as a viable candidate for renewable energy integration, fuel cell-powered systems, and distributed DC microgrid infrastructures.
FORECASTING SOLAR POWER GENERATION WITH MACHINE LEARNING TECHNIQUES Arpn Journal of Engineering and Applied Sciences, 2024 This study underscores the economic and environmental advantages of integrating solar energy into power systems. The unpredictable nature of solar power poses challenges to system operation and planning. To ensure the economic sustainability of newly constructed systems, precise forecasting of Photovoltaic (PV) system effectiveness and energy output is crucial. Addressing variations in solar power consumption, this work presents an enhanced Machine Learning (ML) model. Utilizing Python, the study explores Linear Regressor, Random Forest (RF) Regressor, XGBoost Regression, K-Nearest Neighbor (KNN) Regressor, and AdaBoost Regressor approaches, all proving effective in predicting electricity production. Results highlight the superior performance of ML algorithms over traditional time series methods and two baseline models, emphasizing their efficacy in solar power forecasting.
Hybrid War Strategy Optimization with Power Loss Index for Optimal VAr Compensation in Distribution Feeders with Industrial Load Growth M Devika, V Sai, Geetha Lakshmi, K Jegadish Kumar, K Muthuvel, et al. International Journal of Intelligent Engineering and Systems, 2024 Many countries have benefited from industrialisation and there is a growing global demand for power.For a power system to operate steadily, securely, and dependably, infrastructure must be upgraded and supply and demand must be balanced.In particular, reactive power (VAr) compensation is crucial for balancing the demand for reactive power from industries and, as a result, to ensure a sufficient voltage profile and voltage stability in low-voltage distribution lines.This study suggests the use of switched and fixed capacitors for dynamic volt-var controllers to handle heavy industrial loads.The voltage stability improvement, cost reduction, and loss reduction are the three goals of the multi-objective function.a new, straightforward meta-heuristic for war strategy optimisation (WSO) that combines the power loss index (PLI) to narrow the search space and boost the computing effectiveness.For various industrial load growth scenarios, simulations were performed on IEEE 33-bus low-voltage distribution feeder.A comparative study was also conducted using and compared with WOS (i.e., without reduced search space with PLIs) and whale optimization algorithm (WOA).In terms of global optima, the PLI-WSO findings are superior.In basic 33bus feeder, having 84.78% VAr compensation results in a 34.39 % loss reduction and 33.23 % cost reduction in a 33bus feeder, whereas having maximum 16% of industrial load growth in 10 years, the losses and costs are increased by 24.41 times to the base case, respectively.However, by having optimal VAr compensation, the losses and cost savings are resulted for 8.627% and 8.404%, respectively.Different load increase scenarios showed a similar type of overall benefit, demonstrating the scalability of the suggested methodology for real-time larger systems.
Minimum Component Switched Capacitor Inverter With Modified Pwm Control Sathish Kumar D, Arun V, Vijayakumar M, Elango S, Muthuvel K, Raja A 2nd International Conference on Emerging Research in Computational Science Icercs 2024, 2024 The study presents a minimal component switched capacitor inverter (MCSCI). The proposed Minimum Component Switched Capacitor Inverter (MCSCI) tackles significant concerns with the use of an increased number of semiconductor switches, DC sources, and capacitors. The proposed inverter generates a nine-level resultant voltage using a DC supply, eight switches, and three capacitors. The suggested inverter has the capability to amplify the output voltage with a gain of two. The proposed MCSCI used a variable frequency APOD pulse width modulation technique to augment the resulting voltage and improve the quality of the output voltage. The efficacy and practicality of the proposed MCSCI are evaluated using various modulation indices utilizing MATLABSIMULINK.
A Hybrid U-Net with Active Contours for Plant Leaf Disease Segmentation and Classification P. Muthukumar, Eswaramoorthy, K. Muthuvel, R. Boopathi, M.V. Suganyadevi, Reshma V. K. Proceedings of the 2024 International Conference on Advancement in Renewable Energy and Intelligent Systems Areis 2024, 2024 The economic growth of a nation entirely depends upon the agriculture and agricultural products. In developing countries like India, agriculture is the primary source of income and its contributing 17% to the total GDP. There are plenty of factors lead to plant disease which impacts the quality and yield of plants. Though manual method of detection is time consuming and it may have chance for errors. This method is not enough to identify and limit the spread of plant disease. To establish an automated plant disease detection in farms will reduce the risk of plant disease and promotes real-time monitoring of crops in a daily basis. Artificial Intelligence (AI) took part in supporting farmers to get instant solution in selection of fertilizers, classifying the quality of agricultural products, weather prediction and soil nutrient level detection. In this study, we proposes a novel segmentation approach namely Hybrid U-Net with active contours to segment the disease affected portion on leaf which support farmers to identify disease at early stage. This study provides a comprehensive analysis of plant disease segmentation by proposed method with conventional approaches. The publically available dataset is chosen for this analysis and performance of conventional studies was compared. This study presented current trends of plant disease segmentation and several other image classification techniques. Experimental results evaluating that the proposed study improved segmentation better than conventional methods.
Hybrid Feature Extraction Technique for Electrocardiograph Arrhythmia Signal Classification Muthuvel K, Jerry Alexander T, Muthukumar P, Thomas Thangam, Padma Suresh L Proceedings of the International Conference on Circuit Power and Computing Technologies Iccpct 2023, 2023 This proposed work suggests, a helpful method for classifying arrhythmia beats from an ECG database. Three stages make up the recommended method namely the initial preprocessing phase, hybrid feature extraction stage and the feature classifier. At first, ECG beat signal is acquired from the physiobank ATM and are pre-processed in order to get them ready for feature extraction. A hybrid feature extractor has been employed for effective feature extraction. Two processes comprise the hybrid feature extraction process: (i) Haar wavelet-based feature extraction ii) feature extraction on the basis of tri-spectrum. Once the beat signal has been classified as normal or abnormal, the feature Feed Forward Neural Network (FFNN) classifier is employed. Studies on beat categorization are done using the MIT-BIH Arrhythmia ECG signal database. A classification accuracy of about 73 % is achieved using this beat classification method.
PFoPID CONTROL DESIGN OF GRID-CONNECTED PV INVERTER FOR MPPT USING HYBRID ALGORITHM Thomas Thangam, K. Muthuvel International Journal of Power and Energy Systems, 2022 This paper aims to model a novel “passive fractional-order proportional-integral-derivative (PFoPID) controller” for photovoltaic (PV) inverter via reshaping energy, in such a way that the MPPT is attained through P&O system under diverse atmospheric states. Depending on passivity concept, storage, related to the DC-link current and DC-link voltage, in addition to q -axis current is initially build-up for a PV system, where every variable will be examined systematically when the advantageous terms are retained carefully so as to exploit the features of PV system. Here, the residual energy is reshaped by the FoPID control model, where the control constraints are optimally tuned by a new hybrid algorithm, which hybrids the concept of Cat Swarm Optimization (CSO) and Firefly Algorithm (FF), so that a finest controlling performance could be attained. As both the concepts of FF and CSO are in-cluded, the adopted model is known as Combined FF-CSO scheme (CFF-CSO).
A Survey on Big Marine Wireless Optical Data: Key Factors and Recent Trends K Asha, K Muthuvel 2025 5th International Conference on Expert Clouds and Applications (ICOECA … , 2025 2025
Minimum Component Switched Capacitor Inverter With Modified Pwm Control V Arun, M Vijayakumar, S Elango, K Muthuvel, A Raja 2024 International Conference on Emerging Research in Computational Science … , 2024 2024
A Hybrid U-Net with Active Contours for Plant Leaf Disease Segmentation and Classification P Muthukumar, K Muthuvel, R Boopathi, MV Suganyadevi, R VK 2024 International Conference on Advancement in Renewable Energy and … , 2024 2024
Hybrid War Strategy Optimization with Power Loss Index for Optimal VAr Compensation in Distribution Feeders with Industrial Load Growth. MD Rani, V Lakshmi, KJ Kumar, K Muthuvel, PM Kumar International Journal of Intelligent Engineering & Systems 17 (1) , 2024 2024
Hybrid Feature Extraction Technique for Electrocardiograph Arrhythmia Signal Classification K Muthuvel, P Muthukumar, L Padma Suresh 2023 International Conference on Circuit Power and Computing Technologies … , 2023 2023
PFoPID CONTROL DESIGN OF GRID-CONNECTED PV INVERTER FOR MPPT USING HYBRID ALGORITHM T Thangam, K Muthuvel Int. J. Power Energy Syst 42, 1-8 , 2022 2022 Citations: 2
Effectual evaluation on diabetic retinopathy AT Nair, K Muthuvel, KS Haritha Information and Communication Technology for Competitive Strategies (ICTCS … , 2021 2021 Citations: 7
SEPIC converter with closed loop PI controller for grid utilized PV system T Thangam, K Muthuvel, K Eswaramoorthy Journal of Next Generation Technology (JNxtGenTech) 1 (1), 1-10 , 2021 2021 Citations: 5
Blood vessel segmentation for diabetic retinopathy AT Nair, DK Muthuvel, KS Haritha Journal of Physics: Conference Series 1921 (1), 012001 , 2021 2021 Citations: 7
Automated screening of diabetic retinopathy with optimized deep convolutional neural network: enhanced moth flame model AT Nair, K Muthuvel Journal of Mechanics in Medicine and Biology 21 (01), 2150005 , 2021 2021 Citations: 15
Performance Evaluation of pre-processing techniques for historical Palm Leaf Manuscript image restoration TJ Alexander, SS Kumar, K Muthuvel 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile … , 2020 2020 Citations: 1
Research contributions with algorithmic comparison on the diagnosis of diabetic retinopathy AT Nair, K Muthuvel International Journal of Image and Graphics 20 (04), 2050030 , 2020 2020 Citations: 6
Hybrid algorithm based PFoPID control design of a grid-connected PV inverter for MPPT T Thangam, K Muthuvel 2020 Second International Conference on Inventive Research in Computing … , 2020 2020 Citations: 7
Parametric Analysis on Optimized PFoPID control design of a Grid-connected PV inverter for MPPT T Thangam, K Muthuvel 2020 4th International Conference on Trends in Electronics and Informatics … , 2020 2020 Citations: 3
Research perspectives and state-of-the-art methods in photovoltaic microgrids T Thangam, M K, HA Kazem World Journal of Engineering 17 (2), 223-235 , 2020 2020 Citations: 4
Blood vessel segmentation and diabetic retinopathy recognition: an intelligent approach AT Nair, K Muthuvel Computer Methods in Biomechanics and Biomedical Engineering: Imaging … , 2020 2020 Citations: 21
GABC based neuro-fuzzy classifier with hybrid features for ECG Beat classification K Muthuvel, S Anto, TJ Alexander Multimedia Tools and Applications 78 (24), 35351-35372 , 2019 2019 Citations: 10
PSO based Grid Tied PV System with Three Phase Dual Buck Inverter. N Prasad, SH Silviavinothini, K Muthuvel, S Parthiban International Journal of Psychosocial Rehabilitation 23 (4) , 2019 2019
SFOA: sun flower optimization algorithm to solve optimal power flow T Thangam, HA Kazem, K Muthuvel J Comput Mech Power Syst Control 2 , 2019 2019 Citations: 7
Diabetic retinopathy recognition using enhanced crow search with levy flight algorithm AT Nair, K Muthuvel Multimedia Research 2 (4), 43-52 , 2019 2019 Citations: 18
MOST CITED SCHOLAR PUBLICATIONS
Blood vessel segmentation and diabetic retinopathy recognition: an intelligent approach AT Nair, K Muthuvel Computer Methods in Biomechanics and Biomedical Engineering: Imaging … , 2020 2020 Citations: 21
Diabetic retinopathy recognition using enhanced crow search with levy flight algorithm AT Nair, K Muthuvel Multimedia Research 2 (4), 43-52 , 2019 2019 Citations: 18
Automated screening of diabetic retinopathy with optimized deep convolutional neural network: enhanced moth flame model AT Nair, K Muthuvel Journal of Mechanics in Medicine and Biology 21 (01), 2150005 , 2021 2021 Citations: 15
ECG signal feature extraction and classification using harr wavelet transform and neural network K Muthuvel, SHK Veni, LP Suresh, KB Kannan 2014 International Conference on Circuits, Power and Computing Technologies … , 2014 2014 Citations: 15
Adaptive neuro-fuzzy inference system for classification of ECG signal K Muthuvel, LP Suresh 2013 International Conference on Circuits, Power and Computing Technologies … , 2013 2013 Citations: 15
GABC based neuro-fuzzy classifier with hybrid features for ECG Beat classification K Muthuvel, S Anto, TJ Alexander Multimedia Tools and Applications 78 (24), 35351-35372 , 2019 2019 Citations: 10
Effectual evaluation on diabetic retinopathy AT Nair, K Muthuvel, KS Haritha Information and Communication Technology for Competitive Strategies (ICTCS … , 2021 2021 Citations: 7
Blood vessel segmentation for diabetic retinopathy AT Nair, DK Muthuvel, KS Haritha Journal of Physics: Conference Series 1921 (1), 012001 , 2021 2021 Citations: 7
Hybrid algorithm based PFoPID control design of a grid-connected PV inverter for MPPT T Thangam, K Muthuvel 2020 Second International Conference on Inventive Research in Computing … , 2020 2020 Citations: 7
SFOA: sun flower optimization algorithm to solve optimal power flow T Thangam, HA Kazem, K Muthuvel J Comput Mech Power Syst Control 2 , 2019 2019 Citations: 7
Classification of ECG signal using hybrid feature extraction and neural network classifier K Muthuvel, LP Suresh, TJ Alexander, SHK Veni Power Electronics and Renewable Energy Systems: Proceedings of ICPERES 2014 … , 2014 2014 Citations: 7
Research contributions with algorithmic comparison on the diagnosis of diabetic retinopathy AT Nair, K Muthuvel International Journal of Image and Graphics 20 (04), 2050030 , 2020 2020 Citations: 6
SEPIC converter with closed loop PI controller for grid utilized PV system T Thangam, K Muthuvel, K Eswaramoorthy Journal of Next Generation Technology (JNxtGenTech) 1 (1), 1-10 , 2021 2021 Citations: 5
Research perspectives and state-of-the-art methods in photovoltaic microgrids T Thangam, M K, HA Kazem World Journal of Engineering 17 (2), 223-235 , 2020 2020 Citations: 4
Hybrid Features and Classifier for Classification of ECG Signal K Muthuvel, LP Suresh Research Journal of Applied Sciences, Engineering and Technology 9 (12 … , 2015 2015 Citations: 4
Spectrum approach based hybrid classifier for classification of ECG signal K Muthuvel, LP Suresh, TJ Alexander, SHK Veni 2015 International Conference on Circuits, Power and Computing Technologies … , 2015 2015 Citations: 4
Spectrum approach based classification of ECG signal K Muthuvel, LP Suresh, TJ Alexander 2014 International Conference on Circuits, Power and Computing Technologies … , 2014 2014 Citations: 4
Parametric Analysis on Optimized PFoPID control design of a Grid-connected PV inverter for MPPT T Thangam, K Muthuvel 2020 4th International Conference on Trends in Electronics and Informatics … , 2020 2020 Citations: 3
PFoPID CONTROL DESIGN OF GRID-CONNECTED PV INVERTER FOR MPPT USING HYBRID ALGORITHM T Thangam, K Muthuvel Int. J. Power Energy Syst 42, 1-8 , 2022 2022 Citations: 2
An Ann Based Intelligent System with ABC-GA Optimization for the Classification of ECG Signals K Muthuvel, LP Suresh Australian Journal of Basic and Applied Sciences 9 (16), 396-401 , 2015 2015 Citations: 2
