N VIGNESH PRASANNA

@veltech.edu.in

Associate Professor,Department of Electronics and Communication Engineering
Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology

N VIGNESH PRASANNA


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https://researchid.co/nvignesh

RESEARCH INTERESTS

Wireless Sensor Networks
20

Scopus Publications

Scopus Publications

  • Energy harvesting using piezoelectric sensors for extremely low-power Internet of Things use cases
    R. Sabitha, Vignesh Prasanna Natarajan, P. Sudarsan, B. Dhiyanesh
    Mechanics of Advanced Materials and Structures, 2026
  • Effective VLSI Implementation of Karhunen-Loeve Transform for Medical Electronic Signal Processing
    S. Karthick, Vignesh Prasanna Natarajan
    2025 5th International Conference on Emerging Research in Electronics Computer Science and Technology Icerect 2025, 2025
    The timely diagnosis of heart disease in its early stages provokes the need to develop advanced methods. Traditional methods of electrocardiogram (ECG) signal processing face challenges in computational efficiency and accuracy. Thus, an innovative approach namely, VLSI-based skill optimizer driven gated refined long short-term memory (SO-GRLSTM) method is implemented in this research for enhanced computational efficiency and detection of heart disease based on ECG recordings. Initially, the dataset comprising ECG signal recordings is collected and then preprocessed using a Wiener filter (WF) to reduce noise and improve the quality of the signal. Further, feature extraction is performed by employing the Karhunen-Loeve Transform (KLT), to effectively reduce the dimensionality of the input signal, while preserving the significant features. The extracted features are then fed into SO-GRLSTM, to optimize prediction accuracy and reduce overfitting. To ensure the feasibility of real-time deployment in medical devices, the entire system is implemented in a highly efficient very large-scale integration (VLSI) architecture. This design incorporates parallel processing and optimized hardware resources to provide low latency, frequency, area, and low power consumption. According to the results, the system achieves 97.2% accuracy in distinguishing between normal and abnormal heart rhythms. Thus, to enhance heart disease prediction, this research offers the potential to provide robust solutions for real-world settings.
  • Energy-Efficient Clustering in Wireless Sensor Networks Using Sine-Based Initialization Dynamic Refinement and Mutation-Driven Enhanced Moth-Flame Optimization
    Shashidhara. K S, Qassem Alattabi, Vignesh Prasanna Natarajan, Ajitha. P R, K. R. Kavitha
    2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems Iacis 2025, 2025
    Wireless Sensor Networks (WSNs) are a group of sensors that transmits and collects data from various monitoring fields. Clustering and routing are used in WSNs to minimize energy consumption and increase network lifetime. However, energy efficiency is a crucial problem in WSNs because sensor nodes are typically battery-powered and recharging these batteries frequently is often impossible, particularly in remote and harsh environments. To overcome this problem, this paper proposes a Sine-based Initialization Dynamic Refinement and mutation-driven Enhanced MothFlame Optimization (SDRM-EMFO) algorithm to reduce energy consumption and increase network lifetime and improve the energy efficiency in WSNs. In addition, fitness functions such as residual energy, Cluster member number variance, Ideal communication count, Distance between the node and the base station, are used to enhance the clustering efficiency by selecting optimal Cluster Heads (CHs). The routing algorithm is crucial in WSNs to reduce energy consumption and increase the network lifetime. Experimental results demonstrate that proposed SDRM-EMFO method is consistently outperformed in PDR of 97.72% compare to the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC), significantly reduce energy efficiency and network lifetime in WSNs.
  • Implementing Digital Twins in Intelligent Control Systems for Real-Time Energy Optimization
    Vignesh Prasanna Natarajan, N. Malarvizhi, S. Prabu, Parthiban. K G, Ravi. G, Mitul Patel
    2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems Iacis 2025, 2025
    Digital Twin (DT) technology has emerged as a powerful paradigm for enabling real-time synchronization and intelligent decision-making in energy systems. However, current solutions face challenges in scalability, predictive accuracy, and operational adaptability. This work presents a DT-enabled intelligent control framework that integrates IoT sensors, reinforcement learning, and predictive analytics to optimize energy consumption. The architecture incorporates anomaly detection, load balancing, and adaptive scheduling, supported by mathematical formulations and simulation models. Experimental validation in smart building and industrial environments demonstrates a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 5}-\mathbf{3 0 \%}$</tex> energy efficiency improvement, 18-30% cost reduction, 96.5% fault detection accuracy, and a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 5} \boldsymbol{\%}$</tex> decrease in decision latency compared to conventional approaches. The results highlight DT as a scalable and adaptive solution for energy-intensive domains. The proposed framework addresses sustainability goals and sets the foundation for broader deployment across smart grids, renewable energy systems, and industrial automation.
  • An improving secure communication using multipath malicious avoidance routing protocol for underwater sensor network
    Vignesh Prasanna Natarajan, Senthil Jayapal
    Scientific Reports, 2024
    The Underwater Sensor Network (UWSN) comprises sensor nodes with sensing, data processing, and communication capabilities. Due to the limitation of underwater radio wave propagation, nodes rely on acoustic signals to communicate. The data gathered by these nodes is transmitted to coordinating nodes or ground stations for additional processing and analysis. The characteristics of UWSN with underwater channels make them vulnerable to malicious attacks. UWSN communication networks are particularly susceptible to malicious attacks owing to high bit error rates, significant propagation delay variations, and low bandwidth. Moreover, because of the challenging and erratic underwater conditions, limited bandwidth, slow data transmission speed, and power constraints of underwater sensor nodes establishing secure communication in UWSN presents a significant challenge. To address the issues mentioned above, we have introduced the Multipath Malicious Avoidance Routing Protocol (M2ARP) and Foldable Matrix based Padding Rail Fence Encryption Scheme (FM-PRFES) methods to enhance secure communication in UWSNs. The proposed FM-PRFES method encrypts the input data to prevent unauthorized access during transmission within the network. Subsequently, the proposed Energy Efficiency Node Selection (EENS) method is used to identify the significant nodes in the network. Additionally, the Cuckoo Search Optimization (CSO) method is utilized to select the Cluster Head (CH) for data transmission. Subsequently, M2ARP is employed to analyze various routes and avoid adversarial nodes in the network. As a result, the proposed experimental analysis yields more efficient results regarding security, Packet Delivery Ratio (PDR), and throughput performance than traditional approaches.
  • Switching loss and temperature analysis of MPWM controller for solar PV inverter
    Sivaraj Panneerselvam, Karunanithi Kandasamy, S. Sivakumar, N. Vignesh Prasanna, R. Hushein
    International Journal of Power Electronics and Drive Systems, 2024
    Despite the fact that temperature affects how much power is produced by solar panels, a temperature that exceeds a certain threshold results in a reduction in output. Additionally, there are losses when switching is controlled in inverters using different control approaches like pulse width modulation (PWM), sinusoidal pulse width modulation (SPWM), and multiple pulse width modulation (MPWM). The type of control method and temperature have an impact on these losses. Here, the MPWM approach is used to analyze it at various temperatures. A metal-oxide-semiconductor field-effect transistor or MOSFET-based and an insulated gate bipolar transistor (IGBT)-based inverter are also planned. Their switching losses at various temperatures are contrasted. For a range of temperature values, the IGBT-equipped inverter is discovered to be a low-loss inverter. Compared to an IGBT inverter, the MOSFET inverter has a comparatively higher loss.
  • Chronic Lower Respiratory Diseases detection based on Deep Recursive Convolutional Neural Network
    Prakash P, Dhivya P, Vinitha R, Yogeshwaran A, Vignesh Prasanna Natarajan
    International Journal of Computational and Experimental Science and Engineering, 2024
    Recently, symptoms of Chronic Obstructive Pulmonary Disease (COPD) have been identified concerning long-term continuous treatment. Furthermore, predicting the life probability of patients with COPD is crucial for formative ensuing treatment and conduct plans. Additionally, it plays a vital role in providing complementary solutions using technologies such as Deep Learning (DL) to address experiments in the medical field. Early and timely analysis of clinical images can improve prognostic accuracy. These include COPD, pneumonia, asthma, tuberculosis and fibrosis. Conventional methods of diagnosing COPD often rely on physical exams and tests such as spirometers, chest and genetic analysis. However, respiratory diseases pose an enormous comprehensive health burden for many patients. Thus these methods are not always accurate or obtainable. However, succeeding in their accuracy involves a nonspecific diagnosis rate, time-consuming manual procedures, and extensive clinical imaging knowledge of the radiologist. To solve this problem, we use a Deep Recursive Convolutional Neural Network (DRCNN) method to detect chronic lower respiratory disease. Initially, we collected the images from the Kaggle repository, and evaluate the result based on the following stage. The first stage is pre-processing using a Gaussian filter to reduce noise and detect the edges. The second stage is segmentation used on Image Threshold Based Segmentation (ITBS), used for counting the binary image and separating the regions. In the third stage, we use the chi-square test to select the best features and evaluate the image values for each feature and threshold. Finally, classification using DRCNN detects CLRD classifying better than the previous method. In synthesis, CLRD can be detected by many staging measures, such as sensitivity, specificity, accuracy, precision, and Recall
  • IoT based Energy Management System for Buildings
    Vignesh Prasanna Natarajan, Naveena Aggarapu, Sai Laxman Metta, Siva Tejaswini Kota
    Proceedings of the 5th International Conference on Smart Electronics and Communication Icosec 2024, 2024
    This research study presents an IoT-based energy management system designed for buildings, integrating human detection technology and a pre-paid energy metering system. The system optimizes energy usage by dynamically adjusting devices based on human presence and allows users to monitor and manage their energy consumption in real-time. This approach enhances building energy efficiency, reduces wastage, and provides users with greater control over their energy usage. The combination of IoT technology, human detection, and pre-paid metering offers a comprehensive solution for efficient, cost-effective, and userfriendly energy management in buildings. In order to ensure that energy is used efficiently, the system dynamically adjusts appliances like air conditioning, heating, and lighting based on human presence. Users may also top up energy credits online or using mobile apps, and it gives them the freedom to monitor and manage their energy consumption in real-time. The system’s all-encompassing strategy improves building energy efficiency, lowers waste, and gives users more control over how much energy they use. By providing occupancy information, the system’s human detecting capabilities not only minimizes energy usage but also improves safety and security. By automating security and lighting systems, this data can enhance building management as a whole. Pre-paid energy metering system integration promotes energy conservation in addition to helping consumers budget their energy costs. All things considered, this Internet of Things (IoT)based energy management system provides a complete answer for effective, affordable, and user-friendly building energy management.
  • Hybrid Particle Swarm Optimization Algorithm with Deep Learning for Hyperspectral Images in Crops
    Vignesh Prasanna Natarajan, Myasar Mundher Adnan, Shalini G, Yerrolla Chanti, Sathiya Priya Shanmugam
    2nd IEEE International Conference on Data Science and Network Security Icdsns 2024, 2024
    Mapping is a crucial part of using remote sensing to monitor agricultural resources. Crop maps provide essential baseline data in image categorization for a range of agricultural monitoring and decision support applications. For this reason, the research employs a Hybrid Particle Swarm Optimization-Deep Learning-Driven Crop Type Classification technique (HPSODL-CTC) in Hyper Spectral Image (HSI). The proposed HPSODL-CTC objective is to classify the different crop types on HSI. Initially, the suggested model preprocesses images to increase quality of images. Furthermore, the model under description builds a dilated Convolutional Neural Network (CNN) for extracting the features. The dilated CNN algorithm for hyperparameters are adjusted using the HPSO approach. Furthermore, the HPSODL-CTC model that is being described Extreme Learning Machine (ELM) method for crop categorization. The results showed that the proposed method performed better in terms of accuracy of (99.81%), precision of (99.63%), F1-measure of (99.71%), and recall of (99.86%) than the existing models, such as Multi-Objective Binary Chimp Optimization Algorithm (MOBChOA), Multi-Objective Binary Grey Wolf Optimizer (MOBGWO), and Adaptive Fick's Law technique - SCNN (AFLA-SCNN).
  • Improved Power Effective Node Combined Heterogeneous Path Protocol for Enhance Network Lifetime Based on Cloud Resource Management in WSN
    International Journal of Intelligent Systems and Applications in Engineering, 2023
  • IoT based Detection and Monitoring for Coronary Artery Disease
    Aanandha Saravanan K, Vignesh Prasanna N, Ezilarasan M R, Aloy Anuja Mary G, Sathyasri B, MuthuKumaran D
    IEEE 9th International Conference on Smart Structures and Systems Icsss 2023, 2023
  • Reliable efficient cluster routing protocol based HTDE scheme for UWSN
    Vignesh Prasanna Natarajan, Kavitha Thandapani
    Indonesian Journal of Electrical Engineering and Computer Science, 2022
  • An improvement of communication stability on underwater sensor network using balanced energy efficient joining distance matrix
    Vignesh Prasanna Natarajan, Kavitha Thandapani
    International Journal of System Assurance Engineering and Management, 2022
  • Performance analysis of ICA algorithm for Blind Source Separation
    M.R. Ezilarasan, J. Britto Pari, K. Aanandhasaravanan, N. Vignesh Prasanna, D. Balaji
    8th International Conference on Smart Structures and Systems Icsss 2022, 2022
  • Women Safety Maneuver in Real Time Scenarios
    K. Aanandha Saravanan, B. Sathyasri, G. Aloy Anuja Mary, A. Farithkhan, N. Vignesh Prasanna, M. R. Ezilarasan
    8th International Conference on Smart Structures and Systems Icsss 2022, 2022
  • Adaptive Time Difference of Time of Arrival in Wireless Sensor Network Routing for Enhancing Quality of Service
    Vignesh Prasanna Natarajan, Kavitha Thandapani
    Instrumentation Mesure Metrologie, 2021
  • Implementation of visible light communication for data acquisition using system on chip
    M.R. Ezilzrasan, N.Vignesh Prasanna
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Mean value modeling of turbocharged gasoline direct injection engine
    N. Vignesh Prasanna, P Sivaraj, Surender Dhanasekaran
    Journal of Computational and Theoretical Nanoscience, 2018
  • Low voltage delta-sigma modulator with full range and unidirectional output
    Diwakar Krishnamoorthy, Vignesh Prasanna, Karthi Ramachandran, Soma Pal
    IEICE Electronics Express, 2015
  • Cracker industry fire monitoring system over cluster based WSN
    Journal of Engineering and Applied Sciences, 2014