Jarin T
@jecc.ac.in
Associate Professor/ Department of Electrical and Electronics Engineering
Jyothi Engineering College, Kerala
An Academician with profound skills in Administration, Academics and Research. My research contributions are interpreted through 71 Scholarly Publications in International Peer Reviewed Journals. I am credited with four patents. I am overseeing the Research of Five Scholars under APJ Abdul Kalam Technological University, Kerala as well as acting as Reviewer for International Peer Reviewed Journals. I am an active member of the Board of Studies at State University (Research), and was an Institutional Auditor for State University, Kerala. Coordinated the Institute's National Assessment and Accreditation Council (NAAC),
and Program Specific National Board of Accreditation (NBA) Initiatives.
EDUCATION
PhD / Faculty of Electrical Engineering
Title: Random Carrier Pulse Width Modulation techniques for three phase VSI drives with constant and fluctuating DC links.
National Engineering College (Autonomous Institute with NBA Accredited Programs)/Anna University/2016
MSW
Specialization in Psychology, Students Counselling, and Management
Annamalai University/2012
MBA
Specialization in Human Resource Management
Manonmaniam Sundaranar University/2010
M.E. /Applied Electronics
St. Xavier’s Catholic College of Engineering. (Institute with NBA accredited Programs) / Anna University 2010
B.E. / Electrical & Electronics Engineering.
Vins Christian College of Engineering/ Anna University/2008
RESEARCH, TEACHING, or OTHER INTERESTS
Renewable Energy, Sustainability and the Environment, Electrical and Electronic Engineering, Biomedical Engineering, Energy Engineering and Power Technology
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Meenakshi Sundaram Ulaganathan, Rathinam Muniraj, Vaikundam Suresh, Sundararajan Edwin Raja, and Thankaswamy Jarin
AIP Publishing
Arun Eldho Alias, F.T. Josh, T. Jarin, and Teena Skaria
Elsevier BV
R Sherline Jesie, M.S. Godwin Premi, and T. Jarin
Elsevier BV
J. Mahesh, J. Jerlin Regin, T. Jarin, and S.R. Boselin Prabhu
Inderscience Publishers
A. Cianna, S. Sumathi, and T. Jarin
Springer Science and Business Media LLC
A. Muniappan, T. Jarin, R. Sabitha, Ayman A. Ghfar, I. M. Rizwanul Fattah, Chilala Kakoma Bowa, and Mabvuto Mwanza
IWA Publishing
Abstract Fresh-saline groundwater is distributed in a highly heterogeneous way throughout the world. Groundwater salinization is a serious environmental issue that harms ecosystems and public health in coastal regions worldwide. Because of the complexities of groundwater salinization processes and the variables that influence them, it is challenging to predict groundwater salinity concentrations precisely. It compares cutting-edge machine learning (ML) algorithms for predicting groundwater salinity and identifying contributing factors. It employs bi-directional long short-term memory (BiLSTM) to indicate groundwater salinity. The input variable selection problem has attracted attention in the time series modeling community because it has been shown that information-theoretic input variable selection algorithms provide a more accurate representation of the modeled process than linear alternatives. To generate sample combinations for training multiple BiLSTM models, PMIS-selected predictors are used, and the predicted values from various BiLSTM models are also used to calculate the degree of prediction uncertainty for groundwater levels. The findings give policymakers insights for recommending groundwater salinity remediation and management strategies in the context of excessive groundwater exploitation in coastal lowland regions. To ensure sustainable groundwater management in coastal areas, it is essential to recognize the significant impact of human-caused factors on groundwater salinization.
P. Muthukumar, S. Manikandan, R. Muniraj, T. Jarin, and Ann Sebi
Elsevier BV
N. Amuthan, Marsaline Beno M, P. Velrajkumar, N. Sivakumar, and T. Jarin
Elsevier BV
Gaswin Kastro G., Sreeja Mole S., and Jarin T.
AIP Publishing
Gaswin Kastro G., Anie Pradeeba W., and Jarin T.
AIP Publishing
Rathinam Muniraj, N. Karuppiah, P. V. Nisha, Minju B. Chandran, T. Jarin, and Stephy Akkara
AIP Publishing
Hari Vinayak MV, Jarin T, and Kiruba Thangam Raja
IEEE
A network intrusion detection system (NIDS) is a crucial component of a robust cybersecurity strategy. Its primary purpose is to continuously monitor network traffic and detect suspicious or malicious activity that could be indicative of a cyberattack or unauthorized network access. The effectiveness of NIDS depends heavily on the techniques we use to boost the classification accuracy of intrusion and minimize the computational difficulty while performing training and testing. The high volume of network traffic combined with its large number of features will increase the classification time. With the recent emergence of deep learning techniques, scientists have shown interest in learning dataset features, followed by the classification of intrusions. This study offers a novel method for extracting high-dimensional features from input data by employing a stacked sparse autoencoder. Simple machine learning models are then built using the remaining low-dimensionality features. Simulations were conducted, and the efficacy of binary and multiclass classifications was verified. The proposed method exceeds most of the other existing approaches in terms of performance.
B. Deepanraj, R. Muniraj, T. Jarin, and J. Kohila
IEEE
Disturbance rejection performance optimization with constraints on robustness for a multi-variable process is commonly encountered in industrial control applications. This paper presents the tuning of a multi-loop Proportional Integral (PI) controller method to enhance the performance of load disturbance rejection using evolutionary optimization. The proposed design methodology is formulated to minimize the load disturbance rejection response and the input control energy under the constraints of robust stability. The minimum singular value of multiplicative uncertainty is considered a multi-loop system robust stability indicator. Optimization is performed to achieve the same, or higher level than the most-explored Direct Synthesis (DS) based multi-loop PI controller, which is derived from a conventional criterion. Simulation analysis clearly proved that the proposed multi-loop PI controller tuning method gives better disturbance rejection, and either, the same or a higher level of robust stability when compared to the DS-based multi-loop PI controller.
P. Muthukumar, S. Nageswari, Sabareesa Priya I, Jarin T, and K. Ezhil Vignesh
IEEE
Nowadays the need for electricity is increasing. To full fill the need for electricity by using two ways either non-conventional energy sources or renewable energy sources. Based on the cost and availability renewable energy source is the most possible energy source. The most available as well as cheap renewable energy source is solar. So, recently more research going on solar energy sources to utilize their maximum energy for different applications. The latest research trends in the field of solar are “Solar energy Conversion system”. The LUO converter is employed to increase the performance of the PV system by increasing the voltage that is output. The LUO converter is used to increase the oscillating PV voltage. For an AC load application convert DC-DC Converter voltage to 9 level Inverter Circuit. Analyze the Single phase, three-phase, and induction-based conditions in the proposed model.
K. Sasikala, J Stanly Selva Kumar, K. Ezhil Vignesh, Jarin T, P. Muthukumar, and L. Padmasuresh
IEEE
This research proposes a nonlinear control method for single-phase Unified Power Flow Controller (UPFC) to improve Power Quality (PQ) issues in single-phase power grid. The main objective of this work is to maintain the appropriate level of load voltage minimal distortion and the control aims include the following: (i) compensation for current harmonics and reactive power; (ii) compensation for voltage disturbances (harmonics and swell, sags and flickers of voltage); and (iii) regulate the voltage on the DC bus. For the purpose to reduce harmonics in power systems and generate reference current for AC supply, the Decoupled Double Synchronous Reference Frame (DDSRF) theory has been presented. The influence of harmonics is then lessened by injecting this harmonic into power systems. Artificial Neural Network (ANN) with Hysteresis Current Controller (HCC) is used to create hysteresis Current regulation, which lowers Total Harmonic Distortion (THD) and increases output voltage. Through several simulation outcomes, the suggested system's effectiveness is examined. Hardware results are also confirmed with simulation outcomes using Matlab/Simulink.
D. R. Binu Ben Jose, M. E. Shajini Sheeba, K. Ezhil Vignesh, Jarin T., and P. Muthukumar
IEEE
Now-a-days, society largely depends on sources of clean energy. Nevertheless, incorporating it into the power system is complicated and has technical difficulties. Voltage sag and swell are the main issues with power quality (PQ) that arise from the unpredictability of clean energy sources. In this paper, a D-STATCOM is employed to compensate PQ issues arising due to solar and wind farms. The main contribution of this work is that a photovoltaic (PV) and wind fed D-STATCOM is used to mitigate the issues of PQ, SUC voltage sag, swell, harmonics and flickers. In order to enhance functionality of wind system the squirrel cage induction generator (SCFG) is employed. To stabilize the output voltage of PV system, a Re-Lift Luo converter is introduced. Adaptive neuro-fuzzy interface system (ANFIS) based maximum power point tracking (MPPT) is intended to maximize the power from PV array and is used efficiently to maintain the system reliability. The PWM rectifier increases the current flow through PWM operation and regulates the dc link voltage at the converter side. Here, the recurrent neural network (RNN) is utilized to generate the reference current for hysteresis current controller (HCC), which minimizes the current distortions. The experimental prototype of this work has been realized employing a Node MCU Wi-Fi module and MATLAB simulation platform.
Rajan. VR, Akhil Gilbert, P. Muthukumar, and T. Jarin
IEEE
The potential downside of rapid technological advancement has increased exposure to radiofrequency from wireless transmitting equipment and technologies. Because the radiation is nonionizing, it can disrupt your DNA, and also impact the water molecules or sugar molecules in your body. Normal mobile phone signals and even your Wi-Fi signal are in the microwave range, which means they utilize the same frequency as your microwave to cook food. Since the mobile phone emits a very high amount of radiation it will cause your skin or body to heat up somewhat, So you may actually experience your phone being hot in your hand when you use it too much now. Most human beings claim to be sensitive to the electromagnetic radiation emitted by modern digital devices and mobile phones, they describe symptoms such as headaches, nausea, skin responses, burning eyes, and weariness, nevertheless, these are only impacts claimed on a daily basis. A few studies have shown considerably more disturbing outcomes, such as probable links between the side of the brain used while people are using their phones and the emergence of brain tumors. So an intense review is necessary to look into this problem to get aware and take prevention accordingly.
Shimi Mohan and T. Jarin
IEEE
Cancer is a leading cause of mortality and morbidity worldwide. Approximately 700,000 cancer deaths occur annually in the United States and every year, more than a million new cancer cases are predicted. Chemotherapy and hyperthermia are effective treatment options for patients with high-risk cancers. At disseminated cancer sites, chemotherapeutic drugs can be infused into the bloodstream to stop cancer cell growth and/or spread. The dose of drugs infused into a patient's vein during chemotherapy is often controlled using computerized drug delivery systems. Clinicians typically struggle to identify the correct dosage of intravenous chemotherapy because of unforeseen side effects such as immune response and increased toxicity, and optimization algorithms and automated control approaches have been developed to aid in the safe administration of cancer chemotherapy drugs. To govern the distribution of closed-loop intravenous anticancer medicines, several controllers have been created, and numerous mathematical models have been developed to mimic the behavior of cancer by considering various phases in various treatment options such as chemotherapy and radiation. This study provides a current state-of-the-art review of the function of anti-cancer drug delivery systems, as well as the control approaches and different strategies used in building an anti-cancer drug delivery system, such as mathematical models, optimized control, and hybrid algorithms.
Remya George, Reshma Jose, K. Meenakshy, T. Jarin, and S. Senthil Kumar
IOS Press
Law enforcement teams across the globe experience the highest occupational stress and stress-related diseases. Physical exercise and an active lifestyle are recommended as part of their profession to equip them to fight stress and related health adversities. The research is carried out using objective measures of Heart Rate Variability (HRV), Electro Dermal Activity (EDA), Heart Rate Recovery (HRR), and subjective questionnaires. HRV was generated with an electrocardiogram (ECG) signal acquired using NI myRIO 1900 interfaced with the Vernier EKG sensor. HRR was acquired with the help of a Polar chest strap exercise heart rate monitor and EDA acquisition was carried out with Mindfield E-Sense electrodes. Then statistical features are extracted from the collected data, and feed to the AQCNN (Aquila convolution neural network) classifier to predict the stress. Signal analyses were done in Kubios 4.0, Ledalab V3.x in a MATLAB environment. The results pointed out that exercise training is effective in increasing the vagal tone of the Autonomic Nervous System (ANS) and hence improves the recovery potential of the cardiovascular system from stress. The proposed AQCNN method improves the accuracy by 95.12% which is better than 93.13%, 85.36% and 80.13% from Statistical technique, CNN and ML-SVM respectively. The findings have the potential to influence decision-making in the selection and training of recruits in high-stress positions, hence optimizing the cost and time of training by identifying maladaptive recruits early.
G. Jayahari Prabhu, B. Perumal, and T. Jarin
World Scientific Pub Co Pte Ltd
Medical imaging technology is one of the most critical applications necessitating data protection, particularly if we need to keep track of any important patient information. This medical imaging system employs encryption and decryption. Using several cryptographic techniques, the security key was established to protect the data. Every network that sends and receives data needs to be secure in some way. In this paper, ALO along with the encryption algorithm honey is used to enhance the security of medical imaging technologies, the proposed study uses a variety of ways to protect important health information. In comparison to the existing one, the proposed honey algorithm attains better results. Further, the antlion optimizer uses random keys throughout the encryption and decryption. In the next step, the keys are remodeled using antlion optimization. After that, the updated key is optimized by analyzing every element and generating paths that trigger the traps and latching functions. The mean square error (MSE) is reduced to 1% and the peak signal-to-noise ratio (PSNR) is increased to 98% by using a hybrid strategy.
Reshma Jose, Shanty Chacko, J. Jayakumar, and T. Jarin
World Scientific Pub Co Pte Ltd
Image processing plays a significant role in various fields like military, business, healthcare and science. Ultrasound (US), Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the various image tests used in the treatment of the cancer. Detecting the liver tumor by these tests is a complex process. Hence, in this research work, a novel approach utilizing a deep learning model is used. That is Deep Belief Network (DBN) with Opposition-Based Learning (OBL)-Grey Wolf Optimization (GWO) is used for the classification of liver cancer. This process undergoes five major processes. Initially, in pre-processing the color contrast is improved by Contrast Limited Adaptive Histogram Equalization (CLAHE) and the noise is removed by Wiener Filtering (WF). The liver is segmented by adaptive thresholding following pre-processing. Following that, the kernelizedFuzzy C Means (FCM) method is used to segment the tumor area. The form, color, and texture features are then extracted during the feature extraction process. Finally, these traits are categorized using DBN, and OBL-GWO is employed to enhance system performance. The entire evaluation is done on Liver Tumor Segmentation (LiTS) benchmark dataset. Finally, the performance of the proposed DBN-OBL-GWO is compared to other models and their achievements are proved. The proposed DBN-OBL-GWO achieves a better accuracy of 0.995, precision of 0.948 and false positive rate (FPR) of 0.116, respectively.
Chinju Saju, Parwin Angel Michael, and T. Jarin
Elsevier BV
B. Deepanraj, N. Senthilkumar, T. Jarin, Ali Etem Gurel, L. Syam Sundar, and A. Vivek Anand
Elsevier BV
T. Jarin, Stephy Akkara, S.S. Sreeja Mole, Arthi Manivannan, and A. Immanuel Selvakumar
Elsevier BV
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
Trans Callosal Signal Bypassing and Neuromodulating Brain Machine Interface
Saveetha Medical College and Hospital
Medical and Health Sciences
Filed : 21-01-2019
Published : 11-09-2020
Exoskeleton Hand for Stroke Survivors Rehabilitation and Control via IoT Based Optimization
Jyothi Engineering College, Thrissur
Engineering and Technology Pending
Filed : 28-02-2019
Published : 08-03-2019
Tremor Stabilization Spoon for Parkinson Syndrome Affected Patients, Which Can Function as per the Requirements Based on Monitoring of Conditions
Jyothi Engineering College, Kerala
Engineering and Technology
Filed : 13-11-2018
Published : 23-11-2018
Brain Stitcher: A Compact Wireless Telemetry System to Assess the Behaviour of the Rodents
National Institute of Technology, Manipur, India
Filed : 08-01-2019
Published : 08-02-2019
Industry, Institute, or Organisation Collaboration
Saveetha Medical College and Hospital
National Institute of Technology, Manipur, India
Anna University/National Engineering College
SOCIAL, ECONOMIC, or ACADEMIC BENEFITS
Skill Development Programs - Nodal Co-ordinator of Pradhan Mantri Kaushal Vikas Yojna (PMKVY) – TI
Education Counsellor