Vijayakumar Thangavel

received his B.E (ECE) degree from Bharthiyar University in 2003 .He obtained his M.E (Computer Communication) degree from Anna university Chennai in 2007. He was awarded Ph.D. in Information and Communication Engineering from Anna University Chennai in 2015.
He has 15 years of teaching experience at college level. He also worked as Assistant Professor in IT Department at Bannari Amman Institute of Technology, Sathyamangalam. Currently he is working as Professor ECE at Gurunanak Institute of Technology, Hyderabad.
He has published Twenty Eight papers in International and National Journals and also published around 30 papers in International and National Conferences conducted both in India and abroad. Two monograph book.
His area of interest is Bio signal Processing, Soft computing and Embedded system.
He is life member of IAENG,ISRD and ISTE.

EDUCATION

B.E. Electronics And Communication Engineering Kongu Engineering College, Bharathiar University,Coimbatore. 2003First class
M.E. Computer Communication Tamilnadu college Engineering, Anna University Chennai 2007First class
PhD Information and Communication Engineering,Bio Signal Processing, Anna University Chennai, 2015

RESEARCH INTERESTS

Microprocessor and Microcontroller, Bio Medical Signals Processing, Soft Computing, Digital System Design, Embedded Systems

Scopus Publications

Enhancing User Experience through Emotion-Aware Interfaces: A Multimodal Approach
Vijayakumar T
Journal of Innovative Image Processing, 2024
The ability of a system or entity—such as an artificial intelligence system, computer program, or interface—to identify, comprehend, and react to human emotions is known as emotion awareness. In human-computer interaction, where the aim is to develop more intuitive and sympathetic systems that can comprehend and adjust to users' emotional states, this idea is especially pertinent. Improving user experience with emotion-aware interfaces is a multifaceted problem that calls for a multimodal strategy. Through the integration of several modalities, such as auditory, haptic, and visual feedback, interface designers may develop systems that not only react to user inputs but also identify and adjust based on the emotional states of users. The way users interact in the multimodal domain of emotion awareness will be explained in this research. Following that, a multimodal exploration of the user's experience with emotion awareness will take place.
IoT based Pose detection of patients in Rehabilitation Centre by PoseNet Estimation Control
R. Asokan, T. Vijayakumar
Journal of Innovative Image Processing, 2022
Recently, Virtual rehabilitation has recently emerged as a contemporary option to treating chronic, handicapped, or mobility-impaired patients using virtual reality, augmented reality, and motion capture technology. Using a virtual environment, patients are able to work out in accordance with their treatment plan. This study provides a PoseNet-based in-home rehabilitation telemedicine system with integrated statistical computation allowing clinicians to assess a patient's recovery progress. Using a smartphone camera, patients may undertake rehabilitation activities at home. The angular motions of the patients' elbows and knees are detected and tracked using the PoseNet skeleton-tracking technology. The estimated elbow and other feature poses are recorded during the completion process of rehabilitation activities in front of the mobile camera. Finally, additional performance measurements are gathered and analysed in order to better understand how well the system works.
High performance inventive system for gait automation and detection of physically disabled persons
R. Vinothkanna, T. Vijayakumar, N. Prabakaran
International Journal of Intelligent Enterprise, 2021
Physically challenged persons may face many difficulties in the present modern environment as most of the commercial facilities and utilities for a day to day life is designed for normal people to lead a sophisticated life. Particularly, people with physically disabilities face struggles in escalators in malls and public transportation places. It is very difficult for the disabled individual to be identified as one among in a large crowd and they normally feel unconformable to step inside in a running escalator. This research work proposes a novel method to identify the physically challenged persons from a large crowd by their nature of legs, walking pattern and hand sticks and provide necessary preference for them to get inside the escalators. Gait automation and detection mechanism is used for person identification for all gait events and deep learning-based neural network (DNN) is used for learning the patterns and making the system to automatically identify the physically challenged. Experimental results show that the proposed system automatically measures all the angle of gait events with an accuracy level of 95.4% and thus offers a cost effective solution for gait kinematic analysis for disabled peoples.
Using contourlet transform based RBFN classifier for face detection and recognition
R. Vinothkanna, T. Vijayakumar
Lecture Notes in Computational Vision and Biomechanics, 2019
Face is a highly non-rigid object; in such case, face detection and recognition has become an essential part of biometric systems in the majority of the applications. Numerous applications like robots, tablets, surveillance systems, and cell phones revolve around an efficient face detection and recognition technique in the background for access. Human–computer interaction systems like expression recognition, cognitive state/emotional state, etc. are used. Recognizing with the increased need for security and anticipation of spoofing attacks, almost all techniques have been proposed in the past to successfully detect and recognize the face through a single or combination of facial features, which is a challenging task given the complex nature of the background and the number of facial features involved. Here, the proposed work involves a multi-resolution technique, namely, the Contourlet transform along with linear discriminant analysis for feature detection given to an RBFN classifier for effective classification. It could be clearly seen that the proposed technique outperforms the other conventional techniques by its recognition rate of nearly 99.2%. The observed results indicate a good classification rate in comparison with conventional techniques.
Comparison of Various Face Recognition Techniques in Modelling Associations of Discriminant Factors
T. Vijayakumar, B. Kedarnath, Achampet Harshavardhan
Lecture Notes on Data Engineering and Communications Technologies, 2019
Blockchain technology in cloud computing to overcome security vulnerabilities
Achampet Harshavardhan, T. Vijayakumar, S.R. Mugunthan
Proceedings of the International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2018, 2018
Blockchain, the establishment of Bitcoin, has gotten broad considerations as of late. Blockchain fills in as an unchanging record which permits exchanges occur in a decentralized way. In spite of the fact that the component of blockchain advances may bring us more dependable and advantageous administrations, the security issues and difficulties behind this imaginative strategy is additionally an essential point that we have to concern. We give an outline of blockchain engineering initially and look at some common accord calculations utilized as a part of various blockchains. Moreover, this paper indicates how blockchain is utilized as a part of cloud storage and increase in security measures.
A real time experimental setup for classification of epilepsy risk levels
R. Harikumar, T. Vijayakumar
Applied Soft Computing Journal, 2015
Analysis of wavelet transforms and RBF neural networks for epilepsy risk level classification from EEG signals
International Journal of Applied Engineering Research, 2015
Analysis of wavelet transforms and RBF neural networks for epilepsy risk level classification from EEG signals
International Journal of Applied Engineering Research, 2015
Effective pattern discovery and dimensionality reduction for text under text mining
T. Vijayakumar, R. Priya, C. Palanisamy
Advances in Intelligent Systems and Computing, 2015
Performance analysis of wavelet transforms and morphological operator-based classification of epilepsy risk levels
Rajaguru Harikumar, Thangavel Vijayakumar
Eurasip Journal on Advances in Signal Processing, 2014
Performance analysis of SVD and K-means clustering for optimization of fuzzy outputs in classification of epilepsy risk level from EEG signals
R. Harikumar, T. Vijayakumar, M.G. Sreejith
2012 9th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2012, 2012
Performance analysis of Elman neural networks as post classifiers for wavelet transforms based feature extraction using hard and soft Thresholding methods in the classification of epilepsy risk levels from EEG signals
European Journal of Scientific Research, 2012
Comparison of hierarchical aggregation functions decision trees and rule based AI optimization in the classification of fuzzy based epilepsy risk levels from EEG signals
R. Harikumar, T. Vijayakumar
Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems His 2011, 2011
Performance analysis of SVD and support vector machines for optimization of fuzzy outputs in classification of epilepsy risk level from EEG signals
R. Harikumar, T. Vijayakumar, M.G. Sreejith
2011 IEEE Recent Advances in Intelligent Computational Systems Raics 2011, 2011

Publications

Monograph Published

1. , B.VinothKumar, T.Vijayakumar, Estimation Of Drowsiness From EEG Signals- A Correlation Dimension Approach, LAP LAMBERT Academic Publishing GmbH & Co. KG, Germany, May 2012.
2. , T.Vijayakumar, B.VinothKumar, Performance Analysis Of Fuzzy Genetic Algorithms, Support Vector Machine (SVM) And Statistical Analysis For Classification Of Epilepsy Risk Level In Diabetic Patients From EEG Signals, LAP LAMBERT Academic Publishing GmbH & Co. KG, Germany, May 2012.

Patent published
1. Title:(EN) IPM-SYSTEM: IOT BASED PATIENT MONITORING SYSTEM USING
BEAGLEBONE BLACK WIRELESS (OSD3358)
Application Number: 201941037638
Application Date :18.09.2019
BEC Certification
1. Cleared Cambridge English Qualifications: B1 Business Preliminary (BEC Preliminary)

Journals

1. T.Vijayakumar, R.Vinothkanna, M. Duraipandian,” Fuzzy Logic Based Aeration Control System for Contaminated Water” Journal of Electronics and Informatics (2020), No. 01, Pages: 10-17.
2. T.Vijayakumar, R.Vinothkanna,” Retrieval Of Complex Images Using Visual Saliency Guided Cognitive Classification” Journal of Innovative Image Processing (JIIP) (2020), No. 02, Pages: 102-109.
3. T.Vijayakumar, “Posed Inverse Problem Rectification Using Novel Deep Convolutional Neural Network” Journal of Innovative Image Processing (JIIP) (2020), No. 03, Pages: 121-127.
4. T.Vijayakumar, R.Vinothkanna, “Capsule Network on Font

GRANT DETAILS

Second National Workshop on “Intelligent Data Analytics & Image Processing (IDAIP 2008)” 3-4 October 2008. DRDO
Rs 35000 National