T.vandarkuzhali.

Ph.D – Digital Image Processing

kuzhalivandu@
+91 9600633663
18 year of experience in teaching

EDUCATION

Completed B.E. EEE in Amrita Institute Of Technology& Science, Coimbatore,BHARATHIAR UNIVERSITY. 2000
COMPLETED M.E POWER ELECTRONICS AND DRIVES in Karunya Institute Of Technology, Coimbatore ANNA UNIVERSITY 2005
COMPLETED Ph.D. in Digital Image Processing at Sri Ramakrishna Engineering College, Coimbatore. ANNA UNIVERSITY

RESEARCH INTERESTS

DIGITAL IMAGE PROCESSING,, RENEWABLE ENERGY, POWER ELECTRONICS

Scopus Publications

Cognitive IoT-Driven Antenna Optimization System for Dynamic Wireless Connectivity
N.R. Latha, G. Shyamala, Sonal Borase, N. Gayathri, T. Vandarkuzhali, Ashish Rajesh Polke
Proceedings Iceconf 2025 2025 2nd International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, 2025
In this paper, a Cognitive IoT-based Dynamically Wireless Connections Antenna Optimization System is presented based on a Hybrid Deep Learning and Reinforcement Learning (RL) technique. The system uses TensorFlow and TensorFlow Agents to optimize dynamically the antenna configurations that enhance wireless connections in IoT networks. The system is able to adjust to changing conditions by use of deep learning to predict network conditions as well as reinforcement learning to make real-time decisions to improve signal strength, limit interference, and conserve energy. The outcomes indicate that performance is greatly enhanced and the hybrid approach is more efficient than the traditional optimization approaches in scalability, adaptability and consuming less resources. The adoption of TensorFlow supports easy training and deployment of machine learning models to maintain real-time adaptability in antenna setup. The present paper presents the potential of cognitive IoT systems to optimize dynamic wireless networks, which potentially provides a viable answer to the increasing need of an efficient and reliable IoT-driven connectivity in diverse and dynamic scenarios
Vector Control of an Induction Motor for Speed Regulation
Gurram Satyanarayana, M. Karthikeyan, R. Mahalakshmi, T. Vandarkuzhali
Proceedings 7th International Conference on Computing Methodologies and Communication Iccmc 2023, 2023
In electric motor drives, speed regulation plays an essential part in describing the overall performance of the system drive. To control the motor speed of an Induction motor (IM), an indirect vector control method is implemented in this paper. Scalar control is a simple and effective technique, but it responds slowly to transients and is unsatisfactory for regulating motors with dynamic behavior. The currents are controlled via the field-oriented control (FOC) approach, allowing for quick reactions. This approach meets the demands of dynamic drives, wherein quick response is required. The flux location is calculated indirectly in indirect control methods by rotor speed and slip calculation. The indirect control technique has grown in popularity due to the lack of rotor flux position sensors and the capacity to work at low speeds. A PI controller is utilized in the speed controller to control the motor torque by producing quadrature-axis current reference iq*. The motor's flux is controlled by direct axis current reference id*. The IM is operated by a current-controlled PWM inverter. The designed model is simulated using MATLAB and the results show an accurate speed response of the IM motor.
Subversive power cable fault credential via gsm and gps module for green energy
Journal of Green Engineering, 2021
Detection of fovea region in retinal images using optimisation-based modified FCM and ARMD disease classification with SVM
T. Vandarkuzhali, C.S. Ravichandran
International Journal of Biomedical Engineering and Technology, 2020
The underlying motive resting with the current investigation is invested in designing a superior recognition system for locating the fovea region from the retinal image by significantly steering clear of the roadblocks encountered at present. The significant scheme streams through three specific processes particularly, blood-vessel segmentation, optic-disc detection, fovea detection and ARMD disease classification. In the initial stage, the retinal images are enhanced with the help of AHE approach and then segmented by adaptive-watershed technique. The successive stage opens up with recognition of optic-disc by means of MRG system. And, in the last stage, the fovea region is effectively spotted with the help of OBMFCM technique. Along with the fovea-region segmentation, analysis is made for the classification of dry/wet ARMD with SVM classifier. The record-breaking technique is performed in the platform of MATLAB2014 and its charismatic upshots are assessed and contrasted with those of the parallel fovea recognition approach.
Automated segmentation method for disease identification and fovea detection using GLCM and ELM
Asian Journal of Information Technology, 2016
Fovea detection and disease identification using integreated GF-SVM method
Asian Journal of Information Technology, 2016
Fovea region detection in retinal image using modified watershed and fuzzy C means clustering (MFCM) algorithm
International Journal of Applied Engineering Research, 2015
Fundus-fovea localization image analysis based on automatic screening
Arpn Journal of Engineering and Applied Sciences, 2015
Automatic retinal blood vessel assessment using extreme learning machine approach
Biomedicine India, 2015
ELM based detection of abnormality in retinal image of eye due to diabetic retinopathy
Journal of Theoretical and Applied Information Technology, 2014

RECENT SCHOLAR PUBLICATIONS

Detection of fovea region in retinal images using optimisation-based modified FCM and ARMD disease classification with SVM
T Vandarkuzhali, CS Ravichandran
International Journal of Biomedical Engineering and Technology 32 (1), 83-107 , 2020
2020.0
Citations: 5
Fovea detection and disease identification using integrated GF-SVM method
AS Babu, T Vandarkuzhali, C Ravichandran
Int J Comput Mod T 1 (1) , 2015
2015.0
Citations: 4
Detection of exudates caused by diabetic retinopathy in fundus retinal image using fuzzy k means and neural network
T Vandarkuzhali, CS Ravichandran, D Preethi
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-ISSN … , 2013
2013.0
Citations: 15
Fovea Localisation in Fundus Image using Adaptive Morphology
KA Nyni
2012.0
Fundus-fovea localization image analysis based on automatic screening
T Vandarkuzhali, AS Babu, CS Ravichandran
2006.0
Citations: 1
Elm based detection of abnormality in retinal image of eye due to diabetic retinopathy
DCS Vandarkuzhali, T Ravichandran
Journal of theoretical and applied information technology 6, 423-428 , 2005
2005.0
Citations: 15
Certain investigations on intelligent control techniques to identify diabetic diseases in fundus retinal image
T Vandarkuzhali
Chennai , 0

MOST CITED SCHOLAR PUBLICATIONS

Detection of exudates caused by diabetic retinopathy in fundus retinal image using fuzzy k means and neural network
T Vandarkuzhali, CS Ravichandran, D Preethi
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-ISSN … , 2013
2013.0
Citations: 15
Elm based detection of abnormality in retinal image of eye due to diabetic retinopathy
DCS Vandarkuzhali, T Ravichandran
Journal of theoretical and applied information technology 6, 423-428 , 2005
2005.0
Citations: 15
Detection of fovea region in retinal images using optimisation-based modified FCM and ARMD disease classification with SVM
T Vandarkuzhali, CS Ravichandran
International Journal of Biomedical Engineering and Technology 32 (1), 83-107 , 2020
2020.0
Citations: 5
Fovea detection and disease identification using integrated GF-SVM method
AS Babu, T Vandarkuzhali, C Ravichandran
Int J Comput Mod T 1 (1) , 2015
2015.0
Citations: 4
Fundus-fovea localization image analysis based on automatic screening
T Vandarkuzhali, AS Babu, CS Ravichandran
2006.0
Citations: 1
Fovea Localisation in Fundus Image using Adaptive Morphology
KA Nyni
2012.0
Certain investigations on intelligent control techniques to identify diabetic diseases in fundus retinal image
T Vandarkuzhali
Chennai , 0

Publications

1. K.A. Nyni and T. Vandarkuzhali “Fovea Localisation in Fundus Image using Adaptive Morphology Bonfring International Journal of Advances in Image Processing Online ISSN: 2277-503X | Print ISSN: 2250-1053 | Frequency: 4 Issues/Year PP 80-85, 2012.
2. T.Vandarkuzhali, Dr. C.S Ravichandran, D.Preethi, Detection of Exudates Caused by Diabetic Retinopathy in Fundus Retinal Image Using Fuzzy K-Means and Neural Network, IOSR - Journal of Electrical and Electronics Engineering (IOSR-JEEE) Volume 6, issue 1.pp 22-27, 2013.
3. T.Vandarkuzhali Dr. C.S Ravichandran, "Elm-based detection of abnormality in retinal image of eye due to diabetic retinopathy" ISSN: 1992-8645, Journal of Theoretical and Applied Information Technology volume 66, No2, August 2014.
4. T. Vandarkuzhali, Aswani S. Babu and C. S. Ravichandran “Fundus-Fovea Localization Image Analysis Based on Automatic Screening” ARPN Journal of Engineering and Applied Sciences Volume. 10, NO. 7, APRIL 2015.
5. T.Vandarkuzhali, Ravichandran and A Roosme Wilson. "Automatic Retinal Blood vessel Assessment using Extreme Learning Machine" Biomedicine 11(23), pages 4-9, 2015.
6. T.Vandarkuzhali, Ravichandran "Fovea Localization and Detection of Disease Due to Diabetic Retinopathy in Fundus Retinal Images". Journal of Applied Science Research 35(1), 023-027, 2015.
7. T.Vandarkuzhali, Ravichandran, 'Fovea Region Detection in Retinal Image Using Modified Watershed and Fuzzy C-Means Clustering (MFCM) Algorithm’ Inter