Jenkin Winston

@karunya.edu

Assistant Professor, ECE
Karunya Institute of Technology and Sciences



                 

https://researchid.co/jenkinwinston

RESEARCH INTERESTS

Image Analysis using machine learning and deep learning

18

Scopus Publications

145

Scholar Citations

5

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • A miniaturized uniplanar MIMO antenna for n79/n46/millimeter-wave applications
    Jeevitha Joseph, Gunamony Shine Let, Chandran Benin Pratap, and J Jenkin Winston

    Wiley
    This research suggests a compact uniplanar multiple‐input multiple‐output (MIMO) with four ports for n79/n46/millimeter‐wave (mm‐wave) applications. The size of the quad MIMO is only 30 × 30 × 0.8 mm3. MIMO system consists of four identical Z‐shaped radiators and common ground on the same plane and no decoupling structures are used for isolation. The system covers the bandwidth of 1.9 GHz (4.4–6.3 GHz) with a mid‐frequency of 5.6 GHz and also covers the high‐band frequencies ranging from 18 to 30 GHz with a bandwidth of 12 GHz. The suggested quad MIMO is fabricated on an FR‐4 board, and the measured outcomes are well in line with the simulated results. An isolation value of −11 dB has been achieved for mid‐band frequency and −24 dB has been attained for mm‐wave bands. Through the value of DG = 10 dB, ECC < 0.07, TARC < −3 dB, MEG < −5 dB, and the ratio of MEG = 1 dB, uniplanar quad MIMO shows acceptable MIMO diversity performance. The entire system was evaluated for the users' hand specific absorption rate (SAR) impacts and is within the limits. After the complete analysis of the miniature quad MIMO antenna, an 8‐port, and a 16‐port uniplanar MIMO are simulated for smartphone‐sized dielectric substrates and the performances were examined. The suggested MIMO system provides an efficient single‐layer MIMO antenna to 5G smartphones with high bandwidth and low SAR. The proposed quad MIMO systems are suitable for both the sub‐6 GHz band and the mm‐wave band.

  • Effective Detection of Copy Move Forgery Using Surf
    R A Rakhi, Aldin Justin Sundararaj, R. Catherine Joy, and J. Jenkin Winston

    IEEE
    Copy move forgery is a technique for altering digital images by copying and pasting contents from the original image into different parts of the same image. It is of great importance to detect tampering as the credibility of digital images are being lost. Keypoint based methods proved to be efficient in localizingforgery. The proposed method uses SURF for feature extractionand improved forgery localization method for efficiently localizing forgery. Finally, comparison with the existing keypoint based methods to prove the efficiency of forgery localization in digital images. The tampered image is displayed as binary image for better understanding. The proposed methods efficiently localize forgery even if the tampered region is rescaled, rotated or blurred. The performance is validated by TPR, FPR and F1 score and comparing the same with the existing methods.

  • Hybrid deep convolutional neural models for iris image recognition
    J. Jenkin Winston, D. Jude Hemanth, Anastassia Angelopoulou, and Epaminondas Kapetanios

    Springer Science and Business Media LLC

  • Vulnerabilities and Ethical Issues in Machine Learning for Smart City Applications
    K. Martin Sagayam, Roopa Jeyasingh, J. Jenkin Winston, and Tony Jose

    Springer International Publishing

  • Combination of thermal and sRGB imaging techniques for advanced surveillance system
    K. Martin Sagayam, J. Jenkin Winston, Mohd Helmy Abd Wahab, Bharat Bhushan, Radzi Ambar, and Hazwaj Mhd Poad

    International Association for Educators and Researchers (IAER)
    Surveillance is described as close observation; this term is used mostly when it comes to security observations and recording. Nowadays in our daily life we see the need of security rising up with the constant increase in the world of technology and features. On the other hand, the industries and firms continuously face threats and are pushed to a situation of seeking help for their safety. Also, that there are various blind spots in the regular security cam even the operator is not in a position to identify an emergency or any such need. This work is focused on providing clarity to such issues to the respective personal. It might be an industry, an office workspace, a supermarket or even a house. This concept deals with the enhancement of the surveillance system by infusing two different frames of Thermal and sRGB obtained input and give the output of noise reduced, enhanced and more visible image.

  • Performance-enhanced modified self-organising map for iris data classification
    J. Jenkin Winston and D. Jude Hemanth

    Wiley
    Biometric systems are widely used in applications such as forensics and military. Biometric authentication is a challenging and complex task. These biometric systems must be accurate for practical applications. In this era of artificial intelligence, artificial neural network‐based classifiers are widely used in biometric‐based systems. However, most of the artificial neural network‐based classifiers are less accurate and computationally complex. In this work, two modified self‐organising map (SOM) networks are proposed for iris image classification to improve the performance measures. Particle swarm optimization technique is used in the training process of conventional SOM. The experiments are carried out with conventional and modified classifiers. The proposed modified classifiers provide better performance than the conventional SOM classifier.

  • Novel optimization based hybrid self-organizing map classifiers for iris image recognition
    J. Jenkin Winston, Gul Fatma Turker, Utku Kose, and D. Jude Hemanth

    Springer Science and Business Media LLC
    The concern over security in all fields has intensified over the years. The prefatory phase of providing security begins with authentication to provide access. Inmany scenarios, this authentication is provided by biometric systems.Moreover, the threat of pandemic hasmade the people to think of hygienic systemswhich are noninvasive. Iris image recognition is one such noninvasive biometric system that can provide automated authentication. Self-organizing map is an artificial neural network which helps in iris image recognition. This network has the ability to learn the input features and perform classification. However, from the literature it is observed that the performance of this classifier has scope for refinement to yield better classification. In this paper, heterogeneous methods are adapted to improve the performance of the classifier for iris image recognition. The heterogeneous methods involve the application of Gravity Search Optimization, Teacher Learning Based Optimization, Whale Optimization and Gray Wolf Optimization in the training process of the self-organizing map classifier. This method was tested on iris images from IIT-Delhi database. The results of the experiment show that the proposed method performs better.


  • Moments-Based Feature Vector Extraction for Iris Recognition
    J. Jenkin Winston and D. Jude Hemanth

    Springer Singapore

  • A comprehensive review on iris image-based biometric system
    J. Jenkin Winston and D. Jude Hemanth

    Springer Science and Business Media LLC

  • Classifiers in IRIS Biometrics for Personal Authentication
    S Pradeepa, R Anisha, and Winston J Jenkin

    IEEE
    Machine learning is that which provides the systems the ability to improve on experience without being programmed. Higher accuracy rate is a challenging problem with Iris biometrics. In this paper the best performance based on the classifiers for iris biometric is identified. The normalized iris images are downloaded from IIT Delhi database. The features are extracted from normalized iris images using the techniques such as histogram and wavelet transform. The extracted features are then classified using Neural Networks and Support Vector Machine. The study shows that support vector machine has far better recognition rate than back propagation neural network. The proposed technique provides accuracy at the rate of 96.7% than the neural network.

  • Iris Image Error Correction Techniques
    Jenkin Winston, Prashanthi, Olive Teresa, Shruti Sekar, and Sarah

    IEEE
    In today's world, security has gotten paramount. Many biometric methods like facial expression recognition system and Iris recognition system have been developed. Iris biometry helps in identifying an individual in a more intuitive and natural manner. Iris recognition focuses on recognizing the identity of individuals using the textural based characteristics. But, due to various reasons corruption of texture features of iris often occurs during denoising and deblurring. In this paper we propose maltlab algorithms to overcome these defects as well as extract the features to receive a defect less image. To verify the algorithm white noise is added to the iris dataset and the calculations are done as required. This process shows that denoising and deblurring can improve the quality of the iris image evidently.

  • Iris image recognition using optimized kohonen self organizing neural network
    J.J. Winston, D.J. Hemanth, A. Angelopoulou, and E. Kapetanios

    Institution of Engineering and Technology
    The pursuit to develop an effective people management system has widened over the years to manage the enormous increase in population. Any management system includes identification, verification and recognition stages. Iris recognition has become notable biometrics to support the management system due to its versatility and non-invasive approach. These systems help to identify the individual with the texture information distributed around the iris region. Many classification algorithms are available to help in iris recognition. But those are very sophisticated and require heavy computation. In this paper, an improved Kohonen self-organizing neural network (KSONN) is used to boost the performance of existing KSONN. This improvement is brought by the introduction of optimization technique into the learning phase of the KSONN. The proposed method shows improved accuracy of the recognition. Moreover, it also reduces the iterations required to train the network. From the experimental results, it is observed that the proposed method achieves a maximum accuracy of 98% in 85 iterations.

  • Pyramid-Based Multi-scale Enhancement Method for Iris Images
    J. Jenkin Winston and D. Jude Hemanth

    Springer Singapore

  • Authentication of biometric system using fingerprint recognition with euclidean distance and neural network classifier


  • Prominence of cooperative communication in 5G cognitive radio systems
    G. Shine Let, G. Josemin Bala, J. Jenkin Winston, M. Deepak Raj, and C. Benin Pratap

    IEEE
    Cognitive Radio is a promising way to overcome the spectrum scarcity for wireless communication and improving the spectral efficiency by using the vacant licensed spectrum band. Cooperative communication is a new communication technique which utilizes the help of neighboring nodes to reduce the bit error rate (BER) in a harmful fading environment. The challenging factor is to combine the cooperative communication in cognitive radio to improve the spectral efficiency and to reduce the BER factor of unlicensed user's communication. In this paper, a comparative study of different communication techniques are done by considering Rayleigh fading channel environment and the advantages of cooperative communication is analyzed. Also, the paper deals with the challenges of integrating cooperative communication in cognitive radio network are discussed.

  • Novel local binary textural pattern for analysis and classification of mammogram using support vector machine
    Narain Ponraj, Jenkin Winston, Poongodi, and Merlin Mercy

    IEEE
    Breast cancer is one of the most devastating and deadly diseases for women. It is estimated that between one in eight and one in twelve women will develop breast cancer during their lifetime. The most convenient practical method to detect breast cancer is mammography, because it allows the detection of the cancer at its early stages, a crucial issue for a high survival rate. Mammography is the only technique that has demonstrated the ability to detect breast cancer at an early stage and with high sensitivity and specificity. The search for features in this kind of image is complicated by the higher frequency textural variations in image intensity. In this paper, we have proposed few novel local binary textural patterns for classification of mammogram which was found to have consistent accuracy rate.

  • Improving sensing and throughput of the cognitive radio network
    J. Christopher Clement, D. S. Emmanuel, and J. Jenkin Winston

    Springer Science and Business Media LLC

RECENT SCHOLAR PUBLICATIONS

  • Effective Detection of Copy Move Forgery Using Surf
    RA Rakhi, AJ Sundararaj, RC Joy, JJ Winston
    2023 4th International Conference on Signal Processing and Communication 2023

  • A miniaturized uniplanar MIMO antenna for n79/n46/millimeter‐wave applications
    J Joseph, GS Let, CB Pratap, JJ Winston
    International Journal of Communication Systems, e5477 2023

  • Hybrid deep convolutional neural models for iris image recognition
    JJ Winston, DJ Hemanth, A Angelopoulou, E Kapetanios
    Multimedia Tools and Applications 81 (7), 9481-9503 2022

  • Vulnerabilities and Ethical Issues in Machine Learning for Smart City Applications
    KM Sagayam, R Jeyasingh, JJ Winston, T Jose
    Machine Learning Techniques for Smart City Applications: Trends and 2022

  • Combination of Thermal and sRGB imaging Techniques for Advanced Surveillance System
    KM Sagayam, JJ Winston, MH Abd Wahab, B Bhushan, R Ambar, ...
    Annals of Emerging Technologies in Computing (AETiC) 5 (5) 2021

  • Performance‐enhanced modified self‐organising map for iris data classification
    JJ Winston, DJ Hemanth
    Expert Systems 38 (1), e12467 2021

  • Novel Optimization Based Hybrid Self-Organizing Map Classifiers for Iris Image Recognition
    JJ Winston, GF Turker, U Kose, DJ Hemanth
    International Journal of Computational Intelligence Systems 13 (1), 1048-1058 2020

  • Performance Comparison of Feature Extraction Methods for Iris Recognition
    JJ Winston, DJ Hemanth
    Information Technology and Intelligent Transportation Systems 323, 62 2020

  • Moments-Based Feature Vector Extraction for Iris Recognition
    JJ Winston, DJ Hemanth
    International Conference on Innovative Computing and Communications 2020

  • A comprehensive review on iris image-based biometric system
    JJ Winston, DJ Hemanth
    Soft Computing 23 (19), 9361-9384 2019

  • Iris Image Error Correction Techniques
    J Winston, O Teresa, S Sekar
    2019 2nd International Conference on Signal Processing and Communication 2019

  • Authentication of biometric system using fingerprint recognition with euclidean distance and neural network classifier
    KM Sagayam, DN Ponraj, J Winston, JC Yaspy, DE Jeba, A Clara
    Int. J. Innov. Technol. Explor. Eng 8 (4), 766-771 2019

  • Iris Image Recognition using Optimized Kohonen Self Organizing Neural Network
    JJ Winston, DJ Hemanth, A Angelopoulou, E Kapetanios
    IET Digital Library 2019

  • Pyramid-Based Multi-scale Enhancement Method for Iris Images
    JJ Winston, DJ Hemanth
    Recent Trends in Signal and Image Processing, 13-21 2019

  • Novel local binary textural pattern for analysis and classification of mammogram using support vector machine
    N Ponraj, J Winston, M Mercy
    2017 International Conference on Signal Processing and Communication (ICSPC 2017

  • Prominence of cooperative communication in 5G cognitive radio systems
    GS Let, GJ Bala, JJ Winston, MD Raj, CB Pratap
    2017 International Conference on Circuit, Power and Computing Technologies 2017

  • Improving Sensing and Throughput of the Cognitive Radio Network
    JC Clement, DS Emmanuel, JJ Winston
    Circuits, Systems, and Signal Processing 34 (1), 249-267 2015

MOST CITED SCHOLAR PUBLICATIONS

  • A comprehensive review on iris image-based biometric system
    JJ Winston, DJ Hemanth
    Soft Computing 23 (19), 9361-9384 2019
    Citations: 49

  • Authentication of biometric system using fingerprint recognition with euclidean distance and neural network classifier
    KM Sagayam, DN Ponraj, J Winston, JC Yaspy, DE Jeba, A Clara
    Int. J. Innov. Technol. Explor. Eng 8 (4), 766-771 2019
    Citations: 32

  • Hybrid deep convolutional neural models for iris image recognition
    JJ Winston, DJ Hemanth, A Angelopoulou, E Kapetanios
    Multimedia Tools and Applications 81 (7), 9481-9503 2022
    Citations: 16

  • Prominence of cooperative communication in 5G cognitive radio systems
    GS Let, GJ Bala, JJ Winston, MD Raj, CB Pratap
    2017 International Conference on Circuit, Power and Computing Technologies 2017
    Citations: 12

  • Improving Sensing and Throughput of the Cognitive Radio Network
    JC Clement, DS Emmanuel, JJ Winston
    Circuits, Systems, and Signal Processing 34 (1), 249-267 2015
    Citations: 12

  • Performance‐enhanced modified self‐organising map for iris data classification
    JJ Winston, DJ Hemanth
    Expert Systems 38 (1), e12467 2021
    Citations: 5

  • Novel Optimization Based Hybrid Self-Organizing Map Classifiers for Iris Image Recognition
    JJ Winston, GF Turker, U Kose, DJ Hemanth
    International Journal of Computational Intelligence Systems 13 (1), 1048-1058 2020
    Citations: 5

  • Moments-Based Feature Vector Extraction for Iris Recognition
    JJ Winston, DJ Hemanth
    International Conference on Innovative Computing and Communications 2020
    Citations: 4

  • Performance Comparison of Feature Extraction Methods for Iris Recognition
    JJ Winston, DJ Hemanth
    Information Technology and Intelligent Transportation Systems 323, 62 2020
    Citations: 3

  • Novel local binary textural pattern for analysis and classification of mammogram using support vector machine
    N Ponraj, J Winston, M Mercy
    2017 International Conference on Signal Processing and Communication (ICSPC 2017
    Citations: 3

  • A miniaturized uniplanar MIMO antenna for n79/n46/millimeter‐wave applications
    J Joseph, GS Let, CB Pratap, JJ Winston
    International Journal of Communication Systems, e5477 2023
    Citations: 2

  • Iris Image Recognition using Optimized Kohonen Self Organizing Neural Network
    JJ Winston, DJ Hemanth, A Angelopoulou, E Kapetanios
    IET Digital Library 2019
    Citations: 2