Dr.Pradeep .J

@smvec.ac.in

Associate Professor, Department of Electronics and Communication Engineering,
Sri Manakula Vinayagar Engineering College



                    

https://researchid.co/pradeep.j
10

Scopus Publications

411

Scholar Citations

5

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Absorbers as biosensors: Leveraging absorption phenomena for enhanced biosensing
    Madurakavi Karthikeyan, J Pradeep, M Harikrishnan, R Sitharthan, M Rajesh, Avinash Chandra, and Rajkishor Kumar
    IOP Publishing

  • Underwater Image Enhancement Using Gaussian Pyramid, Laplacian Pyramid and Contrast Limited Adaptive Histogram Equalization
    R. Kurinjimalar, J. Pradeep, and M. Harikrishnan
    IEEE
    Underwater imaging is essential for capturing submerged environments and it faces significant challenges due to light attenuation, water turbidity and color distortion. This paper presents a novel technique to enlighten underwater image quality through a combination of multiscale image processing techniques like Gaussian and Laplacian pyramids, combined with Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance underwater image. In this paper, five different datasets namely, UIEB, EUVP, RUIE, USR and U45 datasets are used to representing various underwater environments and conditions. Four filtering techniques are employed to sharpen images and the resulting enhancements are showcased. From the result, it is found that the parameters including PSNR, UISM, UICM, PCQI and UIQM reach their maximum value for Laplacian of Gaussian method. Conversely, the gamma correction technique exhibits better performance, particularly in terms of UCIQE and CCQ metrics. It is clear from the suggested method that is applied to the UIEB Real World Underwater Datasets various parameter values are evaluated. The PCQI value is 0.0119, the UIQM value is 0.9998, and the UCIQE value is 30.455, according to the results. It shown in comparison to the other approaches that have been proposed earlier, the UCIQE value is improvised. The proposed method offers a superior solution for enhancing underwater images.

  • Recent developments in flexible and printed reconfigurable antennas for medical and Internet of things applications
    Madurakavi Karthikeyan, J Pradeep, M Harikrishnan, R Sitharthan, Naveen Mishra, M Rajesh, and Thamizharasan Sivanesan
    IOP Publishing

  • LIDAR & Thermal Camera Based Braking and Life Saving System With Actuator Shield for Wheels
    R. Kurinjimalar, J. Pradeep, K. Jayapreethi, M. Madhumitha, and S. Narmadha
    IEEE
    Heavy vehicles for public transportation and goods carriers are playing a vital role in the national economy. Heavy motor vehicles, such as buses and trucks, have an inadvertently higher ground clearance, which is a disadvantage during road accidents when people fall and get trapped under the vehicle's wheels. To save people's lives, in this paper an actuator shield based life-saving system is proposed and developed. The system consists of LiDAR ranging system, a thermal camera, and a Raspberry Pi-based fast computing system interfaced to control a dynamic servo-actuated angular shield for wheels. The LiDAR range-sensing sensor detects the road surface to vary the ground clearance of the wheel's shield, which maintained the ground clearance of some height to avoid hitting obstacles. To find the human falling near the wheels, the thermal camera plays a vital role. When the human is detected, it actuates the servo motors and shield to zero ground clearance, and thus people are saved by keeping them from getting under the wheels. The entire system consumes less power and is simple to install, so it can be retrofitted to existing vehicles, resulting in no deaths from vehicle collisions. As a result, the proposed system is used to save lives from the heavy vehicle accidents at low initial cost and low maintenance cost.

  • Fault Detection in Windmills Using Augmented Reality
    Arunagiri P, Pradeep Jayabala, Harikrishnan M, and Martin L
    Bentham Science Publishers Ltd.
    Aim:: Wind energy, being a non-conventional and sustainable renewable resource, provides electrical energy through the rotation of the blades of a wind turbine caused by wind impact. To ensure the sustainability of this resource, maintenance of the wind turbines is essential. Methods:: The incorporation of emerging technologies into the tedious processes has enabled quality improvement in the performance of systems. Augmented reality, which enhances the 3D digital content over the real world, may be used to leverage the tedious process of wind turbine maintenance by providing a user-friendly environment. Results/Discussion: AR utilization provides great insights into the problems occurring in specific parts of a wind turbine, thereby easing out the complexity of field workers. The objective is to create an augmented reality environment to monitor the proper functioning and detect the faultiness in a wind turbine with accuracy. Conclusion:: AR utilization can help facilitate better maintenance service, thereby increasing the life of a wind turbine.

  • Tunable Optimal Dual Band Metamaterial Absorber for High Sensitivity THz Refractive Index Sensing
    Madurakavi Karthikeyan, Pradeep Jayabala, Sitharthan Ramachandran, Shanmuga Dhanabalan, Thamizharasan Sivanesan, and Manimaran Ponnusamy
    MDPI AG
    We present a simple dual band absorber design and investigate it in the terahertz (THz) region. The proposed absorber works in dual operating bands at 5.1 THz and 11.7 THz. By adjusting the graphene chemical potential, the proposed absorber has the controllability of the resonance frequency to have perfect absorption at various frequencies. The graphene surface plasmon resonance results in sharp and narrow resonance absorption peaks. For incident angles up to 8°, the structure possesses near-unity absorption. The proposed sensor absorber’s functionality is evaluated using sensing medium with various refractive indices. The proposed sensor is simulated for glucose detection and a maximum sensitivity of 4.72 THz/RIU is observed. It has a maximum figure of merit (FOM) and Quality factor (Q) value of 14 and 32.49, respectively. The proposed optimal absorber can be used to identify malaria virus and cancer cells in blood. Hence, the proposed plasmonic sensor is a serious contender for biomedical uses in the diagnosis of bacterial infections, cancer, malaria, and other diseases.

  • Automatic Railway Detection and Tracking Inspecting System
    J. Pradeep, M. Harikrishnan, and K. Vijayakumar
    Springer Singapore

  • Voice Recognition Using Natural Language Processing


  • Smart IoT Assistant for Government Schemes and Policies Using Natural Language Processing


  • Artificial neural network based distribution static compensator for THD and reactive power compensation in PV Tied IEEE standard 9-bus system
    P. Mahes Kumar, G. R. Anantha Raman, S Balachandran, J Pradeep, and A Suresh
    American Scientific Publishers

  • Path planning of autonomous mobile robots: A survey and comparison


  • Design of FlexRay communication controller protocol for an automotive application
    R. Radhiga and J. Pradeep
    IEEE
    In recent years modern automobiles integrates numerous number of Electronic components are increased rapidly. These automotive embedded systems have great demand for dependability, on designing the FlexRay protocol. FlexRay (FR) protocol is mainly for scalable, flexible, high speed deterministic, error tolerant communication in order to meet growing safety related challenges in the automobile industry. This paper explores the general issues of functional coverage pertaining to the FlexRay specification. The presented work demonstrated as designed of communication controller of FlexRay node with Finite State Machine (FSM). The simulation and Synthesis result are presented in this paper using Xilinx software as tool.

  • An investigation on the performance of hybrid features for feed forward neural network based english handwritten character recognition system


  • Performance analysis of hybrid feature extraction technique for recognizing English handwritten characters
    J. Pradeep, E. Srinivasan, and S. Himavathi
    IEEE
    In this paper, an off-line handwritten English character recognition system using hybrid feature extraction technique and neural network classifiers are proposed. A hybrid feature extraction method combines the diagonal and directional based features. The proposed system suitably combines the salient features of the handwritten characters to enhance the recognition accuracy. Neural Network (NN) topologies, namely, back propagation neural network and radial basis function network are built to classify the characters. The k-nearest neighbour network is also built for comparison. The Feed forward NN topology exhibits the highest recognition accuracy and is identified to be the most suitable classifier. The proposed system will aid applications for postal/parcel address recognition and conversion of any hand written document into structural text form. The performance of the recognition systems is compared extensively using test data to draw the major conclusions of this paper.

  • Neural network based recognition system integrating feature extraction and classification for English handwritten
    J. Pradeep
    International Digital Organization for Scientific Information (IDOSI)
    Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten character recognition. This paper identifies the most suitable NN for the design of hand written English character recognition system. Different Neural Network (NN) topologies namely, back propagation neural network, nearest neighbour network and radial basis function network are built to classify the characters. All the NN based Recognition systems use the same training data set and are trained for the same target mean square error. Two hundred different character data sets for each of the 26 English characters are used to train the networks. The performance of the recognition systems is compared extensively using test data to draw the major conclusions of this paper

  • Diagonal based feature extraction for handwritten character recognition system using neural network
    J. Pradeep, E. Srinivasan, and S. Himavathi
    IEEE
    An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and twenty different handwritten alphabets characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.

  • Neural network based handwritten character recognition system without feature extraction
    J. Pradeep, E. Srinivasan, and S. Himavathi
    IEEE
    Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognize handwritten characters for English alphabets without feature extraction using multilayer Feed Forward neural network. Each character data set contains 26 alphabets. Fifty different character data sets are used for training the neural network. The trained network is used for classification and recognition. In the proposed system, each character is resized into 30×20 pixels, which is directly subjected to training. That is, each resized character has 600 pixels and these pixels are taken as features for training the neural network. The results show that the proposed system yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition.

RECENT SCHOLAR PUBLICATIONS

  • Absorbers as biosensors: leveraging absorption phenomena for enhanced biosensing
    M Karthikeyan, J Pradeep, M Harikrishnan, R Sitharthan, M Rajesh, ...
    Advances in All-optical Communication, 11-1-11-15 2024

  • Underwater Image Enhancement Using Gaussian Pyramid, Laplacian Pyramid and Contrast Limited Adaptive Histogram Equalization
    R Kurinjimalar, J Pradeep, M Harikrishnan
    2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC 2024

  • Recent developments in flexible and printed reconfigurable antennas for medical and Internet of things applications
    M Karthikeyan, J Pradeep, M Harikrishnan, R Sitharthan, N Mishra, ...
    Advances in Flexible and Printed Electronics: Materials, fabrication, and 2023

  • LIDAR & Thermal Camera Based Braking and Life Saving System With Actuator Shield for Wheels
    R Kurinjimalar, J Pradeep, K Jayapreethi, M Madhumitha, S Narmadha
    2023 International Conference on System, Computation, Automation and 2023

  • Fault Detection in Windmills Using Augmented Reality
    HMML Arunagiri P, Pradeep J
    International Journal of Sensors, Wireless Communications and Control 13 (4) 2023

  • Enhanced Recognition system for Diabetic Retinopathy using Machine Learning with Deep Learning Approach
    J Pradeep, NE Jeffery, M Saranraj, JN Hussain
    Journal of Population Therapeutics and Clinical Pharmacology 30 (11), 452-466 2023

  • Tunable optimal dual band metamaterial absorber for high sensitivity THz refractive index sensing
    M Karthikeyan, P Jayabala, S Ramachandran, SS Dhanabalan, ...
    Nanomaterials 12 (15), 2693 2022

  • Automatic railway detection and tracking inspecting system
    J Pradeep, M Harikrishnan, K Vijayakumar
    Proceedings of International Conference on Data Science and Applications 2022

  • Smart IoT Assistant for Government Schemes and Policies Using Natural Language Processing
    J Pradeep, K Manojkiran, VP Gopi, B Jayakumar
    Big Data Management in Sensing: Applications in AI and IoT 2021

  • Voice Recognition Using Natural Language Processing
    J Pradeep, K Vijayakumar, M Harikrishnan
    Big Data Management in Sensing: Applications in AI and IoT , River 2021

  • Efficient vehicle detection system using OCR and REST API
    J Pradeep, A Muthukumaran, G Nareshraman, R Krishnan, ...
    International Journal of Engineering Research & Technology (IJERT) 9 (7 2020

  • Efficient Vehicle Detection System using OCR and REST API
    RKBSRP J. Pradeep, A. Muthukumaran, G. Nareshraman
    INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09 2020

  • A USER FRIENDLY NAVIGATION SYSTEM FOR PACING URBANIZATION
    K Vijayakumar, S Pushparaj, J Pradeep, V Rohith
    Journal of Critical Reviews 7 (8) 2020

  • AN EARLY DIAGNOSIS OF BREAST CANCER USING FUZZY CLUSTERING AND PNN CLASSIFIER
    PM Kumar, J Pradeep, B Venkateshwarlu
    Journal of Engineering Sciences 10 (12), 893-899 2019

  • BINARY TO DECIMAL FEATURE EXTRACTION BASED HANDWRITTIEN CHARACTER RECOGNITION SYSTEM USING FEED FORWARD NEURAL NETWORK
    PM Kumar, J Pradeep, B Venkateshwarlu
    Journal of Engineering Sciences 10 (9), 900-907 2019

  • AN EFFICIENTANALYSIS AND DETECTION OF BREAST CANCER USING RANDOM FOREST CLASSIFIER
    PM Kumar, J Pradeep, S Mallikarjuna
    Journal of Engineering Sciences 10 (12), 1161-1172 2019

  • Artificial Neural Network Based Distribution Static Compensator for THD and Reactive Power Compensation in PV Tied IEEE Standard 9-Bus System
    PM Kumar, GR Raman, S Balachandran, J Pradeep, A Suresh
    Journal of Computational and Theoretical Nanoscience 15 (11-12), 3223-3230 2018

  • Multi-Objective ABC-NM (Artificial Bee Colony – Nelder Mead) Method for Solving Coverage Connected Node Placement Problem With and Without Critical Target Nodes in Target Based WSN
    RR A. Poonguzhali, J.Pradeep
    International Journal of Pure and Applied Mathematics 119 (14), 289-298 2018

  • Comparison of CAN and Flexray Protocol for Automotive Application
    RD J.Pradeep, S. Richerd Sebasteen
    International Journal of Pure and Applied Mathematics, 119 (14), 1739-1745 2018

  • Optimized Scheme to Detect and Locate the Fault in the Implementation of AES Algorithm
    JPJA P. Majesh
    International Journal of Pure And Applied Mathematics 118 (no.15), pp.295-303 2018

MOST CITED SCHOLAR PUBLICATIONS

  • Diagonal based feature extraction for handwritten character recognition system using neural network
    J Pradeep, E Srinivasan, S Himavathi
    2011 3rd international conference on electronics computer technology 4, 364-368 2011
    Citations: 318

  • Neural network based handwritten character recognition system without feature extraction
    J Pradeep, E Srinivasan, S Himavathi
    2011 international conference on computer, communication and electrical 2011
    Citations: 82

  • Neural network based recognition system integrating feature extraction and classification for english handwritten
    J Pradeep, E Srinivasan, S Himavathi
    International journal of Engineering 25 (2), 99-106 2012
    Citations: 80

  • Tunable optimal dual band metamaterial absorber for high sensitivity THz refractive index sensing
    M Karthikeyan, P Jayabala, S Ramachandran, SS Dhanabalan, ...
    Nanomaterials 12 (15), 2693 2022
    Citations: 61

  • Diagonal feature extraction based handwritten character system using neural network
    J Pradeep, E Srinivasan, S Himavathi
    International Journal of Computer Applications 8 (9), 17-22 2010
    Citations: 55

  • Path Planning of Autonomous Mobile Robots: A Survey and Comparison
    P Victerpaul, D Saravanan, S Janakiraman, J Pradeep
    Journal of Advanced Research in Dynamical and Control Systems 12, 1535-1565 2017
    Citations: 40

  • Performance Analysis of Hybrid Feature Extraction Technique for Recognizing English Handwritten Characters
    J Pradeep, E Srinivasan, S Himavathi
    Proceedings of Second World Congress on Information and Communication 2012
    Citations: 23

  • Design and Implementation of Gesture Controlled Robotic Arm for Industrial Applications
    J PRADEEP, PV PAUL
    International Journal of Advanced Scientific Research & Development 3 (4 2016
    Citations: 18

  • An investigation on the performance of hybrid features for feed forward neural network based English handwritten character recognition system
    J Pradeep, E Srinivasan, S Himavathi
    WSEAS transactions on signal processing 10 (1), 21-29 2014
    Citations: 13

  • Enhanced Recognition system for Diabetic Retinopathy using Machine Learning with Deep Learning Approach
    J Pradeep, NE Jeffery, M Saranraj, JN Hussain
    Journal of Population Therapeutics and Clinical Pharmacology 30 (11), 452-466 2023
    Citations: 7

  • Recent developments in flexible and printed reconfigurable antennas for medical and Internet of things applications
    M Karthikeyan, J Pradeep, M Harikrishnan, R Sitharthan, N Mishra, ...
    Advances in Flexible and Printed Electronics: Materials, fabrication, and 2023
    Citations: 4

  • Comparison of CAN and Flexray Protocol for Automotive Application
    RD J.Pradeep, S. Richerd Sebasteen
    International Journal of Pure and Applied Mathematics, 119 (14), 1739-1745 2018
    Citations: 4

  • Efficient vehicle detection system using OCR and REST API
    J Pradeep, A Muthukumaran, G Nareshraman, R Krishnan, ...
    International Journal of Engineering Research & Technology (IJERT) 9 (7 2020
    Citations: 3

  • E, Shrinivasan, S. Himavathi “Diagonal based feature extraction for handwritten character recognition system using neural network”
    J Pradeep
    IEEE
    Citations: 3

  • Design of FlexRay communication controller protocol for an automotive application
    R Radhiga, J Pradeep
    2015 IEEE 9th International Conference on Intelligent Systems and Control 2015
    Citations: 2

  • Performance Analysis of Wireless Mesh Network in Hospital Environment
    D Prabakar, J Pradeep, GA Elango, N Lakshmy
    IJCA Special Issue on International Conference on Electronics, Communication 2012
    Citations: 2

  • Character Recognition system without feature extraction International Conference on Computer
    J Pradeep, E Srinivasan, SHNN based Handwritten
    Communication and Electrical Technology–ICCCET 2011
    Citations: 2

  • Underwater Image Enhancement Using Gaussian Pyramid, Laplacian Pyramid and Contrast Limited Adaptive Histogram Equalization
    R Kurinjimalar, J Pradeep, M Harikrishnan
    2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC 2024
    Citations: 1

  • Automatic railway detection and tracking inspecting system
    J Pradeep, M Harikrishnan, K Vijayakumar
    Proceedings of International Conference on Data Science and Applications 2022
    Citations: 1

  • Smart IoT Assistant for Government Schemes and Policies Using Natural Language Processing
    J Pradeep, K Manojkiran, VP Gopi, B Jayakumar
    Big Data Management in Sensing: Applications in AI and IoT 2021
    Citations: 1