Qualification: 
Ph.D, M.E
j_aravinth@cb.amrita.edu
Phone: 
9566772677, 9043672677

Dr. J. Aravinth joined the Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Coimbatore campus, in the year 2010, where he is currently an Assistant Professor (Senior Grade). He received his B.E. degree in Electronics and Communication from Periyar University, Salem, India in 2004. He received his M.E degree in Applied Electronics from Anna University, Chennai, India in 2007. He earned his Ph.D. degree from Anna University, Chennai, in December 2017. His Ph.D. thesis was Design of Single and Hybrid Classifier based Score Level Fusion for Multimodal Biometric Recognition. Before joining Amrita, he worked as a lecturer in the Department of ECE at Kongu Engineering College, Erode during the period 2007-2010.

His research areas of interest include Multimodal Biometrics, Hyperspectral Remote Sensing, LiDAR data processing and Soft Computing. He is currently working on Hyperspectral remote sensing for urban development. He has authored around 15 technical papers in reputed conferences and journals indexed in Scopus. He is a member of the Institution of Electronics and Telecommunication Engineers, a professional body. He has organized several national workshops in association with CoreEl Technologies, Bangalore and ISRO sponsored national seminar.

Education

  • 2017: Ph. D. in Multimodal Biometrics
    Anna University
  • 2007: ME in Applied Electronics
    Anna University
  • 2004: B. E. in Electronics and Communication
    Periyar University, Salem

Professional Experience

Year Affiliation
November 1, 2012 to Till Date Assistant Professor(Senior Grade), Amrita Vishwa Vidyapeetham
Domain : Teaching, Research and Projects
July 1, 2010 to October 31, 2012 Assistant Professor, Amrita Vishwa Vidyapeetham
Domain : Teaching, Research and Projects
June 18, 2007 to May 13, 2010 Lecturer, Kongu Engineering College
Domain : Teaching

Academic Responsibilities

SNo Position Class / Batch Responsibility
1. Department OBE Coordinator 2017 CO - PO attainment
3. Class Adviser 2012-16, 2016-20 Class administration, counselling and mark analysis
4. Department Library In-charge 2010 Issuing books

Undergraduate Courses Handled

  1. Signal Processing I
  2. Signal Processing II
  3. Digital Signal Processing
  4. Digital Image Processing
  5. Electronics Engineering
  6. Solid State Devices
  7. Electronic Circuits
  8. Microwave Engineering

Specialized Courses Developed

Course Name Specialization Programme Outcome
Electronics Engineering Basic Electronics PO1, PO2, PO3, PO12 & PSO1
Signal Processing II Signal Processing PO1, PO2, PO3, PO12 & PSO1
Digital Signal Processing Lab Signal Processing PO1, PO2, PO3, PO5, PO9, PO10, PO12 & PSO1

Innovations in Teaching - Learning

SNo Innovation Method Description with Tools used
1. Term work projects in signal processing Mini Projects on recent technologies in signal, Image , video and text under python environment
2. Handbook on Fundamentals of Electronics Engineering This book serves as a guidelines and support for faculty members to give more effective delivering of the subject.

Participation in Faculty Development / STTP / Workshops /Conferences

SNo Title Organization Period Outcome
1. Deep Learning – Academic and Research Perspectives PSG college of Technology 24.01.18 – 25.01.18 Research
2. Advanced Hyperspectral Remote Sensing Techniques for Mineral Exploration NIT, RAipur 01.07.16 – 08.07.16 Elective Course and Research
3. MATLAB & Simulink for Engineering Education Mathworks India 10.02.15 Lab Course and Research
4. Reconfigurable Architectures for Biomedical Signal and Image Processing PSG college of Technology 21.02.15 – 22.02.15 Insight into Xilinx System Generator
5. Technologies for Speaker and Language Recognition SSN College of Engineering 29.04.15 – 30.04.15 Research
6. Soft Computing Techniques Amrita Vishwa Vidyapeetham 17.07.15 – 18.07.15 Research
7. Engineering Failure Analysis Amrita Vishwa Vidyapeetham 26.04.14 Research
8. Artificial Intelligence in Video Image Processing for Warefare Applications Paavai Engineering College 25.02.13 Research
9. MATLAB & Simulink for Engineering Education Mathworks India 06.03.13 Lab Course and Research
10. ISTE workshop on Analog Electronics National Mission on Education through ICT ( MHRD) 04.06.13 – 14.06.13 Core Course and Research
11. ISTE workshop on Signal & Systems National Mission on Education through ICT ( MHRD) 30.09.13 – 04.10.13 Workshop Coordinator
12. ISTE workshop on Writing Effective Conference Papers National Mission on Education through ICT ( MHRD) 18.02.12 – 19.02.12 Research
13. Basic Electrical Engineering Amrita Vishwa Vidyapeetham 20.06.11 – 24.06.11 Faculty Development Programme
14. Digital Video Processing Anna University of Technology, Coimbatore 30.09.11 Elective Course and Research
15. Sparse Image and Signal Processing Amrita Vishwa Vidyapeetham 23.12.11 – 26.12.11 Research
16. An Introduction to LATEX Kongu Engineering College 21.01.10 Research
17. Real Time Image Processing and its Applications PSG college of Technology 30.01.10 Research
18. System Simulation Tools Amrita Vishwa Vidyapeetham 19.07.10 – 20.07.10 Lab Course and Research
19. Faculty Orientation Programme Amrita Vishwa Vidyapeetham 22.12.10 – 23.12.10 Orientation Programme for freshers
20. Content Based Image Retrieval and Fusion Madras Institute of Technology, Anna University 07.08.09 – 09.08.09 Research
21. SAP – Systems, Applications and Products Kongu Engineering College 05.05.08 – 18.05.08 Staff Development Programme
22. Research Issues in Image Processing Kongu Engineering College 20.06.08 – 21.06.08 Research
23. Soft Computing Techniques in Industrial Automation Kongu Engineering College 06.11.08 – 07.11.08 Research
24. Pattern Recognition & Image Processing Bannari Amman Institute of Technology 18.03.06 Elective Course and Research
25. VLSI Design & Technology Bannari Amman Institute of Technology 07.10.05 – 08.10.05 Elective Course and Research

Organizing Faculty Development / STTP / Workshops /Conferences

SNo Title Organization Period Outcome
1. Creative Thinking and User Centred Design Amrita Vishwa Vidyapeetham 15.05.18 – 16.05.18 Research
2. Rural Healthcare in India Amrita Vishwa Vidyapeetham 18.05.18 – 19.05.18 Development of low cost assistive technologies
3. Role of University in Empowering Indian Villages Amrita Vishwa Vidyapeetham 21.09.16 – 23.09.16 Research through social needs
4. Techniques and Applications of Hyperspectral Image Analysis Amrita Vishwa Vidyapeetham 19.04.16 – 20.04.16 New Methodology for processing HSI data
5. Xilinx Vivado System Generator and Analog Discovery Kit Amrita Vishwa Vidyapeetham 21.09.15 – 22.09.15 Insight into analog discovery kit
6. Embedded Design Flow using Xilinx ZynQSoC Amrita Vishwa Vidyapeetham 27.02.15 – 28.02.15 Insight into zynq SOC
7. Signal and Image Processing Applications using Xilinx System Generator Amrita Vishwa Vidyapeetham 10.04.14 – 11.04.14 Insight into Xilinx System Generator
8. Two week ISTE workshop on "Signals & Systems" Amrita Vishwa Vidyapeetham 02.01.14 – 12.01.14 Course Plan development for UG
9. Advances in Signal and Image Processing Amrita Vishwa Vidyapeetham 29.05.12 – 02.06.12 Research

Academic Research – PG Projects

SNo Name of the Scholar Programme Specialization Duration Status
1. Sandra.S.Prasad Communication & Signal Processing Serial Multimodal Biometrics 2015-16 Completed
2. Jeena Elsa George Communication & Signal Processing Hyperspectral Image Processing 2016-17 Completed
3. Diana Abraham C Communication & Signal Processing Hyperspectral&LiDAR 2017-18 Completed
4. AniefMuhammed M A Biomedical Engineering ECG Biometrics 2018-19 Ongoing

Developmental Activities

Research Laboratories – Developed / Associated

Location Name and Year Sponsoring Agency Domain No. of Publications No. of Funded projects No. of PG / PhDs
E-306, AB-II Machine Intelligence Lab (2010) NPMASS Signal Processing 5 2 PG:6 PhD:5

Instructional Materials Developed

SNo Name and Description Outcome
1. Question Bank for Electronics Engineering More than 200 solved problems in basic electronics especially for first year students

Publications

Publication Type: Conference Paper

Year of Publication Title

2017

Aravinth J. and Roopa, S., “Identifying Traces of Copper in Basavakote, Karnataka using Hyperspectral Remote Sensing”, in 2017 International Conference on Technological Advancements in Power and Energy ( TAP Energy), 2017.[Abstract]


The Copper mineral has various uses and it's demand in India is found to be increasing. Copper is essential for human beings, animal and all living organisms. Copper in metal form is also very important. Finding traces of this mineral in India will increase the country's mineral resource and can be used for mining operations. The mineral is generally found in ores like cuprite, azurite, chalcopyrite, bornite and malachite. Copper's property of corrosion resistance and good conductor of heat and electricity makes its purpose most important in the construction field. Using hyperspectral remote sensing techniques, the presence of copper in basavanakote, karnataka is found. The hypespectral remote sensing technique gives high resolution because of its continuous 242 bands. The data is obtained from the USGS - Earth Explorer. This data is being processed using the ENVI software. The presence of copper in the study area is determined by spectral analyst technique. It provides a probability score of 79.43%. The obtained result is then validated using a Spectoradiometer.

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2017

S. Roopa, Narayanan, K. S., Sarvanan, S., Jhanane, M., and Aravinth J., “Detection of Copper in Southern India Using Hyperion Imagery”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


The demand for mineral and energy resources has increased pressure to reduce the environmental and social impact through mining operations. Among various minerals, Copper has served as an effective barometer of the economic health of a particular region or locality. Copper is an essential nutrient for humans and a few mammals for the best functioning of enzymes and carbohydrate metabolism. Copper is also needed for the formation of hemoglobin and hemocyanin which helps in oxygen transportation pigments in the blood of shellfish and many other vertebrates. Copper has high health benefits for a healthy existence, as the mineral allows a normal metabolic process associated with vitamins and amino acids. All these cannot be produced within the body and hence it needs to be added from external sources. This paper indicates the identification of Copper in parts of Karnataka using Hyperspectral remote sensing techniques. ENVI software is used to process and analyze the sensor's (Hyperion) raw data to determine the exact locations of copper that are present in it using different classification techniques.

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2017

J. E. George, Aravinth J., and S. Veni, “Detection of Pollution Content in an Urban Area Using Landsat 8 Data”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.[Abstract]


Pollution control is a challenging task in current scenario. The very first step to control pollution is to detect the sources of pollution. The urban areas are more polluted than rural due to the high population density. The pollutants considered in this paper are aerosol and asbestos sheets. The source of asbestos are building roofs which are mainly in urban area and that of aerosol is combustion of coal. The conventional image processing techniques failed to detect the pollutant in urban environment which can be performed well using multispectral imaging. Since each object has different temperatures using the TIR (Thermal Infrared) bands of Landsat 8 data, the urban objects are classified using the land surface temperature map. The presence of asbestos sheets is detected by change in intensity of images with respect to Band 7 (Short Wave Infrared) and Band 9 (Cirrus). Aerosol is comprised of components that cause air pollution. In this work, the PM10 value is considered as one of the measures to identify the concentration of particulate matters in specific area.

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2016

A. Sathya, Swetha, J., Das, K. A., George, K. K., Dr. Santhosh Kumar C., and Aravinth J., “Robust features for spoofing detection”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016.[Abstract]


It is very important to enhance the robustness of Automatic Speaker Verification (ASV) systems against spoofing attacks. One of the recent research efforts in this direction is to derive features that are robust against spoofed speech. In this work, we experiment with the use of Cosine Normalised Phase-based Cepstral Coefficients (CNPCC) as inputs to a Gaussian Mixture Model (GMM) back-end classifier and compare its results with systems developed using the popular short term cepstral features, Mel-Frequency Cepstral Coefficients (MFCC) and Power Normalised Cepstral Coefficients (PNCC), and show that CNPCC outperforms the other features. We then perform a score level fusion of the system developed using CNPCC with that of the systems using MFCC and PNCC to further enhance the performance. We use known attacks to train and optimise the system and unknown attacks to evaluate and present the results. More »»

2016

S. Prasad and Aravinth J., “Serial multimodal framework for enhancing user convenience using dimensionality reduction technique”, in Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2016, 2016.[Abstract]


Owing to an increased demand for identity management system in day to day applications, considering user convenience as a priority, we conduct a survey on biometric systems and observed serial multimodal systems as the most user convenient and reliable system. For further enhancement of the system performance, the discriminating capacity of the weaker trait can be improved, and we expect that the use of a semi supervised learning based dimensionality reduction method will make the performance of the weaker trait matcher better and hence enhance the overall performance of the system. We also propose a serial biometric system of face and fingerprint into which the dimensionality reduction method can be incorporated.

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2014

H. Nadella, .Sampurna, R., .Susnehalatha, N., Viswanathan, V., T., V. Kumar, and Aravinth J., “Multimodal Biometric Score Fusion Using Cuckoo Search Algorithm”, in Proceedings of RTCSP-2014 , Amrita Vishwa Vidyapeetham, 2014.

2012

A. Vivek S, S, S., P, N., M, V., Ravi, A., and Aravinth J., “Density Based Score Fusion of Fingerprint, Iris and Face for User Authentication”, in Proceedings of fifth National Conference on Recent trends in Information and Communication Technology(RTICT-2012), Bannari Amman Institute of Technology , Sathyamangalam, 2012.

2010

Aravinth J., thangam, M. A., .Aboorva, B., and Banupriya, M., “Database Construction for Facial Expressions using CBIR”, in Third National Conference on Electrical and Instrumnetation Systems , 2010.

2010

D. Zhang and Aravinth J., “Palmprint Authentication System”, in Third National Conference on Electrical and Instrumnetation Systems, 2010.

Publication Type: Conference Proceedings

Year of Publication Title

2017

R. Anand, S. Veni, and Aravinth J., “Big Data Challenges in Airborne Hyperspectral Image for Urban Landuse Classification”, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). pp. 1808-1814, 2017.[Abstract]


In recent years, it was a difficult task to classify a huge set of data due to the increasing population in urban places. As of now, satellite hyperspectral image provides information but this is not sufficient to classify data in urban areas. To develop the urban areas, accurate and timely information is necessary for the government. Hence, airborne hyperspectral data provides sufficient information for urban planning and disaster management. This paper, focuses on the following objectives: (i) To improve the classification accuracy in bigdata images (ii) To reduce the mixed pixels in residential buildings that are surrounded by small trees (iii) To bring down similar pixels of roads and parking lots. In this paper, 15 different classes were classified which are important for the growth in urban areas. The SVM classifier has more accuracy and better kappa coefficient compared with Neural Network (NN) and K-Means clustering. The Overall Accuracy (OA) has improved by 23.3.

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2016

Anand R, S. Veni, and Aravinth J., “An Application of image processing techniques for Detection of Diseases on Brinjal Leaves Using K-Means Clustering Method(2016)”, Fifth International Conference on Recent Trends in Information Technology 2016 (ICRTIT 2016). Anna University, Chennai campus , 2016.

2016

R. Anand, S. Veni, and Aravinth J., “An Application of image processing techniques for Detection of Diseases on Brinjal Leaves Using K-Means Clustering Method”, IEEE International Conference on Circuit, Power and Computing Technologies”, ICCPCT. 2016.

2012

Aravinth J. and .S.Valarmathy, D., “Fusion of fingerprint, Face, and Iris for personal identification based on Expectation Maximization”, International Convention on Innovations in Engineering and Technology for Sustainable Development . Bannari Amman Institute of Technology, pp. 373-378, 2012.

2012

S. A. Vivek, Aravinth J., and Valarmathy, S., “Feature extraction for multimodal biometric and study of fusion using Gaussian mixture model”, IEEE International Conference on Pattern Recognition, Informatics and Medical Engineering(PRIME 2012). Salem, Tamilnadu, pp. 387-392, 2012.[Abstract]


Biometrics consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. This paper describes the feature extraction techniques for three modalities viz. fingerprint, iris and face. The extracted information from each modality is stored as a template. The information are fused at the match score level using a density based score level fusion, GMM followed by the Likelihood ratio test. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm. © 2012 IEEE.

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Publication Type: Journal Article

Year of Publication Title

2016

Aravinth J. and Valarmathy, S., “Multi classifier-based score level fusion of multi-modal biometric recognition and its application to remote biometrics authentication”, The Imaging Science Journal, vol. 64, pp. 1-14, 2016.[Abstract]


Biometric recognition has become a common and reliable way to authenticate the identity of a person. Multimodal biometrics has become an interest of areas for researches in the recent past as it provides more reliability and accuracy. In multimodal biometric recognition, score level fusion has been a very promising approach to improve the overall system's accuracy. In this paper, score level fusion is carried out using three categories of classifiers like, rule classifier (fuzzy classifier), lazy classifier (Naïve Bayes) and learning classifiers (ABC-NN). These three classifiers have their own advantages and disadvantages so the hybridization of classifiers leads to provide overall improvements. The proposed technique consists of three modules, namely processing module, classifier module and combination module. Finally, the proposed fusion method is applied to remote biometric authentication. The implementation is carried out using MATLAB and the evaluation metrics employed are False Acceptance Rate (FAR), False Rejection Rate (FRR) and accuracy. The proposed technique is also compared with other techniques and by employing various combinations of modalities. From the results, we can observe that the proposed technique has achieved better accuracy value and Receiver Operating Characteristic (ROC) curves when compared to other techniques. The proposed technique reached maximum accuracy of having 95% and shows the effectiveness of the proposed technique.

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2015

Aravinth J. and Valarmathy, Sb, “A Natural Optimization Algorithm to Fuse Scores for Multimodal Biometric Recognition”, International Journal of Applied Engineering Research, vol. 10, pp. 21341-21354, 2015.[Abstract]


Multimodal biometrics has become an interest of areas for researches in the recent past as it provides more reliability and accuracy. In this work, we have performed multimodal biometric score fusion with the help of neural networks. The two traits that have been selected for fusion are fingerprint and iris due to their effectiveness and good resistance to spoofing. The type of fusion employed in the system is score level fusion. The neural network classifier approach is chosen to take advantage of its good learning efficiency. The system trains the neural network using a recently developed evolutionary algorithm, the Cuckoo Search Algorithm. The experimental results shown that the proposed fusion system can provide us low FAR, FRR and maximum accuracy of 98.78%. © Research India Publications.

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2013

Aravinth J. and Valarmathy, Sb, “Score-level fusion technique for multi-modal biometric recognition using ABC-based neural network”, International Review on Computers and Software, vol. 8, pp. 1889-1900, 2013.[Abstract]


Biometric recognition has become a common and reliable way to authenticate the identity of a person. Multimodal biometric system utilizes two or more individual modalities so as to improve the recognition accuracy. The key to multimodal biometrics is the fusion of the various biometric data after feature extraction. In this paper, score level fusion technique for multi-modal biometric recognition using Artificial Bee Colony (ABC) based Neural Network (NN) is proposed. The technique consists of two phases namely feature extraction phase and score fusion phase. Features are extracted from the fingerprint, face and iris modalities in the feature extraction phase. Fusion of score value is carried out after obtaining the individual matching scores from the three modalities. Fusion of scores is based on neural network where, ABC algorithm is used as a training algorithm and based on the scores obtained from ABC-based neural network, the recognition is done. The implementation is done using MATLAB and the performance of the proposed technique is evaluated using FRR, FAR, accuracy and ROC curve. The proposed technique is compared with KNN technique and from the results we can see that our proposed technique has achieved better results by having lower FRR and FAR values and higher accuracy measure. © 2013 Praise Worthy Prize S.r.l. - All rights reserved.

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2012

Aravinth J. and S.Valarmathy, D., “A Novel Feature Extraction Techniques for Multimodal Score Fusion Using Density Based Gaussian Mixture Model Approach”, International Journal of Emerging Technology and Advanced Engineering, vol. 2, pp. 123-131, 2012.[Abstract]


An Unimodal biometric systems, which relies only on a single trait of a person for identification is often not able to meet the desired performance. Combining multiple biometrics may enhance the performance of personal authentication system in accuracy and reliability which is adopted in multimodal biometrics. This paper describes the feature extraction techniques for three modalities viz. fingerprint, iris and face. The extracted information is stored as a template which can be fused using density based score level fusion (using GMM followed by likelihood ratio test).

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