Ph.D, MSc, BSc

Dr. G. Jayachandran Nair received his Ph. D. in Electronics from Mumbai University.

Dr. G. Jayachandran Nair joined the Department of Atomic Energy, BARC from 12th batch in 1968 and has ever since been involved in research and developement work in the field of theoretical, experimental, computational and strategic seismology. He served as head Seismology Division, BARC, Mumbai. His major contributions are , new autoregressive deconvolution methods, real time rock-burst monitoring system, digitally communicating seismic array, participation in Pokhran I and II explosions, Vsat based national seismic network and real time Seismic Data Centre and microzonation methods. He has used the novel AR deconvolution method for extracting the features of Sino-Soviet explosions which has become his doctoral work dissertation. He was a visiting scientist to Blacknet, AWRE, Aldermaton,UK Atomic Energy for one year. He was also visiting scientist at Seismic Centres at FOA,Sweden , International Seismic Centre, UK and Seismic Centres in China.

He has designed and installed a PDP11/34 based strata stability monitoring system at Champion Reef Gold mines, KGF, Karnataka and latter developed and installed a 386 based underground rock burst monitoring system at Mysore North Fold region Kolar Gold mines at a depth of 3km, in 80s. As a planned activity under the IX plan he developed and installed a wireless digitally communicating array of seismometers with central recording system ,at Gauribidnur Karnataka. Under the X plan, he has installed a nation wide three component wide band network with real time VSAT communication to Seismic Data Centre at Trombay. The system and software for field systems and Data Centre was indigenously developed and tuned for real time streaming data with high reliability. For events in Indian region this systems reports the events much prior to International seismic data centres. The system also gives a first level tsunami potential for oceanic events and near real time sea level measurements from SOI tide gauges.

His current interests are in the fields of biomedical signal processing especially EEG signals of epileptic patients, brain controlled computer interface and speech signal processing. He has spent many years in teaching in Training school BARC ,guiding PhD students for IITB, Mumbai and Bangalore University and as a member in academic council of Roorkee University.

He is also a member of the committee for tsunami mitigation and modeling measures required for DAE installations. He is also involved in the development of wireless sensor networks for application in seismology like monitoring slope stability and land slides . He was a member of International union of geodesy and geophysics, Shanti Swaroop Bhatnagar award committee, and the DST committee for earth science in addition to some state and departmental committees. He has published over 60 publications in international and Indian journals.


Publication Type: Conference Paper

Year of Publication Title


Poorna S. S., Baba, P. M. V. D. Sai, G. Ramya, L., Poreddy, P., Aashritha, L. S., G.J. Nair, and Renjith, S., “Classification of EEG based control using ANN and KNN-A comparison”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, 2017.[Abstract]

EEG based controls are extensively used in applications such as autonomous navigation of remote vehicles and wheelchairs, as prosthetic control for limb movements in health care, in robotics and in gaming. The work aimed at implementing and classifying the intended controls for autonomous navigation, by analyzing the recorded EEG signals. Here, eye closures extracted from the EEG signals were pulse coded to generate the control signals for navigation. The EEG data was acquired using wireless Emotive Epoc EEG headset, with 14 electrodes, from ten healthy subjects. Preprocessing techniques were applied to enhance the signal, by removing noise and baseline variations. The features from the blinks considered were height of the ocular pulses and their respective widths, from four channels. K-Nearest Neighbor Classifier and Artificial Neural Network Classifier were applied to classify the number of blinks. The results of the study showed that, for the data set under consideration, ANN Classifier gave 98.58% accuracy and 94% sensitivity, compared to KNN Classifier, which gave 96.06 % accuracy and 87.42% sensitivity, to classify the blinks for the control application.

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Poorna S. S., Jeevitha, C. Y., Nair, S. J., Santhosh, S., and G.J. Nair, “Emotion recognition using multi-parameter speech feature classification”, in Proceedings - 2015 International Conference on Computers, Communications and Systems, ICCCS 2015, 2015, pp. 217-222.[Abstract]

Speech emotion recognition is basically extraction and identification of emotion from a speech signal. Speech data, corresponding to various emotions as happiness, sadness and anger, was recorded from 30 subjects. A local database called Amritaemo was created with 300 samples of speech waveforms corresponding to each emotion. Based on the prosodic features: energy contour and pitch contour, and spectral features: cepstral coefficients, quefrency coefficients and formant frequencies, the speech data was classified into respective emotions. The supervised learning method was used for training and testing, and the two algorithms used were Hybrid Rule based K-mean clustering and multiclass Support Vector Machine (SVM) algorithms. The results of the study showed that, for optimized set of features, Hybrid-rule based K mean clustering gave better performance compared to Multi class SVM. © 2015 IEEE. More »»