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Anurag Nishad

goa Electrical & Electronics Engineering

Assistant Professor, Gr-I

Biomedical Signal Processing, Machine Learning and Deep Learning, Time-frequency analysis of non-stationary signals, Wavelet Analysis and its Applications, speech signal processing
D-111, Department of Electrical and Electronics Engineering, BITS Pilani K.K. Birla Goa Campus
anuragn@goa.bits-pilani.ac.in
0832-2580212
https://www.linkedin.com/in/anurag-nishad-019828b5/
Dr. Anurag Nishad

Publications

International Journals:
 
  1. R.B. Pachori and A. Nishad, Cross-terms reduction in Wigner-Ville distribution using tunable-Q wavelet transform, Signal Processing, vol. 120, pp. 288-304, 2016. https://doi.org/10.1016/j.sigpro.2015.07.026
  2. A. Nishad, R.B. Pachori, and U.R. Acharya, Application of TQWT based filter-bank for sleep apnea screening using ECG signals, Journal of Ambient Intelligence and Humanized Computing, 2014. https://doi.org/10.1007/s12652-018-0867-3
  3. A. Nishad, A. Upadhyay, R.B. Pachori, and U.R. Acharya, Automated classification of hand movements using tunable-Q wavelet transform based filter-bank with surface electromyogram signals, Future Generation Computer Systems, vol. 93, pp. 96-110, 2019. https://doi.org/10.1016/j.future.2018.10.005 
  4. A. Nishad and R.B. Pachori, Classification of epileptic electroencephalogram signals using tunable-Q wavelet transform based filter-bank, Journal of Ambient Intelligence and Humanized Computing, vol. 15, pp. 877-891, 2024. https://doi.org/10.1007/s12652-020-01722-8
  5. S.K. Khare, A. Nishad, A. Upadhyay, and V. Bajaj, Classification of emotions from EEG signals using time-order representation based on the S-transform and convolutional neural network, Electronics Letters, vol. 56, pp. 1359-1361, 2020. DOI:  10.1049/el.2020.2380
  6. A. Nishad, A. Upadhyay, G.R.S. Reddy, and V. Bajaj, Classification of epileptic EEG signals using sparse spectrum based empirical wavelet transform, Electronics Letters, vol. 56, pp. 1370-1372, 2020. DOI:  10.1049/el.2020.2526
     
International Conferences:
 
  1. A. Nishad and R.B. Pachori, Instantaneous fundamental frequency estimation of speech signals using tunable-Q wavelet transform, International Conference on Signal Processing and Communication (SPCOM), July 16-19, 2018, Bangalore, India.
  2. V. Gupta, A. Nishad, and R.B. Pachori, Focal EEG signal detection based on constant-bandwidth TQWT filter-banks, IEEE International Conference on Bioinformatics and Biomedicine, December 3-6, 2018, Madrid, Spain.
  3. Y. Chauhan, S. Kakkar, T. Joshia, A. Nishad, A. Upadhyay, Larynx Function prediction using tunable-Q wavelet transform based filter bank using Surface EMG Data, 7th International Conference on Recent Trends in Image Processing & Pattern Recognition, Dec 19-20, 2024
  4. A. S. Kamat, J. G. Fernando Sa, A. Nishad, A. Upadhyay, Unsupervised and supervised machine learning model for cross-terms suppression in Wigner-Ville distribution of non-stationary signals, 5th International Conference on Electronic Engineering and Signal Processing, Dec 2-5, 2024
Book Chapter: 
 
  1. A. Nishad and A. Upadhyay, "Empirical wavelet transform based classification of surface electromyogram signals for hand movements," in Modelling and Analysis of Active Biopotential Signals in Healthcare, Volume 1. Institute of Physics Publishing, 2020, pp. 9.1–9.31.

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