Academic Information
M.Tech (EE)
Department: Electrical Engineering
College: Rashbehari Siksha Prangan (Rajabazar Science College Campus), University of Calcutta
Year: 2016–2018
Ph.D. (EE)
Department: Electrical Engineering (Instrumentation & Signal Processing Department)
University: Indian Institute of Technology Roorkee
Year: 2019–2022
Research Interests
Dr. Soumyendu Banerjee has research interests focused on biomedical signal processing and intelligent embedded systems, with particular emphasis on electrocardiography (ECG) analysis and data compression for efficient storage and transmission. Machine learning–based techniques are employed for signal enhancement, artifact removal, and missing data prediction. Algorithm development and validation are carried out using MATLAB, followed by real-time implementation on Raspberry Pi–based platforms. Applications are also explored in robotics, where adaptive signal processing and learning algorithms are utilized to enhance autonomous and sensor-driven systems in resource-constrained environments.
Courses Taught
2. 8085 microprocessor
3. Analog electronics
Positions Held
Associated Faculty – Department of Robotics & AI (since 2025)
Co-Ordinator of Centre of Excellence (CoE), Indian Knowledge Systems (IKS) (since 2025)
Publications
SCI Peer Reviewed Journal Articles
1. S. Banerjee and G. K. Singh, “A new approach of ECG steganography and prediction using deep learning,” Biomedical Signal Processing and Control, vol. 64, p. 102151, Feb. 2021, doi: 10.1016/j.bspc.2020.102151.
2. S. Banerjee and G. K. Singh, “Quality guaranteed ECG signal compression using tunable-Q wavelet transform and Möbius transform-based AFD,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–11, 2021, doi: 10.1109/TIM.2021.3122119.
3. S. Banerjee and G. K. Singh, “Deep neural network based missing data prediction of electrocardiogram signal using multiagent reinforcement learning,” Biomedical Signal Processing and Control, vol. 67, p. 102508, May 2021, doi: 10.1016/j.bspc.2021.102508.
4. S. Banerjee and G. K. Singh, “Monte Carlo filter-based motion artifact removal from electrocardiogram signal for real-time telecardiology system,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–10, 2021, doi: 10.1109/TIM.2021.3102737.
5. S. Banerjee and G. K. Singh, “A robust bio-signal steganography with lost-data recovery architecture using deep learning,” IEEE Transactions on Instrumentation and Measurement, pp. 1–1, 2022, doi: 10.1109/TIM.2022.3197781.
6. S. Banerjee and G. K. Singh, “A new moving horizon estimation based real-time motion artifact removal from wavelet subbands of ECG signal using particle filter,” Journal of Signal Processing Systems, vol. 95, no. 8, pp. 1021–1035, Aug. 2023, doi: 10.1007/s11265-023-01887-3.
7. S. Banerjee and G. K. Singh, “AUDSER: Auto-detect and self-recovery reversible steganography algorithm for biological signals,” Biomedical Signal Processing and Control, vol. 100, p. 106974, Oct. 2024, doi: 10.1016/j.bspc.2024.106974.
8. S. Banerjee and G. K. Singh, “Agent-based beat-by-beat compression of 12-lead electrocardiogram signal using adaptive Fourier decomposition,” Biomedical Signal Processing and Control, vol. 75, p. 103628, Mar. 2022, doi: 10.1016/j.bspc.2022.103628.
9. S. Banerjee and G. K. Singh, “A new real-time lossless data compression algorithm for ECG and PPG signals,” Biomedical Signal Processing and Control, vol. 79, p. 104127, Jan. 2023, doi: 10.1016/j.bspc.2022.104127.
10. S. Banerjee and G. K. Singh, “Quality aware compression of multilead electrocardiogram signal using 2-mode Tucker decomposition and steganography,” Biomedical Signal Processing and Control, vol. 64, p. 102230, Feb. 2021, doi: 10.1016/j.bspc.2020.102230.
11. N. Patra, S. Banerjee, and S. Bhadra, “On-device compression of multilead electrocardiogram using tunable-Q wavelet transform and MLPNN trained using multi-optima optimization based PSO,” Signal, Image and Video Processing, vol. 19, no. 8, May 2025, doi: 10.1007/s11760-025-04141-4.
SCOPUS Conferences
1. A. Ghosh, S. Banerjee, S. Bhadra, H. Mukherjee, A. Kundu, and P. Ghosh, “Smart control of household appliances using wireless communication and PSO based optimal power consumption control,” in Proc. 2025 Int. Conf. Computing, Intelligence, and Application (CIACON), Durgapur, India, 2025, pp. 1–6.
2. S. Banerjee, A. Ghosh, S. Bhadra, S. Das, S. Gangopadhyay, and M. J. Mondal, “Optimal control of 6-DOF robotic arm using PSO tested on hardware chassis,” in Proc. 2025 Int. Conf. Computing, Intelligence, and Application (CIACON), Durgapur, India, 2025, pp. 1–6.
3. S. Banerjee and G. K. Singh, “Comparative study on R-peak detection over noisy and denoised ECG signal using wavelet transform,” in Proc. 2021 3rd Int. Conf. Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2021, pp. 659–663.
4. S. Banerjee and G. K. Singh, “Feedback control topology of n-DOF robotic manipulator and optimal positioning of end-effector using PSO,” in Proc. 2020 IEEE Applied Signal Processing Conf. (ASPCON), Kolkata, India, 2020, pp. 41–45.
5. S. Banerjee, S. Laskar, A. Chowdhury, S. Sarkar, A. Roy, and A. Das, “Real-time monitoring and control of consumed power for household appliances using Arduino Uno through Bluetooth and Android application,” in Proc. 2019 3rd Int. Conf. Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019, pp. 529–533.
6. S. Banerjee, “A first derivative based R-peak detection and DWT based beat delineation approach of single lead electrocardiogram signal,” in Proc. 2019 IEEE Region 10 Symp. (TENSYMP), Kolkata, India, 2019, pp. 565–570.
7. S. Banerjee, R. Gupta, and J. Saha, “Compression of multilead electrocardiogram using principal component analysis and machine learning approach,” in Proc. 2018 IEEE Applied Signal Processing Conf. (ASPCON), Kolkata, India, 2018, pp. 24–28.
Book Chapter
S. Adhikary, S. Bhadra, K. Chanda, K. Banerjee, J. Aich, and S. Banerjee, “Dynamic Optimization for High-Speed Rail Scheduling: A Novel Human–Computer Interaction Paradigm,” in Recent Advances in Artificial Intelligence and Smart Applications, J. K. Mandal, M. Hinchey, and S. Chakrabarti, Eds., RAAISA 2023, Innovations in Sustainable Technologies and Computing. Singapore: Springer, 2024, doi: 10.1007/978-981-97-3485-6_7.