RAJARSHI GUPTA

About

Rajarshi Gupta is currently a Professor attached to Electrical Engineering Section, Department of Applied Physics, University of Calcutta. He obtained his B.Tech, M.Tech and PhD in Instrumentation Engineering in 1998, 2002 and 2012 from University of Calcutta. His research interests include biomedical signal analysis, and intelligent human health monitoring. His other technical interests are process automation and intelligent sensing. Rajarshi Gupta has 90 publications in National and International peer reviewed journals and conferences (Journals 40, Conferences 51, Books/book chapters 08). He has been associated with PhD guidance of 10 students, out of whom 6 has been awarded/submitted doctoral degrees.
Currently also serving as an associate editor of IEEE Transactions on Instrumentation and Measurement journal, and an Editorial Borad member of Physiological Measurement Journal from IPEM (IoP Science).

Employment

• University of Calcutta University College of Science Technology and Agriculture Professor
  2018 - Present
• University of Calcutta Associate Professor
  2015 - 2017
• University of Calcutta Assistant Professor
  2005 - 2014

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (87)

• MultitaskBP: Reliable PPG-Based Blood Pressure Measurement Under Motion Artifact Corruption
  IEEE Transactions on Instrumentation and Measurement 2026 DOI: 10.1109/TIM.2026.3652724
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• Physiology and Sampling-Frequency Adaptive Type-II Diabetes Detection Using PPG
  IEEE Sensors Journal 2026 DOI: 10.1109/JSEN.2026.3697135
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• An Efficient Multi-Channel EEG Compression Using Intelligent Tunable Q-Wavelet Transform: A Machine Learning-Driven Approach
  Journal of Circuits, Systems and Computers 2026 DOI: 10.1142/S0218126625503967
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• QaPRExt: a unified deep learning framework for quality-aware PPG-derived respiration signal extraction for personalized healthcare
  Measurement Science and Technology 2025 DOI: 10.1088/1361-6501/ae0c05
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• Machine learning assisted classification between diabetic polyneuropathy and healthy subjects using plantar pressure and temperature data: a feasibility study
  Computer Methods in Biomechanics and Biomedical Engineering 2025 DOI: 10.1080/10255842.2024.2359041
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• A2IPPG: A Machine Learning Assisted Real-Time Standalone Continuous Authorization to Identification Using Photoplethysmogram Features
  IEEE Sensors Letters 2025 DOI: 10.1109/LSENS.2025.3586840
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• MemSAR: A New Memory-Based Stacked Autoencoder for Blood Pressure Measurement Using Single Channel Photoplethysmography
  2025 DOI: 10.1007/978-3-031-84331-0_60
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• PPG-Derived Respiration Signal Predictor-Guided Simple Rule-Based Detection of COPD
  IEEE Transactions on Instrumentation and Measurement 2025 DOI: 10.1109/TIM.2025.3611460
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• Detection of Diabetes using Short-duration Photoplethysmography
  IEEE Calcutta Conference (CALCON) 2024 DOI: 10.1109/CALCON63337.2024.10914214
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• Real-Time PPG-to-ECG Reconstruction Model With On-Device Recalibration Facility
  IEEE Transactions on Instrumentation and Measurement 2024 DOI: 10.1109/TIM.2024.3450120
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