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Amit Singhal

Amit Singhal
Associate Professor
Qualification
Ph.D., B.Tech + M.Tech (Dual Degree)
Email
amit@nsut.ac.in

Google Scholar: https://scholar.google.com/citations?user=osJ0bRAAAAAJ&hl=en

 

 

 

 

Bio-Sketch:

Dr. Amit Singhal completed his dual degree with B.Tech. in Electrical Engineering and M.Tech. in Information and Communication Technology from IIT Delhi in 2009. He completed his Ph.D in Molecular Communication from IIT Delhi in 2016. He has a total teaching experience of more than 16 years. Prior to joining NSUT in June 2021, he has worked with JIIT Noida and Bennett University, Greater Noida. In addition to his degrees in the technical domain, he also holds a minor degree in Business Management, obtained from IIT Delhi in 2009.

 

 

Areas of Interest:

Biomedical Signal and Image Processing, Signal and Data Analysis, Molecular Communications, Next Generation Communication Technologies, Image retrieval

 

Publications (International Journal):

  1. A. Singhal A, M. Agarwal, A. K. Paul and B. Lamichhane, “Editorial: Machine learning algorithms and software tools for early detection and prognosis of schizophrenia,” Frontiers in Computational Neuroscience, vol. 20:1966648, 2026. doi: 10.3389/fncom.2026.1966648.
  2. P. Singh, M. Agarwal, N. K. Mishra, A. Singhal, A. Bhattacharyya, A. Gupta and S. D. Joshi, “Novel Time–Frequency Representation for Biomedical Signal Analysis Using Generalized Gaussian COLA Windows,” in IEEE Transactions on Instrumentation and Measurement, vol. 75, pp. 6513610-6513610, 2026, Art no. 6513610, doi: 10.1109/TIM.2026.3728977.
  3. Y. Kaura, B. Lall, R. K. Mallik and A. Singhal, “HALO: Hybrid Adaptive Load Offloading of Grant-Based Traffic to Grant-Free Bandwidth for Spectrum Efficiency in Next Generation Networks,” in IEEE Transactions on Green Communications and Networking, vol. 10, pp. 4266-4281, 2026, doi: 10.1109/TGCN.2026.3727486. 
  4. M. Agarwal, A. Singhal and V. Balyan, “Gaussian filtering based local ternary pattern for efficient classification of crop diseases,” IEEE Canadian Journal of Electrical and Computer Engineering, 2025. 10.1109/ICJECE.2025.3587886
  5. M. Agarwal and A. Singhal, “EEG based classification of sleep cyclic alternating patterns using frequency driven forward ternary encoding,” Sleep and Breathing, vol. 29, no. 6, article no. 327, pp. 1-11, December 2025. https://doi.org/10.1007/s11325-025-03515-9 
  6. V. Tiwari, S. K. Singh, U. Hassan and A. Singhal, “IGF-CNN: An Optimized Deep Learning Model for Covid-19 Classification,” International Journal of Imaging Systems and Technology, vol. 35, no. 6, pp. e70247, November 2025. https://doi.org/10.1002/ima.70247
  7. U. Hassan, A. Singhal and G. Gupta, “Neural network based AI model for lung health assessment,” Scientific Reports, vol. 15, no. 25177, 2025. https://doi.org/10.1038/s41598-025-09524-8 
  8. A. Upadhyay, M. Sharma, P. Mukherjee, A. Singhal and Brejesh Lall, “A comprehensive survey on synthetic infrared image synthesis,” Infrared Physics & Technology, vol. 147, pp. 105745, June 2025. https://doi.org/10.1016/j.infrared.2025.105745
  9. M. Agarwal and A. Singhal, “Efficient system for classifying cyclic alternating pattern phases in sleep,” Cognitive Neurodynamics, vol. 19, no. 79, May 2025. https://doi.org/10.1007/s11571-025-10261-x.
  10. Y. Kaura, B. Lall, R. K. Mallik and A. Singhal, “Adaptive Scheduling of Shared Grant-Free Resources for Heterogeneous Massive Machine type Communication in 5G and Beyond Networks,” IEEE Transactions on Network and Service Management, vol. 22, no. 2, pp. 1188-1204, April 2025. doi: 10.1109/TNSM.2024.3493015.
  11. N. Sharma, M. Sharma, A. Singhal, N. Fatema, V. K. Jadoun and H. Malik, “A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery Classification,” IEEE Access, vol. 13, pp. 2141-2151, 2025, doi: 10.1109/ACCESS.2024.3517639.
  12. M. Agarwal and A. Singhal, “A Gaussian Filtering Approach for Accurate Detection of Schizophrenia,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 48, pp. 1453–1462, December 2024. https://doi.org/10.1007/s40998-024-00738-6.
  13. M. Agarwal and A. Singhal, “Classification of cyclic alternating patterns of sleep using EEG signals,” Sleep Medicine, vol. 124, pp. 282–288, December 2024. https://doi.org/10.1016/j.sleep.2024.09.025
  14. P. Singh, A. Singhal, B. Fatimah, A. Gupta and S. D. Joshi, “On the convergence of Fourier representations and Schwartz distributions,” Franklin Open, vol. 8, September 2024, pp. 100155. https://doi.org/10.1016/j.fraope.2024.100155
  15. U. Hassan and A. Singhal, “Convolutional neural network framework for EEG-based ADHD diagnosis in children,” Health Information Science and Systems, vol. 12, no. 44, August 2024. https://doi.org/10.1007/s13755-024-00305-7.
  16. M. Bakshi, B. Lall, R. K. Mallik and A. Singhal, “Performance of Full-Duplex Cooperative NOMA Network With Direct Link and Battery-Assisted Non-Linear Energy Harvesting Near User,” in IEEE Open Journal of the Communications Society, vol. 5, pp. 3484-3502, June 2024, doi: 10.1109/OJCOMS.2024.3408313.
  17. M. Bakshi, B. Lall, R. K. Mallik and A. Singhal, "Performance Analysis of Downlink Cooperative User Relaying FD/HD NOMA Over Nakagami-m Fading Channels," in IEEE Transactions on Vehicular Technology, vol. 73, no. 6, pp. 8632-8647, June 2024, doi: 10.1109/TVT.2024.3362429
  18. P. Chaudhary, N. Dhankhar, A. Singhal and K.P.S. Rana, “A two-stage transformer based network for motor imagery classification,” Medical Engineering & Physics, vol. 128, pp. 104154, June 2024, doi: https://doi.org/10.1016/j.medengphy.2024.104154. 
  19. U. Hassan, A. Singhal and P. Chaudhary, “Lung disease detection using EasyNet,” Biomedical Signal Processing and Control, vol. 91, pp. 105944, May 2024. https://doi.org/10.1016/j.bspc.2024.105944 
  20. U. Hassan and A. Singhal, “Automated Diagnosis of Pulmonary Diseases Using Lung Sound Signals,” IETE Journal of Research, vol. 70, no. 5, pp. 4792–4800, May 2024, doi: https://doi.org/10.1080/03772063.2023.2258495.
  21. A. Singhal and M. Agarwal, “An automatic risk assessment system for sudden cardiac death using look ahead pattern,” Multimedia Tools and Applications, vol. 83, pp. 27243–27258, March 2024. https://doi.org/10.1007/s11042-023-16548-7.
  22. B. Fatimah, A. Singhal and P. Singh, “ECG arrhythmia detection in an inter-patient setting using Fourier decomposition and machine learning,” Medical Engineering & Physics, vol. 124, pp. 104102, February 2024, doi: https://doi.org/10.1016/j.medengphy.2024.104102.
  23. N. Sharma, A. Upadhyay, M. Sharma and A. Singhal, “Deep temporal networks for EEG-based motor imagery recognition,” Scientific Reports, vol. 13, no. 18813, November 2023, doi: https://doi.org/10.1038/s41598-023-41653-w.
  24. N. Sharma, M. Sharma, A. Singhal, R. Vyas, H. Malik, M. A. Hossaini and A. Afthanorhan, “An Efficient Approach for Recognition of Motor Imagery EEG Signals using the Fourier Decomposition Method,” IEEE Access, vol. 11, pp. 122782-122791, 2023, doi: 10.1109/ACCESS.2023.3299497.
  25. N. Sharma, M. Sharma, A. Singhal, R. Vyas, H. Malik, A. Afthanorhan and M. A. Hossaini, “Recent trends in EEG based motor imagery signal analysis and recognition: A comprehensive review,” IEEE Access, vol. 11, pp. 80518-80542, 2023, doi: 10.1109/ACCESS.2023.3299497.
  26. P. Singh, A. Singhal, B. Fatimah and A. Gupta, “A novel PRFB decomposition for non-stationary time-series and image analysis,” Signal Processing, vol. 207, pp. 108961, June 2023. https://doi.org/10.1016/j.sigpro.2023.108961.
  27. V. K. Mehla, A. Singhal and P. Singh, “An Efficient Classification of Focal and Non-Focal EEG Signals Using Adaptive DCT Filter Bank," Circuits, Systems and Signal Processing, vol. 42, pp. 4691–4712, March 2023. https://doi.org/10.1007/s00034-023-02328-z.
  28. M. Agarwal and A. Singhal, “Fusion of pattern-based and statistical features for Schizophrenia detection from EEG signals,” Medical Engineering & Physics, vol. 112, pp. 103949, February 2023. https://doi.org/10.1016/j.medengphy.2023.103949.
  29. B. Fatimah, A. Singhal and P. Singh, “A multi-modal assessment of sleep stages using adaptive Fourier decomposition and machine learning,” Computers in Biology and Medicine, vol. 148, pp. 105877, September 2022, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2022.105877.
  30. P. Singh, A. Singhal, B. Fatimah, A. Gupta and S. D. Joshi, “Proper Definitions of Dirichlet Conditions and Convergence of Fourier Representations,” in IEEE Signal Processing Magazine, vol. 39, no. 5, pp. 77-84, Sept. 2022, doi: 10.1109/MSP.2022.3172620.
  31. B. Fatimah, P. Singh, A. Singhal and R. B. Pachori, “Biometric Identification From ECG Signals Using Fourier Decomposition and Machine Learning,” in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-9, August 2022, Art no. 4008209, doi: 10.1109/TIM.2022.3199260.
  32. A. Chaoub, M. Giordani, B. Lall, V. Bhatia, A. Kliks, L. Mendes, K. Rabie, H. Saarnisaari, A. Singhal, N. Zhang and Sudhir Dixit, “6G for Bridging the Digital Divide: Wireless Connectivity to Remote Areas,” IEEE Wireless Communications Magazine, vol. 29, no. 1, pp. 160-168, February 2022, doi: 10.1109/MWC.001.2100137.
  33. Harri Saarnisaari, Abdelaali Chaoub, Marjo Heikkilä, Amit Singhal and Vimal Bhatia, “Wireless Terrestrial Backhaul for 6G Remote Access: Challenges and Low Power Solutions,” Frontiers in Communications and Networks, vol. 2, pp. 710781, November 2021. Doi: https://doi.org/10.3389/frcmn.2021.710781.
  34. M. Agarwal and A. Singhal, “Directional local co-occurrence patterns based on Haar-like filters,” Multimedia Tools and Applications, vol. 81, pp. 1109–1123, September 2021. https://doi.org/10.1007/s11042-021-11361-6.
  35. B. Fatimah, P. Singh, A. Singhal, D. Pramanick, Pranav S. and R. B. Pachori, “Efficient detection of myocardial infarction from single lead ECG signal,” Biomedical Signal Processing and Control, vol. 68, pp. 102678, July 2021. https://doi.org/10.1016/j.bspc.2021.102678
  36. B. Fatimah, P. Singh, A. Singhal and R. B. Pachori, “Hand movement recognition from sEMG signals using Fourier decomposition method,” Biocybernetics and Biomedical Engineering, vol. 41, no. 2, pp. 690-703, April 2021. https://doi.org/10.1016/j.bbe.2021.03.004
  37. P. Singh, A. Singhal, B. Fatimah, A. Gupta and S. D. Joshi, “AF-MNS: A novel AM-FM based measure of non-stationarity,” IEEE Communications Letters, vol. 25, no. 3, pp. 990–994, March 2021. doi: 10.1109/LCOMM.2020.3041722
  38. V. K. Mehla, A. Singhal, P. Singh and R. B. Pachori, “An efficient method for identification of epileptic seizures from EEG signals using Fourier analysis," Physical and Engineering Sciences in Medicine, vol. 44, pp. 443-456, March 2021. https://doi.org/10.1007/s13246-021-00995-3.
  39. A. Singhal, M. Agarwal, and R. B. Pachori, “Directional local ternary co-occurrence pattern for natural image retrieval,” Multimedia Tools and Applications, vol. 80, pp. 15901-15920, April 2021. https://doi.org/10.1007/s11042-020-10319-4
  40. V. K. Mehla, A. Singhal and P. Singh, “A novel approach for automated alcoholism detection using Fourier decomposition method,” Journal of Neuroscience Methods, vol. 346, pp. 108945, December 2020. https://doi.org/10.1016/j.jneumeth.2020.108945 
  41. A. Singhal, P. Singh, B. Lall and S. D. Joshi, “Modeling and prediction of COVID-19 pandemic using Gaussian mixture model,” Chaos Solitons & Fractals, vol. 138, pp. 110023, September 2020. https://doi.org/10.1016/j.chaos.2020.110023  
  42. B. Fatimah, P. Singh, A. Singhal and R. B. Pachori, “Detection of apnea events from ECG segments using Fourier decomposition method,” Biomedical Signal Processing and Control, vol. 61, pp. 102005, March 2020. https://doi.org/10.1016/j.bspc.2020.102005 
  43.  A. Singhal, P. Singh, B. Fatimah and R. B. Pachori, “An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique,” Biomedical Signal Processing and Control, vol. 57, pp. 101741, March 2020. https://doi.org/10.1016/j.bspc.2019.101741
  44.  M. Agarwal, A. Singhal and B. Lall, “Multi-channel local ternary pattern for content-based image retrieval,” Pattern Analysis and Applications, vol. 22, no. 4, pp. 1585–1596, November 2019. https://doi.org/10.1007/s10044-019-00787-2
  45.  M. Agarwal, A. Singhal and B. Lall, “3D local ternary co-occurrence patterns for natural, texture, face and bio medical image retrieval,” Neurocomputing, vol. 313, pp. 333–345, November 2018. https://doi.org/10.1016/j.neucom.2018.06.027
  46.  A. Singhal, R. K. Mallik, and B. Lall, “Performance analysis of amplitude modulation schemes for diffusion-based molecular communication,” IEEE Transactions on Wireless Communications, vol. 14, no. 10, pp. 5681–5691, October 2015. doi: 10.1109/TWC.2015.2441067
  47.  A. Singhal, R. K. Mallik, and B. Lall, “Effect of molecular noise in diffusion-based molecular communication,” IEEE Wireless Communications Letters, vol. 3, no. 5, pp. 489–492, October 2014. doi: 10.1109/LWC.2014.2345756
  48. A. Singhal, R. K. Mallik, and B. Lall, “Molecular communication with Brownian motion and a positive drift: performance analysis of amplitude modulation schemes,” IET Communications, vol. 8, no. 14, pp. 2413–2422, September 2014. https://doi.org/10.1049/iet-com.2013.0939

 

 

 

 

 

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