NIST UNIVERSITY
Institute Park, Berhampur, Odisha-761008, India

Harikrushna Gantayat

Dr. Harikrushna Gantayat

Senior Assistant Professor

Electronics and Comm. Engineering (SCHOOL OF ENGINEERING)

harikrushna@nist.edu
9938571121
LHC - LHC-308

Education

B.Tech in ELECTRONICS AND COMMUNICATION ENGINEERING
NATIONAL INSTITUTE OF SCIENCE AND TECHNOLOGY
2008-12-31
M.Tech in ELECTRONICS AND COMMUNICATION ENGINEERING
NATIONAL INSTITUTE OF SCIENCE AND TECHNOLOGY
2012-10-23
PhD in ELECTRONICS AND COMMUNICATION ENGINEERING
Biju Patnaik University of Technology
2024-09-13

Work Experience

SENIOR ASSISTANT PROFESSOR
National Institute Of Science & Technology, Berhampur
19/05/2008 - 23/01/2026

Research Interests

  • Signal Processing,Estimation Theory,Distributed Processing

Publications

  1. Gantayat, H., Panigrahi, T. and Patra, P., 2022, December. Estimation of Direction-of-Arrival with Limited Snapshots in Wireless Sensor Network. In 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) (pp. 1-6). IEEE.
  2. Gantayat, H., Panigrahi, T. and Patra, P., 2022, December. UDM-RBM-Based DOA Estimation in Alpha Unstable Impulse Noise and Multipath Signals. In 2022 6th International Conference on Electronics, Communication and Aerospace Technology (pp. 380-387). IEEE.
  3. Gantayat, H. and Panigrahi, T., 2020. On the Development of Energy-Efficient Distributed Source Localization Algorithm in Wireless Sensor Networks Using Modified Swarm Intelligence. In Nature Inspired Computing for Wireless Sensor Networks (pp. 143-173). Singapore: Springer Singapore.
  4. Gantayat, H., Panigrahi, T. and Patra, P., 2025. An efficient RBF‐DCNN based DOA estimation in multipath and impulse noise wireless environment. Transactions on Emerging Telecommunications Technologies, 36(1), p.e4606.
  5. Gantayat, H., Panigrahi, T. and Patra, P., 2024. An efficient direction‐of‐arrival estimation of multipath signals with impulsive noise using satin bowerbird optimization‐based deep learning neural network. Expert Systems, 41(6), p.e13108.
  6. Panigrahi, T., Roula, S. and Gantayat, H., 2016. Application of comprehensive learning particle swarm optimisation algorithm for maximum likelihood DOA estimation in wireless sensor networks. International Journal of Swarm Intelligence, 2(2-4), pp.208-228.
  7. PRIYANKA, R.R., SHALINI, D., NARESH, E., HAIDARI, M., GANTAYAT, D.H. and THOTTATHYL, H., 2025. INTELLIGENT LAND SUITABILITY ANALYSIS UTILIZING MULTILAYER PERCEPTRON AND IOT SENSORS. Journal of Theoretical and Applied Information Technology, 103(19).
  8. PANDIKUMAR, S., PREMKUMAR, S., GUHAN, T., MARY, T.M., KANNAN, M., SUDHA, V.P., SHABARUDIN, S., SALAM, S., AHMAD, I., MUSA, M.H. and SHAREF, N.M., 2025. AN IMPLEMENTATION OF ENHANCED INCEPTION-RESIDUAL CONVOLUTIONAL NEURAL NETWORK IN LUNG CANCER PREDICTION. Journal of Theoretical and Applied Information Technology, 103(19).
  9. De, D., Mukherjee, A., Das, S.K. and Dey, N. eds., 2020. Nature inspired computing for wireless sensor networks (pp. 1-18). Singapore: Springer.letters, 109(25), p.252301.e Computing, 27(3), pp.203-216.