Position: Associate Professor
School: School of Electrical & Electronics Engineering
Email: shiu.kumar@fnu.ac.fj
Campus: Derrick Campus – Samabula
Phone: 3381044 ext. 1504

Biography:

Dr. Shiu Kumar is an Associate Professor in Electrical Engineering at the School of Electrical and Electronics Engineering, within the College of Engineering and Technical Vocational Education and Training (CETVET) at Fiji National University.

Dr. Kumar has made significant contributions to the fields of Electrical and Electronics Engineering, Brain-Computer Interfaces, and applications of Artificial Intelligence. Additionally, he is well versed and experienced in both Technical and Vocational Education and Training (TVET) and higher education programmes. He also has expertise in curriculum development, national & international accreditation, and developing online data collection tools.

Dr. Kumar is a highly accomplished researcher in his field, and in recognition of his contributions, he was honored with the FNU Vice-Chancellor’s Award for Research Excellence in 2019. Additionally, he was awarded the Vice-Chancellor’s Prize for Best Student Research at the University of the South Pacific in 2021. Apart from these, he also has good knowledge of the electrical field and holds an Electrical Wireman’s License.

Qualification:

• Doctor of Philosophy in Engineering, University of the South Pacific (USP) – Laucala Campus, Suva, Fiji.
• Master of Engineering in Electronics, Mokpo National University (MNU), Mokpo, South Korea.
• Postgraduate Diploma in Electrical & Electronics Engineering, University of the South Pacific (USP) – Laucala Campus, Suva, Fiji.
• Bachelor of Engineering Technology (Electrical & Electronics Engineering), University of the South Pacific (USP) – Laucala Campus, Suva, Fiji.

Area of Expertise:

• Brain-Computer Interface
• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• Signal Processing
• Machine Fault Recognition
• Sleep Stage Classification
• EEG Signal Classification
• Data Mining
• Automation and Control
• Robotics

Journals:

• N. Singh, K. Kothari, S. Kumar, and M. Assaf, “Review on the Enhancement of 5G Communications Using LEO Satellites,” in Lecture Notes in Networks and Systems, December 2024, vol 23, pp. 119-129. (Q4)
• A. Zaman, S. Kumar, S. Shatabda, I. Dehzangi, and A. Sharma, “SleepBoost: a multi‑level tree‑based ensemble model for automatic sleep stage classification,” Medical & Biological Engineering & Computing, May 2024. (Q2)
• M. Kaloumaira et al., “A Real-Time Fall Detection System Using Sensor Fusion,” in Smart Innovation, Systems and Technologies, November 2023, vol. 364, pp. 513-527. (Q4)
• M. O. Miah, R. Muhammod, K. A. A. Mamun, D. M. Farid, S. Kumar, A. Sharma, et al., “CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data,” Journal of Neuroscience Methods, vol. 364, p. 109373, December 2021. (Q2)
• S. Kumar, R. Sharma, T. Tsunoda, T. Kumarevel, and A. Sharma, “Forecasting the spread of COVID-19 using LSTM network,” BMC Bioinformatics, vol. 22, p. 316, June 2021. (Q1)
• S. Kumar, T. Tsunoda, and A. Sharma, “SPECTRA: a tool for enhanced brain wave signal recognition,” BMC Bioinformatics, vol. 22, p. 195, June 2021. (Q1)
• A. L. Tarca, B. Á. Pataki, R. Romero, M. Sirota, Y. Guan, R. Kutum, et al., “Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth,” Cell Reports Medicine, vol. 2, p. 100323, June 2021 (Q1)
• S. Kumar, R. Sharma, A. Sharma, “OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals,” PeerJ Computer Science, 7:e375, 2021 https://doi.org/10.7717/peerj-cs.375. (Q2)
• R. Sharma, S. Kumar, T. Tsunoda, T. Kumarevel, and A. Sharma, “Single-stranded and double-stranded DNA-binding protein prediction using HMM profiles,” Analytical Biochemistry, vol. 612, p. 113954, 2021. (Q2)
• S. Kumar, A. Sharma, and T. Tsunoda, “Subject-Specific-Frequency-Band for Motor Imagery EEG Signal Recognition Based on Common Spatial Spectral Pattern,” PRICAI 2019, Lecture Notes in Artificial Intelligence: Sub-series of Lecture Notes in Computer Science, vol. 11671, August 2019. (Q2)
• S. Kumar, A. Sharma, and T. Tsunoda, “Brain wave classification using long short-term memory network based OPTICAL predictor” Scientific Reports, vol 9(1), June 2019. (Q1)
• S. Kumar and A. Sharma, “A new parameter tuning approach for enhanced motor imagery EEG signal classification,” Medical & Biological Engineering & Computing, April 2018. (Q2)
• S. Kumar, A. Sharma, and T. Tsunoda, “An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information,” BMC Bioinformatics, vol. 18, p. 545, December 28 2017. (Q1)
• S. Kumar, K. Mamun, and A. Sharma, “CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI,” Computers in Biology and Medicine, vol. 91, pp. 231-242, December 2017. (Q1)
• R. Sharma, S. Kumar, T. Tsunoda, A. Patil, and A. Sharma, “Predicting MoRFs in protein sequences using HMM profiles,” BMC Bioinformatics vol. 17, pp. 251-258, September 2016. (Q1)
• S. Kumar, R. Sharma, and E. R. Vans, “Localization for Wireless Sensor Networks: A Neural Network Approach,” International Journal of Computer Networks & Communications (IJCNC), vol. 8, pp. 61-71, January 2016. (Q4)
• S. Kumar and S. R. Lee, “Clock Synchronization: Estimation of Non-deterministic Delays in Wireless Message Delivery,” International Journal of Computer Networks & Communications (IJCNC), vol. 7, pp. 125-134, January 2015. (Q4)
• S. Kumar, Y. Lee and S. R. Lee, Estimation and Compensation of Non-deterministic Delays for Time Synchronization in Wireless Sensor Networks, International Journal of Control and Automation (IJCA), vol. 7 (4), pp. 103-112, April 2014. (Q4)
• S. Kumar, Ubiquitous Smart Home System Using Android Application, International Journal of Computer Networks & Communications (IJCNC), vol. 6, pp. 33-43, January 2014. (Q4)

Book and Book Chapters:

• Kumar, S., Sharma, A. (2024). ‘Advances in Non-invasive EEG-based Brain-Computer Interfaces: Signal Acquisition, Processing, Emerging Approaches, and Applications’ in El-Baz, A. S., Suri, J. (ed.) Advances in Neural Engineering, Volume 1: Signal Processing Strategies. Elsevier, November 2024.
• Zaman, A., Kumar, S., et al. (2024). ‘Recent Development of Single Channel EEG‐Based Automated Sleep Stage Classification: Review And Future Perspectives’ in El-Baz, A. S., Suri, J. (ed.) Advances in Neural Engineering, Volume 2: Brain‐Computer Interfaces. Elsevier, November 2024. (Q2)

Conference:

• N. Chandra, S. Kumar, and R. Sharma, “Predictive controller for PWM-driven Variable Speed Drive”, 11th International Conference on Advanced Engineering and ICT-Convergence, Advanced Engineering and ICT-Convergence Proceedings (AEICP) ISSN: 2635-4586 | Date of Publishing: July 28, 2023. © ICT-Advanced Engineering Society
• M. Kaloumaira et al., “A Real-Time Fall Detection System Using Sensor Fusion,” in Proceedings of Congress on Control, Robotics, and Mechatronics, Singapore, November 2023, pp. 513-527: Springer Nature Singapore.
• H. Tagimae et al., “Machine Learning-based Feature Extraction Method for Sleep Stage Classification” The 18th edition of the IEEE International Symposium on Medical Measurements and Applications (MeMeA), 14th – 16th June, Korea, 2023.
• W. Sukaria et al., “Epileptic Seizure Detection Using Convolution Neural Networks,” in 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2022, pp. 1-5.
• S. Kumar, A. Sharma, K. Mamun, and T. Tsunoda, “A Deep Learning Approach for Motor Imagery EEG Signal Classification,” presented at the 3rd Asia-Pacific World Congress on Computer Science and Engineering, 4th – 6th December, Denarau Island, Fiji, 2016.
• S. Kumar, R. Sharma, A. Sharma, and T. Tsunoda, “Decimation Filter with Common Spatial Pattern and Fishers Discriminant Analysis for Motor Imagery Classification,” in International Joint Conference on Neural Networks (IJCNN), 24th – 29th July, Vancouver, Canada, 2016, pp. 2090-2095.
• S. Kumar, A. Sharma, K. Mamun, and T. Tsunoda, “Application of Cepstrum Analysis and Linear Predictive Coding for Motor Imaginary Task Classification,” in 2nd Asia-Pacific World Congress on Computer Science & Engineering, 2nd – 4th December, Shangri-La Fijian Resort, Fiji, 2015.
• S. Kumar, “Energy and Water Monitoring System for Smart Metering and Consumer Awareness,” in 2nd International Electronic Conference on Sensors and Applications, 15th – 30th November, 2015.
• S. Kumar, H. Jeong, J. Baek, and S. R. Lee, “Voltage Compensated Clock Synchronization in Wireless Sensor Networks,” in International Conference on Future Information & Communication Engineering, 26th – 28th June, Kowloon, Hong Kong, 2014.
• S. Kumar and S. R. Lee, Android Based Smart Home System with Control via Bluetooth and Internet Connectivity, in The 18th IEEE International Symposium on Consumer Electronics, 22nd – 25th June, Jeju, South Korea, 2014.
• S. Kumar and S. R. Lee, Localization with RSSI values for Wireless Sensor Networks: An Artificial Neural Network Approach, in International Electronic Conference on Sensors and Applications, 1st – 16th June, 2014.
• S. Kumar and S. R. Lee, Time Synchronization in Wireless Sensor Networks: Utilizing Supply Voltage for Drift Compensation, in IEEK Fall Conference, November, Seoul, 2013.
• S. Kumar, et al., Estimation of Packet Delay Components for Time Synchronization in Wireless Sensor Networks, in The 1st International Conference on Computer, Information and Application (CIA), 18th October, Pattaya, Thailand, 2013.
• S. Kumar, Y. Lee, and S. R. Lee, Time Synchronization in Wireless Sensor Networks: Estimating Packet Delay, in The 1st International Conference on Convergence and its Applications (ICCA), 12th July, Korea University, Seoul, South Korea, 2013.
• S. Kumar and R. Singh, Advanced Speed Control of an Automated Guided Vehicle, in International Multi-conference of Engineers and Computer Scientists (IMECS), 16th – 18th March, Kowloon, Hong Kong, 2011.

Teaching interest:

• Fundamentals of Electrical & Electronics Engineering
• Digital Electronics
• Digital Signal Processing
• Industrial Automation
• Machine Learning
• Artificial Intelligence
• Pattern Recognition
• Machine Learning
• Data Mining
• Research Methods

Research interest:

• Signal Processing
• Pattern Recognition
• Applications of Artificial Intelligence & Machine Learning Algorithms
• Data Mining
• Automation & Control