Fernando, Dr. B. Rasitha
Dr. B. Rasitha Fernando is passionate for education and research in diverse fields of Electrical and Computer Engineering. He also has sound knowledge in Embedded Systems, Circuit Analysis, VLSI Systems, Power Electronics and Signal Processing. He also specializes in Machine Learning, Deep Learning, and High-Performance Computing and participates in research development in the area.
Dr. Fernando is an active member of IEEE, AAAS and ACM, where he holds the Professional membership. He has acted as a Judge for IEEE Science Fair Awards several times to shape the next generation of scientists and engineers by providing critical feedback. He continued to develop and enhance his knowledge and skills by participating and presenting in numerous conferences.
Dr. Fernando has experiences implementing Machine Learning and Deep Learning algorithms onto hardware devices with MATLAB, Python, C/C++, etc. He enjoys assembling/repairing electrical components and household appliances in his spare time.
Fernando, B. Rasitha, Yangjie Qi, Chris Yakopcic, and Tarek M. Taha. "3D Memristor Crossbar Architecture for a Multicore Neuromorphic System." In 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1-8. IEEE, 2020.
Alam, Md Shahanur, B. Rasitha Fernando, Yassine Jaoudi, Chris Yakopcic, Raqibul Hasan, Tarek M. Taha, and Guru Subramanyam. "Memristor Based Autoencoder for Unsupervised Real-Time Network Intrusion and Anomaly Detection." In Proceedings of the International Conference on Neuromorphic Systems, pp. 1-8. 2019.
C. Yakopcic, B. R. Fernando and T. M. Taha, "Design Space Evaluation of a Memristor Crossbar Based Multilayer Perceptron for Image Processing," 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 2019, pp. 1-8.
B. R. Fernando, R. Hasan and M. Tarek Taha, "Low Power Memristor Crossbar Based Winner Takes All Circuit," 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, 2018, pp. 1-6.
Y. Qi, R. Hasan, R. Fernando and T. Taha, "Socrates-D: Multicore Architecture for On-Line Learning," 2017 IEEE International Conference on Rebooting Computing (ICRC), Washington, DC, 2017, pp. 1-8.
On-Chip Memristor Back-Propagation with detailed Op-Amp Circuit, 2019, Brother Joseph W. Stander Symposium, University of Dayton, OH.
On-chip memristor training with detailed op-amp circuit, 2018, Brother Joseph W. Stander Symposium, University of Dayton, OH.
Memristor-based Neural Learning for Adaptive Control Systems, 2017, Brother Joseph W. Stander Symposium, University of Dayton, OH.