Pedro Machado received his M.Sc. in Electrical and Computers Engineering from the University of Coimbra (2012) and is currently doing a part-time Ph.D. in Computer Sciences at the Nottingham Trent University.
Pedro’s expertise includes FPGA design, computer vision, bio-inspired computing, robotics and computational intelligence. His research interests in computer science are in retinal cell understanding, biological nervous system modelling, spiking neural networks, robotics and autonomous systems, and neuromorphic hardware. Pedro holds a valid Xilinx Alliance Program certificate after having completed, successfully, the Xilinx training for using Xilinx tools to accelerate state-of-the-art AI algorithms in the Xilinx heterogeneous MPSoC/ACAP platforms.
Pedro is a member of the CNCR group.
Modelling of Computational Models of the Retina, Neuromorphic Hardware, ADAS and AGVs, grasping, in-hand manipulation, hand-over algorithms. Robotic tactile and visual perception for completing challenging manipulation tasks delicate with applications in industry and healthcare.
Keywords: SNN, Linux, OpenCV, ROS, Computational Neurosciences, Edge Computing, Cognitive Robotics, BioTac fingertips, WTS-FT fingertips, device drivers, Python, distributed computing.
Pedro is currently the Module Leader of:
- SOFT20091: Software Design & Imp 2.
- SOFT27002: Software Engineering.
- SOFT37001: Advanced Analysis and Design
Pedro, I currently teaching as Lab Tuotr/Supervisor:
- COMP40321: Research Methods | COMP40311: Major Project
- COMP30151: Full Year project
- ISYS30221: Artificial Intelligence
Pedro's teaching experience includes:
- SOFT20091: Software Design & Imp 2 (2015 - Now)
- COMP40321: Research Methods | COMP40311: Major Project (2015 - Now)
- COMP10082: Foundations of Comp & Tech (2018)
- COMP30151: Full Year project (2015 - Now)
- ITEC40091: Embedded Systems (2015 and 2016)
- SOFT20101: Info and Database Engineering (2015)
MACHADO, P.; OIKONOMOU, A.; MCGINNITY, T.M.; HSMD: A bio-inspired object motion detection algorithm using a hybrid Spiking Neural Network architecture; submitted to IEEE transactions on Neural Networks and Learning Systems, IEEE, March 2020.
MACHADO, P.; MCGINNITY, T.M.; “An Analysis of Adaptive Grasping Using High Quality Tactile Sensors”, submitted to Journal of Robotics and Autonomous Systems, Elsevier, Dec 2019.
Costalago-Meruelo, A.,MACHADO, P., Appiah, K., Mujika, A., Leskovsky, P., Alvarez, R., McGinnity, T. M. (2018). Emulation of chemical stimulus triggered head movement in the C. elegans nematode. Neurocomputing, 290, 60–73. https://doi.org/10.1016/j.neucom.2018.02.024
YAHAYA, S.W., LOTFI, A., MAHMUD, M.,MACHADO, P.and KUBOTA, N., 2019. Gesture recognition intermediary robot for abnormality detection in human activities. In: 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019), Xiamen, China, 6-9 December 2019.
BRANDENBURG, S.,MACHADO, P., SHINDE, P., FERREIRA, J.F. and MCGINNITY, T.M., 2019. Object classification for robotic platforms. In: ROBOT 2019: Fourth Iberian Robotics Conference, Porto, Portugal, 20-22 November 2019.
MACHADO, P.; OIKONOMOU, A.; COSMA, G.; MCGINNITY, T.M.; NatCSNN: a convolutional spiking neural network for recognition of objects extracted from natural images. In: ICANN 2019: 28th International Conference on Artificial Neural Networks, Munich, Germany, 17–19 September 2019.
MACHADO, P., OIKONOMOU, A., COSMA, G. and MCGINNITY, T.M., 2018. Bio-inspired ganglion cell models for detecting horizontal and vertical movements. In: 2018 International Joint Conference on Neural Networks (IJCNN 2018), Rio de Janeiro, Brazil, 8-13 July 2018.
APPIAH, K.,MACHADO, P., COSTALAGO MERUELO, A. and MCGINNITY, T.M., 2016. C. elegans behavioural response germane to hardware modelling. In:Proceedings of the 2016 IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, 24-29 July 2016.Piscataway, New Jersey: IEEE, pp. 4743-4750. ISBN 9781509006205
COSTALAGO MERUELO, A.,MACHADO, P., APPIAH, K. and MCGINNITY, T.M., 2016. Challenges in clustering C. elegans neurons using computational approaches. In:Proceedings of the 2016 IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, 24-29 July 2016.Piscataway, New Jersey: IEEE, pp. 4775-4781. ISBN 9781509006205
MACHADO, P., COSTALAGO MERUELO, A., PETRUSHIN, A., FERRARA, L., LAMA, N., ADAMA, D., APPIAH, K., BLAU, A. and MCGINNITY, T.M., 2016. Si elegans: evaluation of an innovative optical synaptic connectivity method for C. elegans phototaxis using FPGAs. In:Proceedings of the 2016 IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, 24-29 July 2016.Piscataway, New Jersey: IEEE, pp. 185-191. ISBN 9781509006205
COSTALAGO MERUELO, A.,MACHADO, P., APPIAH, K. and MCGINNITY, T.M., 2015. Si elegans: a computational model of C. elegans muscle response to light. In: 3rd International Congress on Neurotechnology, Electronics and Informatics, Lisbon, Portugal, 2015.
MACHADO, P., WADE, J., APPIAH, K. and MCGINNITY, T.M., 2015. Si elegans: hardware architecture and communications protocol. In: 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 12-17 July 2015.
SHINDE, P.,MACHADO, P., SANTOS, F.N. and MCGINNITY, T.M., 2018. Online object trajectory classification using FPGA-SoC devices. In:UKCI 2018: 18th Annual UK Workshop on Computational Intelligence, Nottingham Trent University, Nottingham, 5-7 September 2-18.Advances in Intelligent Systems and Computing . Springer.
MACHADO, P., WADE, J. and MCGINNITY, T.M., 2015. Si elegans: Modeling the C. elegans Nematode Nervous System Using High Performance FPGAS. In: A.R. LONDRAL and P. ENCARNAÇÃO, eds.,Advances in Neurotechnology, Electronics and Informatics.Biosystems & Biorobotics, 12 . Cham, Switzerland: Springer International Publishing, pp. 31-45. ISBN 9783319262406See all of Pedro Miguel Baptista Machado's publications...
Course(s) I teach on