Skip to content
Pedro Miguel Baptista Machado

Pedro Machado

Lecturer

Computer Science

Role

Pedro Machado received his M.Sc. in Electrical and Computers Engineering from the University of Coimbra (2012) and his Ph.D. in Computer Sciences from the Nottingham Trent University (2021).
Pedro’s expertise includes Neuromorphic Engineering, Edge Computer Vision, Bio-inspired Computing, robotics and Intelligent Sensors. 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.

Career overview

Research areas

Pedro is a member of the Computational Intelligent and Applications group.

Research interests:

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: OS SNN, Linux, OpenCV, OpenCL, ROS, PYNQ, Vitis AI, Computational Neurosciences, Edge Computing, Cognitive Robotics, Robotic Grasping, Visual Perception, Tensorflow, Keras, Python, C/C++, distributed computing.

Active Research Projects:

Underwater Plastic Detection (PI) [January ~ July 2022]

Plastics thrown away by humans are normally transported to the oceans by rivers. It is not clear how much plastic is transported every year from land to seas because of lack of metrics and standard monitorisation techniques. One of the approaches is on the monitorisation of plastics floating on the water surface. Only a small part of the plastic waste in rivers and oceans floats on the surface, the rest sinks to deeper waters or to rivers/oceans floor, threatening the local flora and fauna. So far, there is no way of detecting plastic at the bottom of rivers/oceans on a large scale. Traditional monitoring methods, in which divers manually collect image data along lines or taut cords (so-called transects) only allow for assertions about very limited areas. In addition, these methods are highly time-consuming, expensive and with very limited results. Generally, such methods neither provide georeferenced data that can be used to find locations again, for example to recover plastic or check its condition. A Xilinx Versal ACAP will be used to accelerate the AI inference algorithm at the edge.

In this research project, we propose a non-evasive approach where an underwater drone is used to collect visual data that will be georeferenced to assess how macro-plastics are transported underwater, how much of that plastic is deposited in the bottom of the Trent River and how those plastics affect the ecosystem. The multidisciplinary team involved in this project will analyse data from different perspectives and use the findings to prepare and submit a research proposal to BBSRC/UKRI to further explore and create understanding how much plastic is carried underwater, how much of the plastic deposits on the Trent’s River soil and how it affects the ecosystem. This research project aims to develop a proof-of-concept validator.

Teaching:

Pedro is the course leader of the new MSc AI starting in September 2022 and he Independent End Point Assessor for the L7 Degree Apprenticeship at the NTU.

Pedro is currently the Module Leader of:

  • COMP20091: Systems Software.
  • SOFT40051 - Advanced Software Engineering.
  • SOFT27002: Software Engineering (DTS).
  • SOFT37001: Advanced Analysis and Design (DTS).

Pedro, I'm currently teaching as Lab Tutor/Supervisor:

  • COMP40321: Research Methods | COMP40311: Major Project
  • COMP30151: Full Year project
  • SOFT27001: Software Design and Implementation (DTS).

Pedro's teaching experience includes:

  • ISYS30221: Artificial Intelligence (2020 - 2021)
  • SOFT20091: Software Design & Imp (2015 - 2021)
  • 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)

Publications

Journals:

P. Machado, A. Oikonomou, J. F. Ferreira and T. M. Mcginnity, "HSMD: An Object Motion Detection Algorithm Using a Hybrid Spiking Neural Network Architecture," in IEEE Access, vol. 9, pp. 125258-125268, 2021, doi: 10.1109/ACCESS.2021.3111005.

YU, Z., MACHADO, P., ZAHID, A., ABDULGHANI, A.M., DASHTIPOUR, K., HEIDARI, H., IMRAN, M.A. and ABBASI, Q.H., 2020. Energy and performance trade-off optimization in heterogeneous computing via reinforcement learning.Electronics, 9 (11): 1812. ISSN 2079-9292

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

Conferences:

Bottcher, W.; Machado, P.; Lama, N.; and McGinnity, T.M.; 2021. Object recognition for robotics from tactile time series data utilising different neural network architectures. In: Proceedings of 2021 International Joint Conference on Neural Networks (IJCNN 2021), Virtual Event, 18-22 July 2021.

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.

Other publications:

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 9783319262406

See all of Pedro Machado's publications...

Course(s) I teach on