Pedro Machado received his MSc in Electrical and Computers Engineering from the University of Coimbra (2012) and his Ph.D. in Computer Science from Nottingham Trent University (2022).
Dr Machado is a Senior Lecturer in Computer Science and Course Leader for MSc Artificial Intelligence, first secretary for the IEEE Systemic Innovation Special Interest Group (SISIG) and the Events Coordinator for the BCS Nottingham and Derby branch.
Dr Machado’s research interests includes neuromorphic engineering, edge computer vision, bio-inspired computing, robotics and intelligent sensors, retinal cell understanding, biological nervous system modelling, spiking neural networks, robotics and autonomous systems, and neuromorphic hardware, aquaculture, endangered/invasive underwater species.
- Research Associate with the University of Ulster (August 2013 - September 2014)
- Research Associate with the University of Coimbra, Portugal (April 2012 - July 2013 and September 2009 - December 2009)
- Research Associate with the Instituto Pedro Nunes, Portugal (January 2010 - March 2012).
Pedro is a member of the Computational Intelligent and Applications group.
Neuromorphic engineering, edge computer vision, bio-inspired computing, robotics and intelligent sensors, retinal cell understanding, biological nervous system modelling, spiking neural networks, robotics and autonomous systems, and neuromorphic hardware, aquaculture, endangered/invasive underwater species.
Mr Dennis Monari (Director of Studies) - 2023 ~ Now
Mr Feliciano Domingues (Director of Studies) - 2023 ~ Now
Mr Michael Gibbs (Supervisor) - 2022 ~ Now
Active Research Projects:
SmartBerry (CoI) [September 2023 - August 2025]
Internet of Water (IoW) (PI) [August 2022 - July 2023]
Underwater Plastic Detection (PI) [January 2022 ~ 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 monitoring techniques. One of the approaches is on the monitoring 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.
Pedro is the course leader of the MSc AI course and the 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.
- COMP40731: Artificial Cognitive Systems.
Pedro is currently teaching as Lab Tutor/Supervisor:
- COMP30271: Cognitive Computing.
- SOFT27002: Software Engineering (DTS).
- COMP40321: Research Methods | COMP40311: Major Project
- COMP30151: Full Year project
- SOFT27001: Software Design and Implementation (DTS).
Pedro's teaching experience includes:
- SOFT37001: Advanced Analysis and Design (DTS) (2021-2023).
- 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)
MURRAY-HILL, N., FONTES, L., MACHADO, P. and IHIANLE, I.K., 2023. Secure video streaming using dedicated hardware. Journal of Signal Processing Systems. ISSN 1939-8018
MACHADO, P., FERREIRA, J.F., OIKONOMOU, A. and MCGINNITY, T.M., 2023. NeuroHSMD: neuromorphic hybrid spiking motion detector. ACM Transactions on Reconfigurable Technology and Systems. ISSN 1936-7406
MAGALHÃES, S.C., SANTOS, F.N., MACHADO, P., MOREIRA, A.P. and DIAS, J., 2023. Benchmarking edge computing devices for grape bunches and trunks detection using accelerated object detection single shot multibox deep learning models. Engineering Applications of Artificial Intelligence, 117 (Part A): 105604. ISSN 0952-1976
MACHADO, P., OIKONOMOU, A., FERREIRA, J.F. and MCGINNITY, T.M., 2021. HSMD: an object motion detection algorithm using a Hybrid Spiking Neural Network Architecture. IEEE Access. ISSN 2169-3536
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., EPELDE, G. and MCGINNITY, T.M., 2018. Emulation of chemical stimulus triggered head movement in the C. elegans nematode. Neurocomputing. ISSN 0925-2312
Course(s) I teach on