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Paul Kirkland

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 https://orcid.org/0000-0001-5905-6816

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20182019
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Personal profile

Personal Statement

Researcher and PhD Student with the Neuromorphic Sensor Signal Processing Lab within the Centre for Signal and Image Processing.

Our lab aims to help develop the fields of Deep Learning and Neuromorphic Engineering with novel algorithms implementations and applications. We cover all types of Deep Learning from supervised learning for object detection, Unsupervised and Self Learning, Reinforcement Learning as well as the new applications Neuromorphic Technology brings with it's Spiking Neural Networks.

 

 

Education/Academic qualification

Master of Engineering, University Of Strathclyde

Keywords

  • Neural Networks
  • Neuromorphic
  • Deep Learning
  • Convolutional Neural Networks
  • Machine Learning
  • Artificial Intelligence

Fingerprint Dive into the research topics where Paul Kirkland is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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Neural networks Engineering & Materials Science
Unmanned aerial vehicles (UAV) Engineering & Materials Science
Plasticity Engineering & Materials Science
Orbits Engineering & Materials Science
Radar Engineering & Materials Science
Satellites Engineering & Materials Science
Space debris Engineering & Materials Science
Sensors Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2018 2019

1 Citation (Scopus)

CubeSat-based passive bistatic radar for space situational awareness: a feasibility study

Persico, A. R., Kirkland, P., Clemente, C., Soraghan, J. J. & Vasile, M., 28 Feb 2019, In : IEEE Transactions on Aerospace and Electronic Systems. 55, 1, p. 476-485 10 p., 8387455.

Research output: Contribution to journal › Article

Open Access
File
Orbits
Radar
Satellites
Space debris
Radar systems

UAV detection: a STDP trained deep convolutional spiking neural network retina-neuromorphic approach

Kirkland, P., Di Caterina, G., Soraghan, J., Andreopoulos, Y. & Matich, G., 18 Sep 2019, p. 724-736. 13 p.

Research output: Contribution to conference › Paper

Open Access
File
Unmanned aerial vehicles (UAV)
Plasticity
Neural networks
Sensors
Lighting