Dr Phil Skelton

Dr Phil Skelton

Research Fellow

School of Computer Science and Information Technology

College of Engineering and Information Technology

Year Citation
2024 Pegoli, S. P., Skelton, P. S. M., & Brinkworth, R. S. A. (2024). Optimising electromechanical whisker design for contact localisation. Sensors and Actuators A: Physical, 376, 1-16.
DOI
2022 Skelton, P. S. M., Finn, A., & Brinkworth, R. S. A. (2022). Contrast independent biologically inspired translational optic flow estimation. Biological Cybernetics, 116(5/6), 635-660.
DOI Scopus1 WoS1 Europe PMC1
2022 Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022). A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance. Sensors, 22(6, article no. 2181), 1-30.
DOI WoS44
2022 Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022). An investigation of preprocessing filters and deep learning methods for vessel type classification with underwater acoustic data. IEEE Access, 10, 117582-117596.
DOI WoS22
2019 Skelton, P. S. M., Finn, A., & Brinkworth, R. S. A. (2019). Consistent estimation of rotational optical flow in real environments using a biologically-inspired vision algorithm on embedded hardware. Image and vision computing, 92(103814), 1-13.
DOI Scopus11 WoS10

Year Citation
2020 Brinkworth, R., Finn, A., Griffiths, D., & Skelton, P. S. M. (2020). 2020900764, A data processing method.

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