Dr Ross Davies
BSc First class Honours in Computer Games Development with the award for outstanding performance
PhD in Integrating Augmented Reality with Dynamic Real World Environments
Studied at the University of Glamorgan where I received a first class honours in computer games development with the British computer society award for outstanding performance. During this time I had programming experience at Fanshawe college in Ontario, Canada when I worked for them for the summer. Completed a PhD in Augmented Reality. Taught in the first International SPEC program ran by Uva Wise in 2018 and returned for the second in 2019.
- Andrew Johnson
- Gethin Dibben
- Jack Whitter-Jones
- Peter Donnelly
- Elliot Naylor
PhD Supervisions Previous:
- Oteng Tabona Big Data Forensics as a Service 2017
- IY1S461 Secure Computer Programming
- IY1S405 Cyber Tools and Processes
- IS1S403 Professional Computer Forensic Security
- IY2S503 Forensic Digital Evidence
- IY2S505 Team Evidential Practice
- IY2S551 Digital Forensics & Security
- IY2S600 Secure Web Programming
- IY2S601 Digital Forensics (E-Crime)
- SE0H01 Software Concepts
- SE0H02 Introduction to Event Driven Programming
- SE1S402 C++ Programming
- IY4S711 Secure Code and Vulnerability Development
- Johnson, C., Davies, R. S., 2019. Using forensic techniques to identify contract cheating: A case study, Vilnius, Lithuania, In Plagiarism across Europe and Beyond.
- Johnson, A., Davies, R., 2019, June. Speculative Execution Attack Methodologies (SEAM): An overview and component modelling of Spectre, Meltdown and Foreshadow attack methods. In 2019 7th International Symposium on Digital Forensics and Security (ISDFS). IEEE.
- Davies, R. S., Ware, J. A. & Wilson, I. D., 2014. Stereoscopic disparity generation reduction using a dilated Laplacian approach. Sousse, Systems, Signals and Image Processing (IWSSIP), 2015 International Conference on, IEEE, pp. 101-104.
- Miknis, M., Davies, R. S., Plassmann, P. & Ware, J. A., 2014. Stereoscopic disparity generation reduction using a dilated Laplacian approach. Sousse, Systems, Signals and Image Processing (IWSSIP), 2015 International Conference on, IEEE, pp. 101-104.
- Davies, R. S., Ware, J. A. & Wilson, I. D., 2014. Stereoscopic Mobile Natural Feature Tracking. Sousse, IEEE, pp. 1-5.
- Davies, R. S., Wilson, I. D. & Ware, A., 2013. Robust Real-Time Stereoscopic Alignment. International Journal of Computer Applications, 81(19), pp. 7-15.
- Wilson, I. D., Davies, R. & Stanton, N., 2013. A Genetic Algorithm based Solution to the Teaching Assignment Problem. International Journal of Computer Applications, 81(19), pp. 1-6.
- Naylor, E., Miknis, M., Davies, R., 2019, Joint Roughness and Wrinkle Detection Using Gabor Filtering and Dynamic Line Tracking, International Journal of Computer Science and Security (IJCSS), 13(5), pp 211-220
- Johnson, C., Davies, R., 2020, Using Digital Forensic Techniques to Identify Contract Cheating: A Case Study, Journal of Academic Ethics, pp 1-9
Software Alliance Wales:
- Workshop on the Internet of Things (11th December 2014)
- Developing Mobile Apps for Android (1st April 2015)
- Learn Python Programming
- 27th-28th January 2015
- 14th-15th April 2015
- CS1H001 Introduction to Integrated Computing Devices (University module)
- Raspberry Pi with Python
- (3rd – 5th December 2014)
Guest workshops for Software Alliance Wales:
- Introduction to Computer Vision using OpenCV 2.4.9
- USW (22nd – 23rd July 2014)
- Pembroke (22nd – 23rd September 2014)
Guest lecture at France University
- Introduction to OpenCV using 2.4.6 (23rd – 24th June 2014)
Guest lecture at Brno University:
- Introduction to OpenCV using 2.4.5 (24th – 25th May 2013)
My primary focus is secure programming and educational based systems. I am the project lead for an in-house development that focuses on using technology to improve the assessment practices within the University and enhance student assessment literacy. My primary focus is software to enhance student experience.
Previously researching a project including Augmented Reality (AR) combined with Stereoscopic Natural Feature Tracking (SNFT). AR is a technology that allows virtual objects to be superimposed over the real-world environment. The goal of the research is to use the benefits of stereoscopic computer vision to provide depth perception abilities, similar to human perception through their two eyes. Depth perception abilities provide a robust and fast NFT. The current avenue of research involves combining the SNFT to make a collision mesh in real-time by compiling an estimate of the depth starting with the extracted key features. The system is capable of allowing interaction of virtual content within the real scene.
Areas of Expertise
- Forensic Investigations
- FTK based Analysis
- Linux based Analysis
- Forensic Professional Report Writing
- Augmented Reality (AR)
- Computer Vision (CV)
- Stereoscopic Vision
- Natural Feature Tracking (NFT)
- Programming (primarily C++, C# and Python)
- Raspberry Pi
- Secure Programming
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