Dr Zonghua Liu
| Biography | Dr Zonghua Liu is now a Lecturer in Electronic and Electrical Engineering in the School of Engineering at the Robert Gordon University. He is a member of IEEE and IET. He is also a committee member of the IEEE OES UKRI Chapter. He received his B.Eng. degree in Applied Physics, M.SC. degree in Laser Technology degrees, and Ph.D. degree in Engineering. He has much research experience in underwater sensors, underwater optical imaging and sensing, and machine-learning-based image processing. He has a good track record and is often involved in national/international collaborations (researcher in a UK-Japan project and an EU project, Co-I in two Japan projects and a Turing Ecosystem Leadership Award). He worked with a UK group and a Japanese group to develop a novel subsea camera which first combined optical holography and Raman spectroscopy into a single subsea device. His work has been highly respected proven by invitations as a speaker at international conferences/workshops. He has published in high international reputation journals and conference proceedings, such as IEEE Journal of Oceanic Engineering, Optical Express, Journal of the Optical Society of America, and OES/MTS OCEANS Conferences. He is an invited Reviewer for MTS/IEEE Oceans. He also often reviews manuscripts for IEEE Journal of Oceanic Engineering, Artificial Intelligence Review, Journal of the Optical Society of America A, Applied Optics, The Visual Computer, and IEEE International Conference on Robotics and Automation. |
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| Research Interests | His current research interests include but are not limited to robotics (e.g., underwater and medical ), underwater/medical sensors, optical fibre sensors, computer vision, machine-learning-based signal and image processing, water/environment quality monitoring, real-time reporting system using AI and cloud computing. |
| PhD Supervision Availability | Yes |
| PhD Topics | Underwater Robotics, Medical Robotics, Water Environment/Quality Monitoring, Optical Fibre Sensing, Optical imaging, Computer Vision, Machine-learning-based Signal and Imaging Processing. |