Keynote Speaker I
Prof. Ian Robert McAndrew
Doctoral Programs at Capitol Technology University, Maryland, USA
Title: Developments and Challenges for Unmanned Aerial Vehicles to Fly beyond the Visual Range of Sight for Commercial applications
Automation is being used more nowadays in the Unmanned Aerial Systems sector. This paper focuses upon the aspects of Cyber security and how the integrity of data automaton is critical to mission success and safety. The existing technology is addressed and how systems are need to make the reliability higher and offer safety levels equal to the commercial aviation sector. Conclusions argue where technology is lacking and protocols to address these with limitations for use and application.
Ph.D. in Mechanical Engineering ; M.Sc. in Manufacturing MA in Education; Pg.D. in Education Training; B.A. (Hons) in Mechanical Engineering; B.A. in Production Engineering A Fellow of the Royal Aeronautical Society and a Member of the Institute of Electrical Engineers. Dr McAndrew spent 12 years in industry as a designer before entering academia. He has over 25 years of teaching experience in the UK, Europe, Middle East and Far East. He has supervised many PhD students and published extensively for over 20 years. He is the author of several books and Editor of several international Journals. Currently he is a tenured full professor in the College of Aeronautics Worldwide at Embry Riddle Aeronautical University. His research interests are in Aerodynamics and Effective Education, which he has published 60 peer reviewed journals and conferences. He has presented at many Conferences and believes these are critical research meetings for those that are new to research and the experienced to mentor the next generation.
Keynote Speaker II
Prof. Ruili Wang
Massey University, New Zealand
Title: Deep learning and Polyphonic sound event detection
The aim of automatic sound event detection is to recognize sound events which present in a continuous acoustic signal. Polyphonic sound detection tackles the situations where multiple sound events happen simultaneously. Recently, polyphonic sound event detection has been utilized in a variety of applications, including scene recognition for mobile robots, monitoring in healthcare, surveillance in living environments, and video captioning. Polyphonic sound event detection can also be used in detecting the songs and calls of birds or whales. In our research, two challenges in polyphonic sound detection are identified and explored. After that, two approaches proposed are to address the challenges. The overall aim of this research is to develop effective and efficient approaches for polyphonic sound event detection.
Ruili Wang is a Professor of Artificial Intelligence at Massey University, Auckland, New Zealand. He received the PhD degree from Dublin City University, Dublin, Ireland, in Computer Science. His current research areas include language and speech processing, machine learning and data mining, computer vison and image processing. He is an Associate Editor (or an editorial board member) for the journals of IEEE Transactions on Emerging Topics in Computational Intelligence, Knowledge and Intelligent Systems (Springer), Applied Soft Computing (Elsevier), and Neurocomputing (Elsevier). He has published more than 120 papers, of which 88 are in peer-reviewed journals. He has supervised 18 PhD and 8 Master’s students to completion. He was awarded a Marsden grant in 2013 in machine learning and its application to speech processing, and the Seed Projects from the National Science Challenges of New Zealand.