Assistant Professor, Faculty of Science and Deputy Dean, Faculty of Science
Ong Wee Hong is an assistant professor in Computer Science in the Universiti Brunei Darussalam (UBD). He joined the UBD in 2007. Before joining the UBD, he taught in the Jefri Bolkiah College of Engineering from 1998 to 2007. His research interests are based around development of intelligent systems for personal robots and ambient intelligence. In particular, he is exploring the application of artificial intelligent techniques and info-communication technologies in developing intelligent systems. He is leading the Robotics and Intelligent Systems Laboratory (Robolab). Current projects include unsupervised human activities recognition, storage cloud-based IoT system, mobile robot navigation, human emotion perception for human-robot interaction and computer vision based automated classification of herbarium species.
Ong Wee Hong received the B.Eng. in Communication and Control Engineering from the University of Manchester, Institute of Science and Technology in 1997. He received the M.Sc. in Computing Science from the Imperial College London in 2004. In 2014, he received the Doctor of Engineering ( PhD ) in Electrical and Information Systems from the University of Tokyo, Japan.
Personal robots, cyber-physical systems, ambient intelligence
Human Activity Recognition (HAR) is an important component in assistive technologies, however, we have not seen wide adoption of HAR technologies in our homes. Two main hurdles to the wide adoption of HAR technologies in our homes are the expensive infrastructure requirement and the use of supervised learning in the HAR technologies. Many HAR researches have been carried out assuming an environment embedded with sensors. In addition, the majority of HAR technologies use supervised approaches, where there are labeled data to train the expert system. In reality, our natural living environment are not embedded with sensors. Labeled data are not available in our natural living environment. We are developing a framework for autonomous HAR suitable in our natural living environment, i.e. the sensor-less homes. The framework uses unsupervised learning approach to enable a robot, acting as a mobile sensor hub, to autonomously collect data and learn the different human activities without requiring manual (human) labeling of the data.
Smart devices in an IoT system, such as the smart home, either connect through their own proprietary server running their server side applications, or they connect through the user home network where a center device is running the necessary server side applications. The second approach is not flexible and not user friendly to setup. There is increasing number of systems that take the first approach of having their own cloud server. However, not all developers are capable of hosting their cloud server to cater for large volume of users. This restricts the development of large scale IoT systems to large companies. There is also the concern of being tied to a proprietary service. To address this issue and to allow amateur developers to build large scale IoT systems, we are developing a new form of connectivity for IoT systems by exploiting storage-cloud services widely used by general public such as DropBox and Google Drive.
Self-driving car technologies are growing and maturing. It will be part of the future transport system. We are initiating our venture into this domain inline with our interest in robot navigation. In this project, we will build a prototype self-driving car and use it to conduct research works in the various aspects of self-driving car.
1. Bacha Rehman, Ong Wee Hong, Abby Tan Chee Hong, Ngo Trung Dung, â€œFace detection and tracking using hybrid margin-based ROI techniquesâ€, The Visual Computer (2019) https://doi.org/10.1007/s00371-019-01649-y
2. Ong, W.-H., Palafox, L. & Koseki, T. Autonomous Learning and Recognition of Human Action based on An Incremental Approach of Clustering. IEEJ Trans. Electron. Inf. Syst. 135, 1136â€“1141 (2015)
3. Ong, W.-H., Palafox, L. & Koseki, T. An Incremental Approach of Clustering for Human Activity Discovery. IEEJ Trans. Electron. Inf. Syst. 134, 1724â€“1730 (2014)