Postgraduate Courses

This course begins with an overview of some fundamental information theory related to data compression. Lossless techniques, including Huffman/ arithmetic coding, and their applications; and lossy techniques, including quantization (both scalar and vector), transform coding, predictive coding and their applications will be discussed. Several international standards (such as JPEG for image coding, and H26x, MPEGx and its variants) will be discussed. Programming demos and exercises on various image and speech codes will be an integral part of this course.

This course introduces the essential fundamentals, including modeling, sensing, signal transmission and conversion, actuation, control, simulation, and implementation technologies used within the mechatronics design for robots and autonomous systems. It will give a holistic view of advanced automation technologies in industrial applications and provide the essential skills to design intelligent mechatronics systems. Through this course, students can enhance their understanding of the cross-disciplinary integration and systematic optimization of mechatronics systems involving the knowledge of mechanics, electronics, control engineering, and computer science.

This course aims to provide an introduction to the fundamental knowledge of physical systems modeled with discrete state space and event driven transitions. Discrete Event Systems (DES) arise in the modeling of many engineering domains, such as automated manufacturing systems, communication networks, software systems, process control systems, and transportation systems. This course will introduce a unified modeling framework and emphasize the analysis and control of DES. Basics of automata and language theory are presented first as mathematical preliminaries. Then comes a detailed treatment of state estimation, diagnosis, security and supervisory control theory of DES based on automata model. Topics of other DES models like Petri nets, timed and hybrid automata are also covered towards the end of the course.

This course introduces the advanced methodologies in the context of motion planning and control for robotics and autonomous systems. Various methodologies are introduced, including search-based methods, grid-based methods, sampling-based methods, optimization-based methods, learning-based methods, etc. In general, this course covers modern approaches, deep theory, and good practice envisions. In addition to the fundamental knowledge in motion planning and control, the students will also have the opportunity to discover and learn cutting-edge methodologies in the related field, aligning with the substantial developments in robotics, autonomous driving, UAVs, etc.

Light traveling in the 3D world interacts with the scene through intricate processes before being captured by a camera. These processes result in the dazzling effects like color and shading, complex surface and material appearance, different weathering, just to name a few. Physics based vision aims to invert the processes to recover the scene properties, such as shape, reflectance, light distribution, medium properties, etc., from the images by modelling and analyzing the imaging process to extract desired features or information. This course introduces the advanced methodologies in the context of physical-based vision for robotics and autonomous systems. We will introduce diverse techniques, covering from traditional methods based on hand-crafted features to recent deep learning methods. Apart from the fundamental knowledge in physical-based vision, the students will also have opportunities to discover and learn cutting-edge methodologies in popular physical-based vision topics (i.e., bad-weather restoration, shadow detection and removal, specular highlight detection and removal, intrinsic image decomposition, reflection detection and removal, and so on) of the physical-based vision, aligning with the substantial developments in robotics, autonomous driving, UAVs, etc.

The course will cover a wide range of analytical methods used in human factors research domain. The students will gain an understanding of the procedures, objectives and limitations of different research methods. The course will also include four case studies so that students would gain first-hand experience in applying the methods in real projects. These contents are required for research investigating users’ behaviors.

The course will cover a wide range of engineering psychology topics as well as how the research in these directions can affect policies and regulations in vehicle design and surface transportation. The students will gain an understanding of the characteristics and limitations of human beings from engineering psychology perspectives of view and how the design of traffic control devices, the roadway, the in-vehicle devices, regulations and traffic rules can be affected by the research in these directions.

Selected topics in robotics of current interest in emerging areas and not covered by existing courses. May be repeated for credit if different topics are covered.

Seminar topics presented by students, faculty and guest speakers. Students are expected to attend regularly and demonstrate proficiency in presentation in accordance with the program requirements. Graded P or F.

An independent study on selected topics carried out under the supervision of a faculty member.

Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.