TEACHING

Course description: In recent years we have been witnessing the remarkable advances of deep networks, computing power and ready access to data, which make Deep Reinforcement Learning one of the most powerful tools for dealing with intelligent decision-making and control of autonomous systems. From the design of automatic control functionalities for robotics and self-driving vehicles to the development of sophisticated game AI, reinforcement has been used to develop a variety of cutting-edge technologies of both practical and theoretical interest. This course will provide a solid introduction to the field of deep reinforcement learning including the core principles, approaches and algorithms, and their applications in robotics, game playing, intelligent transportation, etc. Through a combination of lectures, exercises and a final course project, students will learn the key ideas and techniques for deep RL and gain hands-on experience coding and testing reinforcement systems on a variety of robot control problems.

Course description: Cyber-physical systems (CPS) are physical or engineered systems that are built from and operate upon the seamless integration of sensing, network communication, computing and control. Autonomous vehicles, aircraft, and robots are prime examples, because they move physically in space in a way that is determined by discrete computerized control algorithms. As a new frontier for computer systems, CPS technologies are transforming the way people interact with engineered systems. Autonomous CPS are designed to operate with a high degree of autonomy. Smart and autonomous CPS drive the innovation in application domains including intelligent transportation, manufacturing automation, environmental monitoring and control, healthcare and medicine.

This course is designed to introduce the technical foundations and robotic applications of cyber-physical systems to upper-level undergraduate students or graduate students. This course will prepare the students for pursuing advanced topics in areas such as connected autonomous vehicles, Internet of Things, and smart manufacturing.

Course description: Hardware Description Language design of digital systems. Industrial CAD tools are used to produce a functional description of hardware that is both simulated and then synthesized into hardware. Methods to describe both combinational logic and synchronous devices are given. Devices such as CPLDs and FPGAs are targeted in this design process. 

Course description: Binary logic, digital logic gates, reduction of Boolean expressions, combinational logic design. MSI and LSI combinational logic ICs, flip-flops, synchronous and asynchronous sequential systems design, MSI and LSI sequential system ICs, and algorithmic state machines.