Study of the principles of signals and systems. The course combines lectures and simulation laboratory exercises to expose students to the theories and concepts of both continuous-time and discrete-time forms of signals and systems, as well as their applications in communication systems, control systems, and signal processing.

The course discusses sensing techniques and methods of data fusion for robotics applications. Topics include active and passive sensors, data filtering, deterministic and probabilistic data fusion methods.

This is a graduate-level course on linear dynamical systems with an emphasis on state-space modeling in both discrete and continuous time. Topics covered include equilibrium points and linearization, natural and forced responses, canonical forms and transformations, controllability and observability, and control-theoretic concepts such as pole placement, stabilization, dynamic compensation, and the separation principle. The material in this course is fundamental knowledge for students pursuing research in systems/control theory, signal processing, or mechanical/electrical/industrial engineering.