Electrical Engineering

This course introduces key issues involved in the development of intelligent robotics. It explores issues on spatial transformation, kinematics, software control architectures, sensing, localization, and navigation. Robotics programming theory is backed by programming three types of robots: Pioneer ground vehicle, robotic arm, and a flying drone. Assessment: homework, exams, and a final project.

This course focuses on the design and analysis of algorithms and their applications, as well as the development of problem-solving techniques. The course covers: Algorithmic fundamentals: mathematical foundations, growth of functions, recurrences; Sorting and order statistics; Data structures: heap, binary search trees, RB trees, disjoint sets; Advanced design and analysis techniques: dynamic programming, greedy algorithms, amortized analysis; Graph algorithms: graph representations, searching, minimum spanning trees, shortest paths, network flow, matching (14 hrs); Computational complexity, NP-completeness, and approximation algorithms, and as time permits, general-purpose algorithms: simulated annealing and machine learning. 

This upper-level course introduces the basics of power electronics through a flipped-learning approach that involves pre-class, in-class, and post-class activities. To solidify the concepts covered in the course, a number of problem sets, online simulation problems, and exams will be assigned to assess students' understanding.  The primary goal of the course is for students to finish the semester with a clear understanding of how various power converters operate and how they relate to real applications.