Electrical Engineering

This course develops the ability to understand various physical properties of electronic materials (e.g., semiconductors). Topics include elementary materials science concepts, kinetic molecular theory, thermal process, defects, classical theory of conduction, hall effect and thermal conduction, quantum physics, Schrodinger equation, microscopic physics, modern theory of solid, electronic statistics, waves as a particle, semiconductor fundamentals, conductivity, diffusion and optical properties, and devices.

This course uses the knowledge and skills achieved from earlier courses and independently seek and find solutions for complex mechatronic assignments. The course has two parts. The first one is the development of a concept based on a set of technical and functional specifications from a real problem in industry. The concept can be implemented into a working industrial prototype in another optional course (EIEN70 Mechatronics, industrial product design). This task is solved in groups composed by at least one E-student and at least one M-student to form a mechatronic team with various competences. The other part of the course is an individual assignment to construct a complete mechatronic system aimed at controlling a DC motor from a PC interface, including computer communication, electronic design also on the circuit board, microprocessor programming, sensor technology, and automatic control strategies. The system is built in a lab open 24/7, where cooperation is encouraged but still the final examination is individual. Assumed prior knowledge: Approved basic courses in programming, automatic control and electronics/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.