Master Course Description

No: EE 235

Title: CONTINUOUS TIME LINEAR SYSTEMS

Credits: 5

UW Course Catalog Description

Coordinator: Mari Ostendorf, Professor, Electrical Engineering

Goals: To study analysis of signals and linear systems in the time and frequency domains. To begin learning and using Python for signal analysis and linear system implementation.

Learning Objectives:

At the end of this course, students will be able to:

Textbook: S. K. Mitra, Signals and Systems, Oxford University Press, 2015.

Reference Texts:
A. Oppenheim, A. Willsky and S. H. Nawab, Signals and Systems,Prentice Hall, 1996.
C. Phillips, J. Parr and E. Riskin, Signals, Systems and Transforms, Prentice Hall, 2003.

Prerequisites by Topic: Calculus, complex numbers, computer programming

Topics:

  1. Introduction, continuous-time signals and systems and their properties (2 weeks)
  2. Linear time-invariant systems analysis in the time domain: convolution (2 weeks)
  3. Fourier frequency domain representations (series and transforms) (3 weeks)
  4. Laplace transforms (emphasis on bilateral case), region of convergence, inverse transforms via partial fractions (1.5 weeks)
  5. Applications: Filtering, modulation and sampling (1.5 weeks)

Course Structure: The class meets for four lectures a week (MTWF) and also has a weekly 2-hour computer lab section with a Teaching Assistant. There are weekly homework assignments. Most instructors also offer an optional 1-hour problem solving discussion session weekly to provide opportunity for students to work through additional examples.

Computer Resources: The course uses Python for the laboratory exercises and also for checking homework problems. Students are expected to use their personal laptops in the labs, but they may use remote connections to EE Department computers as needed. Outside of the two-hour lab section, students spend an additional hour per week on average to complete the labs, including prelab assignments and written lab reports.

Laboratory Resources: (see Computer Resources)

Outcome Coverage:

(1) Problems: An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics. (H) The course introduces fundamental mathematical principles used for analysis of continuous-time signals and systems. Students routinely solve problems in systems analysis using mathematical tools of convolution and transforms. They are introduced to computer analysis methods via Python-based computer lab assignments.

(3) Communication: An ability to communicate effectively with a range of audiences. (M) Students are expected to provide clear, concise answers to questions in exams that include only information relevant to the question. In addition, they answer questions about lab assignments orally during laboratory sections and provide written lab reports in electronic notebook format. Some instructors include a brief writing assignment where students are asked to pick an example of modern technology and explain how some aspect of signal processing plays a role in this technology.

(5) Teams: An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives. (M) The computer labs are conducted in teams. Labs constitute about 10-20% of their grade (depending on the instructor).

(7) Learning: An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. (M) Students are expected to use online documentation to learn the Python programming language for use in lab exercises, building on their knowledge of programming in other languages.

Prepared By: Eve A. Riskin

Last revised: 20 August 2018 by Mari Ostendorf and Linda Bushnell