Title: Discrete Time Linear Systems
Coordinator: Tai-Chang Chen, Senior Lecturer, Electrical and Computer Engineering
Goals: To provide students with the fundamental concepts of digital signal processing. To study discrete-time signal and system analysis using time-domain, Fourier and Z-transform techniques. To build proficiency in signal analysis with Python.
Learning Objectives: At the end of this course, students will be able to:
Textbook: Sanjit K Mitra, Signals and Systems 1st Ed., Oxford University Press, 2015.
Prerequisites by Topic:
Course Structure: The class meets 4 times a week for a 50 minute lecture and each student participates in one laboratory session that meets for 2 hours a week. There is weekly homework and several laboratory exercises that must be done in Python. There are midterms and final exam and possibly additional quizzes depending upon the instructor.
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. The students complete an average of 2 hours of computer work per week.
Laboratory Resources: (see Computer Resources)
ABET Student Outcome Coverage: This course addresses the following outcomes:
H = high relevance, M = medium relevance, L = low relevance to course.
(1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics. (H) The majority of the lectures and homework deal with the derivations and application of linear mathematics theory to solve difference equations, perform convolutions and transform signals. The homework involves solving signal processing problems identified by the assignments and exemplified by class discussion.
(2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors. (L) The labs include open-ended assignments in signal synthesis and in digital filter design, with constraints on performance and computation. Social constraints are posed in a music synthesis lab.
(5) 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 20% of their grade, depending on the instructor.
(6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions. (M) The labs include assignments where students experiment with different signals and systems to learn fundamental DSP concepts, including: (i) exploring different system configurations in the Z-domain to learn about the associated time-domain and frequency-domain behavior, and (ii) exploring different configurations of FFT parameters.
(7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. (H) Students use Python and associated data acquisition/display tools to solve homework problems on signal analysis and filter design.
Prepared By: Eve Riskin, Laura Vertatschitsch
Last Revised: 16 February 2019 by Tai-Chang Chen