EECS 152A Digital Signal Processing (2017-2018)

EECS 152A Digital Signal Processing

(Required for CSE. Selected Elective for CpE and EE.)
Catalog Data:

EECS 152A Digital Signal Processing (Credit Units: 3) Nature of sampled data, sampling theorem, difference equations, data holds, z-transform, w-transform, digital filters, Butterworth and Chebychev filters, quantization effects. Prerequisite: EECS50/CSE50. Computer Engineering, Electrical Engineering, and Computer Science and Engineering majors have first consideration for enrollment. Same as CSE 135A. (Design units: 2)

Required Textbook:
. Edition, , 1969, ISBN-13 978-0131988422.

Recommended Textbook:

John Proakis and Dimitris Manolakis, Digital Signal Processing, 4th Edition, Prentice Hall, 2007.

Hamid Jafarkhani
Relationship to Student Outcomes
No student outcomes specified.
Course Learning Outcomes. Students will:

1. Characterize sampled systems in time and frequency domain.

2. Apply z-transform, DTFT, DFT and DWT to analyze and design DSP systems.

3. Design basic FIR digital filters.

4. Design basic IIR digital filters (using the bilinear transformation).

5. Use DSP tools such as MATLAB to analyze discrete systems and design digital filters.

Prerequisites by Topic

Fourier transforms and linear system theory.

Lecture Topics:


Class Schedule:

Meets for 3 hours of lecture and 1 hour of discussion each week for 10 weeks.

Computer Usage:

Students are expected to have sufficient computing to aid with the exercises, although no specific requirements are imposed. MATLAB is strongly encouraged, although people might use C/C++ or Java if they cannot get access to MATLAB.

Laboratory Projects:


Professional Component

Contributes toward the Computer Engineering Topics Courses and Electrical Engineering Topics Courses and Major Design experience.

Design Content Description

This course is devoted to the application of digital analysis techniques to the design of digital processors. In particular, design of basic samples, IIR digital filters and FIR digital filters, including Butterworth and Chebychev filters and sampling filters. Design for quantization effects is also included. The homework problems emphasize the applications of these techniques to design.

Lectures: 0%
Laboratory Portion: 100%
Grading Criteria:
  • Midterm exam : 50%
  • Final exam: 50%
  • Total: 100%
Estimated ABET Category Content:

Mathematics and Basic Science: 0.0 credit units

Computing: 0.0 credit units

Engineering Topics: 3.0 credit units

Engineering Science: 1.0 credit units

Engineering Design: 2.0 credit units

February 22, 2017
Senate Approved:
April 29, 2013
Approved Effective:
2013 Fall Qtr