EE225A Digital Signal Processing

Spring 2012

University of California at Berkeley
Department of Electrical Engineering and Computer Sciences

[Administrative Info | Course Info | Handouts | Homework | Resources ]

Administrative Info

Instructor: Professor Kannan Ramchandran, 269 Cory Hall,

Lectures: Tuesday and Thursday, 11:00 - 12:30 pm,  310 Soda

Office hours: Tuesday, 1:00 pm - 2:00 pm, 258 Cory Hall

GSI: None

Point of Contact: Sameer Pawar,

Course Administrative Assistant: Lea Barker, 253 Cory Hall,

Course website:

Course Info


With signal processing becoming ubiquitous in today's computer literate world, a large number of application areas are growing in importance, both in industry and in the research community, such as signal processing for distributed sensor networks, speech, image and video processing, medical image processing, wavelets and multiresolution signal processing, genomic and biomedical signal processing, financial data signal processing, etc. This course will cover some of the theoretical, algorithmic and practical foundations needed to address this litany of problems and applications in signal processing.


EE 123, EE 126, and familiarity with linear algebra; or equivalent; or consent of instructor.

Course grading

·         There will be bi-weekly homework assignments (15% of course grade), Midterm Exam I (40%), and Midterm Exam II (45%).

·         Homework’s will be assigned every alternate Thursday and due every alternate Wednesday by 4 p.m. (to Lea Barker in 253 Cory Hall).

·         Midterm I will be on March 22, 2012 (6-8 pm). 

·         Midterm II will be on May 4, 2012 (6-8 pm).


Course Outline


Homework’s, solutions and general announcements will be posted on bSpace

The detailed homework grading and submission policy can be downloaded here. Please familiarize yourself with the homework submission policy and the department policy on academic dishonesty.


The following books may be useful. Those marked Reserved are on reserve at the Kresge Engineering Library.

General DSP:

Spectral analysis:

Adaptive filtering:

Multirate signal processing and wavelets:

Quantization and coding