EE225A Digital Signal Processing
Spring 2012
University of California at Berkeley
Department of Electrical Engineering
and Computer Sciences
Administrative Info
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Instructor: Professor Kannan
Ramchandran, 269 Cory Hall, kannanr@eecs.berkeley.edu
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Lectures: Tuesday and Thursday,
11:00 - 12:30 pm, 310 Soda
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Office
hours:
Tuesday, 1:00 pm - 2:00 pm, 258 Cory Hall
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GSI: None
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Point
of Contact:
Sameer Pawar, spawar@eecs.berkeley.edu
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Course
Administrative Assistant: Lea Barker, 253 Cory Hall, leab@eecs.berkeley.edu
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Course
website:
http://inst.eecs.berkeley.edu/~ee225a/sp12
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Course
Info
Description
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.
Prerequisite
EE 123, EE 126, and familiarity with linear algebra; or
equivalent; or consent of instructor.
Course
grading
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There will be bi-weekly homework assignments
(15% of course grade), Midterm Exam I (40%), and Midterm Exam II (45%).
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Homework’s will be assigned every alternate
Thursday and due every alternate Wednesday by 4 p.m. (to Lea Barker in 253 Cory
Hall).
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Midterm I will be on March 22, 2012 (6-8
pm).
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Midterm II will be on May 4, 2012 (6-8 pm).
Textbook
- Monson H. Hayes, Statistical Digital Signal
Processing and Modeling, Wiley, 1996. (ISBN 0471594318) [ Matlab files ] [ Errata ]
Course
Outline
- Overview and Background: linear-time invariant systems,
vector space concepts, sampling
- Multirate signal processing:
filter banks, wavelets, time-frequency analysis
- Transforms, KLT, quantization, sparse signal
representation
- Generalized sampling: finite-rate-of-innovation
sampling
- Spectral Analysis and array processing: MUSIC and
ESPRIT
- Linear estimation: MMSE estimation, Wiener filtering, orthogonality principle
- Adaptive filtering, linear prediction, LMS, convergence
analysis, RLS, Kalman filters
- Advanced topics: compressed sensing
Handouts
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.
Resources
The following books may be useful. Those marked Reserved
are on reserve at the Kresge Engineering Library.
General DSP:
- A. V. Oppenheim and R. W. Schafer with John R. Buck,
Discrete-Time Signal Processing, Second Edition, Prentice-Hall, 1999 Reserved
- J. Proakis and D. Manolakis, Digital Signal Processing: Principles,
Algorithms, and Applications, 4th edition, Prentice-Hall, 2007 Reserved
Spectral analysis:
- P. Stoica and R. Moses,
Introduction to Spectral Analysis, Prentice-Hall, 1997
- S. M. Kay, Modern Spectral Estimation, Theory and
Applications, Prentice-Hall, 1988
Adaptive filtering:
- S. Haykin, Adaptive Filter
Theory, 4th Ed., Prentice-Hall, 2002 Reserved
- P.M. Clarkson, Optimal and Adaptive Signal Processing,
CRC Press, 1993
- B. Widrow and S. D. Stearns,
Adaptive Signal Processing, Prentice-Hall, 1985
Multirate signal processing and
wavelets:
- M. Vetterli and J. Kovacevic, Wavelets and Subband
Coding, Prentice-Hall, 1995 Reserved [ Online
version ]
- G. Strang and T. Nguyen,
Wavelets and Filter Banks, Wellesley, 1997 Reserved
- S. Mallat, A
Wavelet Tour of Signal Processing, Academic Press, 1998.
Quantization and
coding
- N. Jayant and P. Noll,
Digital Coding of Waveforms, Prentice-Hall, 1984
- A. Gersho and R. M. Gray,
Vector Quantization and Signal Compression, Kluwer Academic Publishers, 1992.