EE 225A Spring 2008

Schedule

 

Lecture Date What we talk about Assigned Reading
1 Jan 22 Introduction Hayes, ch. 2.2
2 Jan 24
BASICS and review
Basics of DSP and linear algebra
Hayes, ch. 2
Jan 29 no class
Jan 31 no class
3 Feb 5 Chapter 1: SIGNAL MODELING
Stochastic signal models
Hayes, ch.2, ch.3 (p.57-70); Singular value decomposition; see also Horn and Johnson, p.414ff.
4 Feb 6, 7:30-9 PM, 293 Cory Hall WSS models via autocorrelation and power spectrum Hayes, ch.3 (p.70-99)
5 Feb 7, 293 Cory Hall Power spectrum, spectral factorizationHayes, ch.3 (p.99-108)
6 Feb 12 Models from data; Power Spectrum Estimation Hayes, ch.3 (p.108-119), ch.4 (p.188-200), ch.8 (particularly 8.2.1, 8.2.2, 8.2.4, 8.4, 8.5.1)
7 Feb 13, 7:30-9 PM, 293 Cory Hall Power spectrum estimation
Chapter 2: SIGNAL REPRESENTATION AND APPROXIMATION
8 Feb 14 Hilbert space theory of signal processing: Fourier, Basics of Hilbert space, Hilbert space Projection Theorem See also: Vetterli/Kovacevic, ch. 2 and 4
9 Feb 19 Hilbert space theory of signal processing: Time-Frequency, Uncertainty Principle
10 Feb 21 Hilbert space theory of signal processing: Wavelets (Haar case and its filter bank) O. Rioul and M. Vetterli, Wavelets and Signal Processing, IEEE Signal Prcessing Magazine, vol. 8, no. 4, Oct. 1991, pp. 14-38.
11 Feb 26 Hilbert space theory of signal processing: Wavelets (Design and Algorithms)
12 Feb 28
Hilbert space theory of signal processing: Wavelets (Design and Algorithms); "Optimal Transforms" (Karhunen-Loeve Transform)
M. Unser, Sampling - 50 Years After Shannon, Proceedings of the IEEE, vol. 88, no. 4, April 2000, pp. 569-587.
13 March 4
Sparse signal models and randomized signal processing
R. Baraniuk, Compressive Sensing [Lecture Notes], IEEE Signal Processing Magazine, vol. 24, no. 4, July 2007, pp. 118-121.
14 March 6 Sparsity and "Compressed Sensing"
15 March 11 *** in-class midterm *** Coverage: Lectures 1-13 according to this schedule, with all assigned reading; Homework 1-5. Rules: You can bring any amount of handwritten notes as well as the class textbook and any handouts from the class. No other printed materials are allowed. Computation and communication devices are not permitted.
March 13 no class
16 March 18 Chapter 3: SIGNALS, SYSTEMS, NOISE
Wiener theory of signal processing: FIR and IIR Wiener filters
Hayes, ch. 7.1-7.2
17
March 20
Wiener theory of signal processing: FIR and IIR Wiener filters
Hayes, ch. 7.1-7.2
Spring Break
18 April 1 Wiener theory of signal processing: Wiener filter Hayes, ch. 7.2-7.3
April 2 *** project proposals due ***
19 April 3
Wiener theory of signal processing: Wiener filter
Hayes, ch. 7.3
20
April 8
Wiener theory of signal processing: Wiener filter, Innovations approach
Hayes, ch. 7.3
21 April 10 Wiener theory of signal processing: Kalman filter, Innovations approach Hayes, ch. 7.4
22 April 15 Adaptive Filters (LMS) Hayes, ch. 9.1-9.3
23 April 16 *** take-home midterm due ***
24 April 17 Adaptive Filters (LMS) Hayes, ch. 9.1-9.3
25 April 22 Adaptive Filters (LMS variations, convergence) O. Dabeer and E. Masry, Analysis of mean-square error and transient speed of the LMS adaptive algorithm, IEEE Transactions on Information Theory, vol. 48, no. 7, July 2002
26 April 24 Adaptive Filters (RLS and beyond) Hayes 9.4
April 25,28,30 Project Review Meetings
27 April 29 Elements of General Estimation Theory (CRLB, EM algorithm) Optional Reading: T. M. Cover and J. A. Thomas, Elements of Information Theory. Wiley-Interscience, 1991. Chapter 12
May 1
no class (EECS Faculty Retreat)
28 May 6 Elements of Estimation Theory (Application to Spectrum Estimation) Hayes, ch. 8
29 May 8 DSP in 2008: The Big Picture
30 May 13, 9-12 Project Presentations, Part I (followed by pizza)
May 13, 12:30-3:30 Project Presentations, Part II
31 Friday, May 16, at NOON Project Report Due (firm deadline due to University Regulations!)