| Lecture | Date | What we talk about | Assigned Reading |
|---|---|---|---|
| 1 | Jan 16 | Introduction | Hayes, ch. 2.2 |
| 2 | Jan 18 |
BASICS and review
Basics of DSP and linear algebra |
Hayes, ch. 2 |
| 3 | Jan 23 | Linear Algebra: least-squares solutions, SVD etc. | Hayes, ch. 2 |
| 4 | Jan 25 | SIGNAL MODELING Determinisitic signal models |
Hayes, ch. 4 |
| Jan 31 |
no class
|
||
| 5 | Feb 2 | Stochastic signal models (WSS, LTI systems); Random Processes, Spectral Factorization | Hayes, ch. 3 |
| 6 | Feb 7 | Random Processes, Spectral Factorization | Hayes, ch. 3 |
| 7 | Feb 9, 1-3:30 | Power Spectrum Estimation | Hayes, ch. 8 (particularly 8.2.1, 8.2.2, 8.2.4, 8.4, 8.5.1) |
| 8 | Feb 14 | SIGNAL REPRESENTATION AND APPROXIMATION Fourier Analysis in L1 and L2 - Hilbert space framework |
Vetterli/Kovacevic, ch. 2; Bremaud, Section A and C |
| 9 | Feb 16 | EECS Department Colloquium Prof. Y. Bresler | |
| 10 | Feb 21 | Hilbert Space Framework, Time-Frequency, Uncertainty Principle, Wavelet Transform | O. Rioul and M. Vetterli, Wavelets and Signal Processing, IEEE Signal Prcessing Magazine, vol. 8, no. 4, Oct. 1991, pp. 14-38. |
| Feb 23 |
no class
|
||
| 11 | Feb 28 | Wavelets (Fourier Techniques) | Vetterli/Kovacevic, ch. 4 |
| 12 | March 2 |
Wavelets (Mallat algorithm)
|
|
| 13 | March 7 |
Midterm Exam 12:30-2 in 380 Soda Hall
Rules: 5 double-sided pages of handwritten and not photocopied notes; class textbook; scribe notes. No other books; no homework solutions. |
Coverage: Hayes ch. 2-4 (skip 4.5) and 8 (particularly 8.2.1, 8.2.2, 8.2.4, 8.4, 8.5.1), Fourier, Wavelets (in all cases to the extent covered in class and HW) |
| 14 | March 9, 1-3:30 | Sampling, Models of Sparsity | M. Unser, Sampling - 50 Years After Shannon, Proceedings of the IEEE, vol. 88, no. 4, April 2000, pp. 569-587. |
| 15 | March 14 | Approximation Theory Quantization Theory/Rate-distortion theory |
R. M. Gray and D. L. Neuhoff, Quantization, IEEE Transactions on Information Theory, vol. 44, no. 6, October 1998, pp. 2325-2383. Read Section I, Section II until the end of p.2328, Section III. Optional Reading: T. M. Cover and J. A. Thomas, Elements of Information Theory. Wiley-Interscience, 1991. Chapter 13. |
| 16 | March 16, 1-3:30 | SIGNALS, SYSTEMS, NOISE FIR Wiener Filter, IIR Wiener Filter - [project proposals due] |
Hayes, ch. 7.1-7.3 |
| 17 | March 21 | causal IIR Wiener Filter; Kalman Filter | Hayes, ch. 7.3-7.4 |
|
March 23
|
no class | ||
| Spring Break | |||
| 18 | April 4 | Wiener, Kalman Filter; Innovations approach | Hayes, ch. 7.3-7.4 |
| 19 | April 6 |
Kalman Filter; Innovations approach
| Hayes, ch. 7.3-7.4 |
| 20 |
April 11
|
Adaptive Filters (LMS) - [takehome midterm due]
|
Hayes, ch. 9.1, 9.2 |
| 21 | April 13 | Adaptive Filters (LMS variations, convergence) | Hayes, ch. 9.2, 9.3 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. |
| 22 | April 18 | Adaptive Filters (LMS, RLS) | Hayes, ch. 9.3, 9.4 |
| 23 | April 20 | Adaptive Filters (RLS); MMSE estimation |
Hayes, ch. 9.3, 9.4 |
| 24 | April 24 | Signal Detection (Bayesian Hypothesis testing) | Optional Reading: T. M. Cover and J. A. Thomas, Elements of Information Theory. Wiley-Interscience, 1991. Chapter 12 |
| 25 | April 25 | Signal Detection (Minimax and Neyman-Pearson) | (same) |
| April 27 |
no class (EECS Faculty Retreat)
|
||
| 26 | May 2 | Signal Estimation (Parameter estimation, Cramer-Rao lower bound) | (same) |
| 27 | May 4 | System Identification, and wrap-up |