| Lecture | Date | What we talk about | Assigned Reading |
|---|---|---|---|
| 1 | Jan 17 | Introduction | Hayes, ch. 2.2 |
| 2 | Jan 19 |
SIGNAL MODELING Determinisitic signal models |
Hayes, ch. 2.3, 4.1, 4.2 |
| 3 | Jan 24 | Least-squares approximations | Hayes, ch. 4.1-4.6 |
| 4 | Jan 26 |
Stochastic signal models WSS, LTI systems |
Hayes, ch. 3 |
| 5 | Jan 31 | Random Processes, Spectral Factorization | Hayes, ch. 3 |
| 6 | Feb 2 | Power Spectrum Estimation | Hayes, ch. 8 |
| 7 | Feb 3, 11-12:30, 299 Cory | Power Spectrum Estimation | Hayes, ch. 8 |
| Feb 7 | no class | ||
| 8 | Feb 9 |
SIGNAL REPRESENTATION AND APPROXIMATION Fourier Analysis in L1 and L2 |
Vetterli/Kovacevic, ch. 2; Bremaud, Section A and C |
| 9 | Feb 10, 11-12:30, 400 Cory | Fourier Analysis in L2 - Hilbert space framework | Vetterli/Kovacevic, ch. 2; Bremaud, Section A and C |
| 10 | Feb 14 | Uncertainty Principle/Wavelets | Vetterli/Kovacevic, ch. 4 |
| 11 | Feb 16 | Wavelets/Sampling | M. Unser, Sampling - 50 Years After Shannon, Proceedings of the IEEE, vol. 88, no. 4, April 2000, pp. 569-587. |
| Feb 21 | no class | ||
| Feb 23 | no class | ||
| 12 | Feb 28 | Sampling/Quantization 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. |
| 13 | March 2 | Quantization Theory/Rate-distortion theory | Optional Reading: T. M. Cover and J. A. Thomas, Elements of Information Theory. Wiley-Interscience, 1991. Chapter 13. |
| 14 | March 3, 11-12:30, 299 Cory | Rate-distortion theory | |
| 15 | March 7 |
SIGNALS, SYSTEMS, NOISE FIR Wiener Filter |
Hayes, ch. 7.1-7.2 |
| 16 | March 9 | FIR/IIR Wiener Filter | Hayes, ch. 7.2-7.3 |
| 17 | March 10, 11-12:30, 299 Cory | IIR Wiener Filter | Hayes, ch. 7.3 |
| March 14 | no class | ||
| March 16 | no class | ||
| 18 | March 21 | MIDTERM EXAM | Hayes, ch. 1-4, 8; HW 1-3 (except HW3, Problem 4) |
| 19 | March 23 | causal IIR Wiener Filter; Kalman Filter | Hayes, ch. 7.3-7.4 |
| Spring Break | |||
| 20 | April 4 | Wiener, Kalman Filter; Innovations approach | Hayes, ch. 7.4 |
| 21 | April 6 | Adaptive Filters (LMS) | Hayes, ch. 9.1, 9.2 |
| 22 | April 11 | no class Instead, come to MSRI! | |
| 23 | April 13 | Adaptive Filters (LMS) | Hayes, ch. 9.1, 9.2 |
| 24 | April 18 | 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. |
| 25 | April 20 | Adaptive Filters (RLS) | Hayes, ch. 9.3, 9.4 |
| 26 | April 25 | MMSE estimation; Signal Detection (Bayesian Hypothesis testing) |
Optional Reading: T. M. Cover and J. A. Thomas, Elements of Information Theory. Wiley-Interscience, 1991. Chapter 12 |
| 27 | April 27 | Signal Detection (Minimax and Neyman-Pearson) | " |
| 28 | May 2 | Signal Detection (Parameter estimation, Cramer-Rao lower bound) | " |
| 29 | May 4 | System Identification | |
| 30 | May 9 | Signal processing: The Big Picture. |