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Description: Credit 3U/4G Stochastic signal models, LMS identification, identification of signals from noise, Wiener filtering, blind separation of mixed signal, discrete Wavelet Transforms, compression and denoising, ceptral analysis. Prerequisite: ECE 317 and ECE 341 Recent Textbook: Graupe,
D., Time Series Analysis, Identification and Adaptive Filtering, 2nd Edition,
Kreger Publishing Co., Topics: 1. Introduction to statistical SP 2. Basic concepts in stochastic processes 3. The Yule-Walker Equation 4. AR, MA ARMA signal models & their inter-relations 5. Stability and invertibility, Stochastic convergence
6. Ergodicity 7. The linear Signal Model 8. LMS identification of signal models 9. Nonstationary models 10. Order determination of models 11. The discrete Wiener filter and signal model 12. Blind identification of signal in measurement 13. The Box-Jenkins controller as a filter 14. Time Frequency Methods: Wavelet Transform (WT) 15. Multi-Resolution analysis 16. Discrete WT 17. Inverse WT 18. Debuchies WT in Matrix form 19. Denoising and compression by WT 20. 2D WT 21. Cepstral analysis 22. Cepstral filtering of noise |