Course Contents "Statistical Modeling of Signals - Lecture":
1. Analysis of signals and systems
2. Deconvolution. Invertible systems. Cepstrum
3. Equations of state
4. Stochastic processes
5. Spectral factorization
6. Non-parametric methods in spectral estimation
7. Parametric methods in spectral estimation
8. Wiener filters. The principle of orthogonality
9. Wiener IIR filters. Wiener FIR filters
10. Gradient algorithms. The LMS algorithm
11. Properties of the LMS algorithm
12. Modifications and improvements of the LMS algorithm
13.The RLS algorithm
14. Properties of the RLS algorithm
Lab Contents Contents "Statistical Modeling of Signals - Lab":
1. Analysis of signals and systems
2. Types of systems
3. Equations of state
4. Stochastic signals
5. Spectral factorization of stochastic processes
6. Vector stochastic processes
7. Periodogram
8. Averaging the periodogram
9. Spectral density estimation with AR, MA and ARMA models
10. Gradient algorithms. The LMS algorithm
11. Algorithms derived from LMS
12. Structures and applications of LMS adaptive filters
13. The RLS algorithm
14. Structures and applications of RLS adaptive filters
