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  • 6:44 AM, Tuesday, 18 Feb 2020

Course Undergraduate
Semester Electives
Subject Code AV464
Subject Title Advanced DSP and Adaptive Filter


Discrete Random Process: Expectation, Variance and Co‐variance, Uniform, Gaussian and Exponentially distributed noise, Hillbert space and inner product for discrete signals, Energy of discrete signals, Parseval’s theorem, Wiener Khintchine relation, power spectral density, Sum decomposition theorem, Spectral factorization theorem. Spectrum Estimation : periodogram, Non – parametric methods of spectral estimation Correlation method, WELCH method –AR, MA,ARMA models. Tule – Walker method. Linear Estimation and Prediction: ML estimate – Efficiency of estimator, Cramer Rao bound ‐ LMS criterion. Wiener filter – Recursive estimator – Kalman estimator – Linear prediction, Analysis and synthesis filters, Levinson resursion, Lattice realization. Adaptive filters: FIR adaptive filter – Newton’s Steepest descent algorithm – Widrow Hoff LMS adaptation algorithms – Adaptive noise cancellation, Adaptive equalizer, Adaptive echo cancellors

Text Books

1. M. Hays: Statistical Digital Signal Processing and Modelling, John Willey and Sons, 1996.

2. Simon Haykin: Adaptive Filter Theory, Prentice Hall, 1996

3. "Adaptive Filters :Theory and Applications", by B. Farhang‐Boroujeny, John Wiley and Sons, 1999.

4. John G Proakis and Manolakis, “ Digital Signal Processing Principles, Algorithms and Applications”, Pearson, Fourth Edition, 2007.

5. Sophocles J. Orfanidis, Optimum Signal Processing, An Introduction, McGraw Hill,1990


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