AP7101 ADVANCED DIGITAL SIGNAL PROCESSING - ANNA UNIVERSITY REGULATION 2013 PG SYLLABUS

ANNA UNIVERSITY, CHENNAI
AFFILIATED INSTITUTIONS
REGULATIONS - 2013
M.E. APPLIED ELECTRONICS
SEMESTER - 1 SYLLABUS
ADVANCED DIGITAL SIGNAL PROCESSING   

AP7101 ADSP - SYLLABUS

                                        

COURSE OBJECTIVES:
The purpose of this course is to provide in-depth treatment on methods and techniques in
 Discrete-time signal transforms, digital filter design, optimal filtering
 Power spectrum estimation, multi-rate digital signal processing
 DSP architectures which are of importance in the areas of signal processing, control and
communications.

COURSE OUTCOMES:
Students should be able to:
 To design adaptive filters for a given application
 To design multirate DSP systems.

UNIT I DISCRETE RANDOM SIGNAL PROCESSING 9
Weiner Khitchine relation - Power spectral density – filtering random process, Spectral
Factorization Theorem, special types of random process – Signal modeling-Least Squares
method, Pade approximation, Prony’s method, iterative Prefiltering, Finite Data records,
Stochastic Models.

UNIT II SPECTRUM ESTIMATION 9
Non-Parametric methods - Correlation method - Co-variance estimator - Performance analysis
of estimators – Unbiased consistent estimators - Periodogram estimator - Barlett spectrum
estimation - Welch estimation - Model based approach - AR, MA, ARMA Signal modeling -
Parameter estimation using Yule-Walker method.

UNIT III LINEAR ESTIMATION AND PREDICTION 9
Maximum likelihood criterion - Efficiency of estimator - Least mean squared error criterion -
Wiener filter - Discrete Wiener Hoff equations - Recursive estimators - Kalman filter - Linear
prediction, Prediction error - Whitening filter, Inverse filter - Levinson recursion, Lattice
realization, Levinson recursion algorithm for solving Toeplitz system of equations.

UNIT IV ADAPTIVE FILTERS 9
FIR Adaptive filters - Newton's steepest descent method - Adaptive filters based on steepest
descent method - Widrow Hoff LMS Adaptive algorithm - Adaptive channel equalization -
Adaptive echo canceller - Adaptive noise cancellation - RLS Adaptive filters - Exponentially
weighted RLS - Sliding window RLS - Simplified IIR LMS Adaptive filter.

UNIT V MULTIRATE DIGITAL SIGNAL PROCESSING 9
Mathematical description of change of sampling rate - Interpolation and Decimation -
Continuous time model - Direct digital domain approach - Decimation by integer factor -
Interpolation by an integer factor - Single and multistage realization - Poly phase realization -
Applications to sub band coding - Wavelet transform and filter bank implementation of wavelet
expansion of signals.

L +T= 45+15=60 PERIODS

REFERENCES:
1. Monson H. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley and
Sons Inc., New York, 2006.
2. Sophoncles J. Orfanidis, “Optimum Signal Processing “, McGraw-Hill, 2000.
3. John G. Proakis, Dimitris G. Manolakis, “Digital Signal Processing”, Prentice Hall of India,
New Delhi, 2005.
4. Simon Haykin, “Adaptive Filter Theory”, Prentice Hall, Englehood Cliffs, NJ1986.
5. S. Kay,” Modern Spectrum Estimation Theory And Application”, Prentice Hall, Englehood
Cliffs, Nj1988.
6. P. P. Vaidyanathan, “Multirate Systems And Filter Banks”, Prentice Hall, 1992.