CEC332 Advanced Digital Signal Processing Syllabus:
CEC332 Advanced Digital Signal Processing Syllabus – Anna University Regulation 2021
COURSE OBJECTIVES:
To introduce the concepts of discrete time random signal processing
To know about multirate signal processing and its applications
To understand the spectrum estimation techniques
To learn the concept of prediction theory and filtering
UNIT I MULTIRATE SIGNAL PROCESSING
Review of Convolution, DFT and ZT, Multirate Signal Processing – Decimation, Interpolation, Sampling Rate Conversion by a rational factor – digital filter banks, sub band coding, Quadrature Mirror Filter.
UNIT II DISCRETE TIME RANDOM PROCESSES
Stationary random processes, Autocorrelation, Rational Power Spectra, Filters for generating random Processes from white noise and inverse filter – AR, MA and ARMA processes – relationship between autocorrelation and the filter parameters.
UNIT III LINEAR PREDICTION AND FILTERING
Linear Prediction – Forward and Backward – Wiener filters for filtering and prediction – FIR Wiener Filter – IIR Wiener Filter – Kalman Filter.
UNIT IV ADAPTIVE FILTERING
FIR adaptive filters – adaptive filters based on steepest descent method – LMS algorithm – Variants of LMS algorithm – adaptive echo cancellation – adaptive channel equalization – RLS Algorithm.
UNIT V SPECTRUM ESTIMATION
Estimation of power spectra from finite duration observations of signals – Non parametric methods of spectrum estimation – the Bartlett and the Welch method – Parametric spectrum estimation – AR, MA and ARMA.
30 PERIODS
PRACTICAL EXERCISES: 30 PERIODS
1. Study of autocorrelation and Cross Correlation of random signals
2. Design and Implementation of Multirate Systems.
3. Design and Implementation of Wiener Filter
4. Design and Implementation of FIR Linear Predictor
5. Design of adaptive filters using LMS algorithm
6. Spectrum Estimation using Bartlett and Welch Methods
COURSE OUTCOMES:
Upon successful completion of the course the student will be able to
CO1: Comprehend multirate signal processing and demonstrate its applications
CO2:Demonstrate an understanding of the power spectral density and apply to discrete random signals and systems
CO3: Apply linear prediction and filtering techniques to discrete random signals for signal detection and estimation.
CO4: Analyze adaptive filtering problems and demonstrate its application
CO5: Apply power spectrum estimation techniques to random signals.
TOTAL:60 PERIODS
TEXT BOOKS :
1. John G. Proakis & Dimitris G.Manolakis, ―Digital Signal Processing – Principles, Algorithms & Applications, Fourth Edition, Pearson Education / Prentice Hall, 2007.
2. P. Vaidyanathan, “Multirate systems and filter banks”, Prentice Hall Inc. 1993.
REFERENCES :
1. Monson H. Hayes, “Statistical digital signal processing and modeling”, John Wiley and Sons Inc. New York, Indian reprint 2008.
2. Haykin, Adaptive Filter Theory, 4th Edition, Pearson Education, New Delhi, 2006.
3. Sophoncles J. Orfanidis, “Optimum Signal Processing “, McGraw Hill, 2000.