EC9311 ADVANCED DIGITAL SIGNAL PROCESSING SYLLABUS | ANNA UNIVERSITY ME ECE 1ST SEM SYLLABUS REGULATION 2009 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY FIRST SEMESTER M.E ECE ELECTRONICS AND COMMUNICATION ENGINEERING DEPARTMENT SYLLABUS, TEXTBOOKS, REFERENCE BOOKS,EXAM PORTIONS,QUESTION BANK,PREVIOUS YEAR QUESTION PAPERS,MODEL QUESTION PAPERS, CLASS NOTES, IMPORTANT 2 MARKS, 8 MARKS, 16 MARKS TOPICS. IT IS APPLICABLE FOR ALL STUDENTS ADMITTED IN THE YEAR 2011 2012-2013 (ANNA UNIVERSITY CHENNAI,TRICHY,MADURAI, TIRUNELVELI,COIMBATORE), 2009 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009
EC9311 ADVANCED DIGITAL SIGNAL PROCESSING L T P C
3 0 0 3
[Review of discrete-time signals and systems- DFT and FFT, Z-Transform, Digital Filters is
recommended]
UNIT I DISCRETE RANDOM SIGNAL PROCESSING 9
Discrete Random Processes- Ensemble averages, stationary processes, Autocorrelation and
Auto covariance matrices. Parseval's Theorem, Wiener-Khintchine Relation- Power Spectral
Density-Periodogram Spectral Factorization, Filtering random processes. Low Pass Filtering of
White Noise. Parameter estimation: Bias and consistency.
UNIT II SPECTRUM ESTIMATION 9
Estimation of spectra from finite duration signals, Non-Parametric Methods-Correlation Method ,
Periodogram Estimator, Performance Analysis of Estimators -Unbiased, Consistent Estimators-
Modified periodogram, Bartlett and Welch methods, Blackman –Tukey method. Parametric
Methods - AR, MA, ARMA model based spectral estimation. Parameter Estimation -Yule-Walker
equations, solutions using Durbin’s algorithm
UNIT III LINEAR ESTIMATION AND PREDICTION 9
Linear prediction- Forward and backward predictions, Solutions of the Normal equations-
Levinson-Durbin algorithms. Least mean squared error criterion -Wiener filter for filtering and
prediction , FIR Wiener filter and Wiener IIR filters ,Discrete Kalman filter
UNIT IV ADAPTIVE FILTERS 9
FIR adaptive filters -adaptive filter based on steepest descent method-Widrow-Hoff LMS
adaptive algorithm, Normalized LMS. Adaptive channel equalization-Adaptive echo
cancellation-Adaptive noise cancellation- Adaptive recursive filters (IIR). RLS adaptive filters-
Exponentially weighted RLS-sliding window RLS.
UNIT V MULTIRATE DIGITAL SIGNAL PROCESSING 9
Mathematical description of change of sampling rate - Interpolation and Decimation ,
Decimation by an integer factor - Interpolation by an integer factor, Sampling rate conversion by
a rational factor, Filter implementation for sampling rate conversion- direct form FIR structures,
Polyphase filter structures, time-variant structures. Multistage implementation of multirate
system. Application to sub band coding - Wavelet transform and filter bank implementation of
wavelet expansion of signals.
TOTAL: 45 PERIODS
REFERENCES:
1. Monson H.Hayes, Statistical Digital Signal Processing and Modeling, John Wiley and Sons,
Inc., Singapore, 2002.
2. John G. Proakis, Dimitris G.Manolakis, Digital Signal Processing Pearson Education, 2002.
3. John G. Proakis et.al.’Algorithms for Statistical Signal Processing’, Pearson Education, 2002.
4. Dimitris G.Manolakis et.al.’ Statistical and adaptive signal Processing’, McGraw Hill,
Newyork, 2000.
5. Rafael C. Gonzalez, Richard E.Woods, ‘Digital Image Processing’, Pearson Education, Inc.,
Second Edition, 2004.( For Wavelet Transform Topic)
EC9311 ADVANCED DIGITAL SIGNAL PROCESSING L T P C
3 0 0 3
[Review of discrete-time signals and systems- DFT and FFT, Z-Transform, Digital Filters is
recommended]
UNIT I DISCRETE RANDOM SIGNAL PROCESSING 9
Discrete Random Processes- Ensemble averages, stationary processes, Autocorrelation and
Auto covariance matrices. Parseval's Theorem, Wiener-Khintchine Relation- Power Spectral
Density-Periodogram Spectral Factorization, Filtering random processes. Low Pass Filtering of
White Noise. Parameter estimation: Bias and consistency.
UNIT II SPECTRUM ESTIMATION 9
Estimation of spectra from finite duration signals, Non-Parametric Methods-Correlation Method ,
Periodogram Estimator, Performance Analysis of Estimators -Unbiased, Consistent Estimators-
Modified periodogram, Bartlett and Welch methods, Blackman –Tukey method. Parametric
Methods - AR, MA, ARMA model based spectral estimation. Parameter Estimation -Yule-Walker
equations, solutions using Durbin’s algorithm
UNIT III LINEAR ESTIMATION AND PREDICTION 9
Linear prediction- Forward and backward predictions, Solutions of the Normal equations-
Levinson-Durbin algorithms. Least mean squared error criterion -Wiener filter for filtering and
prediction , FIR Wiener filter and Wiener IIR filters ,Discrete Kalman filter
UNIT IV ADAPTIVE FILTERS 9
FIR adaptive filters -adaptive filter based on steepest descent method-Widrow-Hoff LMS
adaptive algorithm, Normalized LMS. Adaptive channel equalization-Adaptive echo
cancellation-Adaptive noise cancellation- Adaptive recursive filters (IIR). RLS adaptive filters-
Exponentially weighted RLS-sliding window RLS.
UNIT V MULTIRATE DIGITAL SIGNAL PROCESSING 9
Mathematical description of change of sampling rate - Interpolation and Decimation ,
Decimation by an integer factor - Interpolation by an integer factor, Sampling rate conversion by
a rational factor, Filter implementation for sampling rate conversion- direct form FIR structures,
Polyphase filter structures, time-variant structures. Multistage implementation of multirate
system. Application to sub band coding - Wavelet transform and filter bank implementation of
wavelet expansion of signals.
TOTAL: 45 PERIODS
REFERENCES:
1. Monson H.Hayes, Statistical Digital Signal Processing and Modeling, John Wiley and Sons,
Inc., Singapore, 2002.
2. John G. Proakis, Dimitris G.Manolakis, Digital Signal Processing Pearson Education, 2002.
3. John G. Proakis et.al.’Algorithms for Statistical Signal Processing’, Pearson Education, 2002.
4. Dimitris G.Manolakis et.al.’ Statistical and adaptive signal Processing’, McGraw Hill,
Newyork, 2000.
5. Rafael C. Gonzalez, Richard E.Woods, ‘Digital Image Processing’, Pearson Education, Inc.,
Second Edition, 2004.( For Wavelet Transform Topic)
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