EC2057 ADVANCED DIGITAL SIGNAL PROCESSING SYLLABUS | ANNA UNIVERSITY BE E&I 8TH SEMESTER SYLLABUS REGULATION 2008 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY 8TH SEMESTER B.E ELECTRONICS AND INSTRUMENTATION 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), 2008 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009
EC2057 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]
AIM
To provide adequate knowledge in Random signal processing.
OBJECTIVES
i. Detail study of time averaging , ensamble averaging & study of power
spectral density.
ii. Detail study of parametric & non – parametric estimation
iii. Detail study of adaptive filters & its applications
iv. Introduction study of multivariable digital signal processing.
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.
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).
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.
L = 45 T = 15 TOTAL: 60 PERIODS
TEXT BOOKS:
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.
114
REFERENCES:
1. John G. Proakis et.al.’Algorithms for Statistical Signal Processing’, Pearson Education, 2002.
2. Dimitris G.Manolakis et.al.’ Statistical and adaptive signal Processing’, McGraw Hill, New York,
2000.
3. Rafael C. Gonzalez, Richard E.Woods, ‘Digital Image Processing’, Pearson Education, Inc.,
Second Edition, 2004.( For Wavelet Transform Topic)
EC2057 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]
AIM
To provide adequate knowledge in Random signal processing.
OBJECTIVES
i. Detail study of time averaging , ensamble averaging & study of power
spectral density.
ii. Detail study of parametric & non – parametric estimation
iii. Detail study of adaptive filters & its applications
iv. Introduction study of multivariable digital signal processing.
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.
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).
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.
L = 45 T = 15 TOTAL: 60 PERIODS
TEXT BOOKS:
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.
114
REFERENCES:
1. John G. Proakis et.al.’Algorithms for Statistical Signal Processing’, Pearson Education, 2002.
2. Dimitris G.Manolakis et.al.’ Statistical and adaptive signal Processing’, McGraw Hill, New York,
2000.
3. Rafael C. Gonzalez, Richard E.Woods, ‘Digital Image Processing’, Pearson Education, Inc.,
Second Edition, 2004.( For Wavelet Transform Topic)
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