DOC/LP/00/21.01.05
LP – AP7101
LESSON PLAN
LP Rev. No: 01
Sub Code/Name: AP7101 – Advanced Digital Signal Processing
Date: 14.08.2014
Unit : V
Page 01 of 06
Branch : ME(CS & AE)
UNIT V MULTIRATE DIGITAL SIGNAL PROCESSING
Semester: I
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.
Objective:
To understand Multirate systems and their applications.
Session No.
Topics to be covered
Time
Ref
3,6
1.
Mathematical description of change of sampling rate
50m
2.
Interpolation and Decimation, Continuous time model Direct digital domain approach
50m
3.
Decimation by an integer factor
50m
4.
Interpolation by an integer factor
50m
5.
Sampling rate conversion by a rational factor (I/D)
50m
6.
Tutorial
50m
7.
Identities of sampling rate converters
50m
8.
Multistage implementation of multirate system
50m
9.
Polyphase filter structures
50m
10.
Applications -sub band coding, Quadrature Mirror filter
50m
11.
Tutorial
50m
12.
Wavelet transform and filter bank implementation of
wavelet expansion of signals
50m
3,6
3,6
3,6
3,6
3,6
3,6
3,6
3,6
3,6
3,6
Teaching
Method
BB
BB
OHP
OHP
BB
BB
BB
BB
BB
BB
BB
3,6
BB,IPT
DOC/LP/00/21.01.05
LP – AP7101
LESSON PLAN
LP Rev. No: 01
UNIT I
Sub Code/Name: AP7101 – Advanced Digital Signal Processing
Date: 14.08.2014
Unit :I
Page 02 of 06
Branch : ME(CS & AE)
Semester: 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.
Objective:
To compare different types of random processes.
Session No.
13.
14.
15.
16.
Topics to be covered
Introduction to Discrete Random signals, Discrete random
variables
Autocorrelation,Power spectral density and its properties
Filtering Random Process
Weiner Khintchine relation
Time
Ref
Teaching
Method
50m
1
BB
50m
1
BB
50m
1
BB
50m
1
BB
17.
Spectral Factorization
50m
1
BB
18.
Special types of Random process – ARMA, AR, MA process
using Yule-Walker method
50m
2
BB
19.
Tutorial
50m
2
BB
20.
Signal modeling-Least Squares method
50m
1
BB
CAT I
75m
21.
Pade approximation
50m
1
BB
22.
Prony’s method
50m
1
OHP
23.
Iterative Prefiltering
50m
1
OHP
50m
1
BB
24.
Finite Data records and Stochastic Models
DOC/LP/00/21.01.05
LP – AP7101
LESSON PLAN
LP Rev. No: 01
Sub Code/Name: AP7101 – Advanced Digital Signal Processing
Date: 14.08.2014
Unit : II
Page 03 of 06
Branch : ME(CS & AE)
Semester: I
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.
Objective:
To compare nonparametric and parametric methods of spectrum estimation.
Session No.
Topics to be covered
Time
Ref
Teaching
Method
25.
Non-Parametric methods
50m
1,2
BB
26.
Periodogram estimation
50m
1,2
BB
27.
Performance analysis of estimators-Bias,Consistency and
Resolution
50m
1,2
BB
28.
Unbiased consistent estimators
50m
1,2
BB
29.
Barlett spectrum estimation
50m
1,2
BB
30.
Welch estimation
50m
1,2
BB
31.
Parametric method of spectral estimation-Model based
approach - Parameter estimation using Yule-Walker
method
50m
1,2
BB
32.
AR Signal modeling
50m
1,2
BB
33.
MA Signal modeling method
50m
1,2
BB
34.
ARMA Signal modeling
50m
1,2
BB
35.
Tutorial
50m
1,2
BB
50m
1,2
BB
36.
Correlation,Co-variance estimator
DOC/LP/00/21.01.05
LP – AP7101
LESSON PLAN
LP Rev. No: 01
Sub Code/Name: AP7101 – Advanced Digital Signal Processing
Date: 14.08.2014
Unit : III
Page 04 of 06
UNIT-III
Branch : ME(CS & AE)
Semester: I
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.
Objective:
To understand different types of prediction and filtering methods.
Session
No.
Topics to be covered
Time
Ref
Teaching
Method
37.
Maximum likelihood criterion
50m
1
BB
38.
Efficiency of estimator
50m
1
BB
39.
FIR Wiener filters-Least mean squared error
criterion using Discrete Wiener Hoff equations
50m
1
BB
40.
Applications-Filtering, Linear Prediction, Noise
cancellation,Lattice realization
50m
1
BB
41.
-do-
50m
1
BB
42.
Tutorial
50m
1
BB
CAT-II
75m
43.
IIR Wiener filters-Causal & Non causal
types,Causal Linear prediction,Prediction error
50m
1
BB,IPT
44.
Weiner deconvolution -Whitening filter, Inverse
filter
50m
1
BB,IPT
45.
Kalman filter
50m
1
BB
46.
Recursive estimators
50m
1
BB
47.
Levinson recursion
50m
1
BB
48.
Levinson recursion algorithm for solving Toeplitz
system of equations.
50m
1
BB
49.
Tutorial
50m
1
BB
DOC/LP/00/21.01.05
LP – AP7101
LESSON PLAN
LP Rev. No: 01
Sub Code/Name: AP7101 – Advanced Digital Signal Processing
Date: 14.08.2014
Unit : IV
Page 05 of 06
Branch : ME(CS & AE)
UNIT IV ADAPTIVE FILTERS
Semester: I
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.
Objective:
To study and compare different adaptive filter algorithms.
Session No.
Topics to be covered
Time
Ref
Teaching
Method
50.
FIR Adaptive filters
50m
1,4
BB
51.
Newton's steepest descent method
50m
1,4
BB
52.
Adaptive filters based on steepest descent method
50m
1,4
BB
53.
Widrow Hoff LMS Adaptive algorithm
50m
1,4
BB
54.
Tutorial
50m
1,4
BB
55.
Adaptive channel equalization
50m
1,4
BB
56.
Adaptive echo cancellation
50m
1,4
BB
57.
Adaptive noise cancellation
50m
1,4
BB
58.
RLS adaptive algorithm
50m
1,4
BB
50m
1,4
BB
Simplified IIR LMS Adaptive filter
50m
1,4
BB
CAT-III
75m
59.
60.
Exponentially weighted RLS, Sliding window RLS
DOC/LP/00/21.01.05
LP – AP7101
LESSON PLAN
LP Rev. No: 01
Sub Code/Name: AP7101 – Advanced Digital Signal Processing
Date: 14.08.2014
Branch : ME(CS & AE)
Page 06 of 06
Semester: I
Course Delivery Plan:
Week
1
2
3
4
5
6
7
8
9
10
11
12
I II
I II
I II
I II
I II
I II
I II
I II
I II
I II
I II
I II
Units
V
II
I
CAT I
13
I II
14
I
15
II
I
IV
III
CAT II
CAT III
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.
7. http://nptel.ac.in
Prepared by
Approved by
B.SARALA,L.ANJU
Dr.S.GANESH VAIDYANATHAN
Assistant Professor /EC
HoD- EC
14.08.2014
14.08.2014
Signature
Name
Designation
Date
II