BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI
INSTRUCTION DIVISION
FIRST SEMESTER 2018 - 2019
COURSE HANDOUT (PART II)
Date: 01 / 08 / 2018
In addition to Part I (General Handout for all courses appended to the timetable) this
portion gives further specific details regarding the course.
Course No : EEE G613
Course Title : Advanced Digital Signal Processing
Instructor-in-charge : Dr. R.Venkateswaran
1. Course Description :
This course deals with introduction to random processes and spectral representation,
modeling of AR, ARMA time-series processes, spectrum estimation, spectrum analysis
and. design of optimum (Wiener and Kalman) filters for estimating signals in noise,
adaptive filters for estimating & predicting non-stationary signal and linear prediction.
Some applications based on algorithms for adaptive statistical signal processing would be
included.
2. Scope and Objective:
To provide a strong background on most important advanced DSP topics. It will
include topics, which are used in different fields of signal processing applications, which
include linear prediction and optimal filter design using Wiener and Kalman filters. The
focus is on adaptive signal processing. It deals with signal modeling, optimal filtering,
spectrum estimation and adaptive filtering.
3. Text Book:
1. Monson H. Hayes, Statistical Digital Signal Processing and Modeling, Wiley-India,
2008.
Reference books:
1. Manolakis, D., Ingle, M., Kogon, S., Statistical and Adaptive Signal Processing,
McGraw-Hill, 2000.
2. Simon Haykin, Adaptive Filter Theory, Pearson Education, Fourth Edition, 2002.
4. Course Plan:
Lecture Topics to be covered References
No.
1 Introduction to the course, evaluation system 1
2-4 Background: z-transform, DTFT principles, matrix T1: 2
algebra, complex gradients
5-7 Random variables and random processes and basic T1: 3.1-3.3
probability theory for statistical signal analysis
8-10 Special types of random processes, signal modeling T1: 4.1-4.4.4, 4.6
and approximation methods (Pade, Prony)
11-13 Stochastic Models , AR, MA and ARMA T1: 4.7
14-17 Levinson-Durbin Recursion Algorithm and Lattice T1: 5
Filter Structure
18-19 Schur Recursion, Cholesky Decomposition T1: 5.2.6, 5.2.7
20 Non parametric spectrum estimation T1: 8.2
21-23 Minimum variance spectrum estimation, Parametric T1: 8.3,8.5,8.6
spectrum estimation, Frequency estimation:
Pisarenko, MUSIC
24 Introduction to filtering Class notes
25-26 Optimal FIR filtering: Wiener filter T1: 7
27 Acoustic Echo Cancellation Class noes
28, 29 Steepest descent algorithm and convergence T1: 9.2.1
analysis
30,31 The LMS algorithm and convergence analysis T1: 9.2.2, 9.2.3
32 The NLMS algorithm T1: 9.2.4
33 Adaptive equalization and model matching Class notes
34, 35 Least Square methods and The RLS algorithm T1: 9.4
36 LMS Vs. RLS algorithm T1: 9.4
37 Kalman filters T1: 7.4
38-42 Term paper presentations Papers from
Journals/Reputed
Conferences
5. Evaluation Scheme:
EC Evaluation Duration Weightage Date, Time & Nature of
No. Component (min) (%) Venue Component
1 Mid term 90 30 8/10, 11.00 -- 12.30 PM Closed Book
2 Quiz (announced) 5 To be announced in Closed Book
the class
3 Lab performance, & ----- 25 To be announced in Open book
term paper the class
presentations
4 Comprehensive 180 40 01/12 FN Closed Book
6. Chamber Consultation Hours: To be announced in the class.
7. Make-up Policy: Make-up for the tests will be granted as per ID rules. In all cases
prior intimation must be given to IC. There will be no make-up for the term paper
presentations and quizzes.
8. Notices: Notices regarding the course will be displayed in CMS
Instructor - in - charge EEE G613
