Admissions

EE593: Advanced Signal Processing

EE593

To provide stochastic and adaptive signal processing tools required for signal processing systems design and analysis

NO. OF CREDITS: 3
COMPULSORY/OPTIONAL: OPTIONAL
PREREQUISITES: EE257, EE325

MAIN TOPICS AND INTENDED LEARNING OUTCOMES

TOPICS

Introduction to signals
Basics of Stochastic Signal Processing
Weiner Filter
Eigen Analysis and Performance Surface
Iterative algorithms for Optimization
Adaptive signal processing techniques:
Transform Domain Approaches
Recent Advances in Signal Processing

Student will be able to,

ILO1: Calculate basic stochastic signal processing parameters and functions and examine its application to signal processing-based engineering applications.
ILO2: Analyze and deduce the operations of the Wiener filter in relation to channel estimation/ equalization/interference cancellation applications, and evaluate the impact of sensor noise for these applications.
ILO3: Combine the relationship between the Eigen-structure of the ACM, PSD and performance surface.
ILO4: Breakdown the adaptation process of the SD and LMS algorithms to multiple modes and analyze the convergence and transient behaviours of these adaptive techniques and identify the impact of transform domain approaches to convergence behaviour.
ILO5: Design LMS based adaptive approaches to resolve problems relating to channel estimation/equalization/interference cancellation/beamforming.

 

NO RECOMMENDED TEXT
1 B. Farhang-Boroujeny, Adaptive Filters Theory and Applications, John Wiley and sons Ltd., 1998, reprint 2000
2 S. Haykin, Adaptive Filter Theory, Prentice-Hall, 4th ed., 2001
3 Alan V Oppenhiem, Alan S Willsky, Signals and Systems, 2nd ed., Prentice Hall, 1996
TIME ALLOCATION HOURS
Lectures 33
Tutorials 6
Assignments 12
Laboratories 0
ASSESMENT PERCENTAGE
Assignments 30
Laboratory Work -
MID Semester Evaluation 20
END Semester Exam 50
Header Style
Sticky Menu
Color skins
COLOR SCHEMES