Course Catalog 2010-2011
Basic

Basic Pori International Postgraduate Open University

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Course Catalog 2010-2011

SGN-2206 Adaptive Signal Processing, 5 cr

Person responsible

Ioan Tabus

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises


 
 4 h/week
 2 h/week


 


 


 
SGN-2206 2010-01 Tuesday 10 - 12, TB215
Thursday 12 - 14, TB219

Requirements

Assignment and final exam.

Principles and baselines related to teaching and learning

-

Learning outcomes

Student will learn basic adaptive signal processing methods, especially linear adaptive filters and learning of supervised neural networks. After completing the course, the student - Is familiar with the most important adaptive filter generic problems: optimal design, convergence, recursiveness in time, frequency domain implementations; - Is able to start from the formulation of a problem formulation and utilize a number of typical algorithmic tools to derive the solution; - Knows what are the most important structures for adaptive filters: LMS, NLMS,RLS etc. - Acquires practice on simulating adaptive algorithms with given input data and extracting useful performance indices helpful in comparing various algorithms. - Knows how to integrate an adaptive filter in a number of important applications: echo cancelation, noise cancellation, channel equalization etc.

Content

Content Core content Complementary knowledge Specialist knowledge
1. Optimal Wiener filtering     
2. Gradient based adaptation: Steepest descent, LMS, NMLS    Frequency domain adaptive filters 
3. Linear prediction, lattice filters, adaptive lattice filters    Levinson algorithm 
4. Least squares filtering, Recursive least squares     
5. Neural networks as adaptive filters    Backpropagation in time 

Evaluation criteria for the course

Course is graded on the basis of answers to exam questions. Very good grade is obtained when exam questions are correctly answered and homework is accepted. Course acceptance threshold is approx. half of the maximum exam points.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Study material

Type Name Author ISBN URL Edition, availability, ... Examination material Language
Book   "Adaptive Filter Theory"   S. Haykin       Prentice-Hall, 2002.      English  

Prerequisites

Course Mandatory/Advisable Description
MAT-33317 Statistics 1 Advisable    
SGN-1157 Signal Processing Basics Advisable    
SGN-1201 Signaalinkäsittelyn menetelmät Advisable    

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
SGN-2206 Adaptive Signal Processing, 5 cr 8001253 Adaptive Signal Processing, 3 cu  

Additional information

Suitable for postgraduate studies

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-2206 2010-01        

Last modified07.07.2010