Course Catalog 2014-2015
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Course Catalog 2014-2015

SGN-21006 Advanced Signal Processing, 5 cr

Person responsible

Ioan Tabus

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises
 2 h/week
 2 h/week
+2 h/week
+2 h/week


 


 


 
SGN-21006 2014-01 Tuesday 12 - 14 , TB224
Tuesday 12 - 14 , TB214

Requirements

Final examinaion and a homework assignment

Learning Outcomes

Student will learn advanced signal processing methods, especially linear optimal filter design, adaptive filters, spectrum estimation, nonlinear filters and how to select proper methods for signal processing tasks at hand. After completing the course, the student - Is familiar with the most important advanced signal processingr generic problems: optimal design, convergence, recursiveness in time, spectrum estimation; - 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 salgorithms for optimal and adaptive filters: LMS, NLMS,RLS etc. - Acquires practice on simulating optimal and adaptive algorithms with given input data and extracting useful performance indices helpful in comparing various algorithms. - Knows how to integrate an optimal or adaptive filter in a number of important applications: echo cancelation, noise cancellation, channel equalization etc.

Content

Content Core content Complementary knowledge Specialist knowledge
1. 1. Deterministic and random signals: review of Fourier transform, Z transform, random variables, random signals, correlation, AR,MA, ARMA      
2. 2. Optimal filter design (Wiener filter, Least squares, essentials of estimation, MLE, CramerRao)     
3. 3. Adaptive filter design (LMS, NLMS, RLS, Kalman )      
4. 4. Application areas of Optimal filter design and Adaptive filter design     
5. 5. Spectrum estimation:Frequency spectrum (needed in machine function regime diagnosis, finding periodicities in time series), Direction of Arrival spectrum      
6. 6. Nonlinear filters (median and order statistics filter family)     

Instructions for students on how to achieve the learning outcomes

The 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. By volunteering to show exersice solution will be rewarded with increasing the exam result by one grade if the threshold is passed.

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   Simon O. Haykin         No    English  
Book   Optimum Signal Processing   S. J. Orfanidis         No    English  
Book   Spectral analysis of signals   Petre Stoica and Randolph Moses         No    English  
Lecture slides     Ioan Tabus         Yes    English  

Prerequisites

Course Mandatory/Advisable Description
SGN-11000 Signaalinkäsittelyn perusteet Advisable    
SGN-11006 Basic Course in Signal Processing Advisable    

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
SGN-21006 Advanced Signal Processing, 5 cr SGN-2607 Statistical Signal Processing, 6 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-21006 2014-01        

Last modified02.09.2014