Course Catalog 2008-2009 International Basic Pori International Postgraduate Open University |Degrees|     |Study blocks|     |Courses|

Course´s person responsible

Sari Peltonen

Implementations

No implementations

Requirements

Final examination.
Completion parts must belong to the same implementation

Principles and baselines related to teaching and learning

-

Objectives

Student will become familiar with some basic nonlinear filter techniques and will also know how and when to use them.

Content

Content	Core content	Complementary knowledge	Specialist knowledge
1.	median type filters
2.	review of estimation theory (maximum likelihood and M-estimators) and L-, M- and R-filters
3.	trimmed mean, C-, ranked-order and weighted order statistic filters
4.	edge-enhancing selective, rank selection and weighted majority of m values with minimum range filters
5.	nonlinear mean and stack filters (and generalizations)
6.	(soft) morphological, polynomial, data-dependent, decision-based filters
7.	iterative, cascaded and recursive filters
8.	several minor filter classes

Evaluation criteria for the course

Course is graded on the basis of answers to exam questions. Course acceptance threshold is approx. half the maximum exam points. Classroom exercise activity gives bonus points which are added to exam points.

Assessment scale:

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

Partial passing:

Completion parts must belong to the same implementation

Study material

Type	Name	Author	ISBN	URL	Edition, availability, ...	Examination material	Language
Book	"Fundamentals of Nonlinear Digital Filtering"	Astola, J. & Kuosmanen, P.			CRC Press, 1997		English
Lecture slides	"Nonlinear Signal Processing"	Kuosmanen, P.					English

Prerequisites

Correspondence of content

Additional information

Last modified	08.07.2008
Modifier	Sari Peltonen