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SGN-2706 Nonlinear Signal Processing, 5 cr |
Sari Peltonen
Lecture times and places | Target group recommended to | |
Implementation 1 |
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2. vuosikurssi
3.-n. vuosikurssi International Students |
Final examination.
Completion parts must belong to the same implementation
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Student will become familiar with some basic nonlinear filtering techniques and will also know how and when to use them.
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 |
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 in the three exams of this implementation of the course.
Numerical evaluation scale (1-5) will be used on the course
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 |
Course | Mandatory/Advisable | Description |
SGN-1107 Introductory Signal Processing | Mandatory | Either SGN-1107 or SGN-1200 is required. |
SGN-1200 Signaalinkäsittelyn menetelmät | Mandatory | |
SGN-1250 Signaalinkäsittelyn sovellukset | Advisable |
Course | Corresponds course | Description |
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Description | Methods of instruction | Implementation | |
Implementation 1 |
Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 % |