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Course Catalog 2012-2013
SGN-2706 Nonlinear Signal Processing, 5 cr |
Person responsible
Sari Peltonen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Final examination.
Completion parts must belong to the same implementation
Learning outcomes
After completing the course, the student - can list nonlinear filter classes and name filters belonging to each class, - is able to explain the idea or motivation behind each nonlinear filter, - can define each filter mathematically, - is able to derive impulse and step responses for each filter, and - has ability to analyze filter behavior in different noisy cases.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | median type filters | estimation theory | |
| 2. | L-, M- and R-filters | implementation of the 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 in the three exams of this implementation of the course.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Book | Fundamentals of Nonlinear Digital Filtering | Astola, J. & Kuosmanen, P. | 0849325706 | English | |||
| Lecture slides | Nonlinear Signal Processing | Kuosmanen, P. | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-3010 Digital Image Processing I | Advisable | 1 |
| SGN-3016 Digital Image Processing I | Advisable | 1 |
| SGN-1158 Introduction to Signal Processing, short version | Mandatory | 2 |
| SGN-1159 Introduction to Signal Processing, long version | Mandatory | 2 |
| SGN-1201 Signal Processing Methods | Mandatory | 2 |
| SGN-1251 Signal Processing Applications | Advisable |
1 . Either SGN-3010 or SGN-3016 is advisable.
2 . Either SGN-1158, SGN-1159 or SGN-1201 is mandatory.
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
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More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
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Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 % |