|
|
|||||||||||||||||
Course Catalog 2012-2013
SGN-1656 Signal Processing Laboratory, 5 cr |
Person responsible
Katariina Mahkonen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
|
|
|
|
|
|
|
|
|
Requirements
Four laboratory exercises and their reporting.
Completion parts must belong to the same implementation
Principles and baselines related to teaching and learning
-
Learning outcomes
After completing the course, students are able to apply basic signal processing methods to solve problems that are closely related to practical problems. To complete the course, students are required to pass four assignments in groups of two students. The assignments in the course are from various areas of signal processing. During the course, students apply their background knowledge, improve their problem solving strategies and information search abilities in order to solve practical problems. Students will learn to introduce the methods they have used to conduct a given experiment, present results and conclusions of the experiment in a form of written reports and possibly other deliveries such as program code.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | All the individual tasks mentioned are completed successfully (i.e. every questions is given an answer). | Reasons for observed phenomena are analyzed and discussed based on given literature and background knowledge. | Improvements to tested methods are proposed and analyzed by comparison to the tested method numerically. |
| 2. | The report and other possible deliverables fulfill the criteria set by the advisor of each assignment. | The report is formal i.e. follows the structure: introduction, material and methods, results and conclusions. | Besides the previous the layout of the report reminds scientific publications (see e.g. IEEE conference papers). |
Evaluation criteria for the course
To pass the course, it is required that four exercises have been returned and accepted.
Assessment scale:
Evaluation scale passed/failed will be used on the course
Partial passing:
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-2506 Introduction to Pattern Recognition | Mandatory | 1 |
| SGN-2016 Digital Linear Filtering I | Mandatory | 2 |
| SGN-3016 Digital Image Processing I | Mandatory | 3 |
1 . SGN-2500 Johdatus hahmontunnistukseen
2 . SGN-2010 Digitaalinen lineaarinen suodatus I
3 . SGN-3010 Digitaalinen kuvankäsittely I
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
|
|
|
|
|
|
|
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |