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Course Catalog 2010-2011
SGN-1606 Signal Processing and Multimedia laboratory, 5 cr |
Person responsible
Antti Larjo
Lessons
Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Accepted laboratory exercises.
Completion parts must belong to the same implementation
Principles and baselines related to teaching and learning
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Learning outcomes
After passing the course the student has a clear conception of the kinds of signal processing problems that may be found in working life and the student is able to apply the methods learnt in other courses of signal processing in practical problem solving.
Content
Content | Core content | Complementary knowledge | Specialist knowledge |
1. | Using some of the basic signal processing tools and measurement devices (including oscilloscope, signal generator, digital camera, signal processor, Matlab). | Understanding how the devices create the data, what the data is and analysing the data. | |
2. | Better understanding of the process of solving a practical signal processing problem and what are the required knowledge, skills and time to solve it. | Searching for information independently and applying it in problem solving. | |
3. | Assessing the different methods used in what comes to their performance and feasibility (and comparing them to each other, for example which method performs better in practice than another and why). |
Evaluation criteria for the course
Acceptance of all the four laboratory works is needed to pass the course.
Assessment scale:
Evaluation scale passed/failed will be used on the course
Partial passing:
Prerequisites
Course | Mandatory/Advisable | Description |
SGN-1250 Signal Processing Applications | Mandatory | |
SGN-2010 Digital Linear Filtering I | Advisable | |
SGN-2500 Introduction to Pattern Recognition | Mandatory | |
SGN-3010 Digital Image Processing I | 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 |