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Course Catalog 2013-2014
SGN-16006 Bachelor's Laboratory Course in Signal Processing, 5 cr |
Person responsible
Mikko Parviainen, Alpo Värri, Hanna Silen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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| Study type | Hours | Time span | Implementations | Lecture times and places |
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| SGN-16006 2013-02 |
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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. Course can be integrated with KIE-34106 Academic Writing in English.
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). |
Instructions for students on how to achieve the learning outcomes
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-13000 Introduction to Pattern Recognition and Machine Learning | Advisable | 1 |
| SGN-13006 Introduction to Pattern Recognition and Machine Learning | Advisable | 1 |
| SGN-11000 Basic course in Signal Processing | Mandatory | 2 |
| SGN-11006 Basic Course in Signal Processing | Mandatory | 2 |
| SGN-12000 Basic Course in Image and Video Processing | Advisable | 3 |
| SGN-12006 Basic Course in Image and Video Processing | Advisable | 3 |
| ELT-10000 Signals and Measurements | Advisable | |
| FYS-1010 Physics Laboratory I | Advisable |
1 . Courses are equivalent.
2 . Courses are equivalent.
3 . Courses are equivalent.
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 |
| Avoin yliopisto/Avoin yliopisto kesäopetus |