Course - Introduction to Signal Processing in Matlab - BEV3201
BEV3201 - Introduction to Signal Processing in Matlab
About
Examination arrangement
Examination arrangement: Practical examination
Grade: Passed/Failed
| Evaluation form | Weighting | Duration | Examination aids | Grade deviation |
|---|---|---|---|---|
| Practical examination | 100/100 | 30 minutes | HJELPEMIDD |
Course content
The course will provide an introduction to signal analysis in Matlab. The course will include selected topics within signal processing like digital data sampling, signal-to-noise ratio, data filtering, selection of time periods, and calculation of relevant signal characteristics. The course will include introduction to basic operations in Matlab such as importing and exporting data, data visualization, and programing of simple logical structures, use of functions, indexing of data vectors and matrices, and batch processing of larger data sets. The course will use exercises and examples to show the use and interpretation of signal analysis in movement and neuroscience
Learning outcome
After completing the course BEV3201, the student is able to:
- Explain important aspects of signal processing like data sampling, signal-to-noise ratio, data filtering and selection of time periods, and how these factors affect calculation of relevant signal characteristics.
- Apply basic operations in Matlab such as importing and exporting data, data visualization, programming simple logical structures, indexing data vectors and matrices, and batch processing large data sets.
- Create a Matlab script that can read the data, improve data quality, visualize results and compute relevant signal characteristics of various signals relevant in movement and neuroscience
- Perform signal analysis using basic operations in Matlab.
- Explain central concepts of signal processing
- Understand the importance of various aspects in signal analysis for data quality and further statistical analysis
Learning methods and activities
The course contains eight meetings of up to 5 hours which includes some lectures, but most practical exercises in Matlab. Students are expected to work on assigned tasks between the meetings and participate in all activities. The course is taught in English when required and all communications must be in English if international students are enrolled.
Compulsory assignments
- 70% obl oppmøte øving
Further on evaluation
Students should bring their own laptop to show what they have programmed. Otherwise no aids during the examination.
Specific conditions
Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.
Admission to a programme of study is required:
- (MDDIV)
Human Movement Sciences (MBEV)
Neuroscience (MSNEUR)
Recommended previous knowledge
BEV3102, BEV3103, BEV3023, BEV3004
Required previous knowledge
As for admission to the master programmes in medicine, health and social sciences. The course is reserved for students enrolled at master programmes in medicine, health and social sciences.
Credit reductions
| Course code | Reduction | From | To |
|---|---|---|---|
| BEV8003 | 5.0 | 01.09.2016 |
No
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: SPRING 2020
Language of instruction: English
Location: Trondheim
- Health Science
- Sport Science
- Human Movement Science
Department with academic responsibility
Department of Neuromedicine and Movement Science
Phone:
Examination
Examination arrangement: Practical examination
- Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
- Spring ORD Practical examination 100/100 HJELPEMIDD
-
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"