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Blekinge Institute of Technology
Department of Mathematics and Natural Science

Revision: 2
Reg.no: BTH-4.1.14-0033-2025


Course syllabus

Signals and Systems, continuation course

Signals and Systems, continuation course

6 credits (6 högskolepoäng)

Course code: ET2626
Main field of study: Electrical Engineering
Disciplinary domain: Technology
Education level: Second-cycle
Specialization: A1N - Second cycle, has only first-cycle course/s as entry requirements

Language of instruction: Teaching is given in English
Applies from: 2025-02-28
Approved: 2025-02-28

1. Descision

This course is established by Dean 2023-05-03. The course syllabus is approved by Head of Department of Mathematics and Natural Science 2025-02-28 and applies from 2025-02-28.

2. Entry requirements

Admission to the course requires taken Signals and Systems, basic course, 5 credits. English 6.

3. Objective and content

3.1 Objective

The purpose of the course is to provide the student with advanced knowledge in signal processing, including detection of known signal shapes in noise, adaptive filters, and direction estimation using multiple sensors. The course prepares students for further studies and applied projects in electrical engineering, such as machine learning, sensor systems, marine technology, and industrial applications.

3.2 Content

The course includes the following:

• Detection of signals in noise
• Estimation of signal parameters
• Wiener and Kalman filtering
• Adaptive filters, primarily based on the Least Mean Square algorithm
• Brief introduction to artificial intelligence and machine learning

4. Learning outcomes

The following learning outcomes are examined in the course:

4.1. Knowledge and understanding

On completion of the course, the student will be able to:

  • demonstrate knowledge and understanding of signal detection and parameter estimation.
  • demonstrate basic knowledge and understanding of adaptive filters.
  • demonstrate basic knowledge and understanding of Wiener and Kalman filters.
  • demonstrate a general knowledge and understanding of artificial intelligence and machine learning.

4.2. Competence and skills

On completion of the course, the student will be able to:

  • apply methods for signal detection and parameter estimation.
  • use signal processing methods for direction estimation.
  • apply signal processing methods to problem-solving in technical fields.

4.3. Judgement and approach

On completion of the course, the student will be able to:

  • propose and determine appropriate approaches for solving problems in selected areas of technical fields.

5. Learning activities

The course is conducted through lectures, exercises, laboratory sessions, and assignments. A web-based course platform is used to provide students with access to course materials.

6. Assessment and grading

Modes of examinations of the course

Code Module Credit Grade
2510 On-campus Examination[1] 3.5 credits AF
2520 Laboratory session 1.5 credits GU
2530 Written assignment 1 credits GU

[1] Determines the final grade for the course, which will only be issued when all components have been approved.

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Failed result, a little more work required, F Fail.

The examiner may carry out oral follow-up of written examinations.

The information before the start of the course states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.

An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.

7. Course evaluation

The course evaluation should be carried out in line with BTH:s course evaluation template and process.

8. Restrictions regarding degree

The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.

9. Course literature and other materials of instruction

Parts of the following books are included in the course.

Steven M. Kay, Fundamentals of Statistical Signal Processing, Volume 3: Practical Algorithm Development, ISBN: 9780134878409, Pearson, 2018.

Monson H. Hayes, Statistical Digital Signal Processing and Modeling, ISBN: 9780471594314, John Wiley & Sons, 1996.

Material from the department.

This is not a legal document. If you would like a copy of the legal decision regarding this course plan, contact the registrar at Blekinge Institute of Technology.