Digital Signal Processing, 6 credits
Digital signalbehandling, 6 hp
TSRT78
Main field of study
Electrical EngineeringCourse level
Second cycleCourse type
Programme courseExaminer
Fredrik GustafssonDirector of studies or equivalent
Johan LöfbergEducation components
Preliminary scheduled hours: 57 hRecommended self-study hours: 103 h
Available for exchange students
YesMain field of study
Electrical EngineeringCourse level
Second cycleAdvancement level
A1XCourse offered for
- Communication Systems, Master's Programme
- Computer Science and Engineering, M Sc in Engineering
- Industrial Engineering and Management - International, M Sc in Engineering
- Industrial Engineering and Management, M Sc in Engineering
- Information Technology, M Sc in Engineering
- Biomedical Engineering, M Sc in Engineering
- Computer Science and Software Engineering, M Sc in Engineering
- Mechanical Engineering, M Sc in Engineering
- Applied Physics and Electrical Engineering - International, M Sc in Engineering
- Applied Physics and Electrical Engineering, M Sc in Engineering
Entry requirements
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.
Prerequisites
Signal Theory, Signals and Systems.Intended learning outcomes
The student should after the course have the ability to describe the most important methods and algorithms for signal processing, and be able to apply these on signals of various kinds. More specifically, after the course the student should have the ability to
- Compute the discrete Fourier transform (DFT) and understand scaling effects and practical limitations implied by finite data length and sampling.
- Use the DFT for filtering and know how circular convolution is avoided.
- Explain the basic signal models and their relationship.
- Perform transform-based and model-based spectral analysis, and understand the compromise between resolution and noise suppression.
- Describe the theory for model estimation, and be able to apply algorithms for this purpose and validate an estimated model.
- Describe the basics in optimal filtering and be able to compute a Wiener filter for simple examples.
- Describe the premises for Kalman filtering, and be able to apply a Kalman filter to data and tune it to compromise tracking speed, transient behavior and noise suppression.
- Describe the most important adaptive filters, some common applications, and be able to apply and tune an adaptive filter to compromise parameter tracking speed and noise suppression.
Course content
Transforms of signals and systems, in particular the discrete Fourier transform.
Spectral analysis. Filter design. Modelling of signals. Filter design using statistical theory, Wiener and Kalman filters. Methods and applications in adaptive filtering, for example noise cancelling.
Teaching and working methods
The course is organized in lectures/classes and laboratory work.
Examination
LAB1 | Laboratory Work | 1 credits | U, G |
DAT1 | Written/Computer Examination | 5 credits | U, 3, 4, 5 |
Grades
Four-grade scale, LiU, U, 3, 4, 5Other information
Supplementary courses: Sensor fusion
Department
Institutionen för systemteknikDirector of Studies or equivalent
Johan LöfbergExaminer
Fredrik GustafssonCourse website and other links
http://www.control.isy.liu.se/student/tsrt78/Education components
Preliminary scheduled hours: 57 hRecommended self-study hours: 103 h
Course literature
Additional literature
Books
Code | Name | Scope | Grading scale |
---|---|---|---|
LAB1 | Laboratory Work | 1 credits | U, G |
DAT1 | Written/Computer Examination | 5 credits | U, 3, 4, 5 |
Regulations (apply to LiU in its entirety)
The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.
LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva.
Additional literature
Books
Note: The course matrix might contain more information in Swedish.
I | U | A | Modules | Comment | ||
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1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
1.1 Knowledge of underlying mathematics and science (G1X level) |
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X
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DAT1
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1.2 Fundamental engineering knowledge (G1X level) |
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X
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X
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DAT1
LAB1
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1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
X
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X
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LAB1
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1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
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1.5 Insight into current research and development work |
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2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
2.1 Analytical reasoning and problem solving |
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X
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DAT1
LAB1
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2.2 Experimentation, investigation, and knowledge discovery |
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X
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X
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DAT1
LAB1
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2.3 System thinking |
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X
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X
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DAT1
LAB1
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2.4 Attitudes, thought, and learning |
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X
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DAT1
LAB1
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2.5 Ethics, equity, and other responsibilities |
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3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
3.1 Teamwork |
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X
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LAB1
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3.2 Communications |
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X
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LAB1
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3.3 Communication in foreign languages |
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4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
4.1 External, societal, and environmental context |
X
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4.2 Enterprise and business context |
X
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4.3 Conceiving, system engineering and management |
X
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X
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DAT1
LAB1
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4.4 Designing |
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4.5 Implementing |
X
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X
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DAT1
LAB1
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4.6 Operating |
X
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5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
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5.2 Economic conditions for knowledge development |
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5.3 Identification of needs, structuring and planning of research or development projects |
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5.4 Execution of research or development projects |
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5.5 Presentation and evaluation of research or development projects |
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