Course content

The course contains a selection of the following topics:

• Fundamental properties of analog signals in time and frequency domain
• Sampling: discrete-time signals, Nyquist sampling theorem, aliasing, basic quantization
• Frequency analysis: Fourier transform in various forms, spectral leakage, window functions
• Digital LTI systems: LTI criteria, difference equation, impulse response, convolution
• FIR filters: Frequency response and basic design methods
• IIR filters: z-transform, pole-zero maps, stability, and basic design methods
• Implementation of digital filters
• Possibly other topics

More details on the curriculum will provided during the start of semester.

Learning outcome

Knowledge

• The candidate can explain basic properties of discrete-time signals and the connection between representations in the time domain and the frequency domain.
• The candidate can explain the properties of LTI systems and how signals are affected in an LTI system.
• The candidate can explain how digital programming tools can be used for modelling and processing in signal analysis.

Skills

• The candidate can perform basic signal analysis in time and frequency domain with the use of programming tools, and can explain the connection between the two domains.
• The candidate can use programming tools for analysis of digital LTI systems.
• The candidate can calculate the output signal from a simple LTI system.
• The candidate can implement digital filters in a practical setting.

General competence

• The candidate can apply knowledge about signals and systems and their connection to a variety of domains.
• The candidate appreciates and can discuss the overall limitations in systems composed of subsystems from a signals and systems perspective.

Learning methods and activities

Learning activities generally include a mix of lectures, tutorials and practical lab/project work. A constructivist approach for learning is endorsed, with focus on problem solving and practical application of theory.

Compulsory assignments

• A selection of learning activities must be approved

Specific conditions

Recommended previous knowledge

• AIS2102 Dynamiske systemer
• AIS2002 Reguleringsteknikk
• ISTA1002 Statistikk
• IMAA2011 Matematiske metoder 2

Required previous knowledge

The course has no prerequisites.

It is a requirement that students are enrolled in the study programme to which the course belongs.

Course materials

An updated course overview, including curriculum, is presented at the start of the semester and will typically also include English material.

Credit reductions