## Type of course

The course is available as a singular course. The course is also available to exchange students and Fulbright students.

Admission requirements are generell studiekompetanse + SIVING. (matematikk R1 (eller matematikk S1 og S2) og R2 og fysikk 1).

Local admission, application code 9391 - singular courses in engineering sciences. The course is also available to exchange students and Fulbright students.

## Course overlap

If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:

FYS-260 Signal analysis (introduction) 9 ects

## Course content

The course outlines the fundamental importance of sinusoids, in the form of complex exponential functions, as building blocks of signals, and provides an introduction to frequency (spectrum) analysis. Sampling and aliasing is covered, and the processing of discrete signals using FIR and IIR filters both in the time (impulse response), frequency (system function) and Z-domain is explained. The continuous-time Fourier transform is introduced, with applications in amplitude modulation and sampling, leading to the definition of the discrete Fourier transforms (DFT and FFT). Exercises are emphasized. Practical signal processing using programming is also emphasized, hence basic programming skills are advantageous.

## Recommended prerequisites

MAT-1003 Calculus 3, MAT-1004 Linear algebra

## Objectives of the course

Knowledge - The student can

• define the role of sinusoids in signals, e.g. for synthesizing music
• describe the process of sampling and discretization of signals
• define properties of different systems for processing signals
• explain the complementary properties of time and frequency analysis
• understand the Fourier transform of signals and its use

Skills - The student can

• determine the correct sampling frequency for signal discretization
• implement signal processing solutions in Python - process discrete signals using FIR and IIR systems
• design filters for noise removal
• analyze and design processing systems both in terms of time and frequency
• use the Fourier-transform of a signal for frequency analysis and processing

General competence - The student can

• appreciate the importance of signal processing in a society with signals everywhere
• work with signal processing for data analysis
• program scripts and functions in Python

## Language of instruction and examination

The language of instruction is English and all of the syllabus material is in English. Examination questions will be given in English, but may be answered either in English or a Scandinavian language.

## Teaching methods

Lectures: 40 hours Exercises: 40 hours

## Examination

Examination: Date: Weighting: Duration: Grade scale:
Portfolio 31.10.2024 14:00 (Hand in) 3/10 A–E, fail F
School exam 28.11.2024 09:00
7/10 4 Hours A–E, fail F
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