Signal Processing (ELE500)

We are surrounded by signal processing in our everyday lives. We find signal processing in areas such as telecommunication, transfer and storage of digital data (e.g. Jpeg, mpeg, mp3) as well as in interpretation and analysis of various data, such as medical data or seismic data etc. This course will deal with fundamental methods and techniques for digital signal processing.


Course description for study year 2024-2025. Please note that changes may occur.

Facts

Course code

ELE500

Version

1

Credits (ECTS)

10

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

Norwegian

Offered by
Time table

View course schedule

Content

Discrete-time signals and systems. Analysis of linear, time invariant (LTI) systems. The Fourier transform and the Z-transform. Analysis, design, and implementation of digital filters.Sampling and reconstruction of signals. Statistical signal analysis, linear prediction and Wiener filtering. Spectral estimation and adaptive filters.

Learning outcome

Knowledge:

The student shall understand how signals can be studied in time or frequency domains, and that transforms can be applied to transform a signal form one domain to another. The student shall understand the purposes of digital filtering, and how this can be accomplished. Some knowledge of the design of digital filters is also expected. The student shall be familiar with fundamental concepts of stochastic signal analysis.

Skills:

The student shall be capable of using mathematical and statistical analysis in the study and design of signal processing systems and have the ability to expoit the programming environment Matlab in the simulation of such systems.

General competence:

At the end of this course the student shall have an understanding for the fundamental signal processing concepts from a broad perspective and a perception of its possible application areas.

Required prerequisite knowledge

None

Exam

Form of assessment Weight Duration Marks Aid
Written exam 1/1 4 Hours Letter grades No printed or written materials are allowed. Approved basic calculator allowed

Written exam with pen and paper.

Coursework requirements

Exercises
5 out of 9 exercises must be approved by course teacher within the specified deadlines.

Course teacher(s)

Course coordinator:

Sven Ole Aase

Method of work

5-6 hours of lectures 2 hours of problem solving each week.

Overlapping courses

Course Reduction (SP)
Signal Processing (MIK100_1) 10

Open for

Admission to Single Courses at the Faculty of Science and Technology
Industrial Economics - Master of Science Degree Programme Robot Technology and Signal Processing - Master of Science Degree Programme

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

The syllabus can be found in Leganto