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ELE500_1

Signal Processing

This is the study programme for 2019/2020. It is subject to change.


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.

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.

Contents

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. Multirate signal processing and adaptive filters.

Required prerequisite knowledge

One of the following alternatives:
  • BIE250 Signals and systems
  • ELE300 Signals and Systems

Exam

Weight Duration Marks Aid
Written exam1/14 hoursA - FNo printed or written materials are allowed. Approved basic calculator allowed.

Coursework requirements

Exercises
7 out of 10 exercises must be approved by subject teacher within the specified deadlines.

Course teacher(s)

Course coordinator
J.H. Husøy
Head of Department
Tom Ryen

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 to

Master studies at the Faculty of Science and Technology.

Course assessment

Form and/or discussion

Literature

J.G Proakis and D.G. Manolakis, "Digital Signal Processing - Principles, algorithms, and applications", 4th ed. Prentice Hall.


This is the study programme for 2019/2020. It is subject to change.

Sist oppdatert: 13.11.2019

History