This document provides a course syllabus for the Advanced Applied Signal Processing course offered by Blekinge Institute of Technology. The 3 credit, advanced level course aims to provide students practical experience applying signal processing theory. Topics covered include discrete-time signals, sampling theory, the discrete Fourier transform, spectral analysis, system identification methods, digital filter design including FIR and IIR filters, and adaptive filtering using the LMS algorithm. Assessment involves a project graded on a scale of A to F. Prerequisites are completing two introductory signal processing courses. The course is taught in Swedish but may be offered in English.

1. Blekinge Institute of Technology
Department of Applied Signal Processing
COURSE SYLLABUS
Avancerad Tillämpad Signalbehandling
Advanced Applied Signal Processing
7,5 ECTS credit points (7,5 högskolepoäng)
Course code: ET2571
Educational level: Advanced level
Course level: A1N
Field of education: Technology
Subject group: Electrical Engineering
Subject area: Electrical Engineering
Version: 2
Applies from: 2013-10-16
Approved: 2013-10-16
1 Course title and credit points
The course is titled Advanced Applied Signal
Processing/Avancerad Tillämpad Signalbehandling
and awards 7,5 ECTS credits. One credit point
(högskolepoäng) corresponds to one credit point in
the European Credit Transfer System (ECTS).
2 Decision and approval
This course is established by Department for
Electrical Engineering 2013-10-16. The course
syllabus was revised by School of Engineering and
applies from 2013-10-16.
Reg.no: BTH 4.1.1-0738-2013.
3 Objectives
This course is for students to gain practical
experience in signal processing, and applying signal
processing theory into practice.
4 Content
Central items of the course are:
• Basic definitions
• Practical application of basic signal processing
concepts.
• Discrete-time signals
• Relation between input and output: difference
equations, impulse response, convolution, etc.
• sampling theorem, aliasing and reconstruction
• Frequency Features
• Amplitude and fasfunktioner
• Z-transform
• Discrete Fourier transform (DFT)
• Frequency concept
• Spectral
• System identification based on methods:
• deconvolution, correlation, and least squares
adjustment
• Digital filter structures: FIR and IIR filters
• Design of FIR filters
• Adaptive Filtering
• LMS algorithm
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5 Aims and learning outcomes
Knowledge and understanding:
After completion of the course the student will have
a deeper knowledge and understanding of:
•how to practically apply signal processing theory
into practice
• sampling, aliasing and reconstruction
• spectrum estimation
• practical to design and implement digital filters:
FIR and IIR filters
• practical to use and implement adaptive filters
based on the LMS algorithm.
Skills and abilities:
After completion of the course the student will be
able to:
•applying signal processing theory in practical
applications
• demonstrate an understanding of and apply the
sampling and reconstruction
• design, use and implement basic FIR and IIR
filters
• implement and use signal processing algorithms
• implement and use adaptive filters based on the
LMS algorithm
Judgement and approach:
After completion of the course the student will have:
• created an attitude to critically evaluate and apply
signal processing theory into practice.
6 Generic skills
7 Learning and teaching
The teaching language is Swedish. However, the
teaching could be carried out in English.
8 Assessment and grading
Examination of the course
-------------------------------------------------
Code Module Credit Grade
-------------------------------------------------
Project 7.5 ECTS A-F
-------------------------------------------------
The course will be graded A Excellent, B Very good,

2. C Good, D Satisfactory, E Sufficient, FX Insufficient,
supplementation required, F Fail.If grade FX are
given, the student may after consultation with the
course coordinator / examiner get an opportunity to
within 6 weeks complement to grade E for the
specific course element.
9 Course evaluation
The course coordinator is responsible for
systematically gathering feedback from the students
in course evaluations and making sure that the
results of these feed back into the development of
the course.
10 Prerequisites
ET1203 Signal processing I, 7,5 credit points and
ET1303 Signal processing II, 7,5 credit points.
11 Field of education and subject area
The course is part of the field of education and is
included in the subject area Electrical Engineering.
12 Restrictions regarding degree
The course cannot form part of a degree with
another course, the content of which completely or
partly corresponds with the contents of this course.
13 Course literature and other teaching material
Material from the Department.
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