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Syllabus-ET2542-Adaptive Signal Processing

Main Uddin-Al-Hasan
Main Uddin-Al-Hasan
Main Uddin-Al-HasanElectrical Engineer and Professional Programmer at AHIIRO Systems

Syllabus-ET2542-Adaptive Signal Processing

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Blekinge Institute of Technology
Department of Applied Signal Processing
COURSE SYLLABUS
Adaptiv signalbehandling
Adaptive Signal Processing
7,5 ECTS credit points (7,5 högskolepoäng)
Course code: ET2542
Educational level: Advanced level
Course level: A1N
Field of education: Technology
Subject group: Electrical Engineering
Subject area: Electrical Engineering
Version: 5
Applies from: 2013-11-20
Approved: 2013-11-20
Replaces course syllabus approved: 2009-11-01
1 Course title and credit points
The course is titled Adaptive Signal
Processing/Adaptiv 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 2013-11-20. The course
syllabus was revised by School of Engineering and
applies from 2013-11-20.
Reg.no: BTH 4.1.1-0818-2013.
Replaces ET2432.
3 Objectives
The student will acquire the background to and
knowledge of adaptive and optimal systems. The
student will also acquire insights into and
experiences of applied signal processing problems
of which these systems form part.
4 Content
Central items of the course are:
Stochastic signals
Discrete stochastic processes, correlation, spectral
density, cross spectrum, models for stochastic
signals.
Optimal signal processing
Estimation, least square error, the normal equations,
least square filter, prediction, inverted filtration.
Adaptive signal processing
Introduction of the adaptive concept, iterative
solution, the adaptive LMS filter, stability,
convergence. Applications such as noise reduction,
signal improvement and echo extinction.
Algorithms, variants of the LMS.
Laboratory work
Software based laboratory work.
5 Aims and learning outcomes
page 1
On completion of the course the student will be able
to:
•design and implement the Wiener filter
•recognize situations where adaptive systems may
provide a good solution
6 Generic skills
The following generic skills are trained in the
course:
• Capacity for applying knowledge in practice.
• Capacity for analysis and synthesis
• General knowledge in the subject area of the
studies.
7 Learning and teaching
The teaching consists of lectures, exercises, home
assignments, and laboratory work. The home
assignments are compulsory and must be done
individually. During arithmetical problems the
exercise instructor illustrates how the theory that
has been learnt should be applied on signal
processing problems. In order to further explain the
theory and its applications there is a compulsory
laboratory work. The laboratory work may be
carried out individually or in groups.
The teaching language is English.
The teaching language is English.
8 Assessment and grading
Examination of the course
-------------------------------------------------
Code Module Credit Grade
-------------------------------------------------
Written examination[1] 6 ECTS A-F
Laboratory work + written assignment 1.5 ECTS
G-U
-------------------------------------------------
1 Determines the final grade for the course, which
will only be issued when all components have been
approved.
The course will be graded A Excellent, B Very good,
C Good, D Satisfactory, E Sufficient, FX Insufficient,
supplementation required, F Fail.If grade FX or UX
are given, the student may after consultation with
the course coordinator / examiner get an
opportunity to within 6 weeks complement to grade
E or G for the specific course element.
The examination is done through a written exam
together with an account of the compulsory home
assignments, and the laboratory work assignments.
Grading of the laboratory work assignments is done
with the grades Pass or Fail, with the grade of Pass
required for obtaining a final grade of the course.
This final grade will be the same as the examination
grade.
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
Required courses for admission to this course:
ET1303 Signal Processing II and
MS1101 Mathematical Statistics
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 Additional information
The course MS1102 Stochastic Processes is
recommended as previous knowledge but does not
constitute a formal requirement.
14 Course literature and other teaching material
Monson H. Hayes Statistical Digital Signal Processing
and Modeling, Wiley 1996. ISBN 0-471-59431-8.
Material from the department.
s
page 2

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Syllabus-ET2542-Adaptive Signal Processing

  • 1. Blekinge Institute of Technology Department of Applied Signal Processing COURSE SYLLABUS Adaptiv signalbehandling Adaptive Signal Processing 7,5 ECTS credit points (7,5 högskolepoäng) Course code: ET2542 Educational level: Advanced level Course level: A1N Field of education: Technology Subject group: Electrical Engineering Subject area: Electrical Engineering Version: 5 Applies from: 2013-11-20 Approved: 2013-11-20 Replaces course syllabus approved: 2009-11-01 1 Course title and credit points The course is titled Adaptive Signal Processing/Adaptiv 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 2013-11-20. The course syllabus was revised by School of Engineering and applies from 2013-11-20. Reg.no: BTH 4.1.1-0818-2013. Replaces ET2432. 3 Objectives The student will acquire the background to and knowledge of adaptive and optimal systems. The student will also acquire insights into and experiences of applied signal processing problems of which these systems form part. 4 Content Central items of the course are: Stochastic signals Discrete stochastic processes, correlation, spectral density, cross spectrum, models for stochastic signals. Optimal signal processing Estimation, least square error, the normal equations, least square filter, prediction, inverted filtration. Adaptive signal processing Introduction of the adaptive concept, iterative solution, the adaptive LMS filter, stability, convergence. Applications such as noise reduction, signal improvement and echo extinction. Algorithms, variants of the LMS. Laboratory work Software based laboratory work. 5 Aims and learning outcomes page 1 On completion of the course the student will be able to: •design and implement the Wiener filter •recognize situations where adaptive systems may provide a good solution 6 Generic skills The following generic skills are trained in the course: • Capacity for applying knowledge in practice. • Capacity for analysis and synthesis • General knowledge in the subject area of the studies. 7 Learning and teaching The teaching consists of lectures, exercises, home assignments, and laboratory work. The home assignments are compulsory and must be done individually. During arithmetical problems the exercise instructor illustrates how the theory that has been learnt should be applied on signal processing problems. In order to further explain the theory and its applications there is a compulsory laboratory work. The laboratory work may be carried out individually or in groups. The teaching language is English. The teaching language is English. 8 Assessment and grading Examination of the course ------------------------------------------------- Code Module Credit Grade ------------------------------------------------- Written examination[1] 6 ECTS A-F Laboratory work + written assignment 1.5 ECTS G-U ------------------------------------------------- 1 Determines the final grade for the course, which will only be issued when all components have been approved. The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Insufficient, supplementation required, F Fail.If grade FX or UX
  • 2. are given, the student may after consultation with the course coordinator / examiner get an opportunity to within 6 weeks complement to grade E or G for the specific course element. The examination is done through a written exam together with an account of the compulsory home assignments, and the laboratory work assignments. Grading of the laboratory work assignments is done with the grades Pass or Fail, with the grade of Pass required for obtaining a final grade of the course. This final grade will be the same as the examination grade. 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 Required courses for admission to this course: ET1303 Signal Processing II and MS1101 Mathematical Statistics 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 Additional information The course MS1102 Stochastic Processes is recommended as previous knowledge but does not constitute a formal requirement. 14 Course literature and other teaching material Monson H. Hayes Statistical Digital Signal Processing and Modeling, Wiley 1996. ISBN 0-471-59431-8. Material from the department. s page 2