FACULTY OF ENGINEERING

Department of Electrical and Electronics Engineering

EEE 453 | Course Introduction and Application Information

Course Name
Adaptive Filters
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
EEE 453
Fall/Spring
2
2
3
6

Prerequisites
  EEE 309 To succeed (To get a grade of at least DD)
or EEE 301 To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives The objective of this course is to introduce students the methods used for adaptive processing and filtering of biological signals. It includes modeling of random signals, noise cancelling, Wiener filter theory and adaptive filter algorithms.
Learning Outcomes The students who succeeded in this course;
  • explain the analysis and modeling of random signals;
  • define the Wiener filter concept;
  • model adaptive filter theory and its application;
  • recognize adaptive learning algorithms;
  • design adaptive filters for noise reduction problems
Course Description This course covers an introduction and modeling of random processes that generates random signals, stationary processes, linear optimum (Wiener) filtering, linear adaptive filtering, steepest descent, LMS and RLS learning algorithms and Kalman filter theory.

 



Course Category

Core Courses
Major Area Courses
X
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction to random signal processing Chap 0. Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
2 Stationary random processes Chap 1. Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
3 Modeling of random processes Chap 1. Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
4 Auto-regressive (AR) Model Estimation Chap 1. Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
5 Linear Prediction Chap 3. Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
6 Minimum mean square estimation: Wiener filter theory Chap 2 Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
7 Linear adaptive filtering Chap 4 Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
8 Midterm
9 Steepest descent learning algorithm Chap 5. Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
10 Least mean square (LMS) learning algorithm Chap 6. Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
11 Least squares (LS) adaptive filters Chap 9. Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
12 RLS learning algorithm Chap 10. Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
13 Adaptive noise cancelling solutions and applications Chap 13. Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
14 Kalman filter theory Chap 14. Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106
15 Review of the course
16 Final Exam

 

Course Notes/Textbooks

Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106

Suggested Readings/Materials

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
1
32
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
1
28
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
2
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
2
32
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
2
32
Study Hours Out of Class
14
4
56
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
1
5
5
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
20
20
Final Exam
1
35
35
    Total
180

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science and Electrical and Electronics Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

X
2

To be able to identify, define, formulate, and solve complex Electrical and Electronics Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose.

4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Electrical and Electronics Engineering applications; uses computer and information technologies effectively.

X
5

To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Electrical and Electronics Engineering research topics.

6

To be able to work efficiently in Electrical and Electronics Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

X
7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to Electrical and Electronics Engineering; to be aware of the legal ramifications of Electrical and Electronics Engineering solutions.

X
9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications

X
10

To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Electrical and Electronics Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1)

X
12

To be able to speak a second foreign language at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Electrical and Electronics Engineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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