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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
|
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 | |||||
| Course Level | First Cycle | |||||
| Mode of Delivery | Face-to-face | |||||
| Teaching Methods and Techniques of the Course | Narration / Presentation | |||||
| National Occupational Classification Code | - | |||||
| Course Coordinator |
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| Course Lecturer(s) |
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| Assistant(s) |
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| Course Objectives | The aim of this course is to teach students the filtering methods required for adaptive analysis of random signals. It covers modeling of random signals, denoising, Wiener filter theory and adaptive filter algorithms. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning Outcomes |
The students who succeeded in this course;
|
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| Course Description | This course includes introduction to random processes where random biological signals are observed, modeling, stationary processes, linear optimum (Wiener) filtering, linear adaptive filtering, steepest descent, LMS and RLS learning algorithms and Kalman filter theory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Related Sustainable Development Goals |
-
|
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|
|
Core Courses |
|
| Major Area Courses |
X
|
|
| Supportive Courses |
|
|
| Media and Managment Skills Courses |
|
|
| Transferable Skill Courses |
|
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Required Materials | Learning Outcome |
| 1 | Introduction to random signal processing | Chapter 0. Simon Haykin Adaptive Filter Theory 5/E. Pearson, 2013 ISBN: 9780273764106 | LO1 |
| 2 | Stationary random processes | Chapter 1. Simon Haykin Adaptive Filter Theory 5/E. Pearson, 2013 ISBN: 9780273764106 | LO1 |
| 3 | Modeling of random processes | Chapter 1. Simon Haykin Adaptive Filter Theory 5/E. Pearson 2013 ISBN: 9780273764106 | LO1 |
| 4 | Autoregressive (AR) Model Estimation | Chapter 1. Simon Haykin Adaptive Filter Theory 5/E. Pearson 2013 ISBN: 9780273764106 | LO1 |
| 5 | Linear Prediction | Chapter 3. Simon Haykin Adaptive Filter Theory 5/E. Pearson 2013 ISBN: 9780273764106 | LO1 |
| 6 | Linear, least squares estimation, Wiener filter | Chapter 2 Simon Haykin Adaptive Filter Theory 5/E. Pearson 2013 ISBN: 9780273764106 | LO2 |
| 7 | Linear adaptive filtering | Chapter 4 Simon Haykin Adaptive Filter Theory 5/E. Pearson 2013 ISBN: 9780273764106 | LO3 |
| 8 | Midterm exam | - | |
| 9 | Steepest descent learning algorithm | Chapter 5. Simon Haykin Adaptive Filter Theory 5/E. Pearson 2013 ISBN: 9780273764106 | LO4 |
| 10 | Least mean square (LMS) learning algorithm | Chapter 6. Simon Haykin Adaptive Filter Theory 5/E. Pearson, 2013 ISBN: 9780273764106 | LO4 |
| 11 | Least squares (LS) adaptive filters | Chapter 9. Simon Haykin Adaptive Filter Theory 5/E. Pearson 2013 ISBN: 9780273764106 | LO4 |
| 12 | RLS learning algorithm | Chapter 10. Simon Haykin, Adaptive Filter Theory 5/E. Pearson, 2013, ISBN: 9780273764106 | LO4 |
| 13 | Adaptive denoising solutions and applications | Chapter 13. Simon Haykin Adaptive Filter Theory 5/E. Pearson 2013 ISBN: 9780273764106 | LO5 |
| 14 | Kalman filter theory | Chapter 14. Simon Haykin Adaptive Filter Theory 5/E. Pearson 2013 ISBN: 9780273764106 | LO1 |
| 15 | Semester review | - | |
| 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 | Weighting | LO1 | LO2 | LO3 | LO4 | LO5 |
| Laboratory / Application | 1 | 30 | |||||
| Midterm | 1 | 30 | |||||
| Final Exam | 1 | 40 | |||||
| Total | 3 | 100 |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Participation | - | - | - |
| Theoretical Course Hours | 16 | 2 | 32 |
| Laboratory / Application Hours | 16 | 2 | 32 |
| Study Hours Out of Class | 14 | 4 | 56 |
| Field Work | - | - | - |
| Quizzes / Studio Critiques | - | - | - |
| Portfolio | - | - | - |
| Homework / Assignments | - | - | - |
| Presentation / Jury | - | - | - |
| Project | - | - | - |
| Seminar / Workshop | - | - | - |
| Oral Exams | - | - | - |
| Midterms | 1 | 25 | 25 |
| Final Exam | 1 | 35 | 35 |
| Total | 180 |
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
| # | PC Sub | Program Competencies/Outcomes | * Contribution Level | ||||
| 1 | 2 | 3 | 4 | 5 | |||
| No program competency data found. | |||||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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