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	Prof. Dr. Aydın Akan
Course Lecturer(s)	Prof. Dr. Aydın Akan
Assistant(s)	Araş. Gör. Burak Akbuğday

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;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	Explain the analysis and modeling of random signals	1.5		X
LO2	Define the concept of Wiener filter;	1.5				X
LO3	Model the theory and applications of adaptive filters;	1.4			X
LO4	Recognize adaptive learning algorithms;	1.4				X
LO5	Design adaptive filters for denoising problems.	3.1		X

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

Course Category	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
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computation, and related engineering discipline-specific topics; the ability to apply this knowledge to solve complex engineering problems.
	1	Mathematics
	2	Science
	3	Basic Engineering
	4	Computation			LO3	LO4
	5	Related engineering discipline-specific topics		LO1		LO2
	6	The ability to apply this knowledge to solve complex engineering problems
2	Problem Analysis: Ability to identify, formulate and analyze complex engineering problems using basic knowledge of science, mathematics and engineering, and considering the UN Sustainable Development Goals relevant to the problem being addressed.
3	Engineering Design: The ability to devise creative solutions to complex engineering problems; the ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions.
	1	Ability to design creative solutions to complex engineering problems		LO5
	2	Ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions
4	Use of Techniques and Tools: Ability to select and use appropriate techniques, resources, and modern engineering and computing tools, including estimation and modeling, for the analysis and solution of complex engineering problems, while recognizing their limitations.
5	Research and Investigation: Ability to use research methods to investigate complex engineering problems, including literature research, designing and conducting experiments, collecting data, and analyzing and interpreting results.
	1	Literature research for the study of complex engineering problems
	2	Designing experiments
	3	Ability to use research methods, including conducting experiments, collecting data. analyzing and interpreting results
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals; awareness of the legal implications of engineering solutions.
	1	Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals
	2	Awareness of the legal implications of engineering solutions
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility; awareness of being impartial, without discrimination, and being inclusive of diversity.
	1	Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility ethical responsibility
	2	Awareness of being impartial and inclusive of diversity, without discriminating on any subject
8	Individual and Teamwork: Ability to work effectively, individually and as a team member or leader on interdisciplinary and multidisciplinary teams (face-to-face, remote or hybrid).
	1	Ability to work individually and within the discipline
	2	Ability to work effectively as a team member or leader in multidisciplinary teams (face-to-face, remote or hybrid)
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession) on technical issues.
	1	Ability to communicate verbally
	2	Ability to communicate effectively in writing
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
	1	Knowledge of business practices such as project management and economic feasibility analysis
	2	Awareness of entrepreneurship and innovation
11	Lifelong Learning: Lifelong learning skills that include being able to learn independently and continuously, adapting to new and developing technologies, and thinking questioningly about technological changes.