M.SC. in Computer Engineering (Without Thesis)

EEE 506 | Course Introduction and Application Information

Course Name

Adaptive Signal Processing

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
EEE 506	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	Prof. Dr. Aydın AKAN
Course Lecturer(s)	Prof. Dr. Aydın AKAN
Assistant(s)	-

Course Objectives

This course covers the fundamental theories and algorithms of adaptive systems and their applications to engineering problems. Course content includes the topics such as optimal mean-square estimation, Wiener filters. Introduction to adaptive structures and the least squares method. State space models. Kalman filters. Search techniques: Gradient and Newton methods. LMS (least mean squares), RLS (recursive least squares). Analysis of adaptive algorithms: Learning curve, convergence, stability, excess mean square error, mis-adjustment.

Learning Outcomes

The students who succeeded in this course;

know how to design and apply optimal minimum-mean-square-error and linear estimators and evaluate their performance,
know how to design, implement and apply FIR Wiener filters and evaluate their performance,
know how to design, implement and apply LMS, RLS, and Kalman filters to given applications,
be able to evaluate potential applications of different adaptive filtering approaches,
be able to design and implement various adaptive filtering algorithms using Matlab and adaptive filtering toolbox.

Course Description

Optimal mean-square estimation, Wiener filters. Introduction to adaptive structures and the least squares method. State space models. Kalman filters. Search techniques: Gradient and Newton methods. LMS (least mean squares), RLS (recursive least squares).

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Related Preparation	Learning Outcome
1	Review of Digital Signal Processing	S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd ed., 1996 (Ch. 1)
2	Introduction to Stationary Processes	S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd ed., 1996 (Ch. 1)
3	Modeling of Random Processes	S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd ed., 1996 (Ch. 5)
4	AR, MA, and ARMA Models	S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd ed., 1996 (Ch. 5)
5	Linear Prediction	S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd ed., 1996 (Ch. 6)
6	Linear Optimum Filtering	S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd ed., 1996 (Ch. 7)
7	Wiener Filter	S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd ed., 1996 (Ch. 8)
8	Midterm
9	Linear Adaptive Filtering	S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd ed., 1996 (Ch. 9)
10	Steepest Descent and LMS Algorithms	S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd ed., 1996 (Ch. 9,10)
11	RLS Adaptive Filters	S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd ed., 1996 (Ch. 11)
12	Adaptive Noise Canceling Applications	S. Haykin, Adaptive Filter Theory, Prentice Hall, 3rd ed., 1996 (Ch. 19,20)
13	Kalman Filter Theory
14	Nonlinear Adaptive Filtering
15	Review of the Course and Examples
16	Review of the Semester

Course Notes/Textbooks	The textbook referenced above and course slides
Suggested Readings/Materials	Related Research Papers

EVALUATION SYSTEM

Semester Activities	Number	Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	1	30
Presentation / Jury
Project	1	30
Seminar / Workshop
Oral Exams
Midterm	1	40
Final Exam
Total

Weighting of Semester Activities on the Final Grade	2	70
Weighting of End-of-Semester Activities on the Final Grade	1	30
Total

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	5	70
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	8	4	32
Presentation / Jury			0
Project	1	20	20
Seminar / Workshop			0
Oral Exam			0
Midterms	1	20	20
Final Exam			0
		Total	190

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	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	-	X	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	-	-	X
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	X	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	-	X
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	-	-	X
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	-	-	X	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.		-	-	-	-	X
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	X	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	X	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		-	-	X	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	X	-	-	-

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

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