PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
ELK 518	ADAPTIVE SIGNAL PROCESSING - I	3 + 0	2nd Semester	7,5

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	To provide graduate students with the theoretical basis of adaptive signal processing, and to gain students the ability to apply adaptive filtering techniques to real-world problems (e.g. noise cancellation, equalization, interferrence cancellation) in order to improve the performance over static, fixed filtering techniques.
Course Content	Adaptive systems / Fundamentals of adaptive filtering / Newton and Steepest-Descent algorithms / The Least Mean-Squares (LMS) algorithm / LMS-based algorithms / The Recursive Least-Squares (RLS) algorithm / RLS-based algorithms / Adaptive IIR filtering / Subband adaptive filtering / Blind adaptive filtering.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	One knows the concepts of adaptive signal processing.
2	One knows and applies the Steepest descent algorithm.
3	One knows and applies LMS and NLMS based adaptive filters.
4	One knows and applies the method of least squares.
5	One knows and implements RLS based adaptive filters.
6	One can solve noise and echo cancellation problems.

COURSE'S CONTRIBUTION TO PROGRAM

	PO 01	PO 02	PO 03	PO 04	PO 05	PO 06	PO 07	PO 08	PO 09	PO 10	PO 11
LO 001	5	5	3	2	3	4		5
LO 002	5	5	4	2	5	5		3
LO 003	5	5	4	2	5	5		3
LO 004	5	5	4	2	5	5		3
LO 005	5	5	4	2	5	5		3
LO 006	5	5	3	2	4	5		5
Sub Total	30	30	22	12	27	29		22
Contribution	5	5	4	2	5	5	0	4	0	0	0

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	3	42
Assignments	4	10	40
Report / Project	1	71	71
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2010-2011 Spring	1	AYDIN KIZILKAYA
Details	2009-2010 Fall	1	AYDIN KIZILKAYA

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
ELK 518	ADAPTIVE SIGNAL PROCESSING - I	3 + 0	1	Turkish	2010-2011 Spring

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Prof. Dr. AYDIN KIZILKAYA	akizilkaya@pau.edu.tr		Course location is not specified.	%

Goals		To provide graduate students with the theoretical basis of adaptive signal processing, and to gain students the ability to apply adaptive filtering techniques to real-world problems (e.g. noise cancellation, equalization, interferrence cancellation) in order to improve the performance over static, fixed filtering techniques.
Content		Adaptive systems / Fundamentals of adaptive filtering / Newton and Steepest-Descent algorithms / The Least Mean-Squares (LMS) algorithm / LMS-based algorithms / The Recursive Least-Squares (RLS) algorithm / RLS-based algorithms / Adaptive IIR filtering / Subband adaptive filtering / Blind adaptive filtering.

Topics

Weeks	Topics
1	Introduction to adaptive filtering
2	Fundamentals of adaptive filtering and applications
3	Fundamentals of adaptive filtering and applications
4	Fundamentals of adaptive filtering and applications
5	The Least-Mean-Square (LMS) algorithm
6	The Least-Mean-Square (LMS) algorithm
7	LMS-Based algorithms
8	LMS-Based algorithms
9	LMS-Based algorithms
10	Least-Squares (LS) and conventional Recursive Least Square (RLS) algorithm
11	Least-Squares (LS) and conventional Recursive Least Square (RLS) algorithm
12	Least-Squares (LS) and conventional Recursive Least Square (RLS) algorithm
13	Data-Selective adaptive filtering
14	Data-Selective adaptive filtering

Materials

Materials are not specified.

Resources

Course Assessment

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student