COURSE UNIT TITLE

: ADAPTIVE FILTER THEORY

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EEE 5069 ADAPTIVE FILTER THEORY ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR OLCAY AKAY

Offered to

Industrial Ph.D. Program In Advanced Biomedical Technologies
Industrial Ph.D. Program In Advanced Biomedical Technologies
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)
Biomedical Tehnologies (English)
ELECTRICAL AND ELECTRONICS ENGINEERING NON -THESIS (EVENING PROGRAM) (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)
ELECTRICAL AND ELECTRONICS ENGINEERING (ENGLISH)

Course Objective

The main purpose of this course is to provide the mathematical theory and various applications of recursive adaptive filter algorithms.

Learning Outcomes of the Course Unit

1   To be able to explain the linear optimum filters and linear filter structures.
2   To be able to comprehend Wiener filter and linear prediction methods as fundamentals of adaptive filtering.
3   To be able to describe the Least Mean Square and Recursive Least Squares adaptive filtering techniques.
4   To be able to compare different adaptive filtering techniques and clarify the advantages and disadvantages of each technique.
5   To be able to solve signal processing problems on computer environment by performing adaptive filtering techniques.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Background on Discrete-time Signals and Stochastic Processes
2 Wiener Filter
3 Linear Backward and Forward Prediction
4 Levinson Durbin Algorithm, Cholesky Factorization
5 Method of Steepest Descent
6 Least Mean Square (LMS) Adaptive Filters
7 Comparison of LMS Algorithm with Steepest Descent Algorithm
8 Midterm Exam
9 Method of Least Squares
10 Recursive Least Squares (RLS) Adaptive Filters
11 Convergence Analysis of the RLS Algorithm
12 Kalman Filter
13 Term Project Presentations
14 Term Project Presentations

Recomended or Required Reading

Adaptive Filter Theory, Simon Haykin, Prentice Hall, Fourth Edition, 2002.

Planned Learning Activities and Teaching Methods

Lectures+Homework Assignments+Midterm Exam+Term Project Presentations

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 MTE MIDTERM EXAM
3 PRJ PROJECT
4 FCG FINAL COURSE GRADE ASG * 0.30 + MTE * 0.40 + PRJ * 0.30


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Assoc. Prof. Dr. Olcay Akay
olcay.akay@deu.edu.tr
0 232 301 7196

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 3 36
Treating the Projects 1 10 10
Preparations before/after weekly lectures 12 10 120
Preparation for project study 1 5 5
Preparing projects 1 15 15
Preparing assignments 5 2 10
Project Study 1 3 3
Midterm 1 2 2
TOTAL WORKLOAD (hours) 201

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.144
LO.2553
LO.3553
LO.4553
LO.5553

CONTACT INFORMATION
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Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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