• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

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

ASSOCIATE PROFESSOR OLCAY AKAY

Offered to

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

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 Normalized Least Mean Square Adaptive Filters 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 MTE MIDTERM EXAM 2 ASG ASSIGNMENT 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 +ASG * 0.20 +FIN * 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.20 + RST * 0.50

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 project studies during the term 2 5 10 Treating the Projects 1 10 10 Preparations before/after weekly lectures 12 10 120 Preparation for project study 2 6 12 Preparing projects 5 3 15 Project Study 2 3 6 TOTAL WORKLOAD (hours) 209

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 LO.1 5 2 4 3 4 LO.2 4 3 3 3 3 LO.3 2 3 2 3 LO.4 4 5 4 3 LO.5 3 3 4