ECTS - Adaptive Systems and Signal Processing

Adaptive Systems and Signal Processing (EE424) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Adaptive Systems and Signal Processing EE424 3 0 0 3 5
Pre-requisite Course(s)
EE 303 and EE 306
Course Language English
Course Type N/A
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Demonstration, Discussion, Question and Answer, Drill and Practice, Brain Storming.
Course Coordinator
Course Lecturer(s)
  • Asst. Prof. Dr. Hakan Tora
  • Asst. Prof. Dr. İ.Baran Uslu
Course Assistants
Course Objectives •Provide students with an understanding of adaptive filtering applications, structures, algorithms, and performance •Describe the classes of problem where adaptive filtering might be applied •Describe the implementation of the LMS and RLS adaptation algorithms •Introduce the basic principles of Kalman filtering and the Forward Backward algorithm
Course Learning Outcomes The students who succeeded in this course;
  • Ability to design and apply minimum mean square estimators
  • Ability to design, implement and apply Wiener filters and evaluate their performance
  • Ability to use a combination of theory and software implementations to solve adaptive signal problems
  • Ability to identify applications in which it would be possible to use different adaptive filtering approaches
  • Ability to analyze the accuracy and determine advantages and disadvantages of each method
  • Ability to use computer tools (such as Matlab) in developing and testing stochastic DSP algorithms
  • Ability to complete a term project
Course Content Applications of adaptive filtering, autoregressive and moving average processes, linear prediction, lattice filters, Least Mean Square (LMS) algorithm, least squares filtering, convergence analysis, Recursive Least Squares Estimation(RLS), Kalman Filters

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction to Adaptive Filtering: Identification, Inverse Modeling, Prediction, Interference Cancelling Glance this week’s topics from the lecture
2 Linear Optimum Filtering: Wiener Filters Review last week and glance this week’s topics from the lecture
3 Linear Prediction: Forward Linear Prediction, Backward Linear Prediction, Levinson-Durbin Algorithm Glance this week’s topics from the lecture
4 Linear Prediction: Lattice Filters Review last week and glance this week’s topics from the lecture
5 Gradient Based Adaptation: Steepest Descent Algorithm Glance this week’s topics from the lecture
6 Stochastic Gradient Based Adaption: Least Mean Square (LMS) Algorithm Glance this week’s topics from the lecture
7 The LMS Algorithm Review last week and glance this week’s topics from the lecture
8 Variants of the LMS Algorithm: Normalized LMS (NLMS) Algorithm Review last week and glance this week’s topics from the lecture
9 Frequency Domain and Subband Adaptive Filters Glance this week’s topics from the lecture
10 Frequency Domain and Subband Adaptive Filters Review last week and glance this week’s topics from the lecture
11 Linear Least Square (LS) Filtering: Linear LS Estimation Problem; Normal Equations and LS Filters; Properties of Least-Squares Estimates; Singular Value Decomposition Glance this week’s topics from the lecture
12 Recursive Least Squares Estimation: Exponentially Weighted Least Squares; Recursive in time solution; Initialization of the algorithm; Recursion for MSE criterion; Applications: Noise Canceller, Channel Equalization, Echo Cancellation Glance this week’s topics from the lecture
13 Kalman Filters: Statement of the Kalman Filtering Problem; Innovation Process; Estimation of the State; Filtering; Initial Conditions; The Extended Kalman Filter Glance this week’s topics from the lecture
14 Kalman Filters Review last week and glance this week’s topics from the lecture
15 Final examination period Review topics
16 Final examination period Review topics

Sources

Course Book 1. Adaptive Filter Theory, S.Haykin, 4th Edition, Prentice Hall, 2002
Other Sources 2. Adaptive Signal Processing, B.Widrow and S.Stearns, Prentice Hall, 1985

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 15 15
Presentation - -
Project 1 15
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 40
Final Exam/Final Jury 1 30
Toplam 19 100
Percentage of Semester Work 70
Percentage of Final Work 30
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Ability to apply knowledge on Mathematics, Science and Engineering to advanced systems.
2 Implementing long-term research and development studies in major areas of Electrical and Electronics Engineering.
3 Ability to use modern engineering tools, techniques and facilities in design and other engineering applications.
4 Graduating researchers active on innovation and entrepreneurship.
5 Ability to report and present research results effectively.
6 Increasing the performance on accessing information resources and on following recent developments in science and technology.
7 An understanding of professional and ethical responsibility.
8 Increasing the performance on effective communications in both Turkish and English.
9 Increasing the performance on project management.
10 Ability to work successfully at project teams in interdisciplinary fields.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project 1 10 10
Report
Homework Assignments 6 3 18
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 2 6 12
Prepration of Final Exams/Final Jury 1 3 3
Total Workload 123