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 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| EE303 ve EE306 |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Demonstration, Discussion, Question and Answer, Drill and Practice, Brain Storming. |
| Course Lecturer(s) |
|
| 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;
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| 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 | Adequate knowledge of subjects related to mathematics, natural sciences, and Electrical and Electronics Engineering discipline; ability to apply theoretical and applied knowledge in those fields to the solution of complex engineering problems. | X | ||||
| 2 | An ability to identify, formulate, and solve complex engineering problems, ability to choose and apply appropriate models and analysis methods for this. | X | ||||
| 3 | An ability to design a system, component, or process under realistic constraints to meet desired needs, and ability to apply modern design approaches for this. | X | ||||
| 4 | The ability to select and use the necessary modern techniques and tools for the analysis and solution of complex problems encountered in engineering applications; the ability to use information technologies effectively | X | ||||
| 5 | Ability to design and conduct experiments, collect data, analyze and interpret results for investigating complex engineering problems or discipline-specific research topics. | X | ||||
| 6 | An ability to function on multi-disciplinary teams, and ability of individual working. | X | ||||
| 7 | Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; active report writing and understanding written reports, preparing design and production reports, the ability to make effective presentation the ability to give and receive clear and understandable instructions. | X | ||||
| 8 | Awareness of the necessity of lifelong learning; the ability to access knowledge, follow the developments in science and technology and continuously stay updated. | X | ||||
| 9 | Acting compliant with ethical principles, professional and ethical responsibility, and knowledge of standards used in engineering applications. | X | ||||
| 10 | Knowledge about professional activities in business, such as project management, risk management, and change management awareness of entrepreneurship and innovation; knowledge about sustainable development. | X | ||||
| 11 | Knowledge about the impacts of engineering practices in universal and societal dimensions on health, environment, and safety. the problems of the current age reflected in the field of engineering; awareness of the legal consequences of engineering solutions. | X | ||||
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 | ||