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 | Possesses sufficient knowledge in mathematics, natural sciences, and discipline-specific topics in Electrical and Electronics Engineering; uses this theoretical and practical knowledge to solve complex engineering problems. | X | ||||
| 2 | Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analytical and modeling methods for this purpose. | X | ||||
| 3 | Designs complex systems, processes, devices, or products under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, depending on the nature of the design.) | X | ||||
| 4 | Selects and uses modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications; effectively uses information technologies. | X | ||||
| 5 | Designs experiments, conducts tests, collects data, analyzes, and interprets results to investigate complex engineering problems or discipline-specific research topics. | X | ||||
| 6 | Works effectively in disciplinary and interdisciplinary teams; develops the ability to work independently. | X | ||||
| 7 | Communicates effectively in both written and verbal forms; possesses proficiency in at least one foreign language; writes effective reports, understands written reports, prepares design and production reports, delivers effective presentations, and gives and receives clear instructions. | X | ||||
| 8 | Recognizes the need for lifelong learning; accesses information, follows developments in science and technology, and continuously renews oneself. | X | ||||
| 9 | Acts in accordance with ethical principles, assumes professional and ethical responsibility, and possesses knowledge about the standards used in engineering practices. | X | ||||
| 10 | Possesses knowledge about professional practices such as project management, risk management, and change management; gains awareness of entrepreneurship and innovation; understands the principles of sustainable development. | X | ||||
| 11 | Understands the universal and societal impacts of engineering practices on health, environment, and safety; recognizes the contemporary issues reflected in the field of engineering and understands the legal implications 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 | ||