ACADEMICS
Course Details
ELE674 - Adaptive Signal Processing
2024-2025 Fall term information
The course is not open this term
ELE674 - Adaptive Signal Processing
| Program | Theoretıcal hours | Practical hours | Local credit | ECTS credit |
| MS | 3 | 0 | 3 | 8 |
| Obligation | : | Elective |
| Prerequisite courses | : | - |
| Concurrent courses | : | - |
| Delivery modes | : | Face-to-Face |
| Learning and teaching strategies | : | Lecture, Question and Answer, Problem Solving |
| Course objective | : | It is aimed to give the basics of Adaptive Signal Processing from a mathematical perspective. Adaptive Signal Processing is a major field of Statistical Signal Processing which is applied in several areas such as Communications, Control, Radar Signal Processing and Biomedical Engineering. |
| Learning outcomes | : | To make clear of the situations in which Adaptive Signal Processing ought to be used To teach Wiener Filter, and how to use it to filter, predict and smooth a signal To discuss the advantages and disadvantages of several Adaptive Signal Processing techniques and their limitings To discuss the characterics of a time varying signal, and teach how to use Adaptive Signal Processing techniques in such cases To help the student to use the Adaptive Signal Processing techniques learned in the course to his/her thesis and applications in the real world. |
| Course content | : | Statistical Processes and Models, Wiener Filters, Linear Prediction, Steepest Descent Algorithm, LMS (Least-Mean-Square), Normalised LMS, Frequency Domain and Subband Adaptive Filters, Method of Least Squares, Recursive Least Squares Adaptive Filters, Kalman Filters, Tracking of Time-varying Systems |
| References | : | Haykin, Adaptive Filter Theory, Prentice Hall, 2002. ; ; Sayed, Adaptive Filters, 2008. ; ; Farhang-Boroujeny, Signal Processing Techniques for Software Radios, 2010, lulu.com |
| Weeks | Topics |
|---|---|
| 1 | Introduction to Adaptive Signal Processing |
| 2 | Statistical Processes and Models |
| 3 | Wiener Filters |
| 4 | Linear Prediction |
| 5 | Steepest Descent Algorithm |
| 6 | LMS (Least-Mean-Square) |
| 7 | Normalised LMS |
| 8 | Frequency Domain and Subband Adaptive Filters |
| 9 | Method of Least Squares |
| 10 | Recursive Least Squares Adaptive Filters |
| 11 | Midterm |
| 12 | Kalman Filters |
| 13 | Kalman Filters |
| 14 | Tracking of Time-varying Systems |
| 15 | Final exam |
| 16 | Final exam |
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 6 | 30 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Quiz | 0 | 0 |
| Midterms | 1 | 30 |
| Final exam | 1 | 40 |
| Total | 100 | |
| Percentage of semester activities contributing grade success | 60 | |
| Percentage of final exam contributing grade success | 40 | |
| Total | 100 | |
| Course activities | Number | Duration (hours) | Total workload |
|---|---|---|---|
| Course Duration | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, etc.) | 14 | 5 | 70 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 13 | 5 | 65 |
| Quiz | 0 | 0 | 0 |
| Midterms (Study duration) | 1 | 29 | 29 |
| Final Exam (Study duration) | 1 | 34 | 34 |
| Total workload | 43 | 76 | 240 |
| Key learning outcomes | Contribution level | |||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1. | Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge. | |||||
| 2. | Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering. | |||||
| 3. | Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems. | |||||
| 4. | Designs and runs research projects, analyzes and interprets the results. | |||||
| 5. | Designs, plans, and manages high level research projects; leads multidiciplinary projects. | |||||
| 6. | Produces novel solutions for problems. | |||||
| 7. | Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects. | |||||
| 8. | Follows technological developments, improves him/herself , easily adapts to new conditions. | |||||
| 9. | Is aware of ethical, social and environmental impacts of his/her work. | |||||
| 10. | Can present his/her ideas and works in written and oral form effectively; uses English effectively. | |||||
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest