ACADEMICS
Course Details
ELE 674 Adaptive Signal Processing
2020-2021 Spring term information
The course is not open this term
Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.
Course definition tables are extracted from the ECTS Course Catalog web site of Hacettepe University (http://akts.hacettepe.edu.tr) in real-time and displayed here. Please check the appropriate page on the original site against any technical problems. Course data last updated on 25/01/2021.
ELE674 - ADAPTIVE SIGNAL PROCESSING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| ADAPTIVE SIGNAL PROCESSING | ELE674 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prerequisite(s) | None. | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Problem Solving | |||||
| Instructor (s) | Department Faculty | |||||
| 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 |
| |||||
| 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 | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction to Adaptive Signal Processing |
| Week 2 | Statistical Processes and Models |
| Week 3 | Wiener Filters |
| Week 4 | Linear Prediction |
| Week 5 | Steepest Descent Algorithm |
| Week 6 | LMS (Least-Mean-Square) |
| Week 7 | Normalised LMS |
| Week 8 | Frequency Domain and Subband Adaptive Filters |
| Week 9 | Method of Least Squares |
| Week 10 | Recursive Least Squares Adaptive Filters |
| Week 11 | Midterm |
| Week 12 | Kalman Filters |
| Week 13 | Kalman Filters |
| Week 14 | Tracking of Time-varying Systems |
| Week 15 | Final exam |
| Week 16 | Final exam |
Assesment methods
| 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 |
| Midterms | 1 | 30 |
| Final exam | 1 | 40 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 0 | 60 |
| Percentage of final exam contributing grade succes | 0 | 40 |
| Total | 100 | |
Workload and ECTS calculation
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 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, ect) | 14 | 5 | 70 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 13 | 5 | 65 |
| Midterms (Study duration) | 1 | 29 | 29 |
| Final Exam (Study duration) | 1 | 34 | 34 |
| Total Workload | 43 | 76 | 240 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition 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. | X | ||||
| 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. | X | ||||
| 3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems. | X | ||||
| 4. Designs and runs research projects, analyzes and interprets the results. | X | ||||
| 5. Designs, plans, and manages high level research projects; leads multidiciplinary projects. | X | ||||
| 6. Produces novel solutions for problems. | X | ||||
| 7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects. | X | ||||
| 8. Follows technological developments, improves him/herself , easily adapts to new conditions. | X | ||||
| 9. Is aware of ethical, social and environmental impacts of his/her work. | X | ||||
| 10. Can present his/her ideas and works in written and oral form effectively; uses English effectively | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest