| Course Type | Elective Course |
|---|
| Course Level | Doctorate Of Science |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Gain an ability to solve problems.
- Know and use the probablistic applications and probability theory.
- Know and use the structure of basic random processes.
- Gain an ability to calculate density and distribution functions.
- Know and develop the applications on power spectrum/spectral estimation.
- Know and use the structures of Adaptive Filters (LMS, RLS, Kalman).
- Compact the theoretical knowledge with simulations.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Discrete-Time Random Processes. Wiener Filtering. Spectrum Estimation (Minimum Variance, Maximum Entropy, MUSIC, PCA Methods). Adaptive Filtering (LMS, RLS, Square-Root Kalman Filters). Tracking of Time-Varying Systems. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Discrete-Time Random Processes. |
|
2nd Week |
Discrete-Time Random Processes. |
|
3rd Week |
Wiener Filtering. |
|
4th Week |
Wiener Filtering. |
|
5th Week |
Spectrum Estimation (Minimum Variance, Maximum Entropy, MUSIC, PCA Methods). |
|
6th Week |
Spectrum Estimation (Minimum Variance, Maximum Entropy, MUSIC, PCA Methods). |
|
7th Week |
Spectrum Estimation (Minimum Variance, Maximum Entropy, MUSIC, PCA Methods). |
|
8th Week |
Midterm Exam |
|
9th Week |
Adaptive Filtering (LMS, RLS, Square-Root Kalman Filters). |
|
10th Week |
Adaptive Filtering (LMS, RLS, Square-Root Kalman Filters). |
|
11th Week |
Adaptive Filtering (LMS, RLS, Square-Root Kalman Filters). |
|
12th Week |
Tracking of Time-Varying Systems. |
|
13th Week |
Tracking of Time-Varying Systems. |
|
14th Week |
Tracking of Time-Varying Systems. |
|
| Recommended or Required Reading | 1. M.H. Hayes, Statistical Digital Signal Processing, and Modeling, John Wiley, 1996.
2. Haykin, Adaptive Filter Theory, 4th edition , Prentice Hall, 2002.
|
|---|
| Planned Learning Activities and Teaching Methods |
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Quiz |
2 |
10 |
| Project |
1 |
15 |
| Attendance |
1 |
5 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
3 |
3 |
| Preparation for Quiz |
0 |
0 |
0 |
| Individual or group work |
14 |
8 |
112 |
| Preparation for Final exam |
1 |
40 |
40 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
30 |
30 |
| Laboratory (including preparation) |
0 |
0 |
0 |
| Final exam |
1 |
3 |
3 |
| Homework |
2 |
35 |
70 |
| Total Work Load | 300 |
| Total Work Load / 30 (h) | 10 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
