| Course Type | Elective Course |
|---|
| Course Level | Master's Degree With Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Learn z-transform, Laplace transform and Fourier analysis issues.
- Learn digital filter design.
- Learn power spectral estimation methods.
- Applying course units in homeworks on MATLAB or similar programs.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | No |
|---|
| Course Description | Reminder of the basic concepts in signals and systems analysis and teaching the advanced topic. To consolidate the theoretical knowledge with practice in biomedical engineering. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Fourier transform-continuous and discrete time, Discrete Fourier Transform |
|
2nd Week |
Sampling theorem, analysis of linear time invariant systems. |
|
3rd Week |
z- transform and Laplace transform: poles and zeros, stability and causality. |
|
4th Week |
Phase effects in Fourier analysis, group delay |
|
5th Week |
Time-frequency analysis: Short-time Fourier Transforms, Discrete Wavelet Transform (DWT). |
|
6th Week |
Random processes: Auto- and cross-correlation functions, Power density spectra, Energy spectra. |
|
7th Week |
Digital filter design: FIR and IIR filters designs. |
|
8th Week |
Digital filter design: FIR and IIR filters designs. |
|
9th Week |
Linear prediction :and Optimum Linear Filters : Forward and backward linear prediction, properties of linear prediction-error filter. |
|
10th Week |
Linear prediction :and Optimum Linear Filters : Forward and backward linear prediction, properties of linear prediction-error filter. |
|
11th Week |
Power Spectrum Estimation Methods: parametric and non-parametric methods. |
|
12th Week |
Power Spectrum Estimation Methods: parametric and non-parametric methods. |
|
13th Week |
Applications in biomedical signals. |
|
14th Week |
Applications in biomedical signals. |
|
| Recommended or Required Reading | Digital Signal Processing, Principles, Algorithms and Applications, 4th Ed. J.G.Proakis, D.G. Manolakis. Pearson Education, Prentice Hall.
Biomedical Signal Analysis: A Case Study Approach by Rangaraj M. Rangayyan, Wiley Interscience, 2001. |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Case Study
- Practice
- Presentation
- Discussion
- Questions/Answers
- Problem Solving
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Assignment |
3 |
30 |
| 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 |
72 |
72 |
| Preparation for Quiz |
0 |
0 |
0 |
| Individual or group work |
2 |
3 |
6 |
| Preparation for Final exam |
1 |
24 |
24 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
11 |
11 |
| Laboratory (including preparation) |
0 |
0 |
0 |
| Final exam |
1 |
96 |
96 |
| Homework |
3 |
12 |
36 |
| Total Work Load | 287 |
| Total Work Load / 30 (h) | 9.56 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
