ACADEMICS
Course Details
ELE 671 Spectral Estimation
2019-2020 Fall 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.
ELE671 - SPECTRAL ESTIMATION
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| SPECTRAL ESTIMATION | ELE671 | 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 | Successful students are expected to gain : Knowledge of basic spectral estimation methods used for analysis of stochastic processes and signals. | |||||
| Learning outcomes |
| |||||
| Course Content | Review of probability and stochastic processes. Periodogram and Blackman-Tukey spectral estimation. Autoregressive (AR), moving average (MA) and autoregressive-moving average (ARMA) spectral estimation. Minimum-variance spectral estimation. Sinusoidal Parameter Est. Bispectrum and polyspectrum. Spectral estimation of Nonstationary signals. Array processing. | |||||
| References | 1-P. Stoica and R. Moses, Spectral Analysis of Signals, Pearson. 2-S. Kay, Modern Spectral Estimation, Prectice-Hall. 3-L. Marple, Digital Spectral Analysis, Prentice-Hall. 4-Lecture Notes. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Review Of Probability |
| Week 2 | Power Spectral Density. |
| Week 3 | Periodogram, Avg. Periodogram, Blackman-Tukey, Welch Methods. |
| Week 4 | Parametric Modelling, Linear Prediction. |
| Week 5 | Levinson Algorithm, Maximum Entropy. |
| Week 6 | Sinusoid in White Noise. |
| Week 7 | Autocorrelation, Covariance, Modified Cov. Methods, Burg Algoritm. |
| Week 8 | Durbin?s Method(MA) , ARMA spectral Estimation. |
| Week 9 | Term Exam |
| Week 10 | Model Order Est., Minimum Variance Sp. Est., Filterbank. |
| Week 11 | Sinusoidal Parameter Est., Pisarenko, MUSIC, ESPRIT. |
| Week 12 | Higher Order Spcetrum (Bispectrum) |
| Week 13 | Nonstationary Spectral Estimation (Wigner, Wavelet Tr., Evolutionary Sp.) |
| Week 14 | Array Processing. |
| 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 | 8 | 12 |
| Presentation | 0 | 0 |
| Project | 1 | 8 |
| Seminar | 0 | 0 |
| Midterms | 1 | 30 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 50 | 50 |
| Percentage of final exam contributing grade succes | 50 | 50 |
| 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 | 6 | 84 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 1 | 15 | 15 |
| Homework assignment | 8 | 5 | 40 |
| Midterms (Study duration) | 1 | 25 | 25 |
| Final Exam (Study duration) | 1 | 30 | 30 |
| Total Workload | 39 | 84 | 236 |
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