ACADEMICS
Course Details
ELE 770 Statistical Signal Processing
2020-2021 Spring term information
The course is open this term
| Supervisor(s): | Dr. Barış Yüksekkaya | |
| Place | Day | Hours |
|---|---|---|
| Online | Friday | 14:00 - 16:45 |
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 19/04/2021.
ELE770 - STATISTICAL SIGNAL PROCESSING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| STATISTICAL SIGNAL PROCESSING | ELE770 | Any Semester/Year | 3 | 0 | 3 | 10 |
| 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 the following abilities: Knowledge of basic estimation, filtering, prediction methods such as Bayes, MAP, MLE, LMSE, Wiener, Levinson and Kalman filters. | |||||
| Learning outcomes |
| |||||
| Course Content | Metric space, inner product, norm etc. definitions. Review of Probability and stochastic processes. Estimation methods: Bayes, MAP, MLE, LMSE. Filtering, estimation and prediction methods: Wiener, Levinson ve Kalman filters. | |||||
| References | 1-T. Moon and W. Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice-Hall. 2-S.J. Orfanidis, Optimum Signal Processing, McGraww Hill. 3-S. Kay, Fundamentals of Statistical Signal Processing, Vol.I-II, Prentice Hall. 4-Lecture Notes. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Metric Spaces. |
| Week 2 | Norms, Orthogonal Spaces, Projections, Random Vectors. |
| Week 3 | Orthogonal Projections, Gram-Schmidt Orthogonalization. |
| Week 4 | Random Processes, Gaussian Processes, Markov Processes. |
| Week 5 | Random State Models. |
| Week 6 | Analysis of Systems, Spectral Factorization, Rational Modeling. |
| Week 7 | Bayesian Estimation, MAP, MLE,MSE. |
| Week 8 | LMSE. |
| Week 9 | Term Exam. |
| Week 10 | Wiener Filter. |
| Week 11 | Wiener Filter. |
| Week 12 | Levinson Filter. |
| Week 13 | Kalman Filter. |
| Week 14 | Kalman Filter. |
| 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 | 15 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 35 |
| 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 | 9 | 126 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 8 | 8 | 64 |
| Midterms (Study duration) | 1 | 30 | 30 |
| Final Exam (Study duration) | 1 | 40 | 40 |
| Total Workload | 38 | 90 | 302 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Has highest level of knowledge in certain areas of Electrical and Electronics Engineering. | X | ||||
| 2. Has knowledge, skills and and competence to develop novel approaches in science and technology. | X | ||||
| 3. Follows the scientific literature, and the developments in his/her field, critically analyze, synthesize, interpret and apply them effectively in his/her research. | X | ||||
| 4. Can independently carry out all stages of a novel research project. | X | ||||
| 5. Designs, plans and manages novel research projects; can lead multidisiplinary projects. | X | ||||
| 6. Contributes to the science and technology literature. | X | ||||
| 7. Can present his/her ideas and works in written and oral forms effectively; in Turkish or English. | X | ||||
| 8. Is aware of his/her social responsibilities, evaluates scientific and technological developments with impartiality and ethical responsibility and disseminates them. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest