ELE670 - STATISTICAL SIGNAL PROCESSING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| STATISTICAL SIGNAL PROCESSING | ELE670 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prequisites | 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 estimation, filtering, prediction methods such as Bayes, MAP, MLE, LMSE, Wiener, Levinson ve Kalman filters. | |||||
| Learning outcomes |
| |||||
| Course Content | Metric space, inner product, norm etc. definitions. Review of Probability and stochastic processes. Gram_Schmidt ort., Guass, Markov proc. 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 | 6 | 84 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 8 | 7 | 56 |
| Midterms (Study duration) | 1 | 25 | 25 |
| Final Exam (Study duration) | 1 | 30 | 30 |
| Total Workload | 38 | 71 | 237 |
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