ACADEMICS
Course Details
ELE670 - Statistical Signal Processing
2024-2025 Fall term information
The course is not open this term
ELE670 - Statistical Signal Processing
| Program | Theoretýcal hours | Practical hours | Local credit | ECTS credit |
| MS | 3 | 0 | 3 | 8 |
| Obligation | : | Elective |
| Prerequisite courses | : | - |
| Concurrent courses | : | - |
| Delivery modes | : | Face-to-Face |
| Learning and teaching strategies | : | Lecture, Question and Answer, Problem Solving |
| 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 | : | A student completing the course successfully will Recognizes statistical signal processing problems, Models problems encountered in suitable forms, Knows which algorithms be used to solve problems established, knows advantages and disadvantages of these algorithms, Applies the techniques and algorithms learnt in the class in project and other applications, Has the adequate knowledge to follow and understand advanced up-to-date algorithms. |
| 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. |
| Weeks | Topics |
|---|---|
| 1 | Metric Spaces. |
| 2 | Norms, Orthogonal Spaces, Projections, Random Vectors. |
| 3 | Orthogonal Projections, Gram-Schmidt Orthogonalization. |
| 4 | Random Processes, Gaussian Processes, Markov Processes. |
| 5 | Random State Models. |
| 6 | Analysis of Systems, Spectral Factorization, Rational Modeling. |
| 7 | Bayesian Estimation, MAP, MLE,MSE. |
| 8 | LMSE. |
| 9 | Term Exam. |
| 10 | Wiener Filter. |
| 11 | Wiener Filter. |
| 12 | Levinson Filter. |
| 13 | Kalman Filter |
| 14 | Kalman Filter |
| 15 | Final exam |
| 16 | Final exam |
| 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 |
| Quiz | 0 | 0 |
| Midterms | 1 | 35 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade success | 50 | |
| Percentage of final exam contributing grade success | 50 | |
| Total | 100 | |
| Course activities | Number | Duration (hours) | Total workload |
|---|---|---|---|
| Course Duration | 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, etc.) | 14 | 6 | 84 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 8 | 7 | 56 |
| Quiz | 0 | 0 | 0 |
| Midterms (Study duration) | 1 | 25 | 25 |
| Final Exam (Study duration) | 1 | 30 | 30 |
| Total workload | 38 | 71 | 237 |
| Key learning outcomes | Contribution 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. | |||||
| 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. | |||||
| 3. | Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems. | |||||
| 4. | Designs and runs research projects, analyzes and interprets the results. | |||||
| 5. | Designs, plans, and manages high level research projects; leads multidiciplinary projects. | |||||
| 6. | Produces novel solutions for problems. | |||||
| 7. | Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects. | |||||
| 8. | Follows technological developments, improves him/herself , easily adapts to new conditions. | |||||
| 9. | Is aware of ethical, social and environmental impacts of his/her work. | |||||
| 10. | Can present his/her ideas and works in written and oral form effectively; uses English effectively. | |||||
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest