ECTS - Statistical Signal Processing
Statistical Signal Processing (EE422) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Statistical Signal Processing | EE422 | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| EE 213 and EE 303 |
| Course Language | English |
|---|---|
| Course Type | N/A |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Demonstration, Discussion, Question and Answer, Drill and Practice. |
| Course Lecturer(s) |
|
| Course Objectives | To present fundamental skills that target the analysis of signals with stochastic properties to electrical and electronic engineering students. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Introduction to random process, detection and estimation theory, maximum variance unbiased estimation, Cramer-Rao lower bound, general minimum variance unbiased estimation, best linear unbiased estimation, maximum likelihood estimation, Least square methods of estimation, method of moments: second moments analysis, Bayesian philosophy and Bayesian |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction,overview of history and applications of detection and estimation. Review of the requisite mathematical concepts, including concepts in matrix, probability, and statistical analysis. | Glance this week’s topics from the lecture |
| 2 | Introduction and Review | Review last week and glance this week’s topics from the lecture |
| 3 | Sufficiency and Minimum Variance Unbiased (MVUB) Estimators | Glance this week’s topics from the lecture |
| 4 | Neyman-Pearson Detectors: Classifying Tests, The Testing Binary Hypothesis, Neyman-Pearson Lemma, Binary Communication, Matched Filters | Glance this week’s topics from the lecture |
| 5 | Neyman-Pearson Detectors | Review last week and glance this week’s topics from the lecture |
| 6 | Bayes Detectors: Bayes Risks for Hypothesis Testing, Minimax Tests, M-Orthogonal Signals, Likelihood Ratios | Glance this week’s topics from the lecture |
| 7 | Bayes Detectors | Review last week and glance this week’s topics from the lecture |
| 8 | Maximum Likelihood Estimators: Maximum Likelihood Principle, The Fisher Matrix and Cramer-Rao Bound, The Linear Statistical Model, Maximum Likelihood Identification of a Signal Subspace | Glance this week’s topics from the lecture |
| 9 | Maximum Likelihood Estimators | Review last week and glance this week’s topics from the lecture |
| 10 | Bayes Estimators: Bayes Risk for Parameter Estimation, Computing Bayes Risk Estimators, Sequential Bayes, The Kalman Filter, The Wiener Filter | Glance this week’s topics from the lecture |
| 11 | Bayes Estimators | Review last week and glance this week’s topics from the lecture |
| 12 | Minimum Mean-Squared Error (MMSE) Estimators: Conditional Expectation and Orthogonality, Linear MMSE Estimators, Linear Prediction, The Kalman Filter | Glance this week’s topics from the lecture |
| 13 | Minimum Mean-Squared Error (MMSE) Estimators | Review last week and glance this week’s topics from the lecture |
| 14 | Least Squares | Glance this week’s topics from the lecture |
| 15 | Final examination period | Review topics |
| 16 | Final examination period | Review topics |
Sources
| Course Book | 1. Statistical Signal Processing:Detection, Estimation and Time Series Analysis, Louis L. Scharf, Addison-Wesley, 1991. |
|---|---|
| Other Sources | 2. Fundamentals of Statistical Signal Processing: Estimation Theory, S. M. Kay, Prentice Hall, 1993. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 14 | 15 |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 2 | 40 |
| Final Exam/Final Jury | 1 | 30 |
| Toplam | 17 | 85 |
| Percentage of Semester Work | 70 |
|---|---|
| Percentage of Final Work | 30 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Adequate knowledge of subjects related to mathematics, natural sciences, and Electrical and Electronics Engineering discipline; ability to apply theoretical and applied knowledge in those fields to the solution of complex engineering problems. | X | ||||
| 2 | An ability to identify, formulate, and solve complex engineering problems, ability to choose and apply appropriate models and analysis methods for this. | X | ||||
| 3 | An ability to design a system, component, or process under realistic constraints to meet desired needs, and ability to apply modern design approaches for this. | X | ||||
| 4 | The ability to select and use the necessary modern techniques and tools for the analysis and solution of complex problems encountered in engineering applications; the ability to use information technologies effectively | X | ||||
| 5 | Ability to design and conduct experiments, collect data, analyze and interpret results for investigating complex engineering problems or discipline-specific research topics. | X | ||||
| 6 | An ability to function on multi-disciplinary teams, and ability of individual working. | X | ||||
| 7 | Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; active report writing and understanding written reports, preparing design and production reports, the ability to make effective presentation the ability to give and receive clear and understandable instructions. | X | ||||
| 8 | Awareness of the necessity of lifelong learning; the ability to access knowledge, follow the developments in science and technology and continuously stay updated. | X | ||||
| 9 | Acting compliant with ethical principles, professional and ethical responsibility, and knowledge of standards used in engineering applications. | X | ||||
| 10 | Knowledge about professional activities in business, such as project management, risk management, and change management awareness of entrepreneurship and innovation; knowledge about sustainable development. | X | ||||
| 11 | Knowledge about the impacts of engineering practices in universal and societal dimensions on health, environment, and safety. the problems of the current age reflected in the field of engineering; awareness of the legal consequences of engineering solutions. | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 14 | 3 | 42 |
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | 2 | 2 | 4 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 2 | 10 | 20 |
| Prepration of Final Exams/Final Jury | 1 | 20 | 20 |
| Total Workload | 134 | ||