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 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| EE303 ve EE213 |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| 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 | Possesses sufficient knowledge in mathematics, natural sciences, and discipline-specific topics in Electrical and Electronics Engineering; uses this theoretical and practical knowledge to solve complex engineering problems. | X | ||||
| 2 | Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analytical and modeling methods for this purpose. | X | ||||
| 3 | Designs complex systems, processes, devices, or products under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, depending on the nature of the design.) | X | ||||
| 4 | Selects and uses modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications; effectively uses information technologies. | X | ||||
| 5 | Designs experiments, conducts tests, collects data, analyzes, and interprets results to investigate complex engineering problems or discipline-specific research topics. | X | ||||
| 6 | Works effectively in disciplinary and interdisciplinary teams; develops the ability to work independently. | X | ||||
| 7 | Communicates effectively in both written and verbal forms; possesses proficiency in at least one foreign language; writes effective reports, understands written reports, prepares design and production reports, delivers effective presentations, and gives and receives clear instructions. | X | ||||
| 8 | Recognizes the need for lifelong learning; accesses information, follows developments in science and technology, and continuously renews oneself. | X | ||||
| 9 | Acts in accordance with ethical principles, assumes professional and ethical responsibility, and possesses knowledge about the standards used in engineering practices. | X | ||||
| 10 | Possesses knowledge about professional practices such as project management, risk management, and change management; gains awareness of entrepreneurship and innovation; understands the principles of sustainable development. | X | ||||
| 11 | Understands the universal and societal impacts of engineering practices on health, environment, and safety; recognizes the contemporary issues reflected in the field of engineering and understands the legal implications 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 | ||