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 Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Demonstration, Discussion, Question and Answer, Drill and Practice.
Course Coordinator
Course Lecturer(s)
  • Asst. Prof. Dr. Hakan Tora
  • Asst. Prof. Dr. İ.Baran Uslu
Course Assistants
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;
  • Ability to characterize an estimator
  • Ability to design statistical DSP algorithms to meet desired needs
  • Ability to apply vector space methods to statistical signal processing problems
  • Ability to understand Wiener filter theory and design discrete and continuous Wiener filters
  • Ability to understand Kalman Filter theory and design discrete Kalman filters
  • Ability to use computer tools (such as Matlab) in developing and testing stochastic DSP algorithms
  • Ability to complete a term project
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 Ability to apply knowledge on Mathematics, Science and Engineering to advanced systems.
2 Implementing long-term research and development studies in major areas of Electrical and Electronics Engineering.
3 Ability to use modern engineering tools, techniques and facilities in design and other engineering applications.
4 Graduating researchers active on innovation and entrepreneurship.
5 Ability to report and present research results effectively.
6 Increasing the performance on accessing information resources and on following recent developments in science and technology.
7 An understanding of professional and ethical responsibility.
8 Increasing the performance on effective communications in both Turkish and English.
9 Increasing the performance on project management.
10 Ability to work successfully at project teams in interdisciplinary fields.

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