ACADEMICS
Course Details

ELE670 - Statistical Signal Processing

2022-2023 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.
Course Outline Weekly
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
Assessment 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
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
Workload and ECTS Calculation
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
Matrix Of The Course Learning Outcomes Versus Program Outcomes
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
General Information | Course & Exam Schedules | Real-time Course & Classroom Status
Undergraduate Curriculum | Open Courses, Sections and Supervisors | Weekly Course Schedule | Examination Schedules | Information for Registration | Prerequisite and Concurrent Courses | Legal Info and Documents for Internship | Academic Advisors for Undergraduate Program | Information for ELE 401-402 Graduation Project | Virtual Exhibitions of Graduation Projects | Program Educational Objectives & Student Outcomes | ECTS Course Catalog | HU Registrar's Office
Graduate Curriculum | Open Courses and Supervisors | Weekly Course Schedule | Final Examinations Schedule | Schedule of Graduate Thesis Defences and Seminars | Information for Registration | ECTS Course Catalog - Master's Degree | ECTS Course Catalog - PhD Degree | HU Graduate School of Science and Engineering