ACADEMICS
Course Details

ELE774 - Adaptive Signal Processing

2022-2023 Fall term information
The course is not open this term
ELE774 - Adaptive Signal Processing
Program Theoretýcal hours Practical hours Local credit ECTS credit
PhD 3 0 3 10
Obligation : Elective
Prerequisite courses : -
Concurrent courses : -
Delivery modes : Face-to-Face
Learning and teaching strategies : Lecture, Question and Answer, Problem Solving
Course objective : It is aimed to give the basics of Adaptive Signal Processing from a mathematical perspective. Adaptive Signal Processing is a major field of Statistical Signal Processing which is applied in several areas such as Communications, Control, Radar Signal Processing and Biomedical Engineering.
Learning outcomes : To make clear of the situations in which Adaptive Signal Processing ought to be used To teach Wiener Filter, and how to use it to filter, predict and smooth a signal To discuss the advantages and disadvantages of several Adaptive Signal Processing techniques and their limitings To discuss the characterics of a time varying signal, and teach how to use Adaptive Signal Processing techniques in such cases To help the student to use the Adaptive Signal Processing techniques learned in the course to his/her thesis and applications in the real world.
Course content : Statistical Processes and Models, Wiener Filters, Linear Prediction, Steepest Descent Algorithm, LMS (Least-Mean-Square), Normalised LMS, Frequency Domain and Subband Adaptive Filters, Method of Least Squares, Recursive Least Squares Adaptive Filters, Kalman Filters, Tracking of Time-varying Systems
References : Haykin, Adaptive Filter Theory, Prentice Hall, 2002. ; ; Sayed, Adaptive Filters, 2008. ; ; Farhang-Boroujeny, Signal Processing Techniques for Software Radios, 2010, lulu.com
Course Outline Weekly
Weeks Topics
1 Introduction to Adaptive Signal Processing
2 Statistical Processes and Models
3 Wiener Filters
4 Linear Prediction
5 Steepest Descent Algorithm
6 LMS (Least-Mean-Square)
7 Normalised LMS
8 Frequency Domain and Subband Adaptive Filters
9 Method of Least Squares
10 Recursive Least Squares Adaptive Filters
11 Midterm
12 Kalman Filters
13 Kalman Filters
14 Tracking of Time-varying Systems
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 6 30
Presentation 0 0
Project 0 0
Seminar 0 0
Quiz 0 0
Midterms 1 30
Final exam 1 40
Total 100
Percentage of semester activities contributing grade success 60
Percentage of final exam contributing grade success 40
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 9 126
Presentation / Seminar Preparation 0 0 0
Project 0 0 0
Homework assignment 13 5 65
Quiz 0 0 0
Midterms (Study duration) 1 31 31
Final Exam (Study duration) 1 36 36
Total workload 43 84 300
Matrix Of The Course Learning Outcomes Versus Program Outcomes
Key learning outcomes Contribution level
1 2 3 4 5
1. Has highest level of knowledge in certain areas of Electrical and Electronics Engineering.
2. Has knowledge, skills and and competence to develop novel approaches in science and technology.
3. Follows the scientific literature, and the developments in his/her field, critically analyze, synthesize, interpret and apply them effectively in his/her research.
4. Can independently carry out all stages of a novel research project.
5. Designs, plans and manages novel research projects; can lead multidisiplinary projects.
6. Contributes to the science and technology literature.
7. Can present his/her ideas and works in written and oral forms effectively; in Turkish or English.
8. Is aware of his/her social responsibilities, evaluates scientific and technological developments with impartiality and ethical responsibility and disseminates them.
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest
General Information | Course & Exam Schedules | Real-time Course & Classroom Status
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