Course Details

ELE 674 Adaptive Signal Processing
2021-2022 Spring term information

The course is not open this term

Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.

Course definition tables are extracted from the ECTS Course Catalog web site of Hacettepe University ( in real-time and displayed here. Please check the appropriate page on the original site against any technical problems. Course data last updated on 19/05/2022.


Course Name Code Semester Theory
Credit ECTS
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Question and Answer
Problem Solving
Instructor (s)Department Faculty 
Course objectiveIt 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
  1. To make clear of the situations in which Adaptive Signal Processing ought to be used
  2. To teach Wiener Filter, and how to use it to filter, predict and smooth a signal
  3. To discuss the advantages and disadvantages of several Adaptive Signal Processing techniques and their limitings
  4. To discuss the characterics of a time varying signal, and teach how to use Adaptive Signal Processing techniques in such cases
  5. 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 ContentStatistical 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
ReferencesHaykin, Adaptive Filter Theory, Prentice Hall, 2002.

Sayed, Adaptive Filters, 2008.

Farhang-Boroujeny, Signal Processing Techniques for Software Radios, 2010,  

Course outline weekly

Week 1Introduction to Adaptive Signal Processing
Week 2Statistical Processes and Models
Week 3Wiener Filters
Week 4Linear Prediction
Week 5Steepest Descent Algorithm
Week 6LMS (Least-Mean-Square)
Week 7Normalised LMS
Week 8Frequency Domain and Subband Adaptive Filters
Week 9Method of Least Squares
Week 10Recursive Least Squares Adaptive Filters
Week 11Midterm
Week 12Kalman Filters
Week 13Kalman Filters
Week 14Tracking of Time-varying Systems
Week 15Final exam
Week 16Final exam

Assesment methods

Course activitiesNumberPercentage
Field activities00
Specific practical training00
Final exam140
Percentage of semester activities contributing grade succes060
Percentage of final exam contributing grade succes040

Workload and ECTS calculation

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)14570
Presentation / Seminar Preparation000
Homework assignment13565
Midterms (Study duration)12929
Final Exam (Study duration) 13434
Total Workload4376240

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.    X
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.    X
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.   X 
4. Designs and runs research projects, analyzes and interprets the results.   X 
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects. X   
6. Produces novel solutions for problems.   X 
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.  X  
8. Follows technological developments, improves him/herself , easily adapts to new conditions.    X 
9. Is aware of ethical, social and environmental impacts of his/her work.  X  
10. Can present his/her ideas and works in written and oral form effectively; uses English effectively  X  

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

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