EE684 Adaptive Filter Theory

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code EE684
Name Adaptive Filter Theory
Term 2023-2024 Academic Year
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language İngilizce
Level Doktora Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. SAMİ ARICA


Course Goal / Objective

The course provides filtering of stationary and nonstationary signals. The course presents digital systems which tune in automatically and adapt to the environment. The student is given enough theoretical and practical knowledge to independently be able to formulate the mathematical problem, solve it and implement the solution for use with real-life signals.

Course Content

Random process. Moving average (MA), Autoregressive (AR) and ARMA models. Wiener filtering. Linear prediction. Levinson-Durbin algorithm. Lattice filters. Steepest Descent method. Least mean squares (LMS) method and its variants. Finite and infinite response LMS filters. Recursive least squares (RLS). Kalman filters.

Course Precondition

There are no prerequisites for the course.

Resources

Adaptive Filter Theory by Simon O. Haykin

Notes

No suggested additional course notes.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 To have knowledge about and understand the main concepts in optimum and adaptive filter theory.
LO02 To be able to apply the most commonly used methods to real problems and real-life signals.
LO03 To be able to formulate mathematical problems based on described situations.
LO04 Capable of applying the concepts gained in the course for Adaptive Signal Processing to his or her thesis and applications in the real world.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Being able to specialize in at least one of the branches that form the foundations of Electrical and Electronics Engineering by increasing the level of knowledge beyond the master's level 4
PLO02 Bilgi - Kuramsal, Olgusal To comprehend the integrity of all the subjects included in the field of specialization. 3
PLO03 Bilgi - Kuramsal, Olgusal Having knowledge of the current scientific literature in the field of specialization to analyze the literature critically 3
PLO04 Bilgi - Kuramsal, Olgusal To comprehend the interdisciplinary interaction of the field with other related branches, to suggest similar interactions. 3
PLO05 Bilgi - Kuramsal, Olgusal Ability to do theoretical and experimental work 3
PLO06 Bilgi - Kuramsal, Olgusal To create a complete scientific text by compiling the information obtained from the research
PLO07 Bilgi - Kuramsal, Olgusal To work on the thesis topic programmatically, following the logical integrity required by the subject within the framework determined by the advisor.
PLO08 Bilgi - Kuramsal, Olgusal To search for literature in scientific databases, particularly the ability to correctly and accurately scan databases and evaluate and categorize listed items. 3
PLO09 Bilgi - Kuramsal, Olgusal Having a command of English and related English jargon at a level that can easily read and understand a scientific text written in English in the field of specialization and write a similar text
PLO10 Bilgi - Kuramsal, Olgusal Ability to write a computer program in a familiar programming language, generally for a specific purpose, specifically related to the field of expertise.
PLO11 Bilgi - Kuramsal, Olgusal Ability to plan and teach lessons related to the field of specialization or related fields
PLO12 Bilgi - Kuramsal, Olgusal Being able to guide and take the initiative in environments that require solving problems related to the field
PLO13 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Ability to communicate with people in an appropriate language
PLO14 Yetkinlikler - Öğrenme Yetkinliği Adopting the ethical values required by both education and research aspects of academician
PLO15 Yetkinlikler - Öğrenme Yetkinliği To be able to produce projects, policies, and processes in the field of expertise and to evaluate these elements
PLO16 Yetkinlikler - Öğrenme Yetkinliği Ability to research new topics based on existing research experience


Week Plan

Week Topic Preparation Methods
1 Introduction. Linear filtering problem. Adaptive filters. Application of adaptive filters. Stationary discrete-time stochastic processes. Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
2 Stationary discrete-time stochastic processes (cont.). Moving average (MA), Autoregressive (AR) and ARMA models. Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
3 Moving average (MA), Autoregressive (AR) and ARMA models (cont.). Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
4 Wiener filter theory. Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
5 Linear prediction. Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
6 Linear prediction (cont.). Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
7 Steepest Descent method. Least mean squares (LMS) method and its variants. Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
8 Mid-Term Exam Textbook reading/Problem solving. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
9 Kalman filter. Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
10 Kalman filter (cont.). Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
11 Method of least squares. Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
12 Recursive least squares (RLS) filters. Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
13 Recursive least squares (RLS) filters (cont.). Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
14 Lattice filters. Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
15 Lattice filters (cont.). Textbook reading/Problem solving/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
16 Term Exams Textbook reading/Problem solving. Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Textbook reading/Problem solving. Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 6 84
Assesment Related Works
Homeworks, Projects, Others 7 4 28
Mid-term Exams (Written, Oral, etc.) 1 2 2
Final Exam 1 2 2
Total Workload (Hour) 158
Total Workload / 25 (h) 6,32
ECTS 6 ECTS

Update Time: 09.05.2023 05:26