ELECTRICAL-ELECTRONICS ENGINEERING (MASTER) (WITH THESIS) (ENGLISH)
Qualification Awarded Program Süresi Toplam Kredi (AKTS) Öğretim Şekli Yeterliliğin Düzeyi ve Öğrenme Alanı
2 120 FULL TIME TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 7
QF-EHEA:Second Cycle
TR-NQF-HE, ISCED (1997-2013): 52

Ders Genel Tanıtım Bilgileri

Course Code: 3026002016
Ders İsmi: Adaptive Signal Processing
Ders Yarıyılı: Fall
Spring
Ders Kredileri:
Theoretical Practical Labs Credit ECTS
3 0 0 3 6
Language of instruction: EN
Ders Koşulu:
Ders İş Deneyimini Gerektiriyor mu?: No
Other Recommended Topics for the Course:
Type of course: Department Elective
Course Level:
Master TR-NQF-HE:7. Master`s Degree QF-EHEA:Second Cycle EQF-LLL:7. Master`s Degree
Mode of Delivery: Face to face
Course Coordinator : Prof. Dr. Çağatay ULUIŞIK
Course Lecturer(s):
Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives: The aim of the course is to introduce advanced techniques in statistical signal processing and apply them to related fields.
Course Content: Stochastic processes, signal modeling, AR/MA/ARMA processes, Wiener filter, Levinson recursion, Lattice filters, spectrum estimation.

Learning Outcomes

The students who have succeeded in this course;
Learning Outcomes
1 - Knowledge
Theoretical - Conceptual
1) Students will be able to estimate spectrum by various parametric and non-parametric methods
2 - Skills
Cognitive - Practical
1) Students will be able to model signals.
2) Students will be able to implement various random processes.
3) Students will be able to apply Levinson recursion, lattice filters and Wiener filter.
4) Students are able to analyze random processes.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week Subject Related Preparation
1) Introduction, time and frequency domain analysis of signals M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.
2) Discrete-time random processes M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.
3) Stationary random processes, autocorrelation matrices, ergodicity, power spectrum, filtering random processes M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.
4) Spectral factorization, AR/MA/ARMA processes M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.
5) Signal modeling, The Pade approximation M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.
6) Signal modeling, Prony’s method M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.
7) Levinson-Durbin recursion M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.
8) Midterm exam
9) FIR Wiener filtering M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.
10) IIR Wiener filtering M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.
11) Spectrum estimation, Non-parametric methods M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.
12) Spectrum estimation, Parametric methods M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.
13) Student seminars
14) Student seminars
15) Final exam

Sources

Course Notes / Textbooks: M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.
References: M. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley&Sons, 1996. / R. M. Gray, L. D. Davisson, An Introduction to Statistical Signal Processing, 2010. / D.G. Manolakis, V.K. Ingle, S.M. Kogan, “Statistical and Adaptive Signal Processing”, McGraw-Hill, 2000.

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları

1

2

3

5

4
Program Outcomes
1) Through scientific research in the field of Electrical-Electronics Engineering, they expand and deepen their knowledge, evaluate, interpret, and apply the information.
2) They have comprehensive knowledge of the current techniques and methods applied in Electrical-Electronics Engineering, as well as their limitations.
3) Using uncertain, limited, or incomplete data, they complement and apply knowledge through scientific methods; they can integrate information from different disciplines.
4) They are aware of new and emerging applications in Electrical-Electronics Engineering, and when necessary, they investigate and learn about them.
5) They define and formulate Electrical-Electronics Engineering problems, develop methods to solve them, and apply innovative approaches in the solutions.
6) They develop new and/or original ideas and methods; design complex systems or processes and develop innovative/alternative solutions in their designs.
7) They design and apply theoretical, experimental, and modeling-based research; they analyze and solve complex problems encountered during this process.
8) They can work effectively in both interdisciplinary and multidisciplinary teams, lead such teams, and develop solution approaches in complex situations; they can work independently and take responsibility.
9) They communicate effectively in both spoken and written forms using a foreign language at least at the B2 General Level of the European Language Portfolio.
10) They communicate the processes and results of their work in a systematic and clear manner, either in writing or verbally, in national and international contexts, both within and outside their field.
11) They are aware of the social, environmental, health, safety and legal aspects of Electrical and Electronics Engineering applications, project management and business life practices and are aware of the constraints these impose on engineering applications.
12) They observe social, scientific and ethical values in the stages of collecting, interpreting and announcing the data and in all professional activities.

Ders - Öğrenme Kazanımı İlişkisi

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Through scientific research in the field of Electrical-Electronics Engineering, they expand and deepen their knowledge, evaluate, interpret, and apply the information.
2) They have comprehensive knowledge of the current techniques and methods applied in Electrical-Electronics Engineering, as well as their limitations.
3) Using uncertain, limited, or incomplete data, they complement and apply knowledge through scientific methods; they can integrate information from different disciplines.
4) They are aware of new and emerging applications in Electrical-Electronics Engineering, and when necessary, they investigate and learn about them.
5) They define and formulate Electrical-Electronics Engineering problems, develop methods to solve them, and apply innovative approaches in the solutions.
6) They develop new and/or original ideas and methods; design complex systems or processes and develop innovative/alternative solutions in their designs.
7) They design and apply theoretical, experimental, and modeling-based research; they analyze and solve complex problems encountered during this process.
8) They can work effectively in both interdisciplinary and multidisciplinary teams, lead such teams, and develop solution approaches in complex situations; they can work independently and take responsibility.
9) They communicate effectively in both spoken and written forms using a foreign language at least at the B2 General Level of the European Language Portfolio.
10) They communicate the processes and results of their work in a systematic and clear manner, either in writing or verbally, in national and international contexts, both within and outside their field.
11) They are aware of the social, environmental, health, safety and legal aspects of Electrical and Electronics Engineering applications, project management and business life practices and are aware of the constraints these impose on engineering applications.
12) They observe social, scientific and ethical values in the stages of collecting, interpreting and announcing the data and in all professional activities.

Öğrenme Etkinliği ve Öğretme Yöntemleri

Alan Çalışması
Bireysel çalışma ve ödevi
Proje Hazırlama
Seminar

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Homework
Bireysel Proje
Sunum

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Project 1 % 30
Midterms 1 % 30
Semester Final Exam 1 % 40
total % 100
PERCENTAGE OF SEMESTER WORK % 60
PERCENTAGE OF FINAL WORK % 40
total % 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Study Hours Out of Class 14 3 42
Project 1 60 60
Homework Assignments 2 20 40
Midterms 1 2 2
Final 1 2 2
Total Workload 188