Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
SIGNAL ANALYSIS AND PROCESSING 800500805510 3 + 0 3.0 10.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals The course aims to present some fundamental topics in random processes, signal modelling, estimation theory, and discrete time signal processing to give basic knowledge in statistical signal processing.
Course Content Random Processes; random variables, random vectors, random processes, second moment analysis, stationarity, ergodicity, power spectrum, white noise. Some special types of random processes; AR, MA, ARMA, harmonic processes. Linear Transformations; transformations of random processes by linear systems, spectral factorization. Signal Modeling; least squares method, the Yule Walker equations, the autocorrelation and autocovariance methods. Optimal Filtering: linear MSE estimation, linear predictive filters, Wiener filters.
Learning Outcomes 1) Basic knowledge in statistical signal processing
2) Learning of some special types of random process and their properties.
3) Testing statistical signal processing algorithms on experimental or simulated signals

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Matris dönüşümleri, doğrusal uzaylar ve doğrusal işleçler, köşegenleme, özfonksiyonlar, özvektörler Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework
2. Week Sürekli zaman sinyallerinin ayrık zamanda incelenmesi, örnekleme teorisi Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework
3. Week z-dönüşümü, ayrık zaman LTI sistemler, katlama, katlama matrisi Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework
4. Week Rasgele değişkenler, vektörler ve süreçler Lecture; Question Answer; Discussion
Brainstorming
Brain Based Learning 		Homework
5. Week Stokastik süreçlerin ikinci moment analizi, ortalama, varyans, korelasyon, otokorelasyon, güç spektrum yoğunluğu, durağanlık Lecture; Question Answer; Discussion
Brainstorming
Brain Based Learning 		Homework
6. Week Güç spektral yoğunluğu ve özellikleri, spektral çarpanlara ayırma Lecture; Question Answer; Discussion
Brainstorming
Brain Based Learning 		Homework
7. Week WSS süreçlerin doğrusal zamanla değişmez işlenmesi, ergodiklik Lecture; Question Answer; Discussion
Brainstorming
Brain Based Learning 		Homework
8. Week Sinyal modelleme, en küçük kareler yöntemi, Pade, Prony (Deterministik yöntemler) Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework
9. Week AR, MA, ARMA Süreçler (Stokastik yaklaşım), Yule-Walker Eşitlikleri, tüm kutup modelleme Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework
10. Week Harmonik süreçler, harmonik süreçler için Fourier dönüşümü Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework
11. Week Maliyet fonksiyonları: kare ortalama hata, mutlak ortalama hata, en büyük hata Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework
12. Week MSE, ML, mutlak hata kestiricileri Lecture; Question Answer
Brainstorming
Brain Based Learning 		Homework
13. Week Yanlılık, tutarlılık, yanlılık-varyans ödünleşimi Lecture; Question Answer; Discussion
Brainstorming
Brain Based Learning 		Homework
14. Week Wiener filtreleri Lecture
Brainstorming
Project Based Learning 		Project (Including Preparation and presentation Time)

Sources Used in This Course
Recommended Sources
- Louis L. Scharf, Statistical Signal Processing, Addison-Wesley Publishing Company, Inc., Reading, MA, 1991.
- M. H. Hayes, Statistical Signal Processing and Modeling, Wiley, New York, NY, 1996
- Therrien, Charles W., Discrete random signals and statistical signal processing, Prentice Hall, c1992.
A. Papoulis, Probability, Random Variables, and Stochastic Processes, 3rd edition, McGraw Hill, 1991.

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY15000
PY25000
PY35000
PY45000

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 5
Homework 5 10
Presentation (Including Preparation Time) 1 5
Project (Including Preparation and presentation Time) 1 30
Report (Including Preparation and presentation Time) 1 10
Activity (Web Search, Library Work, Trip, Observation, Interview etc.) 5 6
Midterm Exam 1 3
Time to prepare for Midterm Exam 1 20
Final Exam 1 3
Time to prepare for Final Exam 1 30
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload