PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
ELK 590	SPECTRAL ESTIMATION	3 + 0	1st Semester	7,5

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	To provide the graduate students the fundamentals of spectral estimation methods used in frequency domain analysis of random signals.
Course Content	Basic concepts: Energy and power spectral densities, properties of power spectral densities, the spectral estimation problem / Nonparametric methods for spectral estimation / Parametric methods for rational spectra / Parametric methods for line spectra / Filter bank methods / Spatial methods for spectral estimation.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	One knows and classifies the spectral estimation problems.
2	One knows parametric and nonparametric methods for spectral estimation and can apply them to real world data.
3	One can perform frequency domain analysis of random signals and systems and can interpret the implementation outcomes.
4	One has technical background to understand and follow the up-to-date spectral estimation methods and studies based on these methods.

COURSE'S CONTRIBUTION TO PROGRAM

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	5	70
Assignments	1	15	15
Mid-terms	1	23	23
Final examination	1	30	30
Presentation / Seminar Preparation	1	15	15
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2017-2018 Spring	1	AYDIN KIZILKAYA

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
ELK 590	SPECTRAL ESTIMATION	3 + 0	1	Turkish	2017-2018 Spring

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Prof. Dr. AYDIN KIZILKAYA	akizilkaya@pau.edu.tr		MUH A0412	%70

Goals		To provide the graduate students the fundamentals of spectral estimation methods used in frequency domain analysis of random signals.
Content		Basic concepts: Energy and power spectral densities, properties of power spectral densities, the spectral estimation problem / Nonparametric methods for spectral estimation / Parametric methods for rational spectra / Parametric methods for line spectra / Filter bank methods / Spatial methods for spectral estimation.

Topics

Weeks	Topics
1	Spectral Estimation Problem and Its application
2	Basic concepts
3	Nonparametric methods for spectral estimation: Periodogram and Its various versions
4	Parametric methods for spectral estimation: Statistical Models and processes
5	Parametric methods for spectral estimation: AR model approach
6	Parametric methods for spectral estimation: AR model approach
7	Parametric methods for spectral estimation: MA model approach
8	Parametric methods for spectral estimation: ARMA model approach
9	Minimum Variance Spectral Estimation
10	The Maximum Entropy Method
11	Methods for sinüzoidal parameter estimation: Pisarenko, MUSIC, ESPRIT methods
12	Principal Component Analysis
13	Principal Component Analysis and Its applications
14	Final Project presentations

Materials

Materials are not specified.

Resources

Resources	Resources Language
1. A. Kızılkaya, İzge Kestirimi, Ders Notları, Denizli, 2018.	Türkçe
2. P. Stoica, R. Moses, Spectral Analysis of Signals, Prentice-Hall, NJ, 2005.	English
3. M. H. Hayes, Statistical and Digital Signal Processing and Modelling, John Wiley &Sons, Inc, 1996.	English
4. S. M. Alessio, Digital Signal Processing and Spectral Analysis for Scientists, Springer, 2016.	English

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	50	Final Exam
Midterm Exam	50	Midterm Exam

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student