Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Graduate School of Sciences
  • Depart. of Electrical and Electronics Engineering
  • Doctorate Program in Electronics and Electric Eng.
  • Course Structure Diagram with Credits
  • Statistical Signal Processing
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
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Course Introduction Information

Code - Course Title EEM667 - Statistical Signal Processing
Course Type Elective Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ CAN UYSAL
Mode of Delivery face to face lecturing
Prerequisites There is no prerequisite/co-requisite for this course.
Courses Recomended It is recommended that students taking this course take EEM 504 Random Variables and Stochastic Processes.
Required or Recommended Resources Statistical Digital Signal Processing, Monson H. Hayes
Recommended Reading List
Assessment methods and criteria 1 midterm, 1 final exam, project, and weekly assignments
Work Placement None
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Discrete-Time Signal Processing
Week - 2 Discrete-Time Signal Processing
Week - 3 Linear Algebra Review, Vectors, Linear Independence, Matrices
Week - 4 Linear Algebra Review, Linear Equations, Eigenvalues and Eigenvectors
Week - 5 Discrete-Time Random Process; Random Variables; Ensemble Averages; Independent, Uncorrelated and Orthogonal Random Variables; Gaussian Random Variables
Week - 6 Discrete-Time Random Process; Random Processes; Autocovariance and Autocorrelation Matrices; Ergodicity; Spectral Factorization; Special Types of Random Processes
Week - 7 Signal Modeling; Pade Approximation; Prony's Method
Week - 8 Signal Modeling: Pole-Zero Modeling; Shanks's Method; All-Pole Modeling
Week - 9 Signal Modeling: Autocorrelation Methos, Covariance Method; Stochastic Models: Autoregressive Moving Average Models
Week - 10 Signal Modeling: Stochastic Models: Autoregressive Models, Moving Average Models
Week - 11 Levinson Recursion
Week - 12 Spectrum Estimation: Nonparametric Methods, Periodogram, Modified Periodogram, Bartlett's Method
Week - 13 Spectrum Estimation: Nonparametric Methods, Welch's Method, Parametric Methods, Autoregressive Spectrum Estimation, Moving Average Spectrum Estimation
Week - 14 Frequency Estimation; Eigendecomposition of the Autocorrelation Matrix; Pisarenko Harmonic Decomposition; MUSIC

Learning Activities and Teaching Methods

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 30
Homework 1 20
Final Exam 1 50
Toplam (%) 100
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