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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
ELK 517STATISTICAL DIGITAL SIGNAL PROCESSING AND MODELING - I3 + 01st Semester7,5

COURSE DESCRIPTION
Course Level Doctorate Degree
Course Type Elective
Course Objective To provide theoretical background need for the analysis of linear time-invariant random systems and signals.
Course Content Fundamentals of discrete-time signal processing and theory of linear algebra / Random variables, vectors, and sequences / Linear signal models and random process analysis with these models / Deterministic and random signal modelling methods / Levinson-Durbin recursion / Lattice filters.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1One knows the Fundamentals of discrete-time signal processing.
2One knows the concepts of random variables, vectors, and sequences.
3One knows linear signal models and makes random analysis with these models.
4One makes random analysis with linear signals models.
5One knowns nonparametric power spectrum estimation methods.
6One can use spectrum estimation methods in the analysis of signals.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11
LO 00155322223   
LO 0024522332    
LO 003554244 4   
LO 004554455 5   
LO 005553344 4   
LO 006552255 5   
Sub Total293018152323421   
Contribution55334414000

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)14570
Assignments41040
Mid-terms11515
Final examination12828
Total Work Load

ECTS Credit of the Course






195

7,5
COURSE DETAILS
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L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes