Iğdır University Lifelong Learning Programme

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type of Course Unit	Year of Study	Semester	Number of ECTS Credits
EEM-23-120	STATISTICAL SIGNAL PROCESSING	Elective	1	2	6

Level of Course Unit

Second Cycle

Objectives of the Course

To learn statistical estimation, filtering problems that arise in signal processing applications, to put them into an appropriate mathematical formation, and to learn various solution methods.

Name of Lecturer(s)

Dr. Öğr. Üyesi Fesih Keskin

Learning Outcomes

1	Recognizes statistical signal processing problems,
2	Models problems encountered in suitable forms,
3	Knows which algorithms be used to solve problems established, knows advantages and disadvantages of these algorithms,
4	Applies the techniques and algorithms learnt in the class in project and other applications,
5	Has the adequate knowledge to follow and understand advanced up-to-date algorithms.

Mode of Delivery

Daytime Class

Prerequisites and co-requisities

None

Recommended Optional Programme Components

none

Course Contents

Metric Spaces, Norms, Orthogonal Spaces, Projections, Random Vectors., Orthogonal Projections, Gram-Schmidt Orthogonalization., Random Processes, Gaussian Processes, Markov Processes., Random State Models., Analysis of Systems, Spectral Factorization, Rational Modeling., Bayesian Estimation, MAP, MLE, MSE., LMSE., Wiener Filter., Levinson Filter., Kalman Filter.

Weekly Detailed Course Contents

Week	Theoretical	Practice	Laboratory
1	Metric Spaces.
2	Norms, Orthogonal Spaces, Projections, Random Vectors.
3	Orthogonal Projections, Gram-Schmidt Orthogonalization.
4	Random Processes, Gaussian Processes, Markov Processes.
5	Random State Models.
6	Analysis of Systems, Spectral Factorization, Rational Modeling.
7	Bayesian Estimation, MAP, MLE, MSE.
8	Linear MMSE estimator
9	Wiener Filter
10	Wiener Filter
11	Levinson Filter
12	Kalman Filter

Recommended or Required Reading

1. Mathematical Methods and Algorithms for Signal Processing, T. Moon and W. Stirling. Prentice-Hall. 2. Optimum Signal Processing, S.J. Orfanidis. McGraww Hill. 3. Fundamentals of Statistical Signal Processing,Vol.I-II, S. Kay, Prentice Hall.

Planned Learning Activities and Teaching Methods

Assessment Methods and Criteria

Term (or Year) Learning Activities	Quantity	Weight
Midterm Examination	1	100
SUM		100
End Of Term (or Year) Learning Activities	Quantity	Weight
Final Examination	1	100
SUM		100
Term (or Year) Learning Activities		40
End Of Term (or Year) Learning Activities		60
SUM		100

Language of Instruction

Turkish

Work Placement(s)

none

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Attending Lectures	14	3	42
Self Study	14	4	56
Individual Study for Mid term Examination	1	18	18
Individual Study for Final Examination	1	24	24
Homework	4	10	40
TOTAL WORKLOAD (hours)			180

Contribution of Learning Outcomes to Programme Outcomes

LO1

LO2

LO3

LO4

LO5

* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High