ACADEMICS
Course Detail

ELE 670 Statistical Signal Processing
2017-2018 Fall term information

The course is not open this term

Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.

Course definition tables are extracted from the ECTS Course Catalog web site of Hacettepe University (http://ects.hacettepe.edu.tr) in real-time and displayed here. Please check the appropriate page on the original site against any technical problems.

ELE670 - STATISTICAL SIGNAL PROCESSING

Course Name Code Semester Theory
(hours/week)
Application
(hours/week)
Credit ECTS
STATISTICAL SIGNAL PROCESSING ELE670 Any Semester/Year 3 0 3 8
Prerequisite(s)None
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Question and Answer
Problem Solving
 
Instructor (s)Prof. Dr. A. Salim Kayhan 
Course objectiveSuccessful students are expected to gain : Knowledge of basic estimation, filtering, prediction methods such as Bayes, MAP, MLE, LMSE, Wiener, Levinson ve Kalman filters.  
Learning outcomes
  1. A student completing the course successfully will L.O.1. Recognizes statistical signal processing problems,
  2. L.O.2. Models problems encountered in suitable forms,
  3. L.O.3. Knows which algorithms be used to solve problems established, knows advantages and disadvantages of these algorithms,
  4. L.O.4. Applies the techniques and algorithms learnt in the class in project and other applications,
  5. L.O.5. Has the adequate knowledge to follow and understand advanced up-to-date algorithms.
Course ContentMetric space, inner product, norm etc. definitions.
Review of Probability and stochastic processes.
Gram_Schmidt ort., Guass, Markov proc.
Estimation methods: Bayes, MAP, MLE, LMSE.
Filtering, estimation and prediction methods: Wiener, Levinson ve Kalman filters
 
References1-T. Moon and W. Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice-Hall.
2-S.J. Orfanidis, Optimum Signal Processing, McGraww Hill.
3-S. Kay, Fundamentals of Statistical Signal Processing, Vol.I-II, Prentice Hall.
4-Lecture Notes.
 

Course outline weekly

WeeksTopics
Week 1Metric Spaces.
Week 2Norms, Orthogonal Spaces, Projections, Random Vectors.
Week 3Orthogonal Projections, Gram-Schmidt Orthogonalization.
Week 4Random Processes, Gaussian Processes, Markov Processes.
Week 5Random State Models.
Week 6Analysis of Systems, Spectral Factorization, Rational Modeling.
Week 7Bayesian Estimation, MAP, MLE,MSE.
Week 8LMSE.
Week 9Term Exam.
Week 10Wiener Filter.
Week 11Wiener Filter.
Week 12Levinson Filter.
Week 13Kalman Filter
Week 14Kalman Filter
Week 15Final exam
Week 16Final exam

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments815
Presentation00
Project00
Seminar00
Midterms135
Final exam150
Total100
Percentage of semester activities contributing grade succes5050
Percentage of final exam contributing grade succes5050
Total100

Workload and ECTS calculation

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)14684
Presentation / Seminar Preparation000
Project000
Homework assignment8756
Midterms (Study duration)12525
Final Exam (Study duration) 13030
Total Workload3871202

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.    X
2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering.    X
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.    X
4. Designs and runs research projects, analyzes and interprets the results.    X
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects.    X
6. Produces novel solutions for problems.   X 
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.   X 
8. Follows technological developments, improves him/herself , easily adapts to new conditions.    X 
9. Is aware of ethical, social and environmental impacts of his/her work. X   
10. Can present his/her ideas and works in written and oral form effectively; uses English effectively X   

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

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