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://akts.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
|STATISTICAL SIGNAL PROCESSING||ELE670||Any Semester/Year||3||0||3||8|
|Mode of Delivery||Face-to-Face|
|Learning and teaching strategies||Lecture|
Question and Answer
|Instructor (s)||Prof. Dr. A. Salim Kayhan|
|Course objective||Successful students are expected to gain : Knowledge of basic estimation, filtering, prediction methods such as Bayes, MAP, MLE, LMSE, Wiener, Levinson ve Kalman filters.|
|Course Content||Metric 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
|References||1-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.
Course outline weekly
|Week 1||Metric Spaces.|
|Week 2||Norms, Orthogonal Spaces, Projections, Random Vectors.|
|Week 3||Orthogonal Projections, Gram-Schmidt Orthogonalization.|
|Week 4||Random Processes, Gaussian Processes, Markov Processes.|
|Week 5||Random State Models.|
|Week 6||Analysis of Systems, Spectral Factorization, Rational Modeling.|
|Week 7||Bayesian Estimation, MAP, MLE,MSE.|
|Week 9||Term Exam.|
|Week 10||Wiener Filter.|
|Week 11||Wiener Filter.|
|Week 12||Levinson Filter.|
|Week 13||Kalman Filter|
|Week 14||Kalman Filter|
|Week 15||Final exam|
|Week 16||Final exam|
|Specific practical training||0||0|
|Percentage of semester activities contributing grade succes||50||50|
|Percentage of final exam contributing grade succes||50||50|
Workload and ECTS calculation
|Activities||Number||Duration (hour)||Total Work Load|
|Course Duration (x14)||14||3||42|
|Specific practical training||0||0||0|
|Study Hours Out of Class (Preliminary work, reinforcement, ect)||14||6||84|
|Presentation / Seminar Preparation||0||0||0|
|Midterms (Study duration)||1||25||25|
|Final Exam (Study duration)||1||30||30|
Matrix Of The Course Learning Outcomes Versus Program Outcomes
|D.9. Key Learning Outcomes||Contrubition level*|
|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