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Unformatted text preview: 10/2/10 The objective of this course is to introduce students to the fundamental concepts of detection and estimation theory. At the end of the semester, students should be able to cast a generic detection problem into a hypothesis testing framework and to find the optimal test for the given optimization criterion. They should also be capable of finding optimal estimators for various signal parameters, derive their properties and assess their performance. 10/2/10 10/2/10 ELEC6111: Detection and Estimation Theory Detection Theory: Hypothesis testing: Likelihood Ratio Test, Bayes Criterion, Minimax Criterion, Neyman Pearson Criterion, Sufficient Statistics, Performance Evaluation. Multiple hypothesis testing. Composite hypothesis testing. Sequential detection 10/2/10 ELEC6111: Detection and Estimation Theory Text: Vincent Poor, An Introduction to Signal Detection and Estimation: Second Edition, Springer, 1994. References: H. L. Van Trees, Detection, Estimation, and Modulation Theory, John Wiley & Sons, 1968. J. M. Wozencraft, and I. M. Jacob, Principles of Communication Engineering, John Wiley & Sons, 1965. 10/2/10 ELEC6111: Detection and Estimation Theory Project: Turbo Coding. Requirements: Literature Review, Simulation (using a programming language and not Packages and Tool Boxes) References : J. Haguenauer , E. Offer and L. Papke "Iterative decoding of binary block and convolutional codes", IEEE Trans. Inf. Theory , vol. 42, pp. 429 1996. 10/2/10 ELEC6111: Detection and Estimation Theory In order to pass the course, you should get at least 60% (36 out of 60) in the final. The midterm and the Final exams will be open book. Only one text book (any text book covering the material of the course) will be allowed in the exam. No nonauthorized copy of the text will be allowed in the class, in the midterm or in the final. Failing to write the midterm results in losing the 25% assigned to the midterm. In the case 10/2/10 ELEC6111: Detection and Estimation Theory Detection and Estimation deal with extraction of information from Information Bearing Signals (or Data). The difference between the two is that in Detection we deal with discrete results ( Decisions ) while in Estimation we deal with real values. 10/2/10 ELEC6111: Detection and Estimation Theory Applications of Detection Theory Digital Communications....
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 Spring '10
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