| | Standard Course Syllabus | Course Supervisor | Date of Approval |
| | Dept. of Electrical and Computer Engineering | Moses | February 17, 1999 |
| | 805 | Random Processes and Applications |
| | 2. | CATALOG DESCRIPTION |
| | Random processes; autocorrelation and cross-correlation functions; energy and power spectral densities; mean-square |
| | calculus; minimum mean squared error signal estimators; optimal filtering; random processes as inputs to linear systems; |
| | applications to communications and signal processing. |
| | Quarters of Offering | Credits | | Level | Class Meeting |
| | Wi Qtr. | 3 | G | 3 cl. |
| | Course Prerequisites |
| | Prereq: 804. |
| | 3. | PREREQUISITES BY TOPIC |
| | Random variables, expectation operator, cumulative distribution functions, probability distribution functions, conditional |
| | probability and conditional expectation, statistical independence, Gaussian random variables, linear systems theory, Fourier |
| | analysis methods, linear algebra, second order differential equations. |
| | Courses that require this as a direct prerequisite |
| | 800, 806, 851 |
| | 4. | Text(s) and Other Course Materials | Author(s) | Publisher |
| | Probability, Random Variables and Stochastic Processes, 4th | Papoulis and Pillai | McGraw-Hill |
| | Ed., 2002 |
| | ISBN: 0-07-366011-6 |
| | (MSH from 804) |
| | References (supplemental reading) |
| | [1] Stark and Woods, Probability, Random Processes and Estimation Theory for Engineers, Prentice-Hall, 1986. |
| | [2] Papoulis, Probability, Random Processes and Stochastic Processes |
| | [3] Thomas, An Introduction to Communication Theory and Systems |
| | [4] Wong, An Introduction to Random Processes |
| | [5] Viniotis, Probability and Random Processes |
| | 5. | COURSE OBJECTIVES |
| | 1. Students learn the mathematical properties of random processes, mean-square calculus and series representations of |
| | random processes. (Criterion 3(a)) |
| | 2. Students learn the tools necessary for the analysis of random processes through linear systems. (Criterion 3(a)) |
| | 3. Students learn the properties of important random processes: Poisson processes, Wiener processes, and the Markov |
| | property of random processes. (Criterion 3(a)) |
| | 4. Students design and analyze optimal estimation algorithms for random processes in noise. (Criteria 3(b),(c),(e),(k)) |
| | 6. | TOPICS AND (# OF LECTURES) |
| | Basic Concepts of Random Processes (6) |
| | Mean-Square Calculus (5) |
| | Stationary Processes (3) |
| | Spectral Densities (3) |
| | Random Processes through Linear Systems (3) |
| | Filtering and Estimation (6) |
| | Expansions of Periodic and Non-periodic Random Processes (4) |
| | 7. | CLASS MEETING PATTERN | (For example, "3cl." means 3 48-min classes per week.) |
| | 3 cl. |
| | Thursday, August 14, 2008 09:22 AM |
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