EE 640
Applied Random Processes
Fall 2016
Outline

Course

Instructor: Tony Kuh, Office: 205E POST
Office Hours: MW 10:30-11:50 or by appointment
Phone Number: 956-7527
Email: kuh@hawaii.edu

Prerequisites

Math 471 or EE342, (Probability Theory)

Linear Time Invariant Systems

Fourier Transforms, Laplace Transforms

Grading (Approximate)

HW: (quizzes) 20 (assignments)

MT: 30 (exam directory)

Final: 50 (exam directory)

Course Description (lecture summary)

Probability and random variables:

Probability triple, sigma fields, probability axioms, independence, random variables, multivariate distributions, expectation, functions of random variables, Gaussian random variables.

Stochastic convergence, Laws of Large Numbers, Central Limit Theorem.

Bayesian estimation, minimum mean squared error estimation, conditional expectation, projection theorem.

Stationarity, ergodicity, Gaussian random processes, Markov processes, Poisson process

Autocorrelation function, power Spectral density, linear time invariant systems, Karhunen Loeve Expansion, baseband and narrowband processes.

Linear minimum mean square estimation, Wiener filters, Kalman filters, Hypothesis testing, Hidden Markov models, Expectation Maximization algorithm.

B. Hajek, Random Processes for Engineers, Cambridge University Press, 1st. Ed., 2015 (required).

G. Grimmett and D. Stirzaker, Probability and Random Processes, Oxford University Press, 3rd. Ed., 2001 (optional).

R. G. Gallager, Stochastic Processes: Theory for Applications, Cambridge University Press, 1st. Ed., 2014.

H. Stark and J. W. Woods, Probability, Statistics, and Random Processes for Engineers, Prentice Hall, 4th Ed., 2011.

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