Random processes and sequences, stationarity and ergodicity properties of auto- and cross-correlation functions, white noise, power spectral density and spectral estimation simulation of random processes, whitening, linear and non-linear estimation, and Wiener filtering.
Random Processes (EE 550)
Programs\Type | Required | Core Elective | Area Elective |
Computer Science and Engineering - With Bachelor's Degree | * | ||
Computer Science and Engineering - With Master's Degree | * | ||
Computer Science and Engineering - With Thesis | * | ||
Cyber Security - With Bachelor's Degree | * | ||
Cyber Security - With Master's Degree | * | ||
Cyber Security - With Thesis | * | ||
Data Science - With Thesis | * | ||
Electronics Engineering and Computer Science - With Bachelor's Degree | * | ||
Electronics Engineering and Computer Science - With Master's Degree | * | ||
Electronics Engineering and Computer Science - With Thesis | * | ||
Electronics Engineering - With Bachelor's Degree | * | ||
Electronics Engineering - With Master's Degree | * | ||
Electronics Engineering - With Thesis | * | ||
Energy Technologies and Management-With Thesis | * | ||
Industrial Engineering - With Bachelor's Degree | * | ||
Industrial Engineering - With Master's Degree | * | ||
Industrial Engineering - With Thesis | * | ||
Leaders for Industry Biological Sciences and Bioengineering - Non Thesis | * | ||
Leaders for Industry Computer Science and Engineering - Non Thesis | * | ||
Leaders for Industry Electronics Engineering and Computer Science - Non Thesis | * | ||
Leaders for Industry Electronics Engineering - Non Thesis | * | ||
Leaders for Industry Industrial Engineering - Non Thesis | * | ||
Leaders for Industry Materials Science and Engineering - Non Thesis | * | ||
Leaders for Industry Mechatronics Engineering - Non Thesis | * | ||
Manufacturing Engineering - Non Thesis | * | ||
Manufacturing Engineering - With Bachelor's Degree | * | ||
Manufacturing Engineering - With Master's Degree | * | ||
Manufacturing Engineering - With Thesis | * | ||
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) | * | ||
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) | * | ||
Materials Science and Nano Engineering - With Thesis (Pre.Name: Materials Science and Engineering) | * | ||
Mathematics - With Bachelor's Degree | * | ||
Mathematics - With Master's Degree | * | ||
Mathematics - With Thesis | * | ||
Mechatronics Engineering - With Bachelor's Degree | * | ||
Mechatronics Engineering - With Master's Degree | * | ||
Mechatronics Engineering - With Thesis | * | ||
Molecular Biology, Genetics and Bioengineering (Prev. Name: Biological Sciences and Bioengineering) | * | ||
Molecular Biology, Genetics and Bioengineering-(Prev. Name: Biological Sciences and Bioengineering) | * | ||
Molecular Biology,Genetics and Bioengineering-With Thesis (Pre.Name:Biological Sciences and Bioeng.) | * | ||
Physics - Non Thesis | * | ||
Physics - With Bachelor's Degree | * | ||
Physics - With Master's Degree | * | ||
Physics - With Thesis | * |
CONTENT
OBJECTIVE
Graduate level course on probability and stochastic processes. Single and multidimensional discrete and continuous random variables. Probability and moment approximations using limit theorems, basic random processes, wide sense stationary random processes, linear systems and wide sense stationary random processes, multiple wide sense stationary random processes, Gaussian random processes, Poisson random processes. Optimal linear systems. Kalman filter.
LEARNING OUTCOMES
- Solve problems about the results of random experiments using the concept of discrete and continuous random variables
- Use the concept of joint probability distributions, marginal distributions and conditional distributions for multiple random variables for solving complex problems in various engineering domains that involve probability
- Calculate and grasp the significance of the expected value, variance, and standard deviation of a single random variable and mean vector and covariance matrix of a random vector
- Analyze random processes and characterize the response of LTI systems driven by a stationary random process using autocorrelation and power spectral density functions
- Gain sufficient background for the subject areas of detection and estimation, communications, random signal processing, stochastic control, pattern recognition and machine learning
Update Date:
ASSESSMENT METHODS and CRITERIA
Percentage (%) | |
Final | 25 |
Midterm | 60 |
Presentation | 15 |
RECOMENDED or REQUIRED READINGS
Textbook |
Probability, Statistics, and Random Processes For Electrical Engineering (3rd Edition), by Alberto Leon-Garcia |
Readings |
(textbook) Probability, Statistics, and Random Processes For Electrical Engineering (2nd Edition), Prentice Hall, 2008, 978-0131471221 (recommended) Intuitive probability and random processes using Matlab, Steven Kay, Springer, 2006, 0-387-24157-4 (recommended) Probability and random processes for electrical engineering, Alberto Leon-Garcia, Addison-Wesley, 1994, 0-201-50037-X (reference) Introduction to probability, Dimitri P. Bertsekas and John N. Tsitsiklis, Athena Scientific, 2008, 978-1-886529-23-6 |