Download EE-0903321-Probability and Random Variables-Sep-2014

The University of Jordan
School of Engineering & Technology
Department of Electrical Engineering
1st Semester – A.Y. 2014/2015
Course:
Probability & Random Variables (EE 0903321) (3 Cr. – Core Course)
Instructor:
Dr. Loay Khalaf
Telephone: 5355000 ext 22869, Email: [email protected]
Course Website:
N/A
Catalog Data:
Probability, Random Variable, Operation on One Random Variable, Multiple
Random Variables, Operations on Multiple Random Variables, Random Processes,
Spectral Characteristics of Random Processes, linear systems.
Prerequisites by
Course:
Prerequisites
By Topic:
Signals analysis and Systems (EE 0903221)
Textbook:
Probability, Random Variables, and Random Signal Principles by Peyton Z. Peebles, 4th
Edition, McGraw-Hill, 2000.
References:

Students are assumed to have a background of the following topics:
 Calculus skills.
 Solution of ordinary differential equations.
 Fourier transform
 Linear Systems




Probability, Random Variables and Stochastic Processes by Athanasios Papoulis, 3rd
Edition, Mc-Graw-Hill, 1991.
Probability and Random Processes for Electrical Engineers, Y. Viniotis, Ed edition,
WCB McGraw-Hill, 1998.
Probability and Statistics for Engineering and the Sciences by Jay L. Devore, 6th
Edition, Duxbury Press, 2004.
Schaum's Outline of Probability, Random Variables, and Random Processes by Hwei
Hsu, 2nd Edition, McGraw-Hill, 2010.
Lecture Notes and Handouts
Schedule &
Duration:
Minimum Student
Material:
16 Weeks, 42 contact hours (50 minutes each) including exams.
Minimum College
Facilities:
Course
Objectives:
Classroom with whiteboard, library, and computational facilities.
Text book, class handouts, scientific calculator, and an access to a personal computer.
Course work including assignments using software packages, especially MATLAB.
The course objectives are
 Introduce the topics of probability, random variables, and stochastic processes at the
undergraduate level.

To train the student to formulate such problems within the framework of probability
theory
Course Learning Outcomes and Relation to ABET Student Outcomes:
Upon successful completion of this course, a student should:
1.
Convert an Engineering statment problem into a mathematical probabilistic Statement.
[a,e]
2.
Use statistical principles and the properties of random variables to solve Probabilistic problems.
[a,e]
3.
Calculate standard statistics from mass, distribution and density functions.
[a,e]
4.
Recognize, interpret and apply a variety of random processes that occur in engineering.
[a,e]
5.
Calculate the autocorrelation and spectral density of a random process and recognize the
[a,e]
relation between them.
6.
Understand stochastic phenomena such as white and colored noise.
[a,e]
7.
Understand linear systems. And their output characteristics.
[a,e]
Course Topics:
Topic Description
Probability:
Axiomatic definition, use of set concepts, conditional and joint probability, independence, Bays
Rule, Total Probability.
Hrs
5
Random Variables:
Basic concepts, The random variable concept, Distribution function, Density function, The
Gaussian random variable, other distribution and density examples, Conditional distribution and
density functions
Families of distributions.
Operation on One Random Variable
Expectation, Moments, Functions that give Moments, Transformations of a random variable,
Computer generation of one random variable
7
4.
Multiple Random Variables
Vector random variables, Joint distribution and its properties, Joint density and its properties,
Conditional distribution and density, Statistical independence, Distribution and density of a sum of
random variables, Central limit theorem
6
5.
Operations on Multiple Random Variables
Expected value of a function of random variables, Joint characteristic function, Jointly Gaussian
random variables, Transformations of multiple random variables, linear transformation of
Gaussian random variables, Computer generation of multiple random variables
6
6.
Random Processes
The random process concept, Stationarity and independence, Correlation functions, Measurement
of correlation functions, Gaussian random processes, Poisson random process
7
7.
Spectral Characteristics of Random Processes
Power density spectrum and its properties, Relationship between power spectrum and
autocorrelation function, Cross-Power density spectrum and its properties, Relationship between
cross-power spectrum and cross-correlation function , some noise definitions and other topics
5
8.
Response of linear systems to random and deterministic process
Input output relations of linear systems.
5
1.
2.
3.
Ground Rules:
Assessments:
Grading policy:
Class attendance will be taken every class.
Exams, Quizzes, and Assignments.
First Exam
Midterm Exam
Final Exam
Total
Last Updated:
September 2014
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