EEE 350 Random Signal
Analysis Spring 2006
Meeting: Tue/Thu, 12:15 pm – 1:30 pm, PSA 104
Instructor: Prof. Gang Qian (GWC 454, 965-3704 / 727-8742, gang.qian@asu.edu)
Office Hours: Tue, 2 pm – 4 pm; Fri, 1 pm – 3 pm; or by appointment via email
Required:
Roy D. Yates and David J. Goodman, Probability and Stochastic Processes - A Friendly Introduction for Electrical and Computer Engineers, 2nd edition, John Wiley and Sons, 2005.
Other Useful References:
· Alberto Leon-Garcia, Probability and Random Processes for Electrical Engineering, Addison-Wesley, 1994.
· Dimitri P. Bertsekas and John N. Tsitsiklis, Introduction to Probability, Athena Scientific, 2002.
· Henry Stark and John W. Woods, Probability and Random Processes with Applications to Signal Processing (2nd edition), Prentice Hall, 2002.
·
P. Z. Peebles, Probability, random variables,
and random signal principles,
· Sheldon Ross, Introduction to Probability and Statistics for Engineers and Scientists, John Wiley, 1987.
· Homework assignments (approximately weekly), which may involve some simple Matlab programming
· Scheduled quizzes will be given in the class. Tentative quiz dates are available in the lecture schedule on the course website.
· Two midterm exams and one final exam, dates and times:
o Midterm 1: March 1 (tentative, adjustable according to course progress),
o Midterm 2: April 17 (tentative, adjustable according to course progress),
o Final Exam: 12:20 pm - 2:10pm, Tue., May 8
· Homework: 15%; Quizzes: 5%; Midterm 1: 25%; Midterm 2: 25%; Final: 30%
· A = 85.0-100.0; B = 75.0-84.9; C = 65.0-74.9
This course is intended to introduce the concepts of probability and random signals and to discuss their application to engineering problems. It is also intended that this course should be a suitable prerequisite for advanced courses in probability theory and random processes such as EEE554.
Working knowledge of calculus and linear algebra
· Set operations, random experiments, and probability axioms, probability of an event, conditional probability, law of total probability, Bayes’ theorem, independent events (Chapter 1)
· Random variables, cumulative distribution function (pdf), probability mass function (pmf), probability density function (pdf), expectation, variance and standard deviation (std), functions of a random variable, conditional probability distributions given an event (Chapters 2 and 3)
· Pairs of random variables, random vectors, joint probability distributions, marginal distributions, (vector/vector cross-)correlation, (vector/vector cross-)covariance and correlation coefficient, functions of a random vector, conditional probability distributions of a random variable given another r.v., independent random variables (Chapters 4 and 5)
· Moment generating functions (mgf), mgfs of the sum of independent random variables, central limit theorem (Chapter 6)
· Parameter estimation, confidence intervals and hypothesis testing, estimation of a random variable (Chapter 7, part of Chapters 8 and 9)
· Introduction to stochastic processes (if time allows, part of Chapter 10)
· Homework will be assigned approximately once a week and will be collected at the beginning of class on the date specified at the time of assignment. Late homework will not be accepted. If you are sick on the day a paper is due, you should have a friend deliver the paper to the instructor at the beginning of the class.
· Please staple your paper if it has more than one page.
· You are allowed to discuss with your friends about the homework assignments. Nevertheless you must do your own work. Doing your own homework will definitely serve you well in exams and help you secure good grades. Copying homework from others will not be tolerated. Both parties are responsible.
· You are strongly suggested to try to solve the homework independently.
· All exams will be closed-book exams.
· No make-up exams will be granted, except approved in advance by the instructor or in the case of a legitimate unforeseen emergency. If unable to attend for any reason, contact the instructor at least one week before the exam.
· No cheating allowed. Any form of cheating will result in an immediate failure of the course.
· Suggestions:
o Read relevant sections in the textbook before each class.
o Attend all classes.
o Review class notes and work out quiz in the textbook.
o Solve extra problems in the text as exercise.