EE 640 Stochastic Systems
Spring 2013 
.
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Visit course website at http://www.vis.uky.edu/~cheung/courses/ee640/index.html and the course site in Blackboard. 
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If you have never used Blackboard, visit http://wiki.uky.edu/blackboard/Wiki Pages/Accessing Blackboard.aspx to create an account and take the online training as instructed in http://wiki.uky.edu/blackboard/Wiki Pages/Getting Online Training.aspx 
Instructor: Dr. Senching
Cheung (cheung at engr.uky.edu)
Office 
Hours 
Room 217 Marksbury (8592180299) 
By appointment or try your luck 
FPAT 469 
TTh 911am 
Office 
Hours 
FPAT 669 
M and F 11a – 12p 
Class Schedule
Lecture: TTh 11:00am12:15pm (FPAT 265)
Final: 4/30 10:30am12:30pm (FPAT 265)
(From University Bulletin) Random variables, stochastic processes, stationary processes, correlation and power spectrum, meansquare estimation, filter design, decision theory, Markov processes, Simulation
(From Instructor) This
is a graduatelevel course on random (stochastic) processes, which builds on a
firstlevel (undergraduate) course on probability theory, such as MA 320. It
covers the basic concepts of random processes at a theoretically rigorous
manner, and also discusses applications to communications, signal processing
and control systems engineering. To follow the course, in addition to basic
notions of probability theory, students are expected to have some familiarity
with the basic notions of sets, sequences, convergence, linear algebra, linear
systems, and Fourier transforms.
Tentative Syllabus
Week 1 
Mathematical preliminaries (ch. 1) 
Week 2 
Review of probability theory (ch. 1) 
Week 3 
Convergence of random variables (ch. 2) 
Week 4 
Limit theorems and Large Deviation (ch. 2) 
Week 5 
Joint Gaussian Distribution and Orthogonal Principle (ch. 3) 
Week 6 
Kalman Filtering (ch. 3) 
Week 7 
Review and Exam 1 
Week 8 
Random Process I (ch. 4) 
Week 9 
Random Process II (ch. 4) 
Week 10 
Differentiation and Integration of Random Processes (ch. 7) 
Week 11 
KarhunenLoeve Decomposition (ch. 7) 
Week 12 
Stochastic Linear System (ch. 8.18.3) 
Week 13 
Optimal Wiener Filtering (ch. 9) 
Week 14 
Discrete Markov Chain (ch. 6) 
Week 15 
Review & Final Exam 
Your
grade will be based on: 
Weights 
Weekly
Homework 
50% 
Midterm

20% 
Comprehensive
Final 
30% 

Homework will be assigned weekly through the Blackboard site.

While you can discuss with others, you must do your own work.

Late homework will not be accepted without prior notice.

All exams will be closed book.

Two doublesided chat sheets are allowed for midterm, four sheets
for final.

Makeup test will only be given upon permission from the instructor
prior to the test.

The letter grade assignment is based on the following scale:
From
90 to 100 pts => A, from 75 to 89 pts. => B, from 60 to 74 pts => C,
from 0 to 59 pts. => E.
4.
Academic honesty

I have a zerotolerance policy for all forms of plagiarism and
cheating, from copying a homework answer from your friend to cheating in the exams.
Not only you will lose all the points for that assignment, the incident will
also be reported to the Department.
1.
MA 320 or proficiency in basic discrete
probability

I
definitely recommend the following review by Randall Berry: http://www.eecs.northwestern.edu/~rberry/ECE454/Lectures/probreview.pdf
2.
EE 421 or good knowledge about signals and
system

You might want to glance through my old class
notes at http://www.vis.uky.edu/~cheung/courses/ee421G/lectures.html
3.
MA 471G (desirable but not necessary)

Page
160 of the following online book provides a nice foundation of elementary mathematical analysis for
this class:
http://www.princeton.edu/~rvdb/506book/book.pdf

At
the very least, you should be comfortable with Appendix A of the reference 4
above. A hard copy of appendix A will be provided to you during the first day
of lecture.
4.
Matlab
Last
modified: 1/8/2013 11:06:25