ELEC 471: Random Signals and Noise
This is an introductory course in probability and statistics for
undergraduate and graduate students.
Our goal is to study and understand the basic concepts and tools
of probability and statistics.
We will try provide hands-on experience with the course material
through demonstrations and projects using MATLAB.
We will also strive to
relevance and usefulness of probability and statistics in practical
Instructor and Office Hours:
Richard J. Kozick
Office: Room 220 Dana
Phone: (570) 577-1129
FAX: (570) 577-1822
Office hour schedule for Spring, 1999 is MWF 2 - 3
I can also meet MWF 12 - 1 PM, but please
give me advanced warning for these times by sending
MATH 211 (multivariable calculus) and ELEC 320 (Signals and Linear
Probability and Stochastic Processes:
A Friendly Introduction for Electrical & Computer Engineers,
Roy D. Yates and David J. Goodman, John Wiley Publishers, 1999.
** PLEASE BRING THE TEXTBOOK TO CLASS **
Many books are available on the subjects of probability and
The books take many perspectives: some are mathematical,
while others are oriented toward engineering, science, business,
social science, etc.
I can recommend other books with an electrical engineering flavor,
if you are interested.
Course Home Page:
The home page for the ELEC 471 course is located at the URL
It can also be accessed by following the link from
my home page at
The course home page contains the homework assignments,
syllabus, sample MATLAB programs,
and other course information.
The grading will be objective, so you will be evaluated
with respect to an absolute scale rather than in comparison
with your classmates.
There are no limits on the number of A's, B's, etc.
10 quizzes (<= 30 minutes) at 4% each 40%
2 1-hour exams at 10% each 20%
Final exam 15%
Homework and projects 15%
Graduate students will be given additional assignments.
Quizzes and Exams:
We will have two one-hour exams on the following dates:
Exam 1: Thursday, February 4, 1999
Exam 2: Thursday, March 18, 1999
We will also have 10 quizzes during the semester.
The only class dates that will not have a quiz are
February 4 and March 18 (since we will have exams on those days),
and February 11.
These quizzes will follow closely with the material
from the previous class session and the homework assignments.
Missed quizzes cannot be made up, so it is important that you attend
all class sessions.
Some quizzes and exams may require you to use MATLAB.
The course will conclude with a final exam.
Homework, Projects, and Presentations:
Homework will be assigned weekly.
A subset of the homework problems will be
collected and graded.
You are responsible to understand all of the homework
problems, since the quizzes will be based primarily on the
homework assignments and class notes.
There will also be "project" assignments that provide hands-on
experience with the course material, often involving processing
Each student will be asked to prepare a 10-15 minute presentation
to the class. The presentations will be done in groups of 2 or
3 students, and I will assign the topic.
In many cases, the groups will present their solution
to a problem.
Your presentation should be well-prepared and clear.
Late homework and project
assignments will not be accepted, since we will review
the solutions during class.
You are encouraged to work on the homework and projects with groups of
your classmates. However, the work that you submit for
grading must be written individually.
Our goal is to study the following topics related to probability
In the Yates/Goodman text, we will study chapters 1-5 and
selected topics from chapters 6, 7, 9, and 10.
Above is my "wish list" of topics for the ideal course.
I am certain that we will not be able to discuss everything
on the list.
Indeed, the last three topics in the list are typically covered in
five to six graduate-level EE courses!
- Probability (what it means, axioms)
- Random variables
- Probability mass function (pmf) and probability density function (pdf)
- Cumulative distribution function (cdf)
- Expectation, mean, variance, higher moments
- Conditional probability and conditional expectation
- Joint probability distributions
- Independence and correlation of random variables
- Moment generating function
- Useful probability distributions (Gaussian, uniform, binomial,
Poisson, and others)
- Laws of large numbers
- Random processes
- Statistical inference: detection and estimation (least squares,
maximum likelihood, Cramer-Rao bound)
- Applications to digital communications and statistical signal
processing (matched filter, Wiener filter)