EE 640 Course Syllabus

Stochastic Systems

January 11, 2001

Instructor: Laurence Hassebrook

Office/Phone/email: 691 AH/ (606) 257-8040 / (put EE640 in subject)


Class Hours and Location: 3:30-4:45pm TTr, AH 253.

Text: Random Signals Detection, Estimation and Data Analysis

by K. Sam Shannugan and A. M. Breipohl.

 Office hours: 1:00pm-4:00pm W.

Class Content and Objective:

The content of "Stochastic Systems" represents the basic knowledge necessary for signal processing and pattern recognition applications. Chapters 1 through 4 will be covered with possibility of specific material from chapters 5 through 9. There are three areas covered in this course, both as lectures and projects:

1. Random Noise analysis and synthesis: We will cover the concepts of probability, random variables, random vectors and functions of multiple random variables. Specific concepts include Conditional probability, Bayes theorem, expectations and functions of random variables. Theoretical issues such as bounds and convergence definitions will be included. Practical applications in signal and image processing will be given as part of the project 1 problems.

2. Random Processes: We will cover the concepts of random processes, autocorrelation, linear operation on random processes and sampling. Project 2 will cover the concepts and applications of random process theory.

3. Stochastic signal processing: The types of random process models will be detailed and emphasis will be placed on signal detection and discrimination for 1-D and 2-D functions. Optimal signal processing architectures will be developed and analyzed.

There will be three educational components to the course which include lecture, home work and design projects. The lectures will be designed to provide theoretical basis and design equations. The homework will include problems from the text as well as computer problems which will give the students immediate inter-action with the theory and design aspects of Stochastic systems. The design projects will provide the students with individual depth yet experience with a team effort. The student should attain a good theoretical and practical understanding of stochastic systems.

Grading Policy:

Homework: 20% (Once a week, Due one week after assignment. Some homework may be due in parts, such as a single problem due the next lecture session. No late homework, drop the lowest 1).

Computer Project 1: 20%. Synthesis, analysis and parameter estimation of random variables.

Computer Project 2: 20%. Synthesis, sampling, analysis, function estimation and detection theory with random processes.

Midterm Exam: 20% (Date to be announced, open book, open notes).

Final Exam: 20% (Tuesday 5-1-01, 3:30pm to 5:30pm, open book, open notes).


Reference: Probability, Random Processes and Estimation Theory for Engineers by Henry Stark and John W. Woods.

Reference: Introductory Probability and Statistical Applications by P. L. Meyer.

Reference: Probability and Statistics by M. H. DeGroot.

Reference: Discrete-Time Signal Processing by Oppenheim and Schafer.

Reference: Pattern Classification and Scene Analysis by Duda & Hart.

Reference: Detection: Estimation, and Modulation Theory: Part I by Harry L. Van Trees.

Reference: Linear Statistical Inference and Its Applications: 2nd Edition by C. R. Rao.

Reference: Probability, Random Variables, and Stochastic Processes, 2nd and 3rd Editions by A. Papoulis.

Reference: Numerical Recipes in C by Press, Flannery, Teukolsky and Vetterling.