EE445: Statistical Decision Theory
  Course Syllabus, Spring 2011
Lecture: 112 EECH, Tuesdays and Thursdays 2:00 p.m. - 3:15 p.m.
 

Instructors:
Dr. Jun Tao
Dept. of Electrical and Computer Engineering
208 EECH, email: jtb84@mail.mst.edu

Dr. Chengshan Xiao
Dept. of Electrical and Computer Engineering
229 EECH, 573-341-4539 (o), xiaoc@mst.edu

Office Hours: Fridays: 9:00 - 11:00 a.m. or by appointment

Prerequisites:

  • Elec Eng 344 (Stochastic Signal Analysis I).

    Course Description
    Classical detection and estimation theory with appilcations; hypothesis testing, detection of known signals, matched filter receiver implementation, detection of signals with unknown parameters, sequential and nonparametric detection, detection of stochastic signals: Parameter estimation theory with application to modulation.

    Textbook

  • Required textbook: Fundamentals of Statistical Signal Processing: Estimation Theory, by Steven M. Kay, Prentice Hall, 1993. ISBN 0-13-345711-7.

    • References

  • Research articles from IEEE Journals.

    • Lectures, Homeworks, and Exams:

  • You (students) are expected to attend every lecture. You are solely responsible for anything you miss in classes, including announcements, handouts, assignments, and exams, in addition to the course topics discussed in the class.
  • There will be six to eight homework assignments.  
  • Homework assignments are due at 2:00pm on the designated date.
  • There will be two exams.  The exams are closed-book.
  • Makeup exams will not be given unless you have a very unusual excuse with the instructor's permission in advance, or a documented medical/family emergency. 
  • If you disagree with the grading of an exam or a homework, you must contact the instructor within one week from the day the exam/homework is handed back to you.  After that time, no request for regrading will be accepted. A regrade can result in an increase, a decrease, or no change in the grade.

    • Grading:
    The grading scheme is given by

    Homework: 35%
    First Exam: 30%
    Second Exam: 35%

    Important dates:
    Please inform the instructor any religious or traditional holidays that you may wish to observe.

    Feedback: Your feedback is very important to have good lectures. In addition to the semester-end teaching evaluation required by the department, I'll frequently solicit your feedback. Your comments are appreciated and are welcome throughout the semester.
    Feedback and communication with the instructor can be made via in-class questions, office hours, emails, and anonymous letters dropped in my mailbox or in the department office. Your emails will be read everyday during the week. But due to the large volume of emails I receive every day, I may reply only when needed. Common questions will be answered in class.

    Class Behavior and Academic Honesty:

  • When in class, please turn off all cell phones, pagers, and other devices that ring, buzz, or otherwise might disrupt the class.
  • Academic honesty is fundamental to the activities and principles of a university. All members of the academic community must be confident that each person's work has been responsibly and honorably acquired, developed, and presented. Any effort to gain an advantage not given to all students is dishonest whether or not the effort is successful. The academic community regards academic dishonesty as an extremely serious matter, with serious consequences that range from probation to expulsion. When in doubt about plagiarism, paraphrasing, quoting, or collaboration, consult the course instructor.
  • Discussion on homework assignments and project(s) between students is permitted, but each student should solve the problems and write report(s) separately. Other examples of cheating are

    • ADA Statement: If you need assistance or accommodations due to a disability, please notify the instructor immediately.  Reasonable effort will be made to accommodate your special needs.