520.651 Random Signals Analysis

Fall 2007

Thu Fri 9:00-10:30 AM in Latrobe 107

This is an introductory graduate course in stochastic processes, with an emphasis on second-order properties, signal detection and parameter estimation.

The widespread use of statistical models in signal and image processing, speech recognition and language modeling, communication networks, stochastic control and almost every other subfield in Electrical and Computer Engineering makes this a foundation course for further work for many.

Instructor:

E mail:
Telephone:
Fax:
Office Location:
Office Hours:

Course Assistant:

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Office Location:
Office Hours:
Sanjeev Khudanpur

MyLastName at jhu dot edu
410-516-7024
410-516-5050
Barton 223A
10:30-12:00 Thu & Fri (by appointment, please)

S. Krishna Nemala

siris DOT TheBoldFacedName AT gmail DOT com
Barton 223C
2:00-3:00 Mon & Tue (or by appointment)

Textbook and Reference Material

The Fall 2007 offering of the course will utilize the third edition of the book by Stark and Woods as the primary textbook for the first half of the course, with additional reading material from Poor for the second half. Notes from previous offerings of this course may also be provided as needed. Students are expected to be somewhat familiar with probability theory, and an undergraduate-level textbook for self study is

Homeworks and Exams

Evaluation will be based on a combination of

Homework assignments and notes will be posted here throughout the semester. Check this page frequently!

Problem numbers such as 6.13 (and Arabic chapter numbers) refer to the Stark and Woods textbook (3rd Ed.), while problem numbers like IV.F.5 (and Roman chapter numbers) refer to the Poor book.

An Important Note on Academic Ethics:

Cheating is wrong. Cheating hurts our community by undermining academic integrity, creating mistrust, and fostering unfair competition. The university will punish cheaters with failure on an assignment, failure in a course, permanent transcript notation, suspension, and/or expulsion. Offenses may be reported to medical, law or other professional or graduate schools when a cheater applies.

Violations can include cheating on exams, plagiarism, reuse of assignments without permission, improper use of the Internet and electronic devices, unauthorized collaboration, alteration of graded assignments, forgery and falsification, lying, facilitating academic dishonesty, and unfair competition. Ignorance of these rules is not an excuse.

In this course, you will complete all homework and exams without unauthorized assistance from any person, materials or device. If you have questions about this policy, please ask the instructor. For more information, see the guide on "Academic Ethics for Undergraduates" and the Ethics Board web site.

Remark: Most of the students who took this course in the recent past have indicated in their feedback that the workload in this course was much higher (~50%) or higher (~40%) than in other courses they took. Even with a high workload, however, most students said that the overall quality of the course was excellent (~50%), very good (~35%) or good (~15%). Current students should take note, and budget time and effort accordingly to get the most out of this course.

Old Homeworks and Exams

Homeworks and exams from past offerings of this course are listed below to give students a better idea of the concepts covered in this course, and a feel for the pace and level of effort expected. The homeworks and exams assigned this time may vary slightly, but hopefully not significantly, based on material covered in class.

Old Notes

Some excellent notes on Random Processes are available from the person who taught me Probability, Random Variables and Stochastic Processes: Prof Adrian Papamarcou of the University of Maryland, College Park.