Note: This is an archived Handbook entry from 2010.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2010:Semester 2, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: 24 hours; Non-contact time commitment: 96 hours |
Total Time Commitment: 120 hours
|Prerequisites:|| Enrolment in a research higher degree(Masters or PhD) in Engineering |
|Recommended Background Knowledge:||None|
|Non Allowed Subjects:||None|
|Core Participation Requirements:||For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website: http://www.services.unimelb.edu.au/disability/|
CoordinatorDr Marcus Brazil
ContactMelbourne School of Engineering Office
Building 173, Grattan Street
The University of Melbourne
VIC 3010 Australia
General telephone enquiries
+ 61 3 8344 6703
+ 61 3 8344 6507
+ 61 3 9349 2182
+ 61 3 8344 7707
|Subject Overview:||Topics include: a review of stochastic processes detection of random signals in noise; dynamical system models and associated state estimation algorithms (Kalman and Wiener filtering); parameter estimation algorithms (Least Squares, Maximum Likelihood) and their adaptive versions. Other topics to be selected from: nonlinear filtering; hidden Markov model signal processing; expectation maximization algorithm; distributed detection and estimation; information-theoretic aspects of estimation and detection (Cramer Rao bound, Divergence measures); array signal processing.|
|Objectives:||The aim of this subject is to give students a rigorous introduction to the mathematical tools commonly employed in statistical signal processing.|
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
Master of Applied Science (Electrical and Electronic) |
Master of Engineering Science (Electrical and Electronic)
Master of Philosophy - Engineering
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