Note: This is an archived Handbook entry from 2010.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2010:Semester 1, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: Thirty-six hours of lectures, 12 hours of tutorials and 12 hours of laboratory experiment or project work |
Total Time Commitment: 120 hours
431-325 Stochastic Signals and Systems, 431-335 Signal Processing 1 (Fundamentals)
|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/ |
CoordinatorProf Robin Evans
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
On completion of this subject students should have a good understanding of signal processing methods for parameter estimation, and signal estimation and be able to design, analyse and implement such algorithms.
Topics include: Motivation for parameter estimation and filtering with examples. Parameter estimation: least squares and its properties, recursive least squares and least mean squares, optimisation-based methods, maximum likihood methods. Spectral estimation: periodogram, Barlett method, Welch method and Blackman-Tukey method. Optimal filters for signal estimation: Wiener filter, Kalman filter and Hidden Markov Model filter. Examples illustrating the wide application area of signal processing algorithms.
Project: Design, implementation and testing of signal processing algorithms. Implementation and testing of real time signal processing algorithms on a DSP board.
On completing this subject the student should be able to:
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
Bachelor of Engineering (Biomedical)Biosignals |
Bachelor of Engineering (Computer Engineering)
Bachelor of Engineering (Electrical Engineering)
Bachelor of Engineering (Electrical) and Bachelor of Arts
Bachelor of Engineering (Electrical) and Bachelor of Commerce
Bachelor of Engineering (Electrical) and Bachelor of Laws
Bachelor of Engineering (Electrical) and Bachelor of Science
Bachelor of Engineering (EngineeringManagement) Electrical
Bachelor of Engineering (IT) Computer Engineering
Bachelor of Engineering (IT) Electrical Engineering
Bachelor of Engineering (Software Engineering)
Postgraduate Certificate in Engineering
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