Note: This is an archived Handbook entry from 2008.Search for this in the current handbook
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2008:Semester 1, - 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: Not available
|Prerequisites:||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 Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
|Subject Overview:|| |
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.
|Assessment:||Formally supervised written examination 3 hours: 70% (end of semester); project reports (not exceeding 20 pages each): 30% (two projects, one in the first half of the semester and one in the second half of the Âsemester).|
|Recommended Texts:|| |
Information Not Available
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
|Generic Skills:|| |
Bachelor of Engineering (Computer Engineering) |
Bachelor of Engineering (Electrical Engineering)
Bachelor of Engineering (Software Engineering)
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