Note: This is an archived Handbook entry from 2014.
|Dates & Locations:|| |
This subject is not offered in 2014.
|Time Commitment:||Contact Hours: 36 hours of lectures (3 x one hour lectures per week) and up to 24 hours of workshops |
Total Time Commitment:
Prerequisites for this subject are:
ELEN90058 Signal Processing ( prior to 2011, ELEN30008 Signal Processing 1)
ELEN90054 Probability and Random Models( prior to 2011, ELEN30002 Stochastic Signals and Systems )
|Recommended Background Knowledge:|| |
|Non Allowed Subjects:|| |
Anti-requisite for this subject is:
|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/
Students will study topics including:
This material will be complemented with the use of software tools (e.g. MATLAB) for computation and a DSP (Digital Signal Processor) based development platform for the implementation of signal processing algorithms in the laboratory.
INTENDED LEARNING OUTCOMES (ILO)
On completing this subject the student should be able to:
Hurdle requirement: Students must pass the written exam to pass the subject.
Intended Learning Outcomes (ILOs) 1 and 2 are assessed in the final written examination, the mid-semester test, and submitted reports for two projects.
ILOs 3 and 4 are assessed as part of submitted project work and workshops.
|Prescribed Texts:|| |
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
Credit may not be obtained for both ELEN40004(431-461) Signal processing 2 and ELEN90052 Advanced Signal Processing
LEARNING AND TEACHING METHODS
The subject is delivered through lectures and workshop classes that combine both tutorial and hands-on laboratory activities.
INDICATIVE KEY LEARNING RESOURCES
Students are provided with lecture slides, tutorial questions and solutions, project specifications, and reference text lists.
CAREERS / INDUSTRY LINKS
Exposure to industry standard DSP design tools through laboratory activities.
Bachelor of Engineering (Biomedical)Biosignals |
Master of Engineering (Electrical with Business) |
Master of Engineering (Electrical)
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