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Curricular information is subject to change
Having passed this module the student will be able to:
- Expound on the theoretical foundations of a number of important signal processing operations.
- Define fundamental terms that characterise a random signal.
- Describe and analyse linear estimators.
- Specify and design algorithms for the processing of speech and images and implement those algorithms in a high level language.
- Design, implement and analyse adaptive digital filters for a variety of applications.
The following key topics are covered:
- Random signals: random variables, stationary random processes, auto-correlation, cross-correlation, power spectral density.
- Estimation theory: unbiased estimation, minimum variance unbiased estimation, best linear unbiased estimations (BLUE), least squares.
- Linear optimum filtering: Wiener filter, linear prediction.
- Linear adaptive filtering: steepest-descent algorithm, least-mean-square algorithm.
- Channel equalization
- Image processing: image representation, 2D-convolution, 2D-correlation, 2D discrete Fourier transform, template matching, image filtering (smoothing filters, sharpening filters)
|Student Effort Type||Hours|
|Specified Learning Activities||
|Autonomous Student Learning||
Signal Processing (EEEN30050) or equivalent. This course is mathematically challenging, so a strong background in university honours level mathematics in the areas of linear algebra, frequency analysis (transforms), linear time invariant systems is required.
|Description||Timing||Component Scale||% of Final Grade|
|Assignment: Channel Equalization||Varies over the Trimester||n/a||Graded||No||
|Assignment: Image Processing||Varies over the Trimester||n/a||Graded||No||
|Assignment: Spectral Power Density Estimation||Varies over the Trimester||n/a||Graded||No||
|Examination: End of trimester exam||2 hour End of Trimester Exam||No||Standard conversion grade scale 40%||No||
|Resit In||Terminal Exam|
|Spring||Yes - 2 Hour|
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
|Assoc Professor Nam Tran||Lecturer / Co-Lecturer|