431-461 Signal Processing 2 | |
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Credit Points | 12.5 |
Prerequisites | 431-325 Stochastic Signals and Systems, 431-335 Signal Processing 1 (Fundamentals) |
Semester | 1 (view timetable) |
Contact | Twenty-four hours of lectures, 12 hours of tutorials and 12 hours of laboratory experiment or project work |
Subject Description | 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. |
Generic Skills |
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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 semester). |
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