| CODE | SCE5105 | ||||||
| TITLE | Advanced Signal Processing | ||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||
| ECTS CREDITS | 5 | ||||||
| DEPARTMENT | Systems and Control Engineering | ||||||
| DESCRIPTION | This study-unit deals with advanced signal processing methods and their underlying theory. The study -unit has a particular focus on the extraction of information from signals, in particular the information that the signals represent about the physical system that generates them. The study-unit presents digital FIR and IIR filtering methods including various approaches for designing these filters. Signal modelling is presented using auto-regressive moving average models. Signal modelling methods for spectral estimation are presented together with direct analysis spectral estimation methods. The unit goes beyond spectral analysis to present joint time-frequency analysis methods including the short-time Fourier transform and wavelet transforms. Systems with multiple signals, either spatially arranged or not, are considered in this unit presenting multi-channel signal analysis methods. Study-Unit Aims: The aims of this study-unit are to: - Represent processes using auto-regressive (AR), moving average (MA) and auto-regressive moving average (ARMA) models; - Present methods to design FIR and IIR filters; - Present model-based as well as direct signal analysis spectrum estimation methods; - Explain the trade-off between time and frequency resolution; - Explain the theoretical basis of a time-frequency distribution; - Explain the theoretical basis of multi-scale analysis; - Present methods to analyse the time-frequency characteristics of signals; - Present methods to analyse multi-channel and spatial signals. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - Describe how a process may be represented using auto-regressive (AR), moving average (MA), and auto-regressive moving average (ARMA) models, and the spectral interpretation of the ARMA coefficients; - Identify different methods to estimate the spectrum of a signal and perform spectral estimation method using both modelling and direct methods; - Explain the uncertainty principle of time-frequency signal analysis; - Explain the frequency, time and joint time-frequency representations in the context of the uncertainty principle; - Explain how a signal may be analysed at multiple scales; - Compare the nature of multi-channel signals to that of single channel signals; - Explain two multi-channel signal processing methods; - Explain the implications and interpretation of multi-channel signal analysis methods when applied to spatially arranged sources. 2. Skills By the end of the study-unit the student will be able to: - Given a signal, the student will be able to apply FIR and IIR filtering approaches to filter the signal; - Given a signal, the student will be able to model this using auto-regressive, moving average, and auto-regressive moving average models, and to obtain a spectral estimation; - Given a signal, the student will be able to perform a number of spectral estimation method to estimate the spectrum and compare the spectra obtained using different parameters; - Given a signal, the student will be able to determine the short-time Fourier transform of the signal; - Given a signal, the student will be able to determine the continuous wavelet transform and the discrete wavelet transform of the signal.- Given a signal, the student will be able to perform a multi-scale analysis; - Given multi-channel signals, the student will be able to perform a multi-channel signal analysis to extract salient information from the signal; - Given spatially organised multi-channel signals, the student will be able to obtain an optimal spatial filter. Main Text/s and any supplementary readings: Main Texts - Proakis J. G. and Manolakis D. G. (2006). Digital Signal Processing - Principles, Algorithms, and Applications. 4th ed. New Jersey: Prentice-Hall, Inc. Supplementary Readings - Stoica P. and Moses R. (2006). Spectral Analysis of Signals. New Jersey:Prentice-Hall, Inc. - P. S. Addison (2002). The Illustrated Wavelet Transform Handbook. CRC Press. - Hyvarinen A., Karhunen J., Oja E. (2001). Independent Component Analysis. John Wiley & Sons Inc. - Jolliffe I.T. (2002). Principal Component Analysis, 2nd ed. Springer Science. |
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| ADDITIONAL NOTES | Co-requisite Study-unit: SCE5101 Linear Dynamic Systems & Signals | ||||||
| STUDY-UNIT TYPE | Ind Study, Lecture, Practicum, Project & Tutorial | ||||||
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| LECTURER/S | Natasha Padfield Andre Sant |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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