| CODE | CCE5204 | ||||||||||||
| TITLE | Advanced Digital Signal Processing | ||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||
| MQF LEVEL | 7 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Communications and Computer Engineering | ||||||||||||
| DESCRIPTION | This course builds on the foundations of Digital Signal Processing, focusing on advanced techniques for analysing and processing discrete-time signals and time series data. Time-frequency analysis techniques are applied to non-stationary signals. Additionally, stochastic signal modelling methods, including ARIMA models, are explored for analysing and predicting the behaviour of time series. Study-Unit Content: • Discrete Fourier Transform: review of the DFT, time-frequency relationships, the FFT, the discrete cosine transform; • Digital Filters: FIR and IIR designs; • Power Spectrum Estimation: windows, window spectral leakage, narrowband and wideband spectrograms; • Multirate signal processing: decimation and interpolation of the sample points, and their effect on bandwidth and time series; polyphase structures; • Auto-Regressive Moving Average (ARMA) models, parameter estimation and signal modelling; • Auto-Regressive Integrated Moving Average (ARIMA) models, and applications in time series analysis and prediction; • Wavelet transforms, including wavelet bases. Study-unit Aims: The aim of this unit is to offer more advanced topics in digital signal processing to students who already have a basic knowledge of DSP. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: • analyse signals using time-frequency analysis techniques, including wavelet transforms; • compare wavelet-based methods with Fourier-based techniques for signal analysis; • compare different wavelet bases; • explain the theory and applications of ARMA and ARIMA models for stochastic signal modelling. 2. Skills By the end of the study-unit the student will be able to: • design DSP systems using advanced filtering and modelling techniques; • develop ARMA and ARIMA models for time series analysis and prediction; • implement various advanced DSP structures. Main Text/s and any supplementary readings: Main Texts • J. G. Proakis & D. K. Manolakis, Digital Signal Processing, (4th Edition)4th. ed., Prentice Hall, 2006. ISBN: 978-0131873742. Supplementary Readings • S. Mallat, A wavelet tour of signal processing, 3rd. ed., Academic Press, 2009. ISBN: 978-0123743701. • P. S. R. Diniz, Signal processing and machine learning theory, Academic Press, 2024. ISBN: 978-0323917728. |
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| ADDITIONAL NOTES | Prerequisite study-unit: CCE3206 or an equivalent study-unit, as approved by the Board of Studies. | ||||||||||||
| STUDY-UNIT TYPE | Lecture | ||||||||||||
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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 2025/6. It may be subject to change in subsequent years. |
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