Signal and Image Processing - 4EUS4TSI
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Goals
The purpose of this module is to go beyond the basic one-dimensional and deterministic approach of signals, providing theoretical and practical foundations for analysing and processing:
- random signals,
- two-dimensional signals.
Content Statistical signal processing:
- spectral analysis (non-parametric methods, basic parametric methods)
application ? vibroacoustic emissions of an industrial system - optimal filtering (Wiener filtering, adaptive filtering),
application ? filtering of biophysical signals
Image processing:
- digital image processing
- basics of morphological image processing
- Fourier transform and correlation
- inverse filtering
PrerequisitesMathematics for engineers: complex variable functions, Fourier transform, Laplace transform, Z transform.
Basics in continuous-time signal processing: deterministic and random signals, time domain and frequency domain representations, linear and time-invariant filters, modulation, sampling.
Basics in discrete-time signal processing: discrete Fourier transform, analysis and design of digital filters.
Tests First session
- ER assessment : 3 hours supervised written
- EN assessment : work assignments + Lab reports
If distant learning mandatory:
- ER assessment : 3 hours homework
- EN assessment : work assignments + Lab reports
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Second session
- EN assessment: Retaking this assessment is not possible
ER 33% + EN 67%
Bibliography - Modern spectral estimation - theory and application, S.M. KAY, Prentice Hall, 1988
- Optimal Filtering, Brian D. O. Anderson and John B. Moore. Dover Publications, 2005
- Introduction au traitement d'images, D. Lingrand, Vuibert, 2d ed., 2008
- Digital Image Processing, W. K. Pratt, Wiley, 4th ed., 2007.
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Date of update February 8, 2017