Section I: the transforms
Signal Processing I: sampling of the signal (the sampling theorem, the convolution, the correlation, correlation is not causation, decomposition of the signal on the sphere).
Signal Processing II: the Fourier transform (the DFT, the FFT, the power spectrum, the phases, discretization of the signal on the sphere).
The Information: Wavelets, PCA and EMD (data compression, MP3, JPEG2000,JP3D).
Section II: the signal and the noise
The Noise: noise sources, noise types and spectra, SNR maximization, noise suppression.
Data Restoring: image reconstruction vs scanning, speckle imaging, blind deconvolution.
Data Analysis: patterns in data, punctual operatore and filters, more transforms, morphological operators and descriptors.
In the LAB:
Data Access: FITS + multiFITS.
Datafication examples: H-R diagram, Kepler data and star periods.
The Fourier Transform: Fourier spectrum, digital filters, data manipulation (shifts, transform).
Data-cubes Analysis: Wavelet Spectra, EMD analysis.
Data Compression: image quality estimators, image information estimators.
Co-teaching: Dott. De Gasperis Giancarlo
The aim of the course is to provide to the student a broad overview of the various methods and techniques of data analysis, with a deeper insight on those used in modern-time astrophysics. In particular, we will study the aspects of digital information access, handling, restoration, manipulation, compression and transformation into data.