Spectral decomposition of time series (1-D) and image (2-D) data is a commonly used technique across various disciplines that use sensors for data collection. Fourier analysis is the foundation of spectral decomposition methods and provides basis (and intuition) for the more advanced methods in time-frequency analysis such as wavelets and Wigner-Ville decomposition. This workshop will cover 1-D Fourier transform with applications to signals and time series data and will also provide a flavor of applications in image processing.
