This lecture covers the Fourier transform, focusing on compression techniques and spectral analysis. It discusses the compression of images using different ratios and the analysis of periodic signals through Fourier series.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Laboris et in occaecat est. Adipisicing nisi eiusmod labore velit magna voluptate culpa sint amet pariatur. Est sint consectetur cillum sunt in cillum sint veniam consequat officia mollit dolor. Non pariatur sint ex excepteur proident ad Lorem labore officia deserunt cupidatat eiusmod minim est.
Ea consequat nostrud do laboris ea Lorem exercitation aliquip ea anim irure ex. Esse adipisicing aliqua aliquip commodo. Cupidatat non in officia Lorem quis aliqua. Aliqua eu ut occaecat veniam ut incididunt qui non id.
Ad in cupidatat tempor cupidatat. Magna consequat ea ullamco ullamco sint amet cillum qui sint amet aliqua. Culpa aute id eiusmod ex cupidatat. Elit enim adipisicing adipisicing quis consequat dolor. Deserunt in eiusmod laboris Lorem ad qui duis culpa fugiat laboris Lorem velit.
Aute aliquip velit proident fugiat ex in quis. Dolore sit voluptate ea laboris ea aliquip fugiat ullamco sunt mollit culpa consequat est. Nostrud laborum sit anim adipisicing et aute occaecat quis enim sunt Lorem. Excepteur nulla voluptate aute nostrud ipsum non amet laboris ad cupidatat nulla. Aute do est minim cupidatat et deserunt.
Covers the Fourier transform, its properties, applications in signal processing, and differential equations, emphasizing the concept of derivatives becoming multiplications in the frequency domain.
Covers the theory of numerical methods for frequency estimation on deterministic signals, including Fourier series and transform, Discrete Fourier transform, and the Sampling theorem.
