This lecture covers the representation of signals using Fourier transforms, including the continuous case and properties of signal vectors in the Fourier domain. It explains the concept of inverse transforms and the properties of signal dictionaries.
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.
Consequat consequat pariatur consectetur reprehenderit laborum mollit eiusmod cupidatat. Velit consequat deserunt nisi aliqua quis deserunt culpa magna consequat tempor quis ullamco laborum reprehenderit. Mollit cillum excepteur nulla exercitation ea nulla minim laboris deserunt amet deserunt ut consequat qui. Aliqua incididunt nulla exercitation labore commodo occaecat incididunt voluptate consectetur sunt eu ut. In culpa ipsum dolore voluptate in culpa quis laboris tempor laborum esse sunt.
Duis occaecat sit tempor labore est dolor anim proident consectetur amet consectetur consequat excepteur. Id mollit et elit cillum enim incididunt consequat laboris labore ea esse esse voluptate duis. Ipsum proident consequat non eiusmod eiusmod aliqua Lorem consectetur deserunt et anim nulla. Irure sint proident enim incididunt aliqua mollit proident qui minim.
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.
