Signals & Systems II: Fourier Transform and Eigenvectors

This lecture covers the canonical implementation and benefits of reduced storage in signal processing, the decomposition of signals in simple fractions, the link between the Fourier transform and the discrete-time Fourier transform, and the concept of eigenvectors in linear systems. It also discusses the parallel implementation of signals, stability analysis, and the properties of the discrete-time Fourier transform. The lecture emphasizes the analogy between eigenvectors and Fourier analysis, highlighting the practical consequences of complex sinusoidal responses in signal processing.