Signals and Systems: Mathematical Description and Identification Strategies

Related lectures (31)

Page 1 of 4

Covers linear applications, matrices, transformations, and the principle of superposition.

Explores the properties and applications of the Fourier transform in signal processing and mathematics.

Covers linear transformations using matrices, focusing on linearity, image, and kernel.

Explores isometries in Euclidean spaces, including translations, rotations, and linear symmetries, with a focus on matrices.

Explores subspaces in linear algebra and transformations, including kernels and images of linear transformations.

Explores discrete signals, linear systems, categorization examples, and convolution properties in signal processing.

Explores linear transformations, matrices, injective and surjective functions, and their applications.

Covers the reduction of a linear application and finding corresponding reduced forms and bases.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Covers linear transformations, change of bases, and diagonalization of matrices.