Signals & Systems I: Vector Approximation and Signal Comparison

Related lectures (31)

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Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Explores cross-correlation, signal detection, Hilbert spaces, orthogonal approximation, and image compression using DCT.

Introduces mechanics, differential and vector calculus, and historical perspectives from Aristotle to Newton.

Covers the properties of vectors, including commutativity, distributivity, and linearity.

Introduces orthogonal vectors, scalar product, Euclidean norm, Pythagorean theorem, and unit vectors.

Introduces scalars and vectors, explaining their properties and mathematical operations.

Covers landmarks, coordinate systems, frames, and terminology in coordinates, emphasizing geometric angles and orthogonal vectors.

Explores orthogonality, norms, and distances in vector spaces for solving linear systems.

Covers the decomposition of a vector in a base and its applications in speed and distance calculations.