Signal Approximation and Orthogonal Bases

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Explores vector approximation, signal detection, correlation, Fourier series, and signal comparison in signals and systems.

Covers scalar products, orthogonal vectors, norms, and projections in vector spaces, emphasizing orthonormal families of vectors.

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

Covers the concept of 3D vector space, scalar product, bases, orthogonality, and projections.

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.

Explores cross-correlation, convolution, signal approximation, orthonormal functions, and orthogonal approximation theorem in signals and systems.

Covers calculations in coordinates for vectors, including bases, scalar product, and determinants, with geometric interpretations and examples.

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

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

Covers diagonalization of matrices, eigenvectors, linear maps, and least squares method.