Signals & Systems I: Vector Approximation and Signal Comparison

Related lectures (31)

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Explores signal approximation, orthogonal bases, Fourier series, correlation, and vector geometry.

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

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

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

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

Explores orthogonal and orthonormal bases, Gram-Schmidt process, and orthogonal polynomials in physics.

Covers orthogonal vectors, unit vectors, and the Pythagorean theorem in R^m.

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

Covers the fundamental concepts of vectors, including their characteristics and calculation methods.

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