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Lecture
Signal Representations
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Related lectures (30)
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Projection in Vector Spaces
Explores the generalization of projection in vector spaces and its unique properties, emphasizing its role in finding the closest vector in a subspace.
Gram-Schmidt Process
Introduces the Gram-Schmidt orthogonalization process to find orthogonal bases for vector subspaces.
Vector Representation of Signals
Explores the vector representation of signals, orthogonal functions, and Fourier series.
Axonometry: Core Concepts
Covers the core concepts of axonometry, focusing on projection and linear dependence of vectors.
Orthogonality and Projection
Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.
Orthogonal Vectors and Projections
Covers scalar products, orthogonal vectors, norms, and projections in vector spaces, emphasizing orthonormal families of vectors.
Orthogonal Projection Theorems
Covers the theorems related to orthogonal projection and orthonormal bases.
Fourier transforms: Light-matter interaction
Explores Fourier and Laplace transforms in materials science, focusing on light-matter interaction, diffraction patterns, and crystal properties.
Orthogonal Bases in Vector Spaces
Covers the concept of orthogonal bases in vector spaces and Pythagorean theorem applications.
Properties of Fourier Transform
Explores the properties and applications of the Fourier transform in signal processing and mathematics.
