Signal Representations

Related lectures (31)

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

Covers the representation of signals in vector spaces and inner product spaces, including the Projection Theorem.

Explores the postulates of Quantum Mechanics, focusing on states, time evolution, and measurement.

Covers the weak formulation of elliptic partial differential equations and the uniqueness of solutions in Hilbert space.

Covers Dirac's notation in linear algebra and the tensor product concept in Hilbert spaces.

Covers the calculation of Fourier coefficients for periodic complex signals and the concept of a complete orthonormal system.

Covers fundamental concepts in quantum mechanics, including vector spaces, superposition, observables, and inner product.

Explores Hilbert space, scalar product, geometry, bases, complex functions, and quantum mechanics applications.

Explores linear estimation and prediction in AR parametric models, focusing on Yule Walker equations and Wiener filter.

Covers the quantization of topological operators and Ising models on square lattices.