Lecture

Signal Representations

Related lectures (31)

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.

Explores Sobolev spaces in higher dimensions, discussing derivatives, properties, and challenges with continuity.

Explores optimal transport and gradient flows in Rd, emphasizing convergence and the role of Lipschitz and Picard-Lindelöf theorems.

Introduces Hilbert space as a big place where quantum systems evolve with unitaries.

Covers signal representation and manipulation techniques in different domains.

Discusses signal representation, focusing on mathematical expressions and inequalities in signal processing.

Covers the properties of signal/data representations using Fourier transformations and matrices.

Explores the space of bandlimited functions, focusing on 1-bandlimited functions as a basis and their representation through sequences.

Covers the axioms of quantum mechanics and the measurement of quantities in a quantum system.

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

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.