This module extends the ideas of the Discrete Fourier Transform (DFT) into two-dimensions, which is necessary for any image processing.
Introducción al funcionamiento del Algoritmo de GOERtzel y a su implementación en un DSP
This course uses the theory and application of atomistic computer simulations to model, understand, and predict the properties of real materials. Specific topics include: energy models from classical potentials to first-principles approaches; density functional theory and the total-energy pseudopotential method; errors and accuracy of quantitative predictions: thermodynamic ensembles, Monte Carlo sampling and molecular dynamics simulations; free energy and phase transitions; fluctuations and transport properties; and coarse-graining approaches and mesoscale models. The course employs case studies from industrial applications of advanced materials to nanotechnology. Several laboratories will give students direct experience with simulations of classical force fields, electronic-structure approaches, molecular dynamics, and Monte Carlo.
This module offers an introduction to Bayesian networks by means of a worked example of computing a bayesian network from a joint probability distribution (JPD).
Efficient scheme for computing samples of the z-transform. (Important special case: DFTs)
This module describes the circular convolution algorithm and an alternative algorithm
The module looks at circular shifting and how it is can be used as a tool to represent the shifting of a periodic sequence.
The theoretical frameworks of Hartree-Fock theory and density functional theory are presented as approximate methods to solve the many-electron problem. A variety of ways to incorporate electron correlation are discussed. The application of these techniques to calculate the reactivity and spectroscopic properties of chemical systems, in addition to the thermodynamics and kinetics of chemical processes, is emphasized. This course also focuses on cutting edge methods to sample complex hypersurfaces, for reactions in liquids, catalysts and biological systems.
Este modulo describe el elgoritmo de convolucion cicular y un algoritmo alterno
This course gives an outline of the Discrete Fourier Transform and an in-depth look at the Fast Fourier Transform. Many different ways of speeding up a discrete Fourier Transform are examined and evaluated for how efficient they are.
This module will take the ideas of sampling CT signals further by examining how such operations can be performed in the frequency domain and by using a computer.
This module introduces linear algebra, DFT, FFT, matrix and vector.
Este modulo ve el desplazamiento circular y como se puede usar para representar el desplazamiento de secuencias periodicas.
The course treats: the discrete Fourier Transform (DFT), the Fast Fourier Transform (FFT), their application in OFDM and DSL; elements of estimation theory and their application in communications; linear prediction, parametric methods, the Yule-Walker equations, the Levinson algorithm, the Schur algorithm; detection and estimation filters; non-parametric estimation; selective filtering, application to beamforming.
The Fourier transform can be computed in discrete-time despite the complications caused by a finite signal and continuous frequency.
The Fourier transform can be computed in discrete-time despite the complications caused by a finite signal and continuous frequency.
How to compute spectra using DFT.
This module covers the fundamentals of Discrete-Fourier Transformations.
Average stock price as an example of discrete-time filtering.
Definitions and basic algorithms for Fourier analysis for discrete-time signals.
