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Journal ArticleDOI

The Fourier Transform and Its Applications

Ronald N. Bracewell, +1 more
- 01 Aug 1966 - 
- Vol. 34, Iss: 8, pp 712-712
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This article is published in American Journal of Physics.The article was published on 1966-08-01. It has received 2834 citations till now. The article focuses on the topics: Fourier transform.

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Radio Astronomical Polarimetry and the Lorentz Group

TL;DR: In this article, the transformations that commonly arise in radio astronomy are analyzed in the context of the Lorentz group, which is then used to construct a model for the propagation and reception of radio waves.
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Processing of ND NMR spectra sampled in polar coordinates: a simple Fourier transform instead of a reconstruction.

TL;DR: A 2D discrete Fourier transform can be implemented in polar coordinates to obtain directly a frequency domain spectrum and will permit to investigate better compromises in terms of experimental time and lack of artifacts.
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Theory and operational rules for the discrete Hankel transform

TL;DR: The theory of a DHT that is shown to arise from a discretization scheme based on the theory of Fourier-Bessel expansions is proposed and evaluated and can be used to approximate the continuous forward and inverse Hankel transform in the same manner that the discrete Fourier transform is known to be able to approximation the continuous Fouriertransform.
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Results from a double Li-beam technique for measurement of both radial and poloidal components of electron density fluctuations using two thermal beams

TL;DR: In this article, the radial and poloidal components of electron density fluctuation parameters in a tokamak edge plasma were determined by operation of two narrow Li-beams (FWHM?10?mm at a distance of 100?mm from the source), which were separated by 50?mm.
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Calculation of the inverse of the covariance

TL;DR: In this paper, the inverse of the covariance matrix is used to describe the spatial correlation and variation in rock properties or the uncertainty in geostatistics, and it is shown that the inverse is required for simulation and estimation of Gaussian random fields, and can be identified with the differential operator in regularized inverse theory.