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The crucial tool to study the Hilbert transform is to calculate its discrete Laurent transform.
The most effective solving method for the difficulty of Fourier transform is to analyze the signals directly in time domain.
The contribution of such a transform is to leverage the cache performance by enforcing spatial and temporal cache locality.
Fractional complex transform is to renovate the fractional differential equations into ordinary differential equations, yielding a tremendously simple solution procedure.
({mathbf {Psi }}) in Algorithm 1 is the sparsifying matrix for the Fourier domain where Fourier transform is to be taken along the rows.
The core principle of the parallel Hough transform is to divide the range of θ into n pieces, using n processing engines to separately calculate ρ.
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And then a Hilbert transform is applied to these IMFs.
Finally, a PCA transform is applied to extract visual features.
The curvelet transform is used to represent the curved shapes.
Second, curvelet transform is applied to both sets of images.
Fast Fourier transform is used to accelerate the calculation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com