Frequency analysis

https://en.wikipedia.org/wiki/Fourier_analysis https://en.wikipedia.org/wiki/Wavelet|Frequency domain analysis (or spectral density estimation), is the process of decomposing a complex signal into simpler parts. Many physical processes are best described as a sum of many individual frequency components. Any process that quantifies the various amounts (e.g. amplitudes, powers, intensities, or phases), versus frequency can be called spectrum analysis. The Fourier transform of a function produces its frequency spectrum by decomposing the function into its sine and cosine components. Whereas the standard Fourier transform is only localized in frequency, wavelets are localized in both time and frequency. Wavelets are handcrafted to correlate with particular frequencies in a signal; a set of wavelets is usually used for a complete analysis.

Synonyms
Fast Fourier transform
FFT
Wavelet transform
Description

This plugin allows to analyze the local direction and frequency of sinusoidal waves, for example muscle repetitive stripy pattern.

The output are optionally smoothed lambda (inversely proportional to the frequency) and phi (direction in degrees).

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Description

A complete parametric framework and set of MATLAB tools for computing steerable wavelet frames in 2-D.

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Description

A MATLAB package is made available for computing the fractional smoothing spline estimator of a 1D signal, and for generating fBms (fractional Brownian motion).

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Description

Fast Fourier Transform (FFT) for 2D/3D images.

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Description

MonogenicJ performs multiresolution monogenic analyses of 2D images. It extracts wavelet-domain features that characterize the local orientation, the phase and the dominant frequency of an image patch at various levels of resolution.

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