BENOIT™ is a computer program that enables you to measure the fractal dimension
and/or hurst exponent of your data sets using your choice of eleven methods:
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ruler, box, information, perimeter-area, and mass for analysis of self-similar
patterns (2D data);
-
R/S, power-spectral analysis, variogram, roughness-length, and wavelets for
analysis of self-affine traces (1D data);
-
fragmentation for size-frequency data (1D data).
Filtering is provided in the program to permit removal of white noise from your
trace using either Fourier or Wavelet techniques. The user can also generate
synthetic self-affine traces using a built-in trace generator with tuning
parameters.
A user friendly, visual interface makes BENOIT™ a research and study tool for
anyone interested in fractal properties of their data sets. You are just a few
clicks away from the results of the analysis. BENOIT™ automatically calculates
default values for various tuning parameters or you can control the performance
of each method by manual tuning the parameters. Interactive data plots allow
you to deactivate data points outside of the fractal range to increase
accuracy. For those interested in how each method works, which method(s) are
preferable for a given data set, and further information about fractal methods
and their properties, an extensive help file is built into the program that
explores these and other fractal-related topics.
BENOIT for Matlab is a fractal analysis package for Matlab 6.5 or higher
that enables you to measure the fractal dimension and/or hurst exponent of your
data sets using your choice of ten methods:
-
3D box counting for 3D self-similar objects (3D data);
-
ruler, box, information, perimeter-area, and mass for analysis of self-similar
patterns (2D data);
-
R/S, power-spectral analysis, variogram and roughness-length for analysis of
self-affine traces (1D data).
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