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Descriptive statistics (means ± SD) were conducted on the sample features.
This distribution can be used to increase the sample features of the NADJPEG compressed region.
Next, the sample features are extracted by performing wavelet decomposition to obtain the low-frequency coefficients.
It consists of multiple convolution and subsampling layers, with the ability to automatically extract the sample features.
This method is able to provide different approaches of farm ranking in relation to the sample features.
The Fourier transform infrared spectroscopy (FTIR) spectrum of the sample features the peaks characteristic of polypyrrole (Figure 3).
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Our algorithm estimates the component-wise correspondences among the sample feature sets of each user and then learns a set of bins per user based on the distribution of the mutually-corresponding feature instances.
Figures 3(a) and 3(b) show the sample feature points (white cross) that are detected in the corresponding levels.
To summarize, we presented, to the best of our knowledge, the first PS-OCT system that uses a singular circular polarization state at the sample, featuring exclusively conventional SM fibers in both, interferometer and detection unit.
Then, the samples' features are extracted by performing wavelet decomposition to obtain the low-frequency coefficients.
With the aim of better visualization of the samples features revealing the magnetic properties, surfaces were scanned with different polarities of the MFM probe.
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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