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The value I m is interpreted as a robust maximum of the data, which is found by first median filtering each individual A-scan within the same B-scan using a kernel size of 15 pixels (58 μm).
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For the calculation of the correntropy coefficient, we used a kernel size σ equal to 1 2 π for all simulations.
For example, a closing operator using a kernel of size 2, 2, 1 in our set-up would use a rescaled kernel of size 4, 4, 1 in a set-up with voxel sizes of 0.414 μm × 0.414 μm × 0.54 μm.
We estimated traffic density using data from the U.S. Bureau of Transportation Statistics 2005 National Highway Planning Network (NHPN) [ 32] using a kernel density function (ESRI Spatial Analyst) evaluated on a 30 m cell size raster.
Distributions were estimated using a kernel density distribution.
It was better to use a large kernel size when the lung edge was excluded, but a small kernel size was slightly better when the whole lung was analysed.
Unless stated otherwise a kernel size of 30 kb was used in this paper.
This was implemented using the spatial electric field averaging method described in section 4.4 with a kernel size of 40 A-scans.
A kernel size of 300 × 300 m was used to reconstruct the territory of Hong Kong into a gridded surface of 208 columns and 151 rows.
The best size of the kernels is 6 × 6, and an AI CNN with a kernel size of 6 × 6 has a 0.0938 relative error for evaluating the AI using 300 PPI images.
Kernel based learning methods are those methods which use a kernel as a non-linear similarity to perform comparisons.
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