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This paper proposes a two-dimensional regularized locality preserving projection (2DRLPP) algorithm for feature extraction, which combines locality preserving projection (LPP) method with data roughness regularization.
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Based on topographical information, roughness data were evaluated.
Data for roughness parameters were statistically evaluated with repeated measurements analysis of variance (RM-ANOVA).
We used a cubic b-spline basis with a knot placed at every minute and a data adaptive roughness penalty on the second derivative.
A geographic information systems (GIS) software is used to manipulate the massive topographic and roughness data over a fine grid system.
Last year we used an AUV (autonomous underwater vehicle) to collect bathymetry (depth) and sonar (bottom hardness and roughness) data for the lake.
Color scales have been calibrated via AFM roughness data.
The SBS and roughness data were statistically analyzed.
Magnification in all micrographs is 70000×, and color scales have been calibrated using AFM roughness data.
The relationships between roughness data were studied for statistical roughness parameters, spatial roughness parameters and in the frequency domain.
Comparison with surface roughness data from AFM showed a similar trend in roughness across the series of particles.
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