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To address this challenge, Bayesian compressive sampling (BCS) has been recently developed, which is able to reconstruct a complete signal from a remarkably few number of measurement data points on that signal.
Among a variety of interpolation methods (e.g., ordinary kriging (OK), polynomial interpolation (PI), cubic spline interpolation (CSI) and inverse distance weighting (IDW)), OK is one of the widely used methods, and it requires modeling of spatial structure of the measurement data points using a semi-variogram.
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Each measurement data point as shown in the figures and tables was performed at least three times, and reproducibility was satisfactory.
Biomass increase is shown as determined by optical density measurement, unlinked data points indicate dilution.
Each measurement for data points was determined five times; for the relative standard deviations of determination, different DA concentrations were in the range of 2.1 5.1 %; this showed the method is accurate.
Blue and red lines indicate the time of measurement and data points which are located at the yellow and white area represent cells which are still viable at the time of measurement when treated with compound one.
However, for Fig. 6a, only measurement data from point number 2 is presented.
Grouped measurements (252 data points) for APCO, ICO and CCO were analyzed for bias, precision and correlation via Bland Altman analysis.
Afterwards, according to the measurement data, three key points: the handlebar, the saddle and the crank center, were identified and applied to the frame design of various bicycle types.
Field measurement data at all points.
The distance measurement between the data points in the high-dimensional space is defined by the kernel function.
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