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In reverse engineering, curves are constructed by interpolating the data points digitized from existing products.
Next, the evenly sampled spectrum is generated by interpolating the data contained in into the frequency values specified in the space.
Parameter trajectories describing the phenotype transition were determined by interpolating the data between phenotypes.
We constructed continuous-time series by interpolating the data with cubic splines, as implemented in the Matlab Spline Toolbox.
DPOAE thresholds were calculated offline by interpolating the data and identifying when the signal was greater than −5 dB SPL and greater than two S.D. above the noise floor.
Therefore, the protocol described above was extended by not only determining parameter trajectories from phenotype A to phenotype B, but also backwards from phenotype B to phenotype A. A backward trajectory is obtained by interpolating the data from phenotype B to phenotype A, whilst reoptimizing the parameters.
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The non-starved controls values were obtained by linearly interpolating the data from [25], at the corresponding time-points (15 minutes into the cell cycle, as a control for swarmer cells, and 75 minutes for stalked cells).
Using the North Sea benthos survey data, we estimated changes between 1986 and 2000 by spatially interpolating the data for each year using the inverse squared distance method with a search radius of 100 km [ 9] on a grid 0.25° latitudee×0.251°latitude (51° N–60° N, 4° W–10° E).
The P and T data were sampled every 6 min at each station by interpolating the observed data in time, assuming linear behavior between two consecutive datapoints.
By interpolating the kinetic data, relevant subprocesses can be visualized and studied separately in the mixer.
The initial three-dimensional (3D) soil model is automatically generated by interpolating the borehole data for the developed grid points.
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