Therefore it was determined that the Level 4 complexity algorithm was the most relevant, or best suited, to model cattail densities in the region, as it contains the least uncertainty, without increasing sensitivity, and it has less risk of over-parameterisation as is potentially the case with Level 5.
Dabney and Storey determined that the failure of the FDR algorithms was not methodological, rather the distributions of the null p-values corresponding to the most common choices for the Stage II analysis (e.g., t-test, permutation t-test, and Wilcoxon Rank Sum test) were non-uniform for the datasets which were considered.
It has also been determined that the time required by the proposed algorithm to make its prediction from a particular frame does not differ significantly from the time taken by the normal Kalman prediction algorithm.
Using static measurements to determine the accuracy of our pose estimation algorithm we determined that the position of a marker in camera coordinates is very accurate when the marker is on the optical axis at 5 m, that is, less than 0.5 mm in and, and less than 1 cm along the optical axis.
Using the statistical Friedman test again, we determine that the performance of the algorithms deteriorates significantly for an SNR below 20 dB in the presence of CCITT noise for all algorithms except SHRP which becomes significantly worse for an SNR below 25 dB already.
Objects that are determined by the algorithm to be CK positive and CD45 negative were presented to the reviewer for classification (see next section).
The conditions are determined such that the algorithms derived within these two approaches coincide.
Since these two requests (Fig. 8a, b) are quite similar, and the only difference is the submitted value, the algorithm determines that the user has performed this action using a different value, and resubmits the action using the correct value, namely "IPhone".
For example, if the algorithm determines that the most inclusive group of sequences is saturated, then the group is broken up into less inclusive groups using the next level in the taxonomic hierarchy.
The MC cluster was identified by grouping the objects that have been determined by the algorithm to be MC.
