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For small spherical particles, as for graphite, the SAXS method is optimal whereas the image analysis methods are problematic.
Comparing with the existing solutions, we prove that our method is optimal in terms of the search space.
The first method is optimal output feedback control and the second is based on state observer method.
In the first case, we set the probability that OFDM is mistaken for SCLD to 5 × 10-5, so the threshold used in the proposed method is optimal.
However, the Delphi method is optimal because the reasoned reflection process improves the options for reaching some sort of consensus on the value system.
For the density-gradient equations, it is analytically and numerically shown that the convergence rate of the method is optimal in the sense of Gummel's decoupling iteration.
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Planning schemes selected by this method are optimal in the statistical sense, leading to low-probability scenarios being discounted [6].
Thus, these results suggested that our proposed method was optimal for removing redundant contigs and erroneous contigs.
However, the mechanical method was optimal for the retrieval of neurons and microglia, which are the major cell types of interest to us.
We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends.
None of the scaling methods were optimal for all map diagnostics.
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