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We analyse a dynamic (scale-invariant) remeshing method which performs spatial mesh movement based upon equidistribution.
Neutron activation analysis (NAA) is a precise multi-elemental method which performs well for qualitative and quantitative analyses.
The software is based on a finite-volume method, which performs the required three-dimensional simulations, and is also briefly described.
We have designed a method which performs statistical analysis on the differential expression of genes in the Affymetrix GeneChip system at the probe level in order to bypass the assumptions made in other analysis techniques.
It is stated as a generalized pseudo-spectral method which performs exact differentiation by using simultaneously several approximation spaces generated by polynomials bases and suitable choices of port-variables.
Recently, Cheng et al. (2013) [28] introduced an alternative approach, the bi-orthogonal (BO) method, which performs the exact same tasks, i.e. it evolves the spatial basis and the stochastic characteristics of uncertainty.
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RMA was the method which performed consistently well in all comparisons.
This was followed by the single-gene moderated t-statistic method which performed similarly for the RF and DLDA classifiers but much poorer for the SVM classifier and, achieved an error rate that was 10% higher than that for NetRank.
We propose new preprocessing methods, which perform not only normalization and denoising tasks, but also detect reflections and artifacts in the image.
Additionally, this error serves as a rough upper bound for methods which perform pure 3D tracking.
The methods which perform significantly better than our model are indicated with b for p≤0.1.
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