Dotted curves and solid lines are measured samples and linear approximations, respectively.
Field samples and linear kneading compacted samples are more susceptible to moisture than the gyratory compacted samples.
In contrast with traditional Nyquist sampling and linear reconstruction, orthogonal matching pursuit (OMP) algorithm is applied to recover the signal from the chaotic measurements.
Based on the TIN model, applying section scanning sampling and linear interpolation, the transformation from discrete, irregular and diverse data to continuous and regular sampling cross-sectional curve sequence, is realized.
In the offline phase of this approach, the parameter space is sampled and linear ROMs defined by linear reduced operators are pre-computed at the sampled parameter points and stored.
The conventional approach for solving the quadratic program takes time cubic in the number of samples and linear in the number of features (Chapelle, 2007).
The time complexity of each of the above algorithms is linear in the number of samples, loci, and populations, i.e., O NLK); in comparison, the time complexity of principal components analysis is quadratic in the number of samples and linear in the number of loci.
Next, alpha and beta diversity, sample clustering and linear discriminate analysis were estimated in the same way as for viruses (see above).
It was shown that solubility spectra of this sample and a linear homopolymer do not differ significantly.
Once fixed the angle of analysis, for samples spotted on Al-TEOS slide dried, the system is proportional to the concentration of the analyte in the sample and is linear in the range 1 nM–1 μM.
