Sentence examples for scanned datasets from inspiring English sources

We also set up an assay to evaluate the performance of our model to handle large datasets of protein sequences, by scanning three negative test sets of protein sequences yielding recovery values of almost 90% of the true positives with precision values above 80%, and an evident independence of the results from the number of negative instances in the scanned datasets [ 27].

In an equivalent experiment, non-redundant sets of transcript sequences from rice, wheat and barley were scanned (dataset was obtained from the TIGR gene-index databases [ 19]) and those transcripts containing SSRs were collected and mapped in-silico to the rice genome.

We have scanned the datasets with their average length as mentioned above.

This paper presents a fully automatic strategy to calibrate geometrically terrestrial laser scanning datasets.

We demonstrate its robustness and efficiency on several challenging scan datasets with varying degree of noise, outliers, extent of overlap, acquired from indoor and outdoor scenarios.

The robustness, accuracy and efficiency of our method were overall evaluated on several challenging scan datasets acquired from indoor and outdoor scenes as described in Section 7. Given two point sets P and Q, we assume ℬ p = { p i | p i ∈ P } i = 1 N and ℬ q = { q i | q i ∈ Q } i = 1 N to be the two corresponding N-points bases from P and Q, respectively.

After scanning, all datasets were reconstructed at an isotropic CT voxel size of 200 μm and an isotropic SPECT voxel size of 600 μm by using HiSPECT software (Scivis, Göttingen, Germany).

Displayed is the classification scheme used for both the engaged and scanning EM datasets in order to isolate the population of particles used to reconstruct the three maps displayed in the manuscript, highlighted in red.

Experiments with virtual and real environments perceived from 2D laser and sonar scanners are presented, including the validation of the model with a real scans dataset.

The quality of photogrammetric data is evaluated by comparison with a light detection and ranging scanning dataset that acts as ground truth.

All 3 curves are computed over the entire circular B-Scan dataset.