Sentence examples for recover features from inspiring English sources

This method can recover features without complete projections, and the level of understanding sectional views is improved.

These results agree well with the experimental knowledge [ 54, 64] and recover features of different (and sometimes conflicting) numerical studies performed previously [ 17, 19].

As no specific target model needs to be chosen, this approach is suitable for all kinds of applications and, as was shown in the examples, the method can recover features such as multimodality.

The separation performance of the proposed system is further improved through the use of a restoration mechanism designed to recover feature points lost during large pan rotations.

In the previous section, we propose four different optimization methods for recovering features from an original biohash vector that is stolen by an attacker.

Because of the acoustic-phonetic information loss in both noise corruption and feature extraction, it is difficult to perfectly recover clean features from noisy features by using feature compensation algorithms.

Choosing a cutoff at 90%% (gray area in Fig. 4 b) for a feature to be included in the final model, we recover all features (except ϕ0, ϕ1) that were used to generate that dataset.

The key question, however, is whether our intuitive notion (or notions) of unification can be made more precise in a way that allows us to recover the features that we think that good explanations should possess.

Hence, it is necessary to discuss whether the model can recover relevant features in the presence of nonlinearities or how it can be adapted.

These effects appeared conditioned by the presence of microgravity conditions, and after short-term exposures, under ground-conditions, the cells were able to fully recover their features and the ability to form adherent monolayer cultures [ 4, 7].

The presented results certainly require further analysis and experimental validation, however, they reveal the power of BALL-SNP to capture mutual relations and spatial traits of nsSNPs and recover functional features, not available in databases.