Sentence examples for both algorithms was tested from inspiring English sources

Significance of the average difference in A z between both algorithms was tested with the Dorfman-Berbaum-Metz method [ 19, 20] treating both readers and cases as random samples.

Both algorithms were tested for single line to ground faults.

The performance of both algorithms is tested on simulated data sets designed to imitate (normalised) gene expression data, covering a range of biclustering configurations.

Both algorithms are tested in different application setups like complete database scans, substructure-based filter scenarios and individual substructure-molecule searches.

Finally, both algorithms are tested in a clinical situation where the truth is obtained from magnetic resonance images of the patient against which our microwave images are compared.

The validity of the constrained and unconstrained algorithms was tested both qualitatively and quantitatively.

In the last case, both the algorithms were tested under different lighting conditions, and there are once more big FP rate differences between these two methods with a value of 0.001700 for the keypoint model and 0.019453 for the GMM algorithm.

Both of autopanning algorithms were tested, the bandpass filter classifier known as algorithm type A, and the low-pass classifier known as algorithm type B. Algorithms were randomly tested in a double blind fashion.

Both of these algorithms were tested using one replication of 100,000 samples and found to be stable.

Finally, the developed predictive current control algorithms were tested both in simulations and experimentally, and illustrative results are presented here.

The algorithms were tested using both the white Gaussian noise (WGN), and colored noise as inputs.