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The proposed methods achieve high error probability for all the tested payloads.
Some methods achieve high accuracy on autonomous vehicle, but they can't work normally without the aid of high-precision priori map.
Numerical experiments show that the new methods achieve high levels of consistency, maintaining hydrostatic and thermobaric equilibrium in the presence of strongly-sloping layer geometries, non-linear equations-of-state and non-uniform vertical stratification profiles.
The BLAST based methods achieve high accuracy on the non-duplicated nodes since the sequences of nodes are not changed.
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However, parallel implementations of iterative methods achieve higher efficiency, but need suitable preconditioners in order to speed up their convergence.
The methods achieve higher DOA estimation accuracy and resolution performance than the conventional CSSM even if there are fewer sensors or antennas than the incoming signal sources.
While these methods achieve higher detection performance, the weight update is heuristic, making it difficult to predict performance prior to in-field use.
Different from the traditional methods achieving high birefringence, a new highly birefringent hybrid PCF that guides light by both index guiding and bandgap guiding is proposed.
Also in 80% of the cases, if any of the feature selection methods achieves high predictivity, SConES outperforms all other methods including BLUP.
In addition, consistent with what was observed in the targeted sequencing data, all methods achieved high sensitivity in the high-allelic-fraction tumor samples.
Both meta-methods achieve high accuracy and low error at all settings but at the cost of increased runtime compared to NextGenMap and BWA.
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