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Through analysis we demonstrate that our algorithm reduces the system complexity (compared to existing approach using pattern matching and Kalman filter) as it requires only two base station measurements or only the measurement from the closest base station.
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Both methods are compared to existing approaches.
Compared to existing approaches, our algorithm can efficiently generate a wide variety of textures.
Superiority of the proposed results compared to existing approaches is shown by means of an example.
Large area and power savings compared to existing approaches are also obtained.
However, this work has been less warmly received by the statistical community, where many consider it lacking compared to existing approaches.
A simple new approximate solution to this stereological problem is proposed and is compared to existing approaches.
The results demonstrate that the proposed framework achieves higher detection accuracy 87% and clustering quality 0.99 compared to existing approaches.
The prototype system is compared to existing approaches to automated ontology quality ranking to illustrate the usefulness of the research.
Results have shown that our algorithm provides a better performing system with the lowest possible cost compared to existing approaches.
Experimental results indicate that our MLVC method has very low runtimes, with excellent accuracy compared to existing approaches.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com