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Experimental results demonstrate that our method achieves state-of-the-art performance for all these benchmarks whilst requiring less computation resource.
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The detection and tracking of POIs requires less computation resources.
The results showed that both SRE1 and SRE2 required less computation resources to achieve the same level of the accuracy as COL.
The results show that the proposed SRE method requires less computation resources and is able to achieve the same or better accuracy level than the traditional COL.
Compared with the latest work, we can get 1.48 times speedup and use 19% more memory with much less computation resources (34.5% less ALUTs and 46.1% less Regis-ters).
The results from the simulated and real road test data were presented and showed that the proposed auto-calibration method requires less computation resources to achieve equal or better accuracy than applying the traditional collinearity equations despite the fact it using more measurements.
Unlike the existing methods, it takes into account of ESD׳s and consumes much less computational resources and time than full scale CFD computations.
Furthermore, less computation and storage resource are required by the proposed protocol.
This method requires less computation time and fewer resources than the trusted computing group attestation.
The technique thus reduces the computation resource at sensor nodes.
Then, a computation resource in the cloud system is dynamically assigned for the requested offloading computation.
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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