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The schemes efficiently exploit the high correlation between the high and the low bit-depth layers on the macroblock (MB) level.
In the previous work, to investigate different schemes efficiently, we assume that nodes use the distance determinative optimal frequencies, so there may exist several available bit rates in a network which is not very applicable.
We expected the distance-based schemes to be better than the simple broadcasts because the distance-based schemes efficiently determine CW sizes in situation where more senders in a higher network density cause more collisions.
Nanostructures, such as carbon nanotubes and nanowires, have emerged as potential building blocks in nanoscale applications of microscopy tips [1], gas sensors [2], nanoscale interconnects [3], and field emitters [4] mainly because of their tiny size satisfying the needs of down-scale schemes, efficiently and effectively.
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It is also demonstrated that the scheme efficiently improves WZ coding performance for low-delay applications.
The proposed scheme efficiently allocates resources and reduces interference as it adapts to the network conditions.
Experimental results show that the proposed scheme efficiently removes speckle noise from SAR images.
Our scheme efficiently estimates data delivery probabilities of individual nodes through learning and predicting user context information.
Results presented in the aforementioned figures reveal that the proposed MATF scheme efficiently withstands against the colluding attackers up to 30%% of the total nodes.
By use of the inherent characteristics of voice traffic such as frame size and period, the scheme efficiently reduced control channel signaling overhead.
We demonstrate that the use of a surrogate key in the hybrid scheme efficiently identifies data, avoids most time comparisons, and improves query performance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com