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Figure 5 Packet allocation solution for problem P2. Figure 6 compares the power allocation solutions of three different methods solving problem P5 and their computational complexities, where the complexity is represented by the computing time in an Intel Core 3.30-GHz computer (Santa Clara, CA, USA).
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In our study, a vertical mesh size of Δz = 1.0 cm was chosen by taking the computing time into account.
By significantly reducing the computing time, it enables a wider parameter range evaluation for engineering phase.
By doing so, the computing time becomes square to number n of the regions and cubic to their average length.
An algorithm is devised to reduce the computing time by using a vector computer.
To evaluate the performance gains by using the GPU, the computing time on the GPU against its sequential counterpart on the CPU were compared.
SURF reserves most of the features of SIFT, and reduces the computing time by decreasing the number of dimensions.
Several areas of knowledge are being benefited with the reduction of the computing time by using the technology of graphics processing units (GPU) and the compute unified device architecture (CUDA) platform.
As a result, our proposed FSR-SADMM prefers to maintain a better convergence rate which can experimentally reduce the computing time by 40%. 3.
It is noted that the parameters of the solvers are set by default and unit of the computing time in Table 16 is minute.
The sample patterns are restricted in certain map tiles, which is stored in local cache and determined by sensor information, to reduce the computing time.
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CEO of Professional Science Editing for Scientists @ prosciediting.com