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In view of its good performance in improving computational efficiency, parallel computing is considered to be a primary solution for power flow calculation in large-scale power system.
To overcome the limitations of mobile devices, cloud computing is considered the best solution.
Therefore, the above-referenced soft computing is considered to be a foundation emerging in perceptual intelligence [1 3].
Mobile cloud computing is considered a new approach to mobile applications that moves away the mobile device toward centralized and powerful computing platforms within the cloud.
Cloud computing is considered a growing trend in the information and communication technology (ICT) industry, and, of course, risk issues of leveraging this computing model are still in focus.
Optical computing is considered to be a practical and efficient method, so we build an optical information processing-based system to achieve fast visual saliency extraction to generate full resolution saliency maps with fine details of boundaries.
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In particular, wave equations, that are of great interest in seismic computing, are considered; of course, they pose different requirements to the geometry of the data set that lead to a different approach in the CUDA parallelization.
All the parameters computed are considered to be independent as a correlation lower than 0.9 is observed between them in all the databases described above.
The hybrid soft computing method is considered to develop the adaptive neuro-fuzzy inference system (ANFIS).
Therefore, embedded GPU computing platform is considered as a potential real-time implementation platform of minimum-variance beamforming algorithm in this paper.
Computed tomography is considered to be the gold standard[ 5].
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