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For reducing computation cost, we resample the original images at a reduced pixel size and 256 gray levels.
Most of these pipeline systems have ways to help distribute the processing using computers' multicore architectures, or job-scheduling systems installed on clusters, thereby reducing computation time.
This approach offers a means of reducing computation times.
One advantage of using these kernels is that they have closed form integration, reducing computation complexity.
Efforts are made in reducing computation time while keeping an acceptable accuracy.
For reducing computation complexity, we also develop a fast heuristic algorithm to provide highly competitive solutions.
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We also show how to reduce computation time on computers with a cache-based architecture.
Both of these computing architectures reduce computation time by exploiting the parallelism inherent in the application.
The use of parallel computing could drastically reduce computation time for MDR, particularly on a cluster machine.
This innovative approach has undeniable improvements such us computational load distribution and reduced computation time.
Second, it must have low computational cost since we have to reduce computation time to a clinically acceptable level.
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