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Thus, to achieve efficient implementation of an algorithm a matching of the computing capabilities of processors with the computing requirements of the algorithm is required.
Nevertheless, the ideal requirements of the algorithm are for both fast convergence and low misadjustment.
Profiling shows that matrix operations consume the largest portion of the computation requirements of the algorithm.
The CPU time requirements of the algorithm of the present paper are n43, and can be further reduced, making it considerably more practical for large scale problems.
The computational requirements of the algorithm are reduced by restricting the transformation to the template edge points and using invariant information during the comparison process.
Using the H.264/AVC Intra coding tool, we discuss the requirements of the algorithm and the impact on decoded image quality when these requirements are not met.
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In Table 1, we summarize the total communication cost and local memory requirements of the algorithms.
The computational requirements of the algorithms are reduced by restricting the transformation to the image edge points and using invariant information during the comparison process.
The accuracy and frequency of data acquisition directly depend on the electrochemical characteristics of the battery, as well as on the requirements of the algorithms used in the BMS for SOC and SOH estimation.
The performance of SE is compared with a genetic algorithm approach for the same problem with respect to the quality of solutions generated, and timing requirements of the algorithms.
As we show below, the algorithms that implement our stochastic flux analysis are linear in time and space, that is, the time and space requirements of the algorithms are linear functions of the simulation time.
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