Exact(50)
All implementations guarantee tracking error convergence to within prespecified performance limits.
All implementations of the proposed algorithm were done on a Linux PC (Ubuntu 16.04, Core i7, 32 GB RAM), with a GTX 1080 GPU and Torch7.
All implementations involve a single core and platform 1. Figure 9 shows the comparative performance of the sum and cumulative sum components in these two key frameworks.
All implementations involve a single core and platform 1. Figure 8 shows the proportion of the runtime that is associated with redistribution, sort, minimum variance resampling (MVR) and the remaining components (e.g., sum, cumulative sum, diff, scaling).
All implementations of block interleaving following (B.1a) will have low for any that is a multiple of 8. Likewise, implementations following (B.1b) will have low for any that is a multiple of 8.
All implementations that belong to this solution subset represent a trade-off that is not dominated by any other solution in the design space, i.e., a better value for one criterion is automatically bound to a worsening on the second criterion.
Similar(10)
All the implementations showed competitive speedups.
All the implementations and testing was carried out using the Matlab 7.0.1 programming.
Finally, due to the aforementioned dataset propagation, F-zkNN scales worse than all the implementations.
Attempt was made to validate all the implementations through existing analytical solutions.
In all the implementations of the model shown here, the overpopulation limit is set at 0.25.
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