Sentence examples for both algorithms the performance from inspiring English sources

By comparing both algorithms, the performance improvement achieved due to link correlation and network coding is obtained.

Therefore, it is reasonable to assume that by choosing orthogonal binary codes for different users in CMS algorithm and larger separation by TH codes in both algorithms, the performance under MUI may further improve.

The graph also shows that the performance of both classifiers at the EER points is very similar; therefore, it is expected that by combining both algorithms the overall system performance will be improved.

For both the optimal and relaxed algorithms, the performance was evaluated using the root mean square error (RMSE) of the position estimates ({hat {boldsymbol {p}}}_{i}) with respect to the actual positions p i : begin{array}{*{20}l} text{RMSE} = sqrt{frac{1}{N}sumlimits_{i=1}^{N} Vert boldsymbol{p}_{i} - {hat{boldsymbol{p}}}_{i} Vert_{2}^{2} } end{array} (8).

We see that the all-optical perceptron's performance is comparable to GDA's performance for this problem and both algorithms attain performance close to the theoretical optimum.

Both LEAUCH and EEUC consider the density of nodes and the distance between CHs and sink, which make the both algorithms have better performance in network load balance.

We show that both algorithms significantly improve the performance compared to the uncoded version.

On the COG database both algorithms showed the same performance, on PFAM and BALIBASE PHOG-BLAST was much superior to PSI-BLAST.

An iterative structure with fewer multiplications is compared versus the normal structure in terms of complexity where both algorithms provide the same performance.

Both algorithms have the same performance trend as a function of number of pilot symbols.

Both algorithms exhibit the best performance and multirun stability combined with a reasonably fast convergence speed.