Sentence examples for matrices so the from inspiring English sources

Computations in the other steps use data structures for sparse matrices, so the complexity depends on the number c of non-zero values per row/column.

From this numerical experiment, we can find that the false alarm level is comparable with different clutter covariance matrices, so the proposed detector achieves an approximate constant false alarm ratio (CFAR) with respect to different clutter covariance matrices.

For example, both data sets analysed here used saturated models which perfectly predicted the input correlation matrices, so the fit indices based on the discrepancy between observed and SEM-predicted correlation matrices obtained maximum values possible, but this was not particularly informative.

The GB uses a shared covariance matrix, so the separating margin between two classes is also a linear hyperplane.

A key contribution of this paper is that the user selection algorithm uses the determinant of a matrix composed of users' channel matrices so that the orthogonality as well as the channel quality of the selected users are measured.

We show in particular how to design a selection rule for some weighting matrices so that the estimated parameters are consistent with the measurements and the noise constraints.

This approach to community detection is based on rewriting the modularity function of the network in terms of matrices, so that the best partition in two communities can be obtained in terms of spectral analysis of those matrices.

To overcome this difficulty, we introduce in this paper an approximation of the kernel matrices by appropriate multilevel circulant matrices so that the fast Fourier transform can be applied to reduce the computational cost.

The matrices so obtained are the reduced-rank approximation of the sample correlation matrices.

Objective (a) is achieved by computing state feedback matrices so that the closed-loop subsystem evolution matrices generate a solvable Lie algebra (namely, they are all upper triangular in a common coordinate basis).

The kNN method uses Hamming distance to distinguish between the matrices so using feature vectors to describe the recognized objects should comply the character of Hamming distance measure.