Sentence examples for this matrix is estimated from inspiring English sources

During the transmission, this matrix is estimated by means of Chu training sequences [16] presenting good spectral properties.

Normally, this matrix is estimated through math models or using a probabilistic way, by means of people deployed over the transportation infrastructures who take data from buses or freight trucks (road transportation); nevertheless, this fact causes many errors.

This adaptation matrix is estimated using only a small parallel speech corpus.

Nevertheless, the fundamental matrix that was estimated will not be perfect not only because the 8 points chosen cannot be extremely accurate points, but also because this fundamental matrix is estimated using approximate solutions.

This Jacobian matrix is estimated using a first order approximation: ∂ u 3 ∂ x 2 ≅ u 3 (x 1, x 2 + δ, x 3 ) − u 3 (x 1, x 2 − δ, x 3 ) 2 ⋅ δ, where δ is the voxel dimension along the x2 axis.

The residual Cr content of the matrix was estimated, to determine its influence on the thermal properties of the matrix.

Note that since the matrix was estimated in a pairwise fashion it was possible to be non-positive definite.

For this estimator the asymptotic covariance matrix is estimated by (WLSMV) = n-1 (Δ t W-1Δ -1 W-1Δ -1 V2 W-1 Δ (Δ t W-1 Δ)-1     (6) Asymptotically, the WLSMV is not as efficient as the WLS estimator.

In this method, the adaptation matrix is estimated from the fixed source dictionary and source activity matrix, and target speaker information is extracted using the adaptation matrix in this procedure.

The parameters of the mixtures are updated by changing the buffer of the background values in FIFO order by selective update, and the covariance (in this case, a diagonal matrix) is estimated in the time domain by analyzing the set of differences between two consecutive values.

The inverse of the covariance matrix is estimated via the shrinkage estimator [ 96- 99].