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The proposed input design technique optimizes a scalar cost function of the information matrix, by computing the optimal stationary probability mass function (pmf) from which an input sequence is sampled.
We constructed the injection site gene expression correlation matrix by computing the correlation between the rows of the gene signature matrix.
The classical LDA approach involves calculating the between-class scatter matrix by computing the covariance matrix of each class data center.
We outlined a correlation matrix by computing the Pearson correlation coefficient (r) for every pair of data sets.
We then constructed a distance matrix by computing the Euclidean distance between the 4D vectors representing each continental division.
Then, a semantic thesaurus can be constructed from the semantic matrix by computing similarities of each word pair or clusters by clustering algorithms.
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A novel fast-match-search decompression algorithm is used to reconstruct all high-frequency matrices by computing all compressed data probabilities through a binary search algorithm to estimate the data from a look up table.
In this first approach, we summarized the information contained in the correlation matrices by computing their integration.
Form the matrix M j and compute F j using (3.5) for j = 0, 1, …, L. (Here we use R c instead of R f in (3.5).) Form the matrix Q using F0, F 1, …, F L, and obtain the channel impulse response matrix (3.7) by computing the K largest eigenvalues and the associated eigenvectors of Q.
Finally, obtain the channel impulse response matrix H by computing the K largest eigenvalues and the associated eigenvectors of HH ∗.
(4) Use the method given in Section 3.1 to remove the noise components imposed on Q H to obtain HH ∗. (5) Finally, obtain the channel impulse response matrix H by computing the K largest eigenvalues and the associated eigenvectors of HH ∗. .
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