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This is because the angle of a vector from a pole at to the point along the frequency axis is minus the angle of the vector from a zero at to the point.
Each processing element computes a weight vector from a face image region and pre-computed eigenvectors; hence the processing element is also parallelized where each path works on one eigenvector and the face image region to compute one element in the weight vector.
We model any biometric signal as an -dimensional vector from a feature space where is a finite field.
As (G^T tilde{P}^T C^T) returns a Gaussian coefficients vector from a sinogram, only the coefficients are of interest here.
Using (3.23) and (3.24) we can show that the order statistics of the components of a random vector from a bivariate t distribution obey the skew t law.
Among the four proposed methods, L 2-norm minimization is the first to estimate a 200-dimensional feature vector from a biohash of length 1024-bits.
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Since each sample image is a 2-D image, A i ∈ Rm × n, we create a vector from an image by concatenating rows to create a column vector A′ i ∈ R1 × mn.
A methodology for retrieving the stator inductance space-vector from a FEA is proposed as well.
Abstract: The blind deconvolution problem seeks to recover a pair of vectors from a set of rank one bilinear measurements.
We consider the problem of constructing an optimal set of orthogonal vectors from a given set of vectors in a real Hilbert space.
Experiments show that ILS-DLA is capable of reconstructing (most of) the generating dictionary vectors from a sparsely generated data set, with and without noise.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com