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We propose three methods for designing the linear combination coefficients: projection based on orthogonal polynomials, principal component analysis (PCA) based on the EIF matrix, and the quadratic form of the EIFs.
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a linear combination with positive coefficients) of projections if and only if ¯τ(Ra)<∞ for every τ∈T(A), where ¯τ denotes the extension of τ to a tracial weight on A⁎⁎ and Ra∈A⁎⁎ denotes the range projection of a. Assume that A is unital and as above but T(A) has infinitely many extremal points.
To this end, they are divided by the coefficient of projection from the sources to the corresponding electrodes, which are equal for all the sources.
The profiles are extracted using the Modified Sensitivity coefficient Back-Projection (MSBP) method with a sensitivity matrix generated from a realstic phantom in the finite element method software.
Next, project onto and to obtain the projection coefficients as and, respectively.
Based on the factor's projection coefficients, we can also roughly identify the top contributing loci, those with higher coefficients than a threshold (an empirical |Z| score>4, explained later in detail).
Finally, the projection coefficients can be solved by combining the two arrays.
where ({gamma _{j}^{i}}) are the projection coefficients of ({P_{B}^{i}}) over e j.
Then the low-rate high-resolution analog-to-digital convertors (ADCs) are used to sample the projection coefficients.
Let f i d = [ ( R i ∘ e 1 ), ⋯, ( R i ∘ e d ) ] T be the d-dimensional vector of projection coefficients.
where (gamma _{j}^{'i}) are the projection coefficients of the current frame patch P i over the basis e j.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com