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We have shown further that the modular arithmetic for the binary field based on like-trinomials is equivalent to the arithmetic for the field based on trinomials.
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This regression is a binary classifier based on one or more input variables.
We improve the finite field multiplications via using composite field expression and design a finite field inversion based on binary trees described in [41].
Thus, in the proposed approach, we specially develop a binary classifier based on sparse representation.
Several variables were calculated on the binary images based on two-dimensional analysis.
First, we used gender as a binary variable, based on biology (i.e. sex).
Child anaemia status was a binary variable based on the cut off point of 11Hb.
Responses were collapsed into binary variables, based on strongly agree/agree vs. other responses.
This problem is formulated as a binary labeling problem based on a Markov random field (MRF) framework, the solution of which is a binary map for each exposure image, which identifies the pixels to be excluded in the fusion process.
Generating a ghost map is then posed as a binary labeling problem based on a Markov random field (MRF) framework, where the energy to be minimized is designed as a function of the ZNCC distribution parameters.
We also design the binary balloon force based on the four-color theorem.
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CEO of Professional Science Editing for Scientists @ prosciediting.com