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Algorithms of the first category are based on matrix theory concepts, while algorithms of the second one are based on graph theory concepts.
The data in each category are based on at least 45 samples.
To date, all systems in this category are based on the relational data model and existing relational DBMS, and all of them offer some OO extensions.
Information weighted metrics The metrics in this category are based on the modeling of relative local importance of the image information.
Moreover, most beamformers of this RAB category are based on the second-order cone programming (SOCP) problem and needs to use some specific optimization toolboxes [11] to obtain the solution.
However, most beamformers of this RAB category are based on a quadratically constrained quadratic programming (QCQP) problem, whose solution is obtained by using the convex optimization toolboxes such as CVX [19].
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Each category was based on at least one code.
The second category is based on the cortical thickness, including direct, atlas-based, and ROI-based methods.
One category is based on intra macroblock refreshment, and another one is redundant coding.
One category is based on gradient, including finite difference approximation of derivative, adjoint gradient-based methods, and gradient simulator methods.
A third category is based on a common mathematical approach.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com