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The input design problem is converted to linear matrix inequalities by a separation of graphs theorem.
The non-linear regression is converted to linear using log function and after processing converted back to non-linear.
HU were converted to linear attenuation coefficients at 511 keV in Matlab following the method of Carney et al. [3], where LAC = 9.6× 105 × (HU + 1000).
Finally, the synthetic CT is generated using a voxel-wise weighting scheme, and converted to linear attenuation coefficients by a piecewise linear transformation.
The resulting image is converted to linear attenuation coefficients, generating what is known as µ-map, to be used during reconstruction.
Clear image without luminance irregularity can be obtained by that F L (x, y) removed I L (x, y) is converted to linear scale.
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Many of these are badly behaved but a classical core of problems convert to linear matrix inequalities (LMIs).
Mathematically, adsorption equations can either be used in their original forms (generally non-linear) or can be converted to a linear form and then the coefficient of determination is calculated.
Then, the linear fractional multi-objective optimization problem is converted to a linear one using Nykowski and Zolkiewski's approach.
Thus, the finally obtained linear model under uncertainty can be further converted to deterministic linear models, which can be solved by conventional algorithms.
Furthermore, using linearization methods, a nonlinear second-order equation can be converted to a linear second-order ordinary differential equation with known solutions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com