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Our approach uses linear logic as formal framework and is based on a backward and a forward reasoning.
The proposed approach uses linear optimal controllers and, in addition, a fuzzy signal tuner is introduced to achieve adaptiveness.
Under the assumption that the missing data are at least MAR, the regression imputation approach uses linear regression to obtain predicted values for the missing observations.
The standard approach uses linear convolution models that relate experimentally designed inputs, through a haemodynamic response function, to observed blood oxygen level dependent (BOLD) signals.
The second approach uses linear regression to model annual concentrations.
Our approach uses linear unmixing and is similar in spirit to others that have been proposed to solve the problem of autofluorescence during flow cytometry [ 31, 32].
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The approach uses linear-elastic fracture mechanics to relate the crack spacing, material properties and geometry to the energy-release rate of a channeling crack, by means of detailed finite-element calculations.
The aim of the present study was to develop a multistage registration approach using linear registration and nonlinear warping allowing us to normalize microstructural postmortem STN data into a common reference system, and to assess its applicability to the in vivo situation.
A compensation approach using linear interpolation is presented to make a real time feedforward compensation for the cogging force.
Results showing the implementation of the proposed approach using linear, cubic, and Laplacian interpolation are described, and the mesh reconstruction strategy is compared with other methods.
The QSAR studies have been carried out using classical QSAR (physicochemical) approach using linear free energy related (LFER) model and molecular shape analysis using shape, spatial, electronic, thermodynamic and structural descriptors.
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