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In contrast, the new approach formulates the coalescence term as a single entity and directly accounts for the effect of turbulence on the probability of coalescence.
In contrast to previous work, this approach formulates the matching problem as detection of a "bright", coherent disparity surface in a 3D image called the spatio-disparity space (SDS) image.
The proposed 2-BCD approach formulates the subproblem of joint beamformer optimization as a general second-order cone programming problem, which lends itself to standard numerical solvers and which requires no extra assumptions like previous works do.
These models are described briefly below: Linear Model: A linear-model approach formulates the problem of the estimation of a spectral reflectance R ̃ from the camera responses C as finding a transformation matrix (or reconstruction matrix) Q that reconstructs the spectrum from the K measurements as follows: R ̃ = Q C. (2).
Linear Model: A linear-model approach formulates the problem of the estimation of a spectral reflectance R ̃ from the camera responses C as finding a transformation matrix (or reconstruction matrix) Q that reconstructs the spectrum from the K measurements as follows: R ̃ = Q C. (2).
The approach formulates the coupling as a control problem where the states are the solutions of the nonlocal and local equations, the objective is to minimize their mismatch on the overlap of the nonlocal and local domains, and the controls are virtual volume constraints and boundary conditions.
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To increase the precision, we use a machine learning-based approach formulating the relation detection as a binary classification problem: each instance (consisting of a pair of entities) is classified either as not-containing a relation or belonging to one of the four-relation classes.
The most common approaches formulate the problem as purely geometric in nature, though dynamics or quasi-statics of linkages should also be considered.
The approach formulates a continuous meta-optimization problem for parameters; the meta-optimization is constrained by the original data assimilation problem.
Therefore, in this article, we propose a jamming impact collecting-based approach, which formulates the dynamics of jamming.
The so called detection filters approach, formulated as the fundamental problem of residual generation (FPRG) for linear time invariant (LTI) systems is extended for a class of LPV systems.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com