By exploiting the EMV features, introducing a circle cluster, and proposing a novel coherence-pattern, we then form a feature-aided weight coherence (FAWC) optimization to optimize measurement-matrix.
To optimize measurement matrix Φ, we exploit the EMV features and propose a FAWC optimization method in this paper.
Especially, we optimize measurement for the two-particle Green's functions.
The convergence domain was explored to quantify and optimize measurement accuracy in terms of bias and precision.
However, the operational complexities of building modules to optimize measurement precision, balance content, and control exposure (especially over extended periods of time) were not anticipated (Luecht 2013).
Then, a feature-aided weight coherence (FAWC) optimization, based on the algorithm of weighted coherence minimization (WCM) [22], is developed to optimize measurement-matrix without computational complexity increasing.
The complexity of SCSM + SIR method that uses CVX package to optimize measurement matrix is ( O({overset{sim }{N}}^3) ), where ( overset{sim }{N}={overset{sim }{L}}^2 ) [35].
It provides therefore an ideal case to study line selection method to manage the effect of self-absorption, which becomes unavoidable at high concentration level, and to optimize measurement precision.
The aim of the present study was to investigate the suitability of 3D video-fluoroscopy for the analysis of elbow bones kinematics, through a detailed exploration of the convergence domain of the minimization algorithm, in order to quantify and optimize measurement accuracy in terms of bias and precision.
According to (41), to optimize measurement matrix T based on minimizing μ(A) with respect to T, it is only needed to minimize μ(B) with respect to T. Therefore, the optimization problem becomes: widehat{boldsymbol{T}}= arg underset{boldsymbol{T}}{ min }{leftVert {left(boldsymbol{TH}right)}^Hboldsymbol{TH}-{boldsymbol{I}}_{N_r{N}_a}rightVert}_F^2 (42).
