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The roughness estimates are fed to a proportional-integral (PI) controller.
The estimator involves a kinetic MC simulator based on a reduced-order lattice, an adaptive filter used to reduce roughness stochastic fluctuations and an error compensator used to reduce the error between the roughness estimates and the discrete roughness measurements.
There has been a substantial amount of research work exploiting geometrical properties of surfaces including slope and roughness estimates using patterns and contrasts in SAR image tone and texture (e.g., Abdelsalam et al. 2000; Adams et al. 1981; Blom et al. 1984; Berlin et al. 1986; Grandjean et al. 2001; Paillou et al. 2006a, b).
The same was true for the roughness estimates.
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Subsequently, a linear regression function is learned that maps appearance features represented by texton distributions to the roughness estimate.
Another metric developed for 3D mesh quality assessment is called FMPD which is based on local roughness estimated from Gaussian curvature [48].
The surface roughness estimated from AFM for the untreated PEDOT PSS film is 2.08 nm and 2.38 nm for the treated film.
We remark that when we incorporate wall roughness, location estimates are biased.
The significant uncertainties associated with roughness length estimates obtained using indirect velocity-profile-fitting techniques in ABL simulations are documented.
It is shown that the roughness length estimates computed from indirect procedures can be in error by factors of two or more.
Last, the time evolution of surface roughness is estimated.
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