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The scope of this paper is filtering methods with application to estimation of the fundamental frequencies of multiple periodic signals in noise.
The hydrodynamics and heat transfer behavior of this flow was studied by passive, active and compound methods with application of corona wind.
Survival analysis was performed using Kaplan Meier methods with application of log-rank and generalised Wilcoxon tests as appropriate.
Throughout the course of the HDDRISC study, changes in measurement methodology were accompanied by detailed comparisons between methods, with application of correction factors if necessary to maintain continuity of measurement standardization.
Search 1 focused on epidemiological studies (experimental or not) of CKD progression, while Search 2 focused on developments of new statistical methods with application in the field of nephrology.
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In this paper, we present a review on non-gradient optimization methods with applications to structural engineering.
A novel parallel technique for Fourier Galerkin pseudo-spectral methods with applications to two-dimensional Navier Stokes equations and inviscid Boussinesq approximation equations is presented.
Particle filters (PFs) are computationally intensive sequential Monte Carlo estimation methods with applications in the field of mobile robotics for performing tasks such as tracking, simultaneous localization and mapping (SLAM) and navigation, by dealing with the uncertainties and/or noise generated by the sensors as well as with the intrinsic uncertainties of the environment.
Figure 2 shows our method with application to the images with intensity inhomogeneity.
We test the performance of proposed feature learning method with application to facial expression recognition.
The evolving process and the final results of our method with application to the images with intensity inhomogeneity.
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