Three dimensional particles identification and shape parameters research on coarse aggregates of asphalt mixture using X-ray CT images

In this paper a series of digital image processing methods were adopted for getting separated coarse aggregates from asphalt mixture specimen using high-resolution X-ray Computed Tomography (CT) images.The existing three dimensional (3D) particles matching methods based on two dimensional (2D) continuous cross-sections were analyzed and a new 'overlap area method' was presented.After the 3D particles were extracted one by one successfully,the basic parameters of each aggregate:perimeter,area,surface area,and volume were calculated by chain code method.Finally,the 3D mass center coordinates and the sphericity index were introduced.

Bayesian localization in an uncertain ocean environment

In order to improve the ability to localize a source in an uncertain acoustic environment,a Bayesian approach,referred to here as Bayesian localization is used by including the environment in the parameter search space.Genetic algorithms are used for the parameter optimization.This method integrates the a posterior probability density（PPD） over environmental parameters to obtain a sequence of marginal probability distributions over source range and depth,from which the most-probable source location and localization uncertainties can be extracted.Considering that the seabed density and attenuation are less sensitive to the objective function of matched field processing,we utilize the empirical relationship to invert those parameters indirectly.The broadband signals recorded by a vertical line array in a Yellow Sea experiment in 2000 are processed and analyzed.It was found that,the Bayesian localization method that incorporates the environmental variability into the processor,made it robust to the uncertainty in the ocean environment.In addition,using the empirical relationship could enhance the localization accuracy.