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We present a numerical model for dynamic simulation of colloidal particles attached to a fluid interface. A new coupling method is proposed for combining Newtonian dynamics for colloidal particles and the lattice Boltzmann method for fluid phases so as to account for the wetting properties of particle surfaces. With this feature, capillary interaction of colloidal particles, in addition to electrostatic and hydrodynamic interactions, can be simulated.For the validation of the proposed model, we perform numerical simulations of the steady flow around a square array of cylinders, the transient dynamics of a moving particle in the quiescent solvent, as well as the contact angle of a colloidal particle attached to the multi-component fluid interface. Further, we apply the current model to simulate capillary interactions between two colloidal particles at a fluid interface. Effects of the relevant physical parameters on the dynamics of the particles, in particular, wettability and gravity, are investigated.

The lattice Bhatnagar-Gross-Krook modeled uid has an unchangeable unit Prandtl number. A simple method is introduced in this letter to formulate a exible Prandtl number for the modeled uid. The eectiveness was demonstrated by numerical simulations of the Couette ow.

We develop a mesoscopic model for fluid-fluid-solid contact-line motions in the framework of the multi-component lattice Boltzmann model proposed by Gunstensen et al. of immiscible fluid, Phys. Rev. A 43 (1991) 4320-4327]. Regarding a solid wall as a motionless and undeformable fluid, this model can treat both fluid-fluid and fluid-solid interfaces in a unified way. In this research, we investigate fundamental characteristics of our model, such as the dependency of the slip velocity against the contact angles, and the dynamic contact angles against the static contact angles, and compare the simulation results with experimental observation in a qualitative sense.

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Commission I, ICWG I/VaKEY WORDS: Target extraction, close-range photogrammetry, coded target, shape-based matching ABSTRACT:In order to perform precise identification and location of artificial coded targets in natural scenes, a novel design of circle-based coded target and the corresponding coarse-fine extraction algorithm are presented. The designed target separates the target box and coding box totally and owns an advantage of rotation invariance. Based on the original target, templates are prepared by three geometric transformations and are used as the input of shape-based template matching. Finally, region growing and parity check methods are used to extract the coded targets as final results. No human involvement is required except for the preparation of templates and adjustment of thresholds in the beginning, which is conducive to the automation of close-range photogrammetry. The experimental results show that the proposed recognition method for the designed coded target is robust and accurate.

<p><strong>Abstract.</strong> The traditional fast marching algorithm for segmentation of the liver is suitable for processing on the central processing unit (CPU) platform, however, it is not suitable for implementation on Graphics Processing Unit (GPU). The fuzzy connection algorithm is used to extract the blood vessels in the liver, but there is a calculation error. The refinement algorithm is very time consuming when extracting the target skeleton line from the 3D image. In this paper, the fast-marching algorithm and the thinning algorithm are improved, which can be applied to the GPU computing, The fuzzy algorithm is also improved, and the calculation error of the algorithm is solved, making it more suitable for medical image processing. The computing speed of GPU is far faster than CPU. Medical image processing is one of the earliest applications where the computing performance is improved by GPU. These three segmentation methods, fast marching method, fuzzy connecting method and refinement algorithm are very common in medical image segmentation. Because the increment of medical image data results in the extension of computing time for medical image processing, it is necessary to apply the high parallelism of the GPU to speed up these algorithms. The experiment results demonstrate the feasibility of our accelerating algorithm.</p>