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arXiv 1503.03264cs.ET
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数据平滑的材料近似及受黏泥模型启发的样条曲线

Material Approximation of Data Smoothing and Spline Curves Inspired by Slime Mould

Jeff Jones, Andrew Adamatzky

摘要:

使用显示新兴形态适应行为的绒泡菌的粒子模型,我们演示了如何使用最小的集体材料计算方法来转换和总结空间表示的数据集的属性。我们发现,虚拟材料更强烈地松弛到数据中的高频变化,其可用于通过在1D数据集中近似移动平均和低通滤波器来平滑(或滤波)数据。该模型的松弛和最小化属性使B样条曲线(近似样条)在2D数据集中的空间计算成为可能。夹紧和非夹紧样条曲线,开放和封闭的形状,可以表示和样条曲率的程度对应于材料的弛豫时间。样条曲线的材料计算还包括新颖的准机械属性,包括控制点之间的形状展开以及优先粘附到更长、更直的路径。插值样条不能直接近似,由于Steiner点的形成和演变的nar-row顶点,但近似后的直线预处理的源数据。通过将原始数据转换为包含数据库中的材料,进一步简化了预处理。这些示例性结果通过展示一种简单的、集体的和分布式的数据和曲线平滑方法来扩展空间表示的非常规计算设备的库。

英文摘要:

Using a particle model of Physarum displaying emer- gent morphological adaptation behaviour we demonstrate how a minimal approach to collective material computation may be used to transform and summarise properties of spatially represented datasets. We find that the virtual material relaxes more strongly to high-frequency changes in data which can be used for the smoothing (or filtering) of data by ap- proximating moving average and low-pass filters in 1D datasets. The relaxation and minimisation properties of the model enable the spatial computation of B-spline curves (approximating splines) in 2D datasets. Both clamped and unclamped spline curves, of open and closed shapes, can be represented and the degree of spline curvature corresponds to the relaxation time of the material. The material computation of spline curves also includes novel quasi-mechanical properties including unwind- ing of the shape between control points and a preferential adhesion to longer, straighter paths. Interpolating splines could not directly be ap- proximated due to the formation and evolution of Steiner points at nar- row vertices, but were approximated after rectilinear pre-processing of the source data. This pre-processing was further simplified by transform- ing the original data to contain the material inside the polyline. These exemplar results expand the repertoire of spatially represented uncon- ventional computing devices by demonstrating a simple, collective and distributed approach to data and curve smoothing.

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