论文标题
电磁组织模型中有效材料特性的限制
Limits of effective material properties in the context of an electromagnetic tissue model
论文作者
论文摘要
大多数用于基于反射的组织光谱的校准方案基于MM-WAVE/THZ频率范围的基于均质的,频率依赖性的组织模型,在这些模型中,宏观材料参数已通过测量确定或使用有效的材料理论来确定。但是,由于在这些频率下的测量分辨率捕获了组织的基本微观结构,因此,我们将在这里研究这种有效材料模型在较大频率范围内的有效性极限(10 MHz -200 GHz)。我们嵌入了可参数化的虚拟工作台中,我们使用层次多尺度方法实现了一种数值均质化方法,同时捕获组织的分散性和张力电磁性能,并确定该频率频率与单波电磁参考模型偏离鸟类Carlo Carlo Carlo Carlo分析的框架。使用模仿微观结构形态的通用皮下组织进行了模拟。结果表明,有效性极限发生在令人惊讶的低频率下,因此与均质组织模型的传统使用相矛盾。详细解释了其原因,因此显示了如何使用有监督的机器学习方法的特定材料和结构特性的频率选择性分类/识别频率选择性分类/识别。使用实现的分类器,我们开发了一种方法来识别禁止频率范围内的特定频段,以优化材料分类的可靠性。
Most calibration schemes for reflection-based tissue spectroscopy in the mm-wave/THz-frequency range are based on homogenized, frequency-dependent tissue models where macroscopic material parameters have either been determined by measurement or calculated using effective material theory. However, as the resolution of measurement at these frequencies captures the underlying microstructure of the tissue, here we will investigate the validity limits of such effective material models over a wide frequency range (10 MHz - 200 GHz) . Embedded in a parameterizable virtual workbench, we implemented a numerical homogenization method using a hierarchical multiscale approach to capture both the dispersive and tensorial electromagnetic properties of the tissue, and determined at which frequency this homogenized model deviated from a full-wave electromagnetic reference model within the framework of a Monte Carlo analysis. Simulations were carried out using a generic hypodermal tissue that emulated the morphology of the microstructure. Results showed that the validity limit occurred at surprisingly low frequencies and thus contradicted the traditional usage of homogenized tissue models. The reasons for this are explained in detail and thus it is shown how both the lower "allowed" and upper "forbidden" frequency ranges can be used for frequency-selective classification/identification of specific material and structural properties employing a supervised machine-learning approach. Using the implemented classifier, we developed a method to identify specific frequency bands in the forbidden frequency range to optimize the reliability of material classification.