UWB based dielectric material characterization using hardware/software co-design based ANN

S. Sardar, A. Mishra
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引用次数: 4

Abstract

The feasibility of using reflected Ultra Wideband (UWB) waves in Hardware/Software (Hw/Sw) Co-design based Artificial Neural Network (ANN) framework as a non-destructive method for dielectric material characterization by estimating their relative dielectric constant, is discussed in this paper. The property of an electromagnetic wave changes owing to the effects of relative dielectric constant & conductivity of a dielectric material. Depending on the relative dielectric constant & conductivity of a dielectric material, the reflection or transmission signal changes in terms of it's amplitude and spread. This property can be utilized to estimate the relative dielectric constant of a dielectric material. First, software implementation was carried out for feasibility analysis. In the next step, Hw/Sw co-design implementation was proposed to overcome the limitations of software implementation of ANN. These approaches are discussed and validated using FDTD simulation.
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基于人工神经网络的硬件/软件协同设计的超宽带介质材料表征
本文讨论了在基于硬件/软件(Hw/Sw)协同设计的人工神经网络(ANN)框架中利用反射超宽带(UWB)波作为一种无损方法,通过估计介质的相对介电常数来表征介质材料的可行性。电磁波的性质受介电材料的相对介电常数和电导率的影响而发生变化。根据介电材料的相对介电常数和电导率,反射或透射信号的振幅和传播会发生变化。这一性质可用于估计介电材料的相对介电常数。首先进行软件实现,进行可行性分析。下一步,提出软硬件协同设计实现,克服人工神经网络软件实现的局限性。对这些方法进行了讨论,并通过时域有限差分仿真进行了验证。
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