The finite-difference time-domain (FDTD) method has been widely used in recent years to analyze the propagation and scattering of electromagnetic waves. Because the FDTD method has second-order accuracy in space, its numerical dispersion error arises from truncated higher-order terms of the Taylor expansion. This error increases with the propagation distance in cases of large-scale analysis. The numerical dispersion error is expressed by a dispersion relation equation. It is difficult to solve this nonlinear equation which have many parameters. Consequently, a simple formula is necessary to substitute for the dispersion relation error. In this study, we have obtained a simple formula for the numerical dispersion error of 2-D and 3-D FDTD method in free space propagation.

Pre-Cantor bar, the one-dimensional fractal media, consists of two kinds of materials. Using the transmission-line theory we will explain the double-exponential behavior of the minimum of the transmittance as a function of the stage number n, and obtain formulae of two kinds of scaling behaviors of the transmittance. From numerical calculations for n=1 to 5 we will find that the maximum of field amplitudes of resonance which increases double-exponentially with n is well estimated by the theoretical upper bound. We will show that after sorting field amplitudes for resonance frequencies of the 5th stage their distribution is a staircase function of the index.

Finite difference time domain (FDTD) method has been accelerated on the Cell Broadband Engine (Cell B.E.). However the problem has arisen that speedup is limited by the bandwidth of the main memory on large-scale analysis. As described in this paper, we propose a novel algorithm and implement FDTD using it. We compared the novel algorithm with results obtained using region segmentation, thereby demonstrating that the proposed algorithm has shorter calculation time than that provided by region segmentation.

Ground penetrating radar (GPR) has the advantage of non-destructively and quickly inspecting internal structures such as voids and buried pipes under roads. However, it is necessary to estimate the internal structures from the GPR images. Recently, recognition and detection methods for GPR images using deep learning have been studied. This paper examines a data augmentation method using a cutout method necessary to estimate GPR images with deep learning accurately. We find that the cutout augmentation exhibits higher detection rates for all objects used in this study than a commonly used horizontal shift augmentation.