Surface wave distribution over electromagnetic bandgap (EBG) plate is measured and suppression of surface wave propagation over the EBG is investigated. We used a micro current probe that detects H-field strength of the propagating transverse magnetic (TM) microwave up to 6 GHz. By scanning with the probe over the EBG, we visualized surface wave distribution at various frequencies. This visualized map shows that the EBG plate suppresses the surface wave propagation within the bandgap frequency. We utilized this effect for the antenna reflective shield. By combining the EBG with a microstrip patch antenna, this EBG works as a reflective shield and the front-to-backward radiation ratio of antenna is increased. In this experiment, we fabricated three types of shield board; mushroom type of EBG that has hexagonal textured patches connected with via-holes, textured surface without via-holes, and plane metal. By comparing the surface wave distributions and beam patterns of antenna with various shields, we found that the visualized map of TM surface wave gives us direct and intuitive information and helpful tips in designing the EBG reflective shield for patch antenna.

This paper proposes a new approach to recover the sign of local surface curvature of object from three shading images using neural network. The RBF (Radial Basis Function) neural network is used to learn the mapping of three image irradiances to the position on a sphere. Then, the learned neural network maps the image irradiances at the neighbor pixels of the test object taken from three illuminating directions of light sources onto the sphere images taken under the same illuminating condition. Using the property that basic six kinds of surface curvature has the different relative locations of the local five points mapped on the sphere, not only the Gaussian curvature but also the kind of curvature is directly recovered locally from the relation of the locations on the mapped points on the sphere without knowing the values of surface gradient for each point. Further, two step neural networks which combines the forward mapping and its inverse mapping one can be used to get the local confidence estimate for the obtained results. The entire approach is non-parametric, empirical in that no explicit assumptions are made about light source directions or surface reflectance. Results are demonstrated by the experiments for real images.

This paper proposes a new method to recover the sign of local Gaussian curvature from multiple (more than three) shading images. The information required to recover the sign of Gaussian curvature is obtained by applying Principal Components Analysis (PCA) to the normalized irradiance measurements. The sign of the Gaussian curvature is recovered based on the relative orientation of measurements obtained on a local five point test pattern to those in the 2-D subspace called the eigen plane. Using multiple shading images gives a more accurate and robust result and minimizes the effect of shadows by allowing a larger area of the visible surface to be analyzed compared to methods using only three shading images. Furthermore, it allows the method to be applied to specular surfaces. Since PCA removes linear correlation among images, the method can produce results of high quality even when the light source directions are not widely dispersed.