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Masaya Kaneko

Publications and source records attributed to Masaya Kaneko.

2 recordsLinked to original sources

Design guidelines for the SPICE parameters of waveform-selective metasurfaces varying with the incident pulse width at a constant oscillation frequency

In this study, we numerically demonstrate how the response of recently reported circuit-based metasurfaces is characterized by their circuit parameters. These metasurfaces, which include a set of four diodes as a full wave rectifier, are capable of sensing different waves even at the same frequency in response to the incident waveform, or more specifically the pulse width. This study reveals the relationship between the electromagnetic response of such waveform-selective metasurfaces and the SPICE parameters of the diodes used. First, we show that reducing a parasitic capacitive component of the diodes is important for realization of waveform-selective metasurfaces in a higher frequency regime. Second, we report that the operating power level is closely related to the saturation current and the breakdown voltage of the diodes. Moreover, the operating power range is found to be broadened by introducing an additional resistor into the inside of the diode bridge. Our study is expected to provide design guidelines for circuit-based waveform-selective metasurfaces to select/fabricate optimal diodes and enhance the waveform-selective performance at the target frequency and power level.

physics.app-ph

TriDepth: Triangular Patch-based Deep Depth Prediction

We propose a novel and efficient representation for single-view depth estimation using Convolutional Neural Networks (CNNs). Point-cloud is generally used for CNN-based 3D scene reconstruction; however it has some drawbacks: (1) it is redundant as a representation for planar surfaces, and (2) no spatial relationships between points are available (e.g, texture and surface). As a more efficient representation, we introduce a triangular-patch-cloud, which represents the surface of the 3D structure using a set of triangular patches, and propose a CNN framework for its 3D structure estimation. In our framework, we create it by separating all the faces in a 2D mesh, which are determined adaptively from the input image, and estimate depths and normals of all the faces. Using a common RGBD-dataset, we show that our representation has a better or comparable performance than the existing point-cloud-based methods, although it has much less parameters.

cs.CV