The malignancy rate of GGN is different according to the presence and the size of a solid component. Thus, it is important to differentiate part-solid GGN with a variable sized solid component from pure GGN. In this paper, we propose a method of classifying the GGNs according to presence or size of solid component using multiple 2.5- dimensional deep CNNs. First, to consider not only intensity but also texture, and shape information, we propose an enhanced input image using image augmentation and removing background. Second, we proposed GGN-Net which can classify GGNs into three classes using multiple input images in chest CT images. Finally, we comparatively evaluate the classification performance according to different type of input images. In experiments, the accuracy of the proposed method using multiple input images was the highest at 82.76% and it was 10.35%, 13.79%, and 6.90% higher than that of using three single input image such as intensity-based, texture- and shape-enhanced input images, respectively.
Within the index matching framework, the overview of two architectures of liquid crystal (LC)-based lenticular lens
arrays developed previously is given. The first type exhibits the polarization-dependent focusing effect which comes
from the index matching between a polymer convex lens structure on the bottom substrate and a LC on it. It shows the
high quality two-dimensional image and the focusing effect depending on the polarization of the incident light. The
second type is capable of laterally shifting the focusing effect in a complementary geometry of a convex lens on the
bottom substrate and a concave lens on the other. The lateral offset between the centers of both lenses was one half of
lens pitch such that the lateral shift of the focusing effect is spontaneously achieved at either the interface of LC-concave
lens or that of LC-convex lens. The two architectures of the LC-based lenticular lenses would be applicable for devising
a new class of advanced 2D-3D convertible systems.
Optical and electronic devices for optoelectronic integrated circuits have been extensively studied, and now, more efforts for the conversion between optical and electrical signals are accordingly required. In this work, a silicon (Si)-compatible optically drivable III-V-on-Si metal-oxide-semiconductor field-effect transistor (MOSFET) is studied by simulation. The proposed optoelectronic device provides a strong interface between the optical and the electronic platforms as a key component of the optical interconnect. The optically driven MOSFET device is analogously analyzed into a photodetector and its complementary device, getting rid of receiver circuitry, which improves the integration density and simplifies the fabrication processes. To realize the optical switching with maximized photo-sensing region, a bottom gate is formed to modulate the channel, where germanium (Ge) and gallium arsenide (GaAs) are the active materials on Si platform. Both direct-current (DC) and alternating-current (AC) performances of an optimized device are evaluated.
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