Multi Computer Vision-Driven Testing Platform: Structural Reconstruction and Material Identification with Ultrabroadband Carbon Nanotube Imagers (Adv. Mater. Technol. 7/2025)

IF 6.4 3区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Advanced Materials Technologies Pub Date : 2025-04-04 DOI:10.1002/admt.202570040

Daiki Shikichi, Raito Ota, Miki Kubota, Yuya Kinoshita, Noa Izumi, Mitsuki Kosaka, Tomoki Nishi, Daiki Sakai, Yuto Matsuzaki, Leo Takai, Minami Yamamoto, Yuto Aoshima, Ryoga Odawara, Takeru Q. Suyama, Hiroki Okawa, Zhenyu Zhou, Tomoya Furukawa, Shota Wada, Satoshi Ikehata, Imari Sato, Yukio Kawano, Kou Li

引用次数: 0

Abstract

Carbon Nanotubes

In article number 2401724, Yukio Kawano, Kou Li, and co-workers synergize material-identifying non-destructive inspections under non-invasive millimeter-wave–infrared irradiation with computer vision sensing. The system selectively extracts 3D structural information per constituent material from opaque complicated targets by carbon nanotube imagers.

Abstract Image

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多计算机视觉驱动测试平台：基于超宽带碳纳米管成像仪的结构重构和材料识别。抛光工艺。7/2025)

在文章编号2401724中，Yukio Kawano， Kou Li和同事将非侵入性毫米波红外辐射下的材料识别无损检测与计算机视觉传感相结合。该系统利用碳纳米管成像仪从不透明复杂目标中选择性地提取各组成材料的三维结构信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advanced Materials Technologies Materials Science-General Materials Science

CiteScore

10.20

自引率

4.40%

发文量

566

期刊介绍： Advanced Materials Technologies Advanced Materials Technologies is the new home for all technology-related materials applications research, with particular focus on advanced device design, fabrication and integration, as well as new technologies based on novel materials. It bridges the gap between fundamental laboratory research and industry.