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Yasuyuki Kobayashi

Publications and source records attributed to Yasuyuki Kobayashi.

2 recordsLinked to original sources

Prototyping of 6.2-mm-Pitch Fiber Positioner Modules for Stage-V Telescope Instrumentation

Small-pitch populated focal planes are essential enabling technologies for the next generation of highly multiplexed astronomical instruments. As modern astrophysics relies on massive spectroscopic surveys to study dark energy, dark matter, and galactic assembly, the ability to observe thousands of targets simultaneously has become paramount. To achieve these ambitious scientific goals, optical fibers must be packed into the telescope's focal plane with unprecedented density and accuracy. This work reports on comprehensive prototyping activities for novel 6.2 mm-pitch alpha-beta (theta-phi) fiber positioner modules. Achieving reliable operation at this extremely miniaturized scale presents formidable mechanical and control-system challenges. We provide a detailed comparative analysis of two primary architectural approaches: trillium-based mechanisms and independently actuated robotic designs. A rigorous quantitative assessment was conducted for both prototype models. Critical metrics such as XY positioning repeatability, non-linearity, and gear backlash were evaluated, as these directly dictate the targeting accuracy of the fiber on the sky. Furthermore, we analyzed fiber tilt angles, a crucial factor given its severe implications for Focal Ratio Degradation and the subsequent loss of optical throughput to the spectrographs. Our analysis contextualizes these mechanical constraints with their direct implications for overall instrument performance and survey efficiency. Initial results are highly encouraging, indicating that these miniaturized positioners can successfully overcome spatial limitations while maintaining stringent tolerances. These promising metrics demonstrate that 6.2 mm-pitch modules are highly suitable for the next generation telescopes and the massive multi-object spectroscopic facilities.

astro-ph.IM↗

All-in-one platform for AI R&D in medical imaging, encompassing data collection, selection, annotation, and pre-processing

Deep Learning is advancing medical imaging Research and Development (R&D), leading to the frequent clinical use of Artificial Intelligence/Machine Learning (AI/ML)-based medical devices. However, to advance AI R&D, two challenges arise: 1) significant data imbalance, with most data from Europe/America and under 10% from Asia, despite its 60% global population share; and 2) hefty time and investment needed to curate proprietary datasets for commercial use. In response, we established the first commercial medical imaging platform, encompassing steps like: 1) data collection, 2) data selection, 3) annotation, and 4) pre-processing. Moreover, we focus on harnessing under-represented data from Japan and broader Asia, including Computed Tomography, Magnetic Resonance Imaging, and Whole Slide Imaging scans. Using the collected data, we are preparing/providing ready-to-use datasets for medical AI R&D by 1) offering these datasets to AI firms, biopharma, and medical device makers and 2) using them as training/test data to develop tailored AI solutions for such entities. We also aim to merge Blockchain for data security and plan to synthesize rare disease data via generative AI. DataHub Website: https://medical-datahub.ai/

cs.CV↗