Dr. Kentaro Kitamura Profile

Dr. Kentaro Kitamura

at Kyushu Institute of Technology

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Publications (2)

Proceedings Article | 23 August 2024 Presentation + Paper

Astronomical 6U CubeSat mission VERTECS: scientific objective and project status

Kei Sano, Takao Nakagawa, Shuji Matsuura, Koji Takimoto, Aoi Takahashi, Tetsuhito Fuse, Rodrigo Cordova, Victor Schulz, Pooja Lepcha, Necmi Cihan Örger, Daisuke Nakayama, Joseph Ofosu, Reynel Josue Galindo Rosales, Eyoas Areda, Pema Zangmo, Ezra Fielding, Keenan Chatar, Yukihisa Otani, Hisataka Kawasaki, Bastien Morelle, John Paul Almonte, Shunsuke Nakagawa, Yuto Tome, Shohei Karaki, Chinathip Narongphun, Hari Ram Shrestha, Marco Rosa, David Dai, Wenceslao Bejarano, Akihiro Ikeda, Rin Sato, Kentaro Hayashida, Hiroki Miyagawa, Masahiro Nishioka, Kana Kurosaki, Isami Kato, Satoshi Ikari, Kohji Tsumura, Ichiro Jikuya, Hideo Matsuhara, Umi Enokidani, Hayato Tanaka, Yuki Hirose, Akimasa Ojika, Akane Tsumoto, Taiko Iwaki, Yuki Ohara, Mengu Cho, Kentaro Kitamura, Hirokazu Masui, Mariko Teramoto, Takashi Yamauchi, Ryo Hashimoto, Emino Fukumoto, Zamba Leonel, Arisa Oho, Shoki Yabumoto, Hayato Masuno, Chisato Arakawa, Kouta Miyamoto, Takehiko Wada, Naoki Isobe, Yasuyuki Miyazaki, Ryu Funase, Hajime Kawahara, Keiichi Hirako, Yoichi Yatsu, Yoshihide Aoyanagi

Proceedings Volume 13092, 130920W (2024) https://doi.org/10.1117/12.3014708

KEYWORDS: Satellites, Visible radiation, Galactic astronomy, Stars, Astronomy, Point spread functions, X band, Telescopes, Data transmission

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Proceedings Article | 19 October 2023 Presentation + Paper

Data downlink prioritization using image classification on-board a 6U CubeSat

Keenan A. Chatar, Ezra Fielding, Kei Sano, Kentaro Kitamura

Proceedings Volume 12729, 127290K (2023) https://doi.org/10.1117/12.2684047

KEYWORDS: Satellites, Education and training, Data modeling, Image classification, Image compression, Stars, Quantization, Electron beam lithography, Data transmission, Imaging systems

Read Abstract +

Nanosatellites are proliferating as low-cost dedicated sensing systems with lean development cycles. Kyushu Institute of Technology (Kyutech) and collaborators have launched a joint venture for a nanosatellite mission, Visible Extragalactic background RadiaTion Exploration by CubeSat (VERTECS). The primary mission is to elucidate the formation history of stars by observing the optical-wavelength cosmic background radiation. The VERTECS satellite will be equipped with a small-aperture telescope and a high-precision attitude control system to capture the astronomical data for analysis on the ground. However, nanosatellites are limited by their onboard memory resources and downlink speed capabilities. Additionally, due to a limited number of ground stations, the satellite mission will face issues meeting the required data budget for mission success. To alleviate this issue, we propose an on-orbit system pipeline to autonomously classify and then compress desirable image data for downlink prioritization and optimization. The system comprises a prototype Camera Controller Board (CCB) which carries a Raspberry Pi Compute Module four which is used for classification and compression. The system uses a lightweight Convolutional Neural Network (CNN) model to classify and determine the desirability of captured image data. The model is designed to be lean and robust to reduce the computational and memory load on the satellite. The model is trained and tested on a novel star field dataset consisting of data captured by the Sloan Digital Sky Survey (SDSS). The dataset is meant to simulate the expected data produced by the 6U satellite. The compression step implements GZip, RICE or HCOMPRESS compression, which are standards for astronomical data. Preliminary testing on the proposed CNN model results in a validation classification accuracy of 100% on the star field dataset, with compression ratios of 3.99, 5.16 and 5.43 for GZip, RICE and HCOMPRESS that were achieved on tested FITS image data.

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