Mr. Ezra Fielding Profile

Ezra Fielding

Master's Student at Kyushu Institute of Technology

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Author

Area of Expertise:

Artificial Intelligence , Machine Learning , Computer Science , Space Systems Engineering , Satellite Development

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Company Website

Publications (2)

Proceedings Article | 25 July 2024 Presentation + Paper

VERTECS: A COTS-based payload interface board to enable next generation astronomical imaging payloads

Ezra Fielding, Victor Schulz, Keenan A. Chatar, Kei Sano, Akitoshi Hanazawa

Proceedings Volume 13101, 131010J (2024) https://doi.org/10.1117/12.3019471

KEYWORDS: Cameras, X band, Transmitters, Interfaces, Data transmission, Satellites, Design, Data communications, Astronomical imaging, Power consumption

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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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