Dr. Victor Hugo Schulz Profile

Dr. Victor Hugo Schulz

at Kyushu Institute of Technology

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Publications (2)

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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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Due to advances in observation and imaging technologies, modern astronomical satellites generate large volumes of data. This necessitates efficient onboard data processing and high-speed data downlink. Reflecting this trend is the Visible Extragalactic background RadiaTion Exploration by CubeSat (VERTECS) 6U Astronomical Nanosatellite. Designed for the observation of Extragalactic Background Light (EBL), this mission is expected to generate a substantial amount of image data, particularly within the confines of CubeSat capabilities. This paper introduces the VERTECS Camera Control Board (CCB), an open-source payload interface board leveraging Commercial Off-The-Shelf (COTS) components, with a Raspberry Pi Compute Module four at its core. The VERTECS CCB hardware and software have been designed from the ground up to serve as the sole interface between the VERTECS bus system and astronomical imaging payload, while providing compute capability not usually seen in nanosatellites of this class. Responsible for mission data processing, it will facilitate high-speed data transfer from the imaging payload via gigabit Ethernet, while also providing a high-bitrate serial connection to the payload x-band transmitter for mission data downlink. Additional interfaces for secondary payloads are provided via USB-C and standard 15-pin camera connectors. The Raspberry Pi embedded within the VERTECS CCB operates on a standard Linux distribution, streamlining the software development process. Beyond addressing the current mission’s payload control and data handling requirements, the CCB sets the stage for future missions with heightened data demands. Furthermore, it supports the adoption of machine learning and other compute-intensive applications in orbit. This paper delves into the development of the VERTECS CCB, offering insights into the design and validation of this next-generation payload interface, to ensure that it can survive the rigors of space flight.

Proceedings Article | 14 November 2021 Presentation + Paper

Wildfire detection CubeSat based on convolution neural network

Muhammad Hasif Bin Azami, Necmi Cihan Orger, Victor Hugo Schulz, Mengu Cho

Proceedings Volume 11914, 119140K (2021) https://doi.org/10.1117/12.2603906

KEYWORDS: Satellites, Neural networks, Convolution, Earth observing sensors, Satellite imaging, Cameras, Systems modeling, Commercial off the shelf technology, Image processing

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Wildfires burn millions of hectares of land every year globally. Most of them are caused by humans, while only 10-15% occur naturally due to the climate change. The hotter weather dries out forests and plants, making them more prone to fire. The “frontline wildfire defense” has fully utilized satellite imagery to monitor, map, and control the fire spread and damage. However, there are three major challenges of using traditional satellite data: (1) the spatial resolution, (2) the temporal resolution, and (3) the downlink and analyzing data on the ground. In recent technology, the satellites are developed into small-size CubeSats that supporting the resolution issues. By exploiting the deep learning (DL) technique, the CubeSat can become sufficiently “intelligent” to detect wildfire events. This paper discusses a potential approach for implementing a Convolution Neural Network (CNN) onboard a CubeSat to sense wildfire. The DL model has been tested on the Camera Controller Board (CCB) embedded with Raspberry Pi Compute Module (RPi CM3+), that interfacing with the imaging mission of a 6U CubeSat named KITSUNE. In addition, the space environment test of radiation Total Ionizing Dose (TID) with functional tests of the board has been discussed. The results have shown no anomaly observed on the RPi while the DL model achieved a 94% overall accuracy with 16 minutes of learning time and 32 seconds of classification time. Hence, the state-of-art processing images onboard CubeSat will improve the valuable downlink data as the limited time window passes through the ground station.

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