• Journal of Positioning, Navigation, and Timing
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• 제13권2호
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• pp.149-158
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• 2024

Global Navigation Satellite System (GNSS) receivers are becoming increasingly sophisticated, equipped with advanced features and precise specifications, thus demanding efficient and high-performance hardware platforms. This paper presents the design and implementation of a Field-Programmable Gate Array (FPGA)-based GNSS receiver development platform for multi-band signal processing. This platform utilizes a FPGA to provide a flexible and re-configurable hardware environment, enabling real-time signal processing, position determination, and handling of large-scale data. Integrated signal processing of L/S bands enhances the performance and functionality of GNSS receivers. Key components such as the RF frontend, signal processing modules, and power management are designed to ensure optimal signal reception and processing, supporting multiple GNSS. The developed hardware platform enables real-time signal processing and position determination, supporting multiple GNSS systems, thereby contributing to the advancement of GNSS development and research.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2024년도 춘계학술발표대회
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• pp.199-201
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• 2024

많은 양의 데이터 처리를 요구하는 오늘날, 메모리 입/출력 없이 데이터를 처리할 수 있는 Processing-In Memory가 많은 관심을 받고 있다. Processing-In Memory는 소프트웨어 라이브러리를 통해 접근할 수 있는데, 적절히 구현되지 않은 라이브러리는 공격 대상이 된다. 본 논문에서는 Processing-In Memory 소프트웨어 라이브러리에 존재하는 시간적 접근 취약점을 분석하고 그에 대한 완화기법을 제시한다.

• 한국자기학회:학술대회 개요집
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• 한국자기학회 2003년도 somma/kms meeting
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• pp.253-253
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• 2003

• Bulletin of the Korean Chemical Society
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• 제34권6호
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• pp.1887-1890
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• 2013

• 한국약용작물학회:학술대회논문집
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• 한국약용작물학회 2011년도 춘계학술발표회
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• pp.400-401
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• 2011

• 한국식물생명공학회:학술대회논문집
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• 한국식물생명공학회 2003년도 추계학술대회 발표논문 초록집
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• pp.89-89
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• 2003

• 한국식물생명공학회:학술대회논문집
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• 한국식물생명공학회 2003년도 추계학술대회 발표논문 초록집
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• pp.90-90
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• 2003

• 한국자원리싸이클링학회:학술대회논문집
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• 한국자원리싸이클링학회 2005년도 추계정기총회 및 제26회 학술발표대회 고분자리싸이클링기술 특별심포지엄
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• pp.153-157
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• 2005

• 전자통신동향분석
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• 제33권4호
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• pp.54-60
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• 2018

In this paper, we analyze the trends of deep-learning based plant data processing technologies. In recent years, the deep-learning technology has been widely applied to various AI tasks, such as vision (image classification, image segmentation, and so on) and natural language processing because it shows a higher performance on such tasks. The deep-leaning method is also applied to plant data processing tasks and shows a significant performance. We analyze and show how the deep-learning method is applied to plant data processing tasks and related industries.

• Bulletin of the Korean Chemical Society
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• 제32권9호
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• pp.3509-3512
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• 2011