• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1243-1257
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• 2019

As current algorithms unable to perform effective fusion processing of unknown complex radar signals lacking database, and the result is unstable, this paper presents a multi-level fusion processing algorithm for complex radar signals based on evidence theory as a solution to this problem. Specifically, the real-time database is initially established, accompanied by similarity model based on parameter type, and then similarity matrix is calculated. D-S evidence theory is subsequently applied to exercise fusion processing on the similarity of parameters concerning each signal and the trust value concerning target framework of each signal in order. The signals are ultimately combined and perfected. The results of simulation experiment reveal that the proposed algorithm can exert favorable effect on the fusion of unknown complex radar signals, with higher efficiency and less time, maintaining stable processing even of considerable samples.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.2
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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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.

• Proceedings of the Korean Magnestics Society Conference
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• 2003.12a
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• pp.253-253
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• 2003

• Bulletin of the Korean Chemical Society
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• v.34 no.6
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• pp.1887-1890
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• 2013

• 한국약용작물학회:학술대회논문집
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• 2011.04a
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• pp.400-401
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• 2011

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2003.10a
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• pp.89-89
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• 2003

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2003.10a
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• pp.90-90
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• 2003

• Proceedings of the Korean Institute of Resources Recycling Conference
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• 2005.10a
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• pp.153-157
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• 2005

• Electronics and Telecommunications Trends
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• v.33 no.4
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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.