인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
In this paper, we propose a novel hardware architecture specifically designed to enable real-time response in disaster environments, where rapid decision-making and low-latency processing are critical. Our approach focuses on optimizing a GAN-based radar voice imaging system, which processes radar signals and generates visual representations in real time. To enhance accuracy and robustness, we developed a simulation framework that assigns dynamic weights to each incoming signal, enabling the system to identify the signal path with the minimum loss through iterative optimization. The proposed architecture was implemented using Register-Transfer Level (RTL) coding, targeting efficient hardware utilization while maintaining high-speed operation. To validate the design, we conducted logic synthesis and timing verification using the Xilinx Vivado<sup>R</sup> synthesis environment. This step ensured that the proposed system not only met functional correctness but also satisfied timing and area constraints required for deployment in edge computing scenarios, such as portable disaster response units. The synthesis results demonstrated that the design is highly resource-efficient, consuming only 2% of available Look-Up Tables (LUTs), 2% of flip-flops (FFs), 13% of input/output (I/O) resources, and 1% of global clock buffers (BUFGs). These results indicate the architecture’s suitability for integration into FPGA-based platforms with limited hardware resources, offering a practical solution for real-time radar signal processing in critical field applications.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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