• Title/Summary/Keyword: u-Blaze

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Implementation of SoC for NMEA2000 Ship Standard Network Protocol Using FPGA

  • Park, Dong-Hyun;Hong, Ji-Tae;Kim, Kyung-Yup;Yu, Yung-Ho
    • Journal of Advanced Marine Engineering and Technology
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    • v.34 no.1
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    • pp.125-132
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    • 2010
  • IEC61162-3 known as NMEA2000 protocol is approved as a standard network of SOLAS ship by ISO and used for the instrument network which exchanges data in real-time. For easy the development of ship network equipments, this study is focused on the development of SoC which can convert to NMEA2000 protocol from various kind of protocols such as TCP/IP, NMEA0183, RS422 and others using FPGA and u-Blaze. In this paper, we composed NMEA2000 protocol stack on FPGA and verified NMEA2000 network communication of FPGA system by connecting with commercialized devices through PC Hyper-terminal and network monitoring program.

Avatar Generation from 3D Motion (3차원 모션을 통한 아바타 생성 기술)

  • So-Hyun Park;U-Chae Jun;Jae-Eun Ko;Ji-Woo Kang
    • Proceedings of the Korea Information Processing Society Conference
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    • 2023.11a
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    • pp.733-734
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    • 2023
  • 버츄얼 유튜버로서 자신의 동작을 3D 가상 캐릭터로 나타내고, SNS 에서 춤을 공유하는 경우가 많아졌다. 본 논문에서는 2D 영상에서 MediaPipe BlazePose 모델로 추정된 사람 포즈를 3D 인체 모델인 SMPL 에 피팅하여 사용자 정의 3D 모델을 생성하는 방법을 제안한다. 이를 통해 자신의 춤 영상으로 3D 모델을 생성하여 공유하거나, 기존의 춤 동영상으로 3D 모델을 생성하여 댄스 게임에 사용할 수 있다. 이처럼 본 기술은 예술 및 엔터테인먼트 분야에서 다양하게 활용될 수 있다.

Analysis of Understanding Using Deep Learning Facial Expression Recognition for Real Time Online Lectures (딥러닝 표정 인식을 활용한 실시간 온라인 강의 이해도 분석)

  • Lee, Jaayeon;Jeong, Sohyun;Shin, You Won;Lee, Eunhye;Ha, Yubin;Choi, Jang-Hwan
    • Journal of Korea Multimedia Society
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    • v.23 no.12
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    • pp.1464-1475
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    • 2020
  • Due to the spread of COVID-19, the online lecture has become more prevalent. However, it was found that a lot of students and professors are experiencing lack of communication. This study is therefore designed to improve interactive communication between professors and students in real-time online lectures. To do so, we explore deep learning approaches for automatic recognition of students' facial expressions and classification of their understanding into 3 classes (Understand / Neutral / Not Understand). We use 'BlazeFace' model for face detection and 'ResNet-GRU' model for facial expression recognition (FER). We name this entire process 'Degree of Understanding (DoU)' algorithm. DoU algorithm can analyze a multitude of students collectively and present the result in visualized statistics. To our knowledge, this study has great significance in that this is the first study offers the statistics of understanding in lectures using FER. As a result, the algorithm achieved rapid speed of 0.098sec/frame with high accuracy of 94.3% in CPU environment, demonstrating the potential to be applied to real-time online lectures. DoU Algorithm can be extended to various fields where facial expressions play important roles in communications such as interactions with hearing impaired people.