CC* Data Storage: Distributed, Fast, Scalable Infrastructure for Emerging Media Research Data

CC* 数据存储：新兴媒体研究数据的分布式、快速、可扩展基础设施

• 批准号: 2232817
• 负责人: Qi Sun
• 金额: $ 49.99万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232817&HistoricalAwards=false
• 关键词: CC; Data; Storage; Distributed; Fast

The project develops a fast, scalable, distributed cyberinfrastructure (CI) across New York University’s New York City buildings to meet the specialized demands of laboratory and classroom media researchers, including: 1) large-scale storage; 2) fast, low latency, two-way edge-server communications; and 3) scalable system design to rapidly adapt to emerging research frameworks, such as virtual/augmented reality (VR/AR), machine learning, and data science. The project architecture is based on research with colleagues operating the large Flatiron Institute Ceph clusters. The CI fosters and supports data-driven, high concurrency, and collaborative research and educational activities. With VR/AR as an example use case, the CI generates a new service to provide an immediate enabling impact on real-time cloud-edge data transmission and storage, relaxing the computational load on low-power untethered headsets. The high concurrency data volume support enables remote virtual environment co-development by synchronously sharing data and models. The strategic CI design supports other broad emerging research disciplines, including robotics, educational technologies, biomedical experimentation, and wireless networks. To broaden participation, the CI is accessible via the New York State Education and Research Network, as well as to other NYU collaborating K-12 educational institutions and government agencies. The technology helps to support the training and education of students from low-income households and backgrounds underrepresented in STEM. Through the integration of ultra-large and instantaneous public and citizen data analysis, it also supports just-in-time data-driven urban policy-making and public emergency response.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目在纽约大学纽约市的建筑中开发了一个快速、可扩展的分布式网络基础设施(CI)，以满足实验室和课堂媒体研究人员的专业需求，包括：1)大规模存储；2)快速、低延迟、双向边缘服务器通信；以及3)可扩展的系统设计，以快速适应新兴的研究框架，如虚拟/增强现实(VR/AR)、机器学习和数据科学。该项目的架构是基于与运营大型熨斗研究所Cave集群的同事的研究。CI促进和支持数据驱动、高并发性以及协作研究和教育活动。以VR/AR为例，CI生成了一项新服务，以提供对实时云边缘数据传输和存储的即时支持影响，从而减轻低功耗自由耳机的计算负载。高并发数据量支持，通过同步共享数据和模型，实现远程虚拟环境的协同开发。战略性CI设计支持其他广泛的新兴研究学科，包括机器人学、教育技术、生物医学实验和无线网络。为了扩大参与，可以通过纽约州教育和研究网络以及纽约大学其他合作的K-12教育机构和政府机构访问CI。该技术有助于支持来自低收入家庭和STEM中代表性不足的背景的学生的培训和教育。通过集成超大和即时的公共和公民数据分析，它还支持实时数据驱动的城市政策制定和公共应急响应。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Reconstructing room scales with a single sound for augmented reality displays

使用单一声音重建房间尺度以进行增强现实显示

• DOI: 10.1080/15980316.2022.2145377
• 发表时间: 2023
• 期刊: Journal of Information Display
• 影响因子: 3.7
• 作者: Liang, Benjamin S.;Liang, Andrew S.;Roman, Iran;Weiss, Tomer;Duinkharjav, Budmonde;Bello, Juan Pablo;Sun, Qi
• 通讯作者: Sun, Qi

Force-Aware Interface via Electromyography for Natural VR/AR Interaction

• DOI: 10.1145/3550454.3555461
• 发表时间: 2022-10
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: Yunxiang Zhang;Benjamin Liang;Boyuan Chen-;P. Torrens;S. F. Atashzar;Dahua Lin;Qinghong Sun

Imperceptible Color Modulation for Power Saving in VR/AR

难以察觉的色彩调制，可在 VR/AR 中实现省电

• DOI: 10.1145/3588037.3595388
• 发表时间: 2023
• 期刊: ACM SIGGRAPH 2023 Emerging Technologies
• 作者: Chen, Kenneth;Duinkharjav, Budmonde;Ujjainkar, Nisarg;Shahan, Ethan;Tyagi, Abhishek;He, Jiayi;Zhu, Yuhao;Sun, Qi
• 通讯作者: Sun, Qi