SPIRIT - Scalable Platform for Innovations on Real-time Immersive Telepresence
SPIRIT - Scalable Platform for Innovations on Real-time Immersive Telepresence
批准号:
10043737
负责人:
金额:
$55.98万
依托单位:
依托单位国家:
英国
项目类别:
EU-Funded
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --
中文摘要
沉浸式网真技术将在虚拟世界和物理世界之间界限模糊的网络空间中,对个人之间或与非人类对象(例如机器)之间的交互产生改变游戏规则的影响。这项技术预计将在各种垂直行业产生影响,包括教育和培训、娱乐、医疗保健、制造业等。主要挑战包括应用平台和底层网络支持的局限性,无法实现大规模沉浸式网真内容的无缝呈现、处理和交付。创新的设计、严格的验证和测试工作旨在满足确定的关键技术要求,如低延迟通信、高带宽需求和实时复杂的内容编码/呈现任务。行业领先的SPIRIT财团将建立在项目合作伙伴开发的现有TRL4应用平台和网络基础设施上,旨在应对关键的技术挑战,并进一步开发远程呈现技术的所有主要方面,以实现目标TRL7。SPIRIT项目将重点在网络层、传输层、应用/内容层技术以及安全和隐私机制方面进行创新,以促进远程呈现应用的大规模运营。该项目团队将在德国和英国的两个地理位置开发一个完全分布式、互联的测试基础设施,允许在现实生活的互联网环境中对不同类型的网真应用进行大规模测试。网络基础设施将托管基于WebRTC和低延迟DASH的两种主流应用环境。除了项目指定的用例场景外,项目组还将通过FSTP参与测试涵盖不同垂直领域的各种额外用例。
英文摘要
Immersive telepresence technologies will have game-changing impacts on interactions amongst individuals or with non-human objects (e.g. machines), in cyberspace with blurred boundaries between the virtual and physical world. The impacts of this technology are expected to range in a variety of vertical sectors, including education and training, entertainment, healthcare, manufacturing industry, etc. The key challenges include limitations of both the application platform and the underlying network support to achieve seamless presentation, processing and delivery of immersive telepresence content at a large scale. Innovative design, rigorous validation and testing exercises aim to fulfil the key technical requirements identified such as low-latency communication, high bandwidth demand, and complex content encoding/rendering tasks in real-time. The industry-leading SPIRIT consortium will build on the existing TRL4 application platforms and network infrastructures developed by the project partners, aiming to address key technical challenges and further develop all major aspects of telepresence technologies to achieve targeted TRL7. The SPIRIT Project will focus its innovations in network-layer, transport-layer, application/content-layer techniques, as well as security and privacy mechanisms to facilitate the large scale operation of telepresence applications. The project team will develop a fully distributed, interconnected testing infrastructure across two geographical sites in Germany and UK, allowing large-scale testing of heterogeneous telepresence applications in real-life Internet environments. The network infrastructure will host two mainstream application environments based on WebRTC and low-latency DASH. In addition to the project-designated use case scenarios, the project team will test a variety of additional use cases covering heterogeneous vertical sectors through FSTP participation.
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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批准号:--
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项目类别:合作创新研究团队
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资助金额:--
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批准年份:2024
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负责人:姚韬
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依托单位: