SPIRIT - Scalable Platform for Innovations on Real-time Immersive Telepresence
SPIRIT - Scalable Platform for Innovations on Real-time Immersive Telepresence
批准号:
10039387
负责人:
金额:
$27.72万
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
沉浸式远程呈现技术将对个人之间或与非人类物体(如机器)之间的互动产生改变游戏规则的影响,在虚拟世界和物理世界之间的界限模糊的网络空间中。预计该技术的影响将涉及各种垂直行业,包括教育和培训、娱乐、医疗保健、制造业等。关键的挑战包括应用平台和底层网络支持的限制,以实现大规模沉浸式远程呈现内容的无缝表示、处理和交付。创新的设计、严格的验证和测试练习旨在满足确定的关键技术需求,如低延迟通信、高带宽需求和复杂的内容编码/实时呈现任务。业界领先的SPIRIT联盟将建立在现有的TRL4应用平台和项目合作伙伴开发的网络基础设施上,旨在解决关键技术挑战,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项目将把创新重点放在网络层、传输层、应用/内容层技术以及安全和隐私机制上,以促进远程呈现应用程序的大规模操作。该项目团队将在德国和英国的两个地理站点上开发一个完全分布式、相互连接的测试基础设施,允许在真实的互联网环境中大规模测试异构远程呈现应用程序。网络基础设施将承载基于WebRTC和低延迟DASH两种主流应用环境。除了项目指定的用例场景之外,项目团队还将通过参与FSTP测试各种额外的用例,这些用例涵盖了异构垂直部门。
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 largescale 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: