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I-Corps: Software-Defined Distributed Wireless Charging

I-Corps: Software-Defined Distributed Wireless Charging
I-Corps:软件定义的分布式无线充电
批准号:
1644598
负责人:
Kaushik Chowdhury
金额:
$5.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-08-01 至 2017-01-31

项目摘要

项目成果

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中文摘要
翻译
I-Corps项目的更广泛影响/商业潜力是促进消费设备的持续运行,并实现不间断的安全关键应用,如老年人护理、病人和结构健康监测。该项目可以通过消除对低功率电子设备频繁充电的担忧,潜在地提高生活质量。通过对小尺寸传感器进行无线充电,还可以减少与电池更换相关的费用和环境问题。该项目将产生一个广泛的客户发现过程,通过该过程,将从医疗保健、可穿戴设备、汽车行业和智能家居/办公室等行业中筛选出具有高影响力的行业。它将通过解决网络的关键能源需求,在新兴的物联网范式背景下促进广泛应用场景的创新。I-Corps项目定义了一种新的网络架构,能够通过结合硬件和软件平台进行分布式无线射频充电。所提出的协同波束形成技术允许无线充电速率的指数增长,这是由于建设性干扰和使用最小接收器反馈的能量信号的实时适应。该项目利用软件定义的分布式能量波束形成平面,以确保用于选择高充电速率的能量发射器的各种参数的连续反馈。最后,它评估整个网络的能源需求,并能够主动安排能源广播。虽然最近研究了来自单个充电器的全向和基于mimo的波束形成,但I-Corps项目试图探索来自多个空间能量发射器(et)的分布式和并发能量传输的创新应用。
英文摘要
The broader impact/commercial potential of this I-Corps project is to facilitate the continuous operation of consumer devices as well as enable uninterrupted safety-critical applications such as elderly care, patient and structural health monitoring. The project can potentially enhance the quality of life by eliminating concerns of frequent charging of low-power electronic devices. By wireless charging of small form factor sensors, the expenses and environmental concerns associated with battery-replacements can also be mitigated. This project will result in an extensive customer discover process, through which high-impact sectors will be shortlisted from the sectors of healthcare, wearables, automative industry, and smart homes/offices. It will spur innovations across the wide spectrum of applications scenarios in context of the emerging Internet of Things paradigm by addressing the critical energy needs of the networkThis I-Corps project defines a new network architecture that is capable of distributed wireless RF-based charging by combining both hardware and software platforms. The proposed technique of collaborative beamforming allows an exponential increase in wireless charging rates due to constructive interference and real-time adaptation of energy signals using minimal receiver feedback. This project leverages a software-defined distributed energy beamforming plane to ensure continuous feedback used for selecting various parameters at the energy transmitters that give high charging rates. Finally, it assesses energy needs over the network and is able to schedule energy broadcasts proactively. While omni-directional and MIMO-based beamforming from a single charger have been investigated recently, this I-Corps project attempts to explore innovative applications of distributed and concurrent energy transfers from multiple spatially-located energy transmitters (ETs).
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NSF-SNSF: Rapid Beamforming for Massive MIMO using Machine Learning on RF-only and Multi-modal Sensor Data
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  • 资助金额:
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