Compressive Sensing Based Inference in Distributed Networks

分布式网络中基于压缩感知的推理

基本信息

  • 批准号:
    1307775
  • 负责人:
  • 金额:
    $ 32.49万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2013
  • 资助国家:
    美国
  • 起止时间:
    2013-08-15 至 2016-07-31
  • 项目状态:
    已结题

项目摘要

ObjectiveThe objective of this program is to develop efficient algorithms and obtain performance boundsfor decentralized inference in the reduced dimensional subspace obtained via Compressive Sens-ing based measurement schemes in resource-constrained distributed networks.Intellectual MeritThe intellectual merit of the proposed program is that it will advance the theory of CompressiveSensing to resource-constrained distributed systems with multiple measurement vectors. Whilethere is a considerable amount of work in the literature for Compressive Sensing based signalprocessing with a single measurement vector, its extension to multiple measurement vectors inresource-constrained distributed systems is still at its infancy. The transformative nature of theproposed research lies in its potential to identify and solve decentralized inference problems basedon low dimensional sampling schemes exploiting structured models of the multiple measurementvectors collected at distributed nodes.Broader ImpactsThe proposed research will have direct impact on advancing the theory and practice, as well ason education and training. The broader impacts are: 1) Development of efficient low dimensional signal processing techniques for decentralized inference in resource-constrained distributed networks exploiting structured properties (sparsity as well as models that go beyond the simple sparsity model) of large data sets. 2) Efficient design approaches for distributed inference networks with a wide variety of applications including environmental monitoring, remote sensing, radar networks, cognitiveradio networks and bio-medical applications with high dimensional data. 3) Development of graduate level courses on signal processing and inference for large data sets. Mentoring of graduate student in research methods.
该计划的目标是开发有效的算法,并获得性能boundsfor分散推理的降维子空间中获得通过压缩感知的测量方案在资源受限的分布式networks.Intellectual MeritThe提出的计划的智力优点是,它将推进压缩感知理论的资源受限的分布式系统与多个测量向量。虽然在文献中有相当多的工作基于压缩感知的信号处理与一个单一的测量向量,其扩展到多个测量向量在资源受限的分布式系统仍处于起步阶段。所提出的研究的变革性在于它的潜力,以确定和解决分散的推理问题的基础上,低维抽样方案,利用结构化模型的多个测量向量收集在分布式节点。更广泛的影响所提出的研究将有直接的影响,推进理论和实践,以及ason教育和培训。更广泛的影响是:1)开发有效的低维信号处理技术,用于在资源受限的分布式网络中利用大型数据集的结构化属性(稀疏性以及超越简单稀疏性模型的模型)进行分散式推理。2)分布式推理网络的有效设计方法具有广泛的应用,包括环境监测,遥感,雷达网络,认知无线电网络和生物医学应用与高维数据。3)开发关于大型数据集的信号处理和推理的研究生课程。指导研究生的研究方法。

项目成果

期刊论文数量(0)
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会议论文数量(0)
专利数量(0)

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Pramod Varshney其他文献

Pramod Varshney的其他文献

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{{ truncateString('Pramod Varshney', 18)}}的其他基金

SFC Award (Indian Currency) for a Guest Scientist at the Indian Institute of Technology Delhi, in Communication and Computer Engineering
印度德里印度理工学院通信与计算机工程客座科学家荣获 SFC 奖(印度货币)
  • 批准号:
    8407317
  • 财政年份:
    1984
  • 资助金额:
    $ 32.49万
  • 项目类别:
    Standard Grant
Sfc Travel Support (In Indian Currency) to Attend the International Symposium on Microwaves and Communications, I.I.T. Kharagpur, India, Dec. 1981
证监会出差支持(以印度货币计)出席 I.I.T. 微波与通信国际研讨会
  • 批准号:
    8120193
  • 财政年份:
    1981
  • 资助金额:
    $ 32.49万
  • 项目类别:
    Standard Grant

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    60776795
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