CIF: Small: Task-Cognizant Sparse Sensing for Inference

CIF：小型：用于推理的任务认知稀疏感知

• 批准号: 1527396
• 负责人: Xiang Chen
• 金额: $ 40万
• 依托单位: George Mason University
• 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
• 起止时间: 2016-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527396&HistoricalAwards=false
• 关键词: CIF; Small; Task; Cognizant; Sparse

As big data applications continue to grow in size and number, it is of crucial importance to deal only with measurements that are informative for a specific inference task in order to limit the required sensing cost, as well as the related costs of storing or communicating the data. To this end, this project develops a new paradigm of compressive sensing for random processes, where the signal compression and reconstruction strategies are designed to be task-cognizant, hinging on useful signal statistics rather than the original random signals. This research leads to major sensing energy savings, and can benefit a plethora of energy-efficient sensing applications such as location-aware services, weather monitoring, spectrum monitoring, and radio astronomy.The main goal of this project is to significantly reduce the cost of sensing as well as the related storage and communication requirements by offering innovative sensing approaches tailored to the inference task of interest. Different from existing compressive sampling, the proposed research leverages the inherent structures of signal statistical information in order to enable reliable inference from sparsely sampled data, rather than solely relying on signal sparsity to enable compression. As such, a new approach to sparse sensing is introduced for random processes, which reveals the fundamental limits of compression in relation to the degrees of freedom inherent to the underlying statistical structure, even in the absence of signal sparsity. Efficient inference techniques and deterministic compressive sampler designs are put forth to affect major savings in the sensing costs while achieving the desired inference quality. These basic results open up opportunities for efficient handling of data-intensive sensing applications in which inference from random processes is of foremost importance.

随着大数据应用程序的规模和数量继续增长，仅处理针对特定推理任务的测量值，以限制所需的感应成本以及存储或传达数据的相关成本至关重要。为此，该项目为随机过程开发了一种新的压缩感应范式，在该过程中，信号压缩和重建策略的设计为任务认知，呈现有用的信号统计信息，而不是原始的随机信号。这项研究可导致大量的感应节能节省，并可以使大量节能的感应应用（例如位置感知服务，天气监测，频谱监测和射电天文学）等。该项目的主要目的是显着降低感应成本，以降低相关的存储和通信需求，以及提供创新的感应需求，以实现量身定制的兴趣，以实现量身定制的兴趣。与现有的压缩抽样不同，拟议的研究利用了信号统计信息的固有结构，以便从稀疏采样的数据中可靠推断，而不是仅仅依靠信号稀少度来启用压缩。因此，为随机过程引入了一种新的稀疏感测方法，该方法揭示了与基本统计结构固有的自由度相关的基本压缩限制，即使在没有信号稀少度的情况下也是如此。提出有效的推理技术和确定性的压缩采样器设计，以影响感应成本的主要节省，同时达到所需的推理质量。这些基本结果为有效处理数据密集型传感应用程序提供了机会，其中从随机过程中推断至关重要。