CAREER: A Generalized Compressive Sensing Approach to Data Acquisition and Ad-Hoc Sensor Networking

职业：数据采集和自组织传感器网络的通用压缩传感方法

• 批准号: 1056065
• 负责人: Nazanin Rahnavard
• 金额: $ 40万
• 依托单位: Oklahoma State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-01 至 2014-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1056065&HistoricalAwards=false
• 关键词: CAREER; Generalized; Compressive; Sensing; Approach

The objective of this project is to advance the field of compressive sensing and broaden its scope into a multitude of new applications, such as ad hoc networking, by significantly enhancing the efficiency through the utilization of problem-specific signals and systems properties. The proposed approach is to design a novel rateless-coding-inspired generalized compressive sensing (GCS) framework and to integrate it into different stages of sensing, information processing, and cross-layer designs in the network design processes.Intellectual Merit: The proposed research bridges the gap between the advanced theoretical research on modern coding and compressive sensing, as to significantly enhance the efficiency of data acquisition and ad hoc networking. GCS offers the flexibility of exploiting problem-specific properties, such as non-uniform sparsity and non-uniform importance of data in the sensing and recovery phases. This is expected to result in a significant reduction in sensing and communication costs. Through cross-layer design of GCS and underlying network protocols, efficient data acquisition and distributed data storage problems in sensor networks will be investigated.Broader Impacts: This proposal is expected to have a significant direct impact on advancing the theory and practice of compressive sensing and an indirect impact on many areas in which compressive sensing has potential application, such as medical imaging, hyper-spectral imaging, and bio-informatics. Furthermore, it expected to have an impact on increasing the participation of under-represented students in the PI's research group, as well as in promoting engineering education among high school students, in integration of research and education through curriculum development/revision, in training graduate students, and in promoting multidisciplinary research and collaboration through initiating ECE Seminar Day Program at OSU.

该项目的目标是推进压缩感知领域，并将其范围扩大到许多新的应用，如ad hoc网络，通过利用特定问题的信号和系统属性来显着提高效率。所提出的方法是设计一种新颖的无码率编码启发的广义压缩感知（GCS）框架，并将其集成到网络设计过程中的感知、信息处理和跨层设计的不同阶段。所提出的研究弥合了现代编码和压缩感知的先进理论研究之间的差距，以显著提高数据采集和特设联网的效率。GCS提供了利用特定问题属性的灵活性，例如在感测和恢复阶段数据的非均匀稀疏性和非均匀重要性。预计这将大大降低传感和通信成本。通过GCS和底层网络协议的跨层设计，研究传感器网络中的高效数据采集和分布式数据存储问题。预计该提案将对推进压缩感知的理论和实践产生重大的直接影响，并对压缩感知具有潜在应用的许多领域产生间接影响，例如医学成像，高光谱成像，和生物信息学。此外，它预计将对增加PI研究小组中代表性不足的学生的参与产生影响，并促进高中生的工程教育，通过课程开发/修订整合研究与教育，培训研究生，以及通过在俄勒冈州立大学发起欧洲经济委员会研讨会日计划来促进多学科研究和合作。