BRIGE: Simultaneous Modeling and Calibration for Environmental Sensor Data

BRIGE：环境传感器数据的同步建模和校准

• 批准号: 1342121
• 负责人: Laura Balzano
• 金额: $ 17.5万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1342121&HistoricalAwards=false
• 关键词: BRIGE; Simultaneous; Modeling; Calibration; Environmental

ECCS-1342121Balzano, LauraUniversity of MichiganBRIGE: Simultaneous Modeling and Calibration for Environmental Sensor DataABSTRACTIntellectual Merit: This BRIGE project is aimed at the investigation of signal processing theory and methods for blindly calibrating sensors on a massive scale, after they are deployed, and as their calibrations change over time. The massive deployment of sensors is exciting for the prospect of what may be learned from the data; a critical challenge in using those data is knowing their quality and reliability. In deployments of even a hundred sensors, it becomes infeasible to hand-calibrate each one in order to maintain confidence in the sensor output. Thus blind calibration, i.e. calibration without the need for controlled stimulus or high fidelity ground-truth data, is of critical importance. This proposal focuses both on theory of blind calibration and the concrete practical application to air quality sensing. Realistic sensing environments are considered, where phenomena are non-stationary, and data are streaming, with corruptions and missing values. A calibration dataset will be collected and shared with the community. The two major theoretical contributions will be (1) extending theory of blind calibration to models which capture the great variety exhibited by environmental phenomena, and (2) online modeling for the practical scenario where the phenomenon of interest is non-stationary. Broader Impacts: Environmental sensing is an important contemporary application of statistical signal processing that attracts a great deal of interest from people with diverse backgrounds. This application gets people involved in the technology, the climate, and their local community; therefore it has potential to truly broaden participation in engineering. The syllabus for Digital Signal Processing at the University of Michigan, a central course in all signal processing curricula, will be expanded to include this application and useful fundamentals like spatial models, auto-regressive models, and matrix decomposition. Additionally, signal processing for environmental sensing in particular has the potential to attract students who may typically go into science or environmental policy, but who have a strong interest in mathematics as well. Interaction with the Marian Sarah Parker Scholars program at Michigan will expose outstanding young female scientists to the wide variety of possibilities with signal processing. Beyond the classroom, exploring one's environment using sensing has the potential to make a very positive social impact. The projects developed for the Parker scholars and other Michigan educational programs will build an infrastructure for future interactions with all grade levels and the greater public.

ECCS-1342121Balzano，Laura 密歇根大学BRIGE：环境传感器数据的同步建模和校准摘要智力价值：该 BRIGE 项目旨在研究大规模盲目校准传感器的信号处理理论和方法，在传感器部署后，并作为其校准随着时间的推移而改变。传感器的大规模部署对于从数据中获取信息的前景来说是令人兴奋的；使用这些数据的一个关键挑战是了解它们的质量和可靠性。即使部署一百个传感器，也无法手动校准每个传感器以保持对传感器输出的信心。因此，盲校准，即不需要受控刺激或高保真地面实况数据的校准至关重要。该提案重点关注盲标定理论和空气质量传感的具体实际应用。考虑现实的传感环境，其中现象是非平稳的，数据是流动的，存在损坏和缺失值。将收集校准数据集并与社区共享。两个主要的理论贡献将是（1）将盲校准理论扩展到捕获环境现象所表现出的多样性的模型，以及（2）针对感兴趣的现象非平稳的实际场景进行在线建模。更广泛的影响：环境传感是统计信号处理的重要当代应用，吸引了不同背景的人们的极大兴趣。该应用程序让人们参与技术、气候和当地社区；因此，它有潜力真正扩​​大工程领域的参与。密歇根大学的数字信号处理教学大纲是所有信号处理课程的核心课程，将进行扩展以包括该应用程序和有用的基础知识，例如空间模型、自回归模型和矩阵分解。此外，环境传感的信号处理尤其有可能吸引那些通常从事科学或环境政策但对数学也有浓厚兴趣的学生。与密歇根州玛丽安·莎拉·帕克学者计划的互动将使杰出的年轻女科学家接触到信号处理的各种可能性。在课堂之外，利用传感探索环境有可能产生非常积极的社会影响。为帕克学者和其他密歇根教育项目开发的项目将为未来与所有年级和广大公众的互动建立基础设施。