BRIGE: Simultaneous Modeling and Calibration for Environmental Sensor Data

bridge：环境传感器数据的同步建模与校准[摘要]智力优势：该bridge项目旨在研究用于大规模、部署后和随时间变化的传感器盲目校准的信号处理理论和方法。传感器的大规模部署令人兴奋，因为我们可以从数据中了解到什么；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。在部署100个传感器时，为了保持对传感器输出的信心，手动校准每个传感器变得不可行。因此，盲校准，即不需要受控刺激或高保真真实数据的校准，是至关重要的。本文重点介绍了盲校准的理论和在空气质量传感中的具体实际应用。考虑了现实的传感环境，其中现象是非平稳的，数据是流的，有损坏和缺失值。将收集校准数据集并与社会分享。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)对感兴趣的现象是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，吸引了来自不同背景的人们的极大兴趣。这个应用程序让人们参与到技术、气候和当地社区中来；因此，它有潜力真正扩大工程领域的参与。作为所有信号处理课程的中心课程，密歇根大学的《数字信号处理》的教学大纲将会扩展到包括这一应用和一些有用的基础知识，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会进入科学或环境政策领域的学生，但他们对数学也有浓厚的兴趣。与密歇根大学Marian Sarah Parker学者项目的互动将使杰出的年轻女性科学家接触到信号处理的各种可能性。在课堂之外，利用感知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更大的公众互动建立一个基础设施。

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.