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Development of low-cost sensor suite for fog monitoring

Development of low-cost sensor suite for fog monitoring
开发用于雾监测的低成本传感器套件
批准号:
535880-2018
负责人:
Lee, Regina
金额:
$1.82万
依托单位:
依托单位国家:
加拿大
项目类别:
Engage Grants Program
财政年份:
2018
资助国家:
加拿大
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31

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中文摘要
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英文摘要
Wood Environment and Infrastructure Solutions (referred to as 'Wood Environment' hereinafter) is currently investigating improved technologies to forecast fog in Atlantic Canada and offshore areas including Grand Banks in collaboration with various academic, government and industry partners. Fog forecasting is not a simple problem. In the past, most of the fog prediction heavily relied on theoretical mathematical models with limited field measurement. More recently, efforts are being made to improve the accuracy and responsiveness using extensive real-time, large network of wireless sensors. A team of researchers at the University of Notre Dame program recently embarked on a new initiative to understand the fundamentals and microphysics behind fog using field measurement onboard research vehicles. ** Our research team at York University is proposing to extend our current engineering research to design and prototype a low-cost, integrated, reconfigurable sensor suite to complement Wood Environment's ongoing effort to measure and predict fog. In the past 11 years, our research has been focused on designing low-cost solution for space-borne and air-borne measurement units to enhance real-time monitoring of various atmospheric constituents. Our research expertise is mostly in the nanosatellite technologies where commercial electronics are characterized for the space environment. Building on this experience we are confident that we can offer innovative and reliable engineering solution for Wood Environment that can complement their current research on fog-prediction.** Through the proposed Engage program, we will design, prototype and characterize a sensor suite that will form a basis for network of sensors - a natural step in providing measurement techniques over a large area over long period of time. The proposed research is in direct support of Wood Environment's current efforts to improve the accuracy of their fog prediction model.****
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