EXP-LA: High Precision Detection and Prediction of Explosives Based on Multiple Sensing Systems and Data Fusion

EXP-LA：基于多传感系统和数据融合的爆炸物高精度检测和预测

• 批准号: 0731125
• 负责人: Pradeep Kurup
• 金额: --
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0731125&HistoricalAwards=false
• 关键词: EXP; LA; Precision; Detection; Prediction

The rapid increase in the level of sophistication and types of explosives necessitate the development of sensing systems that strategically combine multiple, ultra-sensitive detection technologies to significantly improve sensitivity and probability of detection. Through a multidisciplinary collaborative effort the researchers propose to develop and integrate novel sensor arrays based on different sensing principles with multi sensor data fusion (MSDF) techniques for detecting and predicting explosives threat with high precision. Intellectual Merit:The proposed methodology addresses some of the fundamental challenges in sensing of explosives using several strategies. First, the probability of detection is significantly enhanced by integration of multiple types of detectors [fluorescent polymer nanofibrous sensors, nanowire sensors, surface acoustic wave sensors (SAW)] that operate simultaneously. Secondly, each of these sensors will be specifically designed to maximize analyte-receptor interaction, thus improving the sensitivity. Ultra-high surface area polymeric nanofibers and metal oxide nanowires will achieve extremely high sensitivity due to enhanced analyte-receptor interaction. SAW sensors with chemoselective polymer coatings will provide complementary detection capabilities. Finally, for analyzing data acquired by multiple sensing systems we plan to develop cognitive-based MSDF models and perform inferences that may not be possible from a single sensing system alone. This approach will reduce the uncertainties associated with the interpretation of data gathered by the individual sensing systems by identifying coincidences, thus making the overall detection system extremely precise, and significantly lower the incidence of false alarms. Broader Impacts:Several graduate and undergraduate students will be actively involved in the multidisciplinary tasks of the proposed research. Educational activities also include the addition of new modules into existing courses, participation in conferences, interaction with researchers and industry professionals, and K-12 outreach involving high school students and teachers (through the UMass K-12 outreach program, Summer Opportunities in Sciences, and the Lowell Regional Physics Alliance). The novel sensing technologies that will be developed in this project will significantly add to the existing infrastructure for research and education. Partnerships with industry (Foster-Miller Inc., and Linden Photonics Inc.) will facilitate technology transfer and commercialization. The results from this project will be broadly disseminated to benefit a large audience, enhancing their scientific and technological understanding, and increasing their awareness on safety and security issues.

复杂水平和爆炸物类型的迅速增加，需要开发传感系统，这些传感系统从策略上结合了多种超敏感的检测技术，以显着提高敏感性和检测概率。通过一项多学科的协作努力，研究人员建议根据不同的传感原理与多传感器数据融合（MSDF）技术开发和整合新的传感器阵列，以高精度地检测和预测爆炸物威胁。智力优点：拟议的方法可以解决使用几种策略来感知炸药的一些基本挑战。首先，通过多种类型的检测器的整合[荧光聚合物纳米传感器，纳米线传感器，表面声波传感器（SAW）]，可显着提高检测的可能性。其次，这些传感器中的每一个都将专门设计以最大化分析物 - 受体相互作用，从而提高灵敏度。超高的表面积聚合物纳米纤维和金属氧化物纳米线将由于增强的分析物受体相互作用而达到极高的灵敏度。具有化学选择性聚合物涂层的锯传感器将提供互补的检测功能。最后，为了分析多个感应系统获取的数据，我们计划开发基于认知的MSDF模型，并执行仅凭单个感应系统而言可能无法进行的推论。这种方法将减少与单个传感系统收集的数据的解释相关的不确定性，从而使整体检测系统非常精确，并大大降低了错误警报的发生率。更广泛的影响：几名研究生和本科生将积极参与拟议研究的多学科任务。教育活动还包括在现有课程中添加新模块，参与会议，与研究人员和行业专业人员的互动，以及涉及高中生和老师的K-12外展（通过UMass K-12外展计划，科学夏季机会，以及洛厄尔地区的夏季机会以及洛厄尔地区物理学联盟）。 该项目将开发的新型传感技术将大大增加研究和教育的基础设施。与工业公司（Foster-Miller Inc.和Linden Photonics Inc.）的合作伙伴关系将促进技术转移和商业化。该项目的结果将被广泛传播，以使大量受众受益，增强他们的科学和技术理解，并提高他们对安全和安全问题的认识。