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.

爆炸物的复杂程度和类型的迅速增加使得有必要开发传感系统，该传感系统战略性地联合收割机结合多种超灵敏检测技术，以显著提高检测的灵敏度和概率。通过多学科的合作努力，研究人员建议开发和集成基于不同传感原理的新型传感器阵列，并采用多传感器数据融合（MSDF）技术，以高精度检测和预测爆炸物威胁。智力优点：拟议的方法解决了一些基本的挑战，在传感爆炸物使用几种策略。首先，通过集成同时操作的多种类型的检测器[荧光聚合物纳米纤维传感器、纳米线传感器、表面声波传感器（SAW）]，检测概率显著提高。其次，这些传感器中的每一个都将被专门设计为最大化分析物-受体相互作用，从而提高灵敏度。超高表面积的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-受体相互作用而实现极高的灵敏度。具有化学选择性聚合物涂层的SAW传感器将提供互补的检测能力。最后，为了分析由多个传感系统获得的数据，我们计划开发基于认知的MSDF模型，并执行可能无法单独从单个传感系统进行的推断。这种方法将通过识别重合来减少与由各个感测系统收集的数据的解释相关联的不确定性，从而使整个检测系统非常精确，并且显著降低假警报的发生率。更广泛的影响：一些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，参加会议，与研究人员和行业专业人士互动，以及涉及高中学生和教师的K-12外展（通过马萨诸塞大学K-12外展计划，夏季科学机会和洛厄尔地区物理联盟）。 该项目将开发的新型传感技术将显着增加现有的研究和教育基础设施。与工业界的伙伴关系（福斯特-米勒公司，和林登光子公司）将促进技术转让和商业化。该项目的成果将广泛传播，以使广大受众受益，提高他们对科学和技术的理解，并提高他们对安全和安保问题的认识。