Unsupervised learning for spectral data analysis as a novel sensor for identifying rodent infestation in urban environments
Unsupervised learning for spectral data analysis as a novel sensor for identifying rodent infestation in urban environments
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DOI：
10.1109/icsens.2017.8234207
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Omar Costilla-Reyes;Z. Coldrick;B. Grieve
中科院分区：
文献类型：
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作者：
Omar Costilla-Reyes;Z. Coldrick;B. Grieve
啮齿动物的尿液会发出荧光。本研究旨在利用光谱成像数据通过发色团检测啮齿动物活动。我们引入无监督学习技术，直接从光谱数据的啮齿动物尿液样本的分类和聚类。我们将啮齿动物的尿液与其他化合物（如人类尿液和咖啡）进行分类和比较，以验证我们的分析和模型。为了便于可视化的化合物的光谱数据，我们使用多种技术的光谱聚类可视化。
Rodent urine is known to fluoresce. This research aims to use spectral imaging data to detect rodent activity via chromophores. We introduce unsupervised learning techniques for classification and clustering of rodent urine samples from the spectral data directly. We classify and compare the rodent urine against additional chemical compounds such as human urine and coffee to validate our analysis and models. In order to facilitate the visualisation of the chemical compound's spectral data, we use manifold techniques for spectral clustering visualisation.