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- W2066392362 abstract "In this paper, we show that objects of interest, like pipes and cylinders, reminiscent of guns and rifles, can be classified based on their acoustic vibration signatures. That is, if the acoustic returns are measurable, one can indeed classify objects based on the physical principle of resonance. We consider classifiers which are both training independent and those that are training dependent. The statistical classifier belongs to the former category, whereas, the neural network classifier belongs to the latter. Comparisons between the two approaches are shown to render both classifiers as suitable classifiers with small classification errors. We use the probability of correct classification as a measure of performance. We demonstrate experimentally that unique features for classification are the resonant frequencies. The measured data are obtained by exciting mechanical vibrations in pipes of different lengths and of different metals, for example, copper, aluminum, and steel, and the measuring of the acoustic returns, using a simple microphone. Autoregressive modeling is applied to the data to extract the respective object features, namely, the vibration frequencies and damping values. We consider two classification problems, 1) Classifying objects comprised of different metals, and 2) Classifying objects of the same material, but made of different lengths. It is shown that classification performance can be improved by incorporating additional features such as the damping coefficients." @default.
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- W2066392362 date "2009-05-01" @default.
- W2066392362 modified "2023-10-02" @default.
- W2066392362 title "Detection and classification of indoor objects using acoustic excitations" @default.
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- W2066392362 doi "https://doi.org/10.1117/12.819124" @default.
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