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The advent of fiber sensor has opened up a plenty of opportunities to perimeter security. Damaging activities can cause fiber vibrate whose signals will be collected as data source for detection and identification. In this paper we formulate identifying damaging activities by vibration signals as a classification problem. We design features that characterize vibration signals by combining both the statistic and time-frequency information. In addition, a novel multiclass classification tree of Support Vector Machine (SVM) is introduced to recognize vibration signals. Experimental results show that the proposed feature extraction scheme and classification method yields a high recognition rate of 94.6% for nine different kinds of damaging activities, much better than other results reported ever since.

Water is key to the evolution of the Earth's mantle because it could significantly affect the physical properties of the rocks in the Earth's interior, including viscosity, melting temperature, electrical conductivity, diffusion rate, and phase boundary (e.g.