Zebra mussels’ behaviour detection,extraction and classification using wavelets and kernel methods |
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| Affiliation: | 1. Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain;2. Cantabrian Basin Authority, Spanish Ministry of Agriculture, Food and Environment, 33071 Oviedo, Spain;3. Department of Electrical Engineering, Campus de Viesques, University of Oviedo, 33204 Gijón, Spain |
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| Abstract: | This paper concerns the detection, feature extraction and classification of behaviours of Dreissena polymorpha. A new algorithm based on wavelets and kernel methods that detects relevant events in the collected data is presented. This algorithm allows us to extract elementary events from the behaviour of a living organism. Moreover, we propose an efficient framework for automatic classification to separate the control and stressful conditions. |
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| Keywords: | Wavelet Zebra mussel Feature extraction Classification BEWS |
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