Application of Wavelets and Kernel Methods to Detection and Extraction of Behaviours of Freshwater Mussels

摘要: Some species of mussels are well-known bioindicators and may be used to create a Biological Early Warning System. Such systems use long-term observations activity for monitoring purposes. Yet, many these based on statistical methods do not all the potential that stays behind data derived from observations. In paper we propose an algorithm wavelets kernel detect behaviour events in collected data. We present our together with discussion influence various parameters received results. The study describes obtaining pre-processing raw feature extraction algorithm. Other papers which applied mathematical apparatus Systems much simpler their effectiveness was questionable. verify results using system prepared tags specified events. This leads us classification creating Dreissena polymorpha dictionary Results preliminary experiments show, such formulation problem, allows extracting relevant information given signal yields effective solution considered problem.