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dc.creatorMonte, Gustavo Eduardo
dc.date.accessioned2023-12-06T21:01:06Z
dc.date.available2023-12-06T21:01:06Z
dc.date.issued2008-11-13
dc.identifier.isbn978-1-4244-1767-4
dc.identifier.urihttp://hdl.handle.net/20.500.12272/9128
dc.description.abstractThis paper presents a signal processing technique that employs oversampling and identification of important samples to determine signal behavior and tendency. Sensor signal windows of random lengths are vectorized and classified to fit into only eight predefined types, and in conjunction with time indexes vectors, they can predict future values, steady state value and an estimation of the sensor signal function. The techniques developed allow the representation of any class of sensor signal for further analysis. The computational cost is quite low so they can be implemented in real time into smart sensors with low cost microcontrollers. Therefore, it is also an ideal technique to preprocess the sensor signal to mark regions of interest to more sophisticated processes.es_ES
dc.formatpdfes_ES
dc.language.isospaes_ES
dc.rightsopenAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.rights.uriAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.subjectSensor signal preprocessing techniques for analysis and predictiones_ES
dc.titleSensor signal preprocessing techniques for analysis and predictiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.affiliationFil: Monte Gustavo, Eduardo. Universidad Tecnológica Nacional. Facultad regional del Neuquen; Argentina.es_ES
dc.description.peerreviewedPeer Reviewedes_ES
dc.type.versionacceptedVersiones_ES
dc.rights.useCreative Commoses_ES


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