The need for the use of sensor networks in ever more efficient manner drives research methods for better information management. It would be useful to decrease the amount of managed data. Often, we are interested in few noteworthy information of a signal (for example, period, amplitude, time constant, steady state value, and so on) not in the whole waveform. The idea is to take less data, but acquire the same information. In a highly oversampled signal, each single sample does not carry a lot of information. From this point, two different algorithms are compared, in which only few samples are stored or transferred. This paper describes these two algorithms: 1) the first one is the segmentation and labeling algorithm, also proposed for the definition of the new standard of the IEEE 1451 and 2) the second one is based on compressive sensing theory. These two algorithms are compared, the simulations results are shown, and it is discussed which case could be more suitable for.
A comparison between sensor signal preprocessing techniques
Paciello V.
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2015-01-01
Abstract
The need for the use of sensor networks in ever more efficient manner drives research methods for better information management. It would be useful to decrease the amount of managed data. Often, we are interested in few noteworthy information of a signal (for example, period, amplitude, time constant, steady state value, and so on) not in the whole waveform. The idea is to take less data, but acquire the same information. In a highly oversampled signal, each single sample does not carry a lot of information. From this point, two different algorithms are compared, in which only few samples are stored or transferred. This paper describes these two algorithms: 1) the first one is the segmentation and labeling algorithm, also proposed for the definition of the new standard of the IEEE 1451 and 2) the second one is based on compressive sensing theory. These two algorithms are compared, the simulations results are shown, and it is discussed which case could be more suitable for.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
