Stephan da Costa Ribeiro

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This paper addresses the challenge of energy efficiently acquiring data on mobile platforms in order to recognize human motion. The method presented is based on the theory of compressive sensing and experimental results show that a significant reduction of the number of needed samples, in comparison to common methods, is possible. There are many situations(More)
In selected scenarios, sensor data capturing with mobile devices can be separated from the data processing step. In these cases, Compressive Sensing allows a significant reduction of the average sampling rate below the Nyquist rate, if the signal has a sparse frequency representation. This can be motivated in order to increase the energy efficiency of the(More)
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