Raphael Ventura

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Noise pollution is a significant problem in cities due to its various effects on health, but the modeling of noise data and the generation of accurate noise pollution maps suffer from the high cost and restricted scale of sensing performed using static municipal sensors. In this paper, we present our approach for augmenting municipally sensed data using(More)
Mobile phone sensing contributes to changing the way we approach science: massive amount of data is being contributed across places and time, and paves the way for advanced analyses of numerous phenomena at an unprecedented scale. Still, despite the extensive research work on enabling resource-efficient mobile phone sensing with a very-large crowd, key(More)
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