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Analysis of Similarity Measurements in CBIR Using Clustered Tamura Features for Biomedical Images Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Soft Sensor Network for Environmental Monitoring

Authors : Umberto Maniscalco, Giovanni Pilato, Filippo Vella

Published in: Intelligent Interactive Multimedia Systems and Services 2016

Publisher: Springer International Publishing

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Abstract

This paper shows the application of a soft sensor network for the detection of meteorological events. A set of hard (real) sensor are placed in a territory, where they measure heterogeneous quantities. Starting from their measurements, a soft sensor network provides useful information coming from the data. In this contribution we show how prediction and validation of data can be done through machine learning approach by collecting data from the historical series. Furthermore, we show how the cluster based on correlation analysis among the data achieved by the sensors can be sensibly different from the ones simply drawn on geographical distance.

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previous chapter A Multiple Data Stream Management Framework for Ambient Assisted Living Emulation
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Footnotes
2
In this manuscript we use the term “cluster” to indicate the set of hardware sensors used to forecast the measures of another hardware sensor.
 
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Metadata
Title
Soft Sensor Network for Environmental Monitoring
Authors
Umberto Maniscalco
Giovanni Pilato
Filippo Vella
Copyright Year
2016
Book
Intelligent Interactive Multimedia Systems and Services 2016

Print ISBN: 978-3-319-39344-5

Electronic ISBN: 978-3-319-39345-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-39345-2_63

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