000			03212nam a2200433 i 4500
999			_c386930 _d386930
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003			ES-MaUEC
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007			cr nn 008mamaa
008			220601s2018 sz | o |||| 0|eng d
020			_a9783031015267
024	7		_a10.1007/978-3-031-01526-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7872.D48 _b2018 EB
100	1		_aStanley, Michael _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686138
245	1	0	_aSensor Analysis for the Internet of Things _cby Michael Stanley, Jongmin Lee
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XXIII, 113 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Algorithms and Software in Engineering _x1938-1735
505	0		_aList of Figures -- List of Tables -- Preface -- Acknowledgments -- Nomenclature -- Introduction -- Sensors -- Sensor Fusion -- Machine Learning for Sensor Data -- IoT Sensor Applications -- Concluding Remarks and Summary -- Bibliography -- Authors' Biographies.
520			_aWhile it may be attractive to view sensors as simple transducers which convert physical quantities into electrical signals, the truth of the matter is more complex. The engineer should have a proper understanding of the physics involved in the conversion process, including interactions with other measurable quantities. A deep understanding of these interactions can be leveraged to apply sensor fusion techniques to minimize noise and/or extract additional information from sensor signals. Advances in microcontroller and MEMS manufacturing, along with improved internet connectivity, have enabled cost-effective wearable and Internet of Things sensor applications. At the same time, machine learning techniques have gone mainstream, so that those same applications can now be more intelligent than ever before. This book explores these topics in the context of a small set of sensor types. We provide some basic understanding of sensor operation for accelerometers, magnetometers, gyroscopes, and pressure sensors. We show how information from these can be fused to provide estimates of orientation. Then we explore the topics of machine learning and sensor data analytics.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9441179 _aRedes de sensores inalámbricas
650		7	_2embne _9483083 _aInternet de los objetos
700	1		_aLee, Jongmin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686139
776	0	8	_iPrinted edition: _z9783031000140
776	0	8	_iPrinted edition: _z9783031003981
776	0	8	_iPrinted edition: _z9783031026546
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01526-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eb _zSI