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008			220128s2022 sz | s |||| 0|eng d
020			_a9783030909031
024	7		_a10.1007/978-3-030-90903-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5105.8857 _b2022
100	1		_aVelasco-Montero, Delia _eautor _0(orcid)0000-0003-3487-1712 _1https://orcid.org/0000-0003-3487-1712 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9683393
245	1	0	_aVisual Inference for IoT Systems : _bA Practical Approach _cby Delia Velasco-Montero, Jorge Fernández-Berni, Angel Rodríguez-Vázquez
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIII, 159 páginas) _b59 ilustraciones, 57 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
505	0		_aIntroduction -- Embedded Vision for the Internet of the Things: State-of-the-Art -- Hardware, Software, and Network Models for Deep-Learning Vision: A Survey -- Optimal Selection of Software and Models for Visual Interference -- Relevant Hardware Metrics for Performance Evaluation -- Prediction of Visual Interference Performance -- A Case Study: Remote Animal Recognition.
520			_aThis book presents a systematic approach to the implementation of Internet of Things (IoT) devices achieving visual inference through deep neural networks. Practical aspects are covered, with a focus on providing guidelines to optimally select hardware and software components as well as network architectures according to prescribed application requirements. The monograph includes a remarkable set of experimental results and functional procedures supporting the theoretical concepts and methodologies introduced. A case study on animal recognition based on smart camera traps is also presented and thoroughly analyzed. In this case study, different system alternatives are explored and a particular realization is completely developed. Illustrations, numerous plots from simulations and experiments, and supporting information in the form of charts and tables make Visual Inference and IoT Systems: A Practical Approach a clear and detailed guide to the topic. It will be of interest to researchers, industrial practitioners, and graduate students in the fields of computer vision and IoT.
988			_aSpringer_Engineering_2022
650		7	_2embne _9483083 _aInternet de los objetos
650		7	_2embne _9141143 _aGráficos de ordenador
700	1		_aFernández-Berni, Jorge _eautor _0(orcid)0000-0003-0476-4676 _1https://orcid.org/0000-0003-0476-4676 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9683394
700	1		_aRodríguez-Vázquez, Angel _eautor _0(orcid)0000-0002-1006-5241 _1https://orcid.org/0000-0002-1006-5241 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9683478
776	0	8	_iPrinted edition: _z9783030909024
776	0	8	_iPrinted edition: _z9783030909048
776	0	8	_iPrinted edition: _z9783030909055
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-90903-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2022 _dz _esc _zSI