000			03398nam a2200397 c 4500
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988			_aSpringer_Engineering_2019
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020			_a9783319923840
024	7		_a10.1007/978-3-319-92384-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7872.D48 _b2019 EB
245	0	0	_aMission-Oriented Sensor Networks and Systems : Art and Science : _bVolume 2: Advances _cedited by Habib M. Ammari.
250			_a1st ed. 2019.
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XVIII, 794 páginas) _b303 ilustraciones, 188 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aStudies in Systems Decision and Control _x2198-4182 _v164
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction -- Autonomous Cooperative Routing for Mission-Critical Applications -- Using Models for Communication in Cyber-Physical Systems -- Urban Micro-Climate Monitoring Using IoT Based Architecture -- Digital Forensics for IoT and WSNs -- An Overview of Wearable Computing -- Wearable Computing and Human Centricity -- Wireless transfer of energy alongside information in wireless sensor networks -- Efficient Protocols for Peer-to-Peer Wireless Power Transfer and Energy Aware Network Formation -- DeepCharge: Next-generation Software-defined Wireless Charging Systems -- Robotic Wireless Sensor Networks -- Robot and Drone Localization in GPS-Denied Areas -- Middleware for Multi-Robot System -- Interference Mitigation Techniques in Wireless Body Area Networks -- Radiation Control Algorithms in Wireless Networks -- Subspace based Encryption.
520	3		_aThis book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain-computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
650		7	_2embne _aRedes de sensores inalámbricas _9441179
700	1		_aAmmari, Habib M _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783319923833
776	0	8	_iPrinted edition: _z9783319923857
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-92384-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
998			_aSI _cm _dz _feng _ggw _h0 _b12/2019 _ek _zSI