Sprache: Englisch
Verlag: Neal-Schuman Publishers, Incorporated, 2011
ISBN 10: 1555707726 ISBN 13: 9781555707729
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Zustand: Good. Pap/Cdr. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
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In den WarenkorbPaperback. Zustand: Brand New. 208 pages. 9.00x6.00x0.50 inches. In Stock.
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2024
ISBN 10: 3659768251 ISBN 13: 9783659768255
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Verlag: LAP LAMBERT Academic Publishing, 2024
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Sprache: Englisch
Verlag: Springer-Nature New York Inc, 2022
ISBN 10: 3031127102 ISBN 13: 9783031127106
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Sprache: Englisch
Verlag: Touchladybirdlucky Studios Aug 2018, 2018
ISBN 10: 1622651561 ISBN 13: 9781622651566
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Taschenbuch. Zustand: Neu. Neuware.
Sprache: Englisch
Verlag: Unc Department of Romance Studies, 2024
ISBN 10: 1469677717 ISBN 13: 9781469677712
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Sprache: Englisch
Verlag: Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3031127102 ISBN 13: 9783031127106
Anbieter: moluna, Greven, Deutschland
Zustand: New. This SpringerBrief presents a brief introduction to probabilistic risk assessment (PRA), followed by a discussion of abnormal event detection techniques in industrial control systems (ICS). It also provides an introduction to the use of game theory for the .
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Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing Nov 2015, 2015
ISBN 10: 3319219200 ISBN 13: 9783319219202
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Taschenbuch. Zustand: Neu. Neuware -This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive distribution of a scalar environmental field of interest. New techniques are introduced to avoid computationally prohibitive Markov chain Monte Carlo methods for resource-constrained mobile sensors. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks starts with a simple spatio-temporal model and increases the level of model flexibility and uncertainty step by step, simultaneously solving increasingly complicated problems and coping with increasing complexity, until it ends with fully Bayesian approaches that take into account a broad spectrum of uncertainties in observations, model parameters, and constraints in mobile sensor networks. The book is timely, being very useful for many researchers in control, robotics, computer science and statistics trying to tackle a variety of tasks such as environmental monitoring and adaptive sampling, surveillance, exploration, and plume tracking which are of increasing currency. Problems are solved creatively by seamless combination of theories and concepts from Bayesian statistics, mobile sensor networks, optimal experiment design, and distributed computation.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 128 pp. Englisch.
Sprache: Englisch
Verlag: Springer International Publishing, Springer International Publishing, 2015
ISBN 10: 3319219200 ISBN 13: 9783319219202
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive distribution of a scalar environmental field of interest. New techniques are introduced to avoid computationally prohibitive Markov chain Monte Carlo methods for resource-constrained mobile sensors. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks starts with a simple spatio-temporal model and increases the level of model flexibility and uncertainty step by step, simultaneously solving increasingly complicated problems and coping with increasing complexity, until it ends with fully Bayesian approaches that take into account a broad spectrum of uncertainties in observations, model parameters, and constraints in mobile sensor networks. The book is timely, being very useful for many researchers in control, robotics, computer science and statistics trying to tackle a variety of tasks such as environmental monitoring and adaptive sampling, surveillance, exploration, and plume tracking which are of increasing currency. Problems are solved creatively by seamless combination of theories and concepts from Bayesian statistics, mobile sensor networks, optimal experiment design, and distributed computation.
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Sprache: Englisch
Verlag: Springer International Publishing, 2015
ISBN 10: 3319219200 ISBN 13: 9783319219202
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks | Online Environmental Field Reconstruction in Space and Time | Yunfei Xu (u. a.) | Taschenbuch | xii | Englisch | 2015 | Springer International Publishing | EAN 9783319219202 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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In den WarenkorbPaperback. Zustand: Brand New. 168 pages. 10.00x7.25x0.50 inches. In Stock.
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Taschenbuch. Zustand: Neu. Hybrid Solar Cells Based on Quantum Dots and Polymers | Yunfei Zhou | Taschenbuch | 148 S. | Englisch | 2016 | Scholars' Press | EAN 9783659837913 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
Zustand: new.
Sprache: Englisch
Verlag: Springer International Publishing, 2020
ISBN 10: 3030306577 ISBN 13: 9783030306571
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Sprache: Englisch
Verlag: Springer International Publishing, 2019
ISBN 10: 3030306542 ISBN 13: 9783030306540
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In den WarenkorbHardcover. Zustand: Brand New. 350 pages. 9.75x7.00x1.00 inches. In Stock.
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In den WarenkorbHardcover. Zustand: Brand New. 357 pages. 8.27x5.83x8.50 inches. In Stock.