9783319219202 - bayesian prediction and adaptive sampling algorithms for mobile sensor networks: online environmental field reconstruction in space and time di xu, yunfei (12 risultati)
Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2015
Serie: SpringerBriefs in Electrical and Computer Engineering, Libro 16 di 27. Libro 16 di 27 - SpringerBriefs in Electrical and Computer Engineering
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Paperback. Condizione: new. Paperback. 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 commun…ication 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Lingua: Inglese
Editore: Springer, 2015
Serie: SpringerBriefs in Electrical and Computer Engineering, Libro 16 di 27. Libro 16 di 27 - SpringerBriefs in Electrical and Computer Engineering
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Condizione: New. pp. 118.
Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks: Online Environmental Field Reconstruction in Space and Time
Xu, Yunfei (Author)/ Choi, Jongeun (Author)/ Dass, Sarat (Author)/ Maiti, Tapabrata (Author)
Lingua: Inglese
Editore: Springer, 2015
Serie: SpringerBriefs in Electrical and Computer Engineering, Libro 16 di 27. Libro 16 di 27 - SpringerBriefs in Electrical and Computer Engineering
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Paperback. Condizione: Brand New. 128 pages. 9.75x6.50x0.50 inches. In Stock.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing, 2015
Serie: SpringerBriefs in Electrical and Computer Engineering, Libro 16 di 27. Libro 16 di 27 - SpringerBriefs in Electrical and Computer Engineering
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Taschenbuch. Condizione: 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 maxim…ize 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.
Lingua: Inglese
Editore: Springer, 2015
Serie: SpringerBriefs in Electrical and Computer Engineering, Libro 16 di 27. Libro 16 di 27 - SpringerBriefs in Electrical and Computer Engineering
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Taschenbuch. Condizione: Neu. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks | Online Environmental Field Reconstruction in Space and Time | Yunfei Xu (u. a.) | Taschenbuch | SpringerBriefs in Electrical and Computer Engineering | xii | Englisch | 2015 | Springer | EAN 9783319219202 | Verantwortl…iche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2015
Serie: SpringerBriefs in Electrical and Computer Engineering, Libro 16 di 27. Libro 16 di 27 - SpringerBriefs in Electrical and Computer Engineering
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Paperback. Condizione: new. Paperback. 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 commun…ication 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Lingua: Inglese
Editore: Springer, 2015
Serie: SpringerBriefs in Electrical and Computer Engineering, Libro 16 di 27. Libro 16 di 27 - SpringerBriefs in Electrical and Computer Engineering
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing Nov 2015, 2015
Serie: SpringerBriefs in Electrical and Computer Engineering, Libro 16 di 27. Libro 16 di 27 - SpringerBriefs in Electrical and Computer Engineering
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 128 pp. Englisch.
Lingua: Inglese
Editore: Springer, 2015
Serie: SpringerBriefs in Electrical and Computer Engineering, Libro 16 di 27. Libro 16 di 27 - SpringerBriefs in Electrical and Computer Engineering
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Condizione: New. Print on Demand pp. 118.
Lingua: Inglese
Editore: Springer, 2015
Serie: SpringerBriefs in Electrical and Computer Engineering, Libro 16 di 27. Libro 16 di 27 - SpringerBriefs in Electrical and Computer Engineering
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Condizione: New. PRINT ON DEMAND pp. 118.
Lingua: Inglese
Editore: Springer International Publishing, 2015
Serie: SpringerBriefs in Electrical and Computer Engineering, Libro 16 di 27. Libro 16 di 27 - SpringerBriefs in Electrical and Computer Engineering
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides the reader with modeling and predictive tools of use in a number of applications of current interestProblems and solutions gradually increase in complexity throughout the brief so that learning can take plac…e in easy stepsNew techn.
Lingua: Inglese
Editore: Springer, Springer Nov 2015, 2015
Serie: SpringerBriefs in Electrical and Computer Engineering, Libro 16 di 27. Libro 16 di 27 - SpringerBriefs in Electrical and Computer Engineering
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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 sens…ors 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 KG, Sachsenplatz 4-6, 1201 Wien 128 pp. Englisch.
