Zhu yunmin (35 risultati)

Hardcover. Condizione: Fine. Kluwer Academic hardcover, 2003, unused/unmarked, some surface rub to glossy cover otherwise as New. We will bubble wrap the book and ship it in a New BOX- Not a plastic bag like the zombie sellers. "Multisensor Decision And Estimation Fusion" by Zhu (as pictured).

Condizione: New. pp. 264.

Condizione: New. pp. 264 Illus.

Condizione: New. pp. 264.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: New.

Condizione: New.

Condizione: New.

Gebunden. Condizione: New.

Hardcover. Condizione: new. Hardcover. Due to the increased capability, reliability, robustness, and survivability of systems with multiple distributed sensors, multi-source information fusion has become a crucial technique in a growing number of areasincluding sensor networks, space technology, air traffic control, military engineering, agriculture and environmental engineering, and industrial control. Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods presents advanced mathematical descriptions and methods to help readers achieve more thorough results under more general conditions than what has been possible with previous results in the existing literature. Examining emerging real-world problems, this book summarizes recent research developments in problems with unideal and uncertain frameworks. It presents essential mathematical descriptions and methods for multisensory decision and estimation fusion. Deriving thorough results under general conditions, this reference book: Corrects several popular but incorrect results in this area with thorough mathematical ideasProvides advanced mathematical methods, which lead to more general and significant resultsPresents updated systematic developments in both multisensor decision and estimation fusion, which cannot be seen in other existing booksIncludes numerous computer experiments that support every theoretical resultThe book applies recently developed convex optimization theory and high efficient algorithms in estimation fusion, which opens a very attractive research subject on minimizing Euclidean error estimation for uncertain dynamic systems. Supplying powerful and advanced mathematical treatment of the fundamental problems, it will help to greatly broaden prospective applications of such developments in practice. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New. In English.

Condizione: New. Num Pages: 437 pages, 79 black & white illustrations, 18 black & white tables. BIC Classification: THRB. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 238 x 157 x 29. Weight in Grams: 748. . 2012. 1st Edition. Hardcover. . . . .

Gebunden. Condizione: New.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - YUNMIN ZHU In the past two decades, multi sensor or multi-source information fusion tech niques have attracted more and more attention in practice, where observations are processed in a distributed manner and decisions or estimates are made at the individual processors, and processed data (or compressed observations) are then transmitted to a fusion center where the final global decision or estimate is made. A system with multiple distributed sensors has many advantages over one with a single sensor. These include an increase in the capability, reliability, robustness and survivability of the system. Distributed decision or estimation fusion prob lems for cases with statistically independent observations or observation noises have received significant attention (see Varshney's book Distributed Detec tion and Data Fusion, New York: Springer-Verlag, 1997, Bar-Shalom's book Multitarget-Multisensor Tracking: Advanced Applications, vol. 1-3, Artech House, 1990, 1992,2000). Problems with statistically dependent observations or observation noises are more difficult and have received much less study. In practice, however, one often sees decision or estimation fusion problems with statistically dependent observations or observation noises. For instance, when several sensors are used to detect a random signal in the presence of observation noise, the sensor observations could not be statistically independent when the signal is present. This book provides a more complete treatment of the fundamentals of multi sensor decision and estimation fusion in order to deal with general random ob servations or observation noises that are correlated across the sensors.…

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - YUNMIN ZHU In the past two decades, multi sensor or multi-source information fusion tech niques have attracted more and more attention in practice, where observations are processed in a distributed manner and decisions or estimates are made at the individual processors, and processed data (or compressed observations) are then transmitted to a fusion center where the final global decision or estimate is made. A system with multiple distributed sensors has many advantages over one with a single sensor. These include an increase in the capability, reliability, robustness and survivability of the system. Distributed decision or estimation fusion prob lems for cases with statistically independent observations or observation noises have received significant attention (see Varshney's book Distributed Detec tion and Data Fusion, New York: Springer-Verlag, 1997, Bar-Shalom's book Multitarget-Multisensor Tracking: Advanced Applications, vol. 1-3, Artech House, 1990, 1992,2000). Problems with statistically dependent observations or observation noises are more difficult and have received much less study. In practice, however, one often sees decision or estimation fusion problems with statistically dependent observations or observation noises. For instance, when several sensors are used to detect a random signal in the presence of observation noise, the sensor observations could not be statistically independent when the signal is present. This book provides a more complete treatment of the fundamentals of multi sensor decision and estimation fusion in order to deal with general random ob servations or observation noises that are correlated across the sensors.…

Condizione: New. pp. 264.

Condizione: New. pp. 437 1st Edition.

Hardcover. Condizione: Brand New. 1st edition. 352 pages. 9.33x6.42x1.14 inches. In Stock.

Hardcover. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condizione: New. Num Pages: 437 pages, 79 black & white illustrations, 18 black & white tables. BIC Classification: THRB. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 238 x 157 x 29. Weight in Grams: 748. . 2012. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: new. Hardcover. Due to the increased capability, reliability, robustness, and survivability of systems with multiple distributed sensors, multi-source information fusion has become a crucial technique in a growing number of areasincluding sensor networks, space technology, air traffic control, military engineering, agriculture and environmental engineering, and industrial control. Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods presents advanced mathematical descriptions and methods to help readers achieve more thorough results under more general conditions than what has been possible with previous results in the existing literature. Examining emerging real-world problems, this book summarizes recent research developments in problems with unideal and uncertain frameworks. It presents essential mathematical descriptions and methods for multisensory decision and estimation fusion. Deriving thorough results under general conditions, this reference book: Corrects several popular but incorrect results in this area with thorough mathematical ideasProvides advanced mathematical methods, which lead to more general and significant resultsPresents updated systematic developments in both multisensor decision and estimation fusion, which cannot be seen in other existing booksIncludes numerous computer experiments that support every theoretical resultThe book applies recently developed convex optimization theory and high efficient algorithms in estimation fusion, which opens a very attractive research subject on minimizing Euclidean error estimation for uncertain dynamic systems. Supplying powerful and advanced mathematical treatment of the fundamental problems, it will help to greatly broaden prospective applications of such developments in practice. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Hardcover. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Hardcover. Condizione: New.

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -YUNMIN ZHU In the past two decades, multi sensor or multi-source information fusion tech niques have attracted more and more attention in practice, where observations are processed in a distributed manner and decisions or estimates are made at the individual processors, and processed data (or compressed observations) are then transmitted to a fusion center where the final global decision or estimate is made. A system with multiple distributed sensors has many advantages over one with a single sensor. These include an increase in the capability, reliability, robustness and survivability of the system. Distributed decision or estimation fusion prob lems for cases with statistically independent observations or observation noises have received significant attention (see Varshney's book Distributed Detec tion and Data Fusion, New York: Springer-Verlag, 1997, Bar-Shalom's book Multitarget-Multisensor Tracking: Advanced Applications, vol. 1-3, Artech House, 1990, 1992,2000). Problems with statistically dependent observations or observation noises are more difficult and have received much less study. In practice, however, one often sees decision or estimation fusion problems with statistically dependent observations or observation noises. For instance, when several sensors are used to detect a random signal in the presence of observation noise, the sensor observations could not be statistically independent when the signal is present. This book provides a more complete treatment of the fundamentals of multi sensor decision and estimation fusion in order to deal with general random ob servations or observation noises that are correlated across the sensors. 264 pp. Englisch.…

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -YUNMIN ZHU In the past two decades, multi sensor or multi-source information fusion tech niques have attracted more and more attention in practice, where observations are processed in a distributed manner and decisions or estimates are made at the individual processors, and processed data (or compressed observations) are then transmitted to a fusion center where the final global decision or estimate is made. A system with multiple distributed sensors has many advantages over one with a single sensor. These include an increase in the capability, reliability, robustness and survivability of the system. Distributed decision or estimation fusion prob lems for cases with statistically independent observations or observation noises have received significant attention (see Varshney's book Distributed Detec tion and Data Fusion, New York: Springer-Verlag, 1997, Bar-Shalom's book Multitarget-Multisensor Tracking: Advanced Applications, vol. 1-3, Artech House, 1990, 1992,2000). Problems with statistically dependent observations or observation noises are more difficult and have received much less study. In practice, however, one often sees decision or estimation fusion problems with statistically dependent observations or observation noises. For instance, when several sensors are used to detect a random signal in the presence of observation noise, the sensor observations could not be statistically independent when the signal is present. This book provides a more complete treatment of the fundamentals of multi sensor decision and estimation fusion in order to deal with general random ob servations or observation noises that are correlated across the sensors. 264 pp. Englisch.…

Buch. Condizione: Neu. Multisensor Decision And Estimation Fusion | Yunmin Zhu | Buch | The International Series on Asian Studies in Computer and Information Science | xxi | Englisch | 2002 | Springer | EAN 9781402072581 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. …

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -YUNMIN ZHU In the past two decades, multi sensor or multi-source information fusion tech niques have attracted more and more attention in practice, where observations are processed in a distributed manner and decisions or estimates are made at the individual processors, and processed data (or compressed observations) are then transmitted to a fusion center where the final global decision or estimate is made. A system with multiple distributed sensors has many advantages over one with a single sensor. These include an increase in the capability, reliability, robustness and survivability of the system. Distributed decision or estimation fusion prob lems for cases with statistically independent observations or observation noises have received significant attention (see Varshney's book Distributed Detec tion and Data Fusion, New York: Springer-Verlag, 1997, Bar-Shalom's book Multitarget-Multisensor Tracking: Advanced Applications, vol. 1-3, Artech House, 1990, 1992,2000). Problems with statistically dependent observations or observation noises are more difficult and have received much less study. In practice, however, one often sees decision or estimation fusion problems with statistically dependent observations or observation noises. For instance, when several sensors are used to detect a random signal in the presence of observation noise, the sensor observations could not be statistically independent when the signal is present. This book provides a more complete treatment of the fundamentals of multi sensor decision and estimation fusion in order to deal with general random ob servations or observation noises that are correlated across the sensors.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 264 pp. Englisch.…