1 edition of Mathematics of Data Fusion found in the catalog.
Published
1997
by Springer Netherlands in Dordrecht
.
Written in English
Data fusion or information fusion are names which have been primarily assigned to military-oriented problems. In military applications, typical data fusion problems are: multisensor, multitarget detection, object identification, tracking, threat assessment, mission assessment and mission planning, among many others. However, it is clear that the basic underlying concepts underlying such fusion procedures can often be used in nonmilitary applications as well. The purpose of this book is twofold: First, to point out present gaps in the way data fusion problems are conceptually treated. Second, to address this issue by exhibiting mathematical tools which treat combination of evidence in the presence of uncertainty in a more systematic and comprehensive way. These techniques are based essentially on two novel ideas relating to probability theory: the newly developed fields of random set theory and conditional and relational event algebra. This volume is intended to be both an update on research progress on data fusion and an introduction to potentially powerful new techniques: fuzzy logic, random set theory, and conditional and relational event algebra. Audience: This volume can be used as a reference book for researchers and practitioners in data fusion or expert systems theory, or for graduate students as text for a research seminar or graduate level course.
Edition Notes
| Statement | by I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen |
| Series | Theory and Decision Library, Series B: Mathematical and Statistical Methods -- 37, Theory and Decision Library, Series B: Mathematical and Statistical Methods -- 37 |
| Contributions | Mahler, Ronald P. S., Nguyen, Hung T. |
| Classifications | |
|---|---|
| LC Classifications | T57-57.97 |
| The Physical Object | |
| Format | [electronic resource] / |
| Pagination | 1 online resource (xii, 508 p.) |
| Number of Pages | 508 |
| ID Numbers | |
| Open Library | OL27073376M |
| ISBN 10 | 9048148871, 9401589291 |
| ISBN 10 | 9789048148875, 9789401589291 |
| OCLC/WorldCa | 851372440 |
The data fusion techniques discussed in this book are closely related to that for biomedical image registration/fusion. Major challenges and new opportunities are enormous ahead of us. In that context, I hope this book will have a significant impact on the biomedical imaging areas, and recommend it to biomedical imaging researchers in Cited by: 3. Elementary Algebra Exercise Book I. Essential Engineering Mathematics. Mathematics for Computer Scientists. Introduction to Complex Numbers. Essential Mathematics for Engineers. Quantitative Analysis. Mathematics Fundamentals. Introduction to Vectors. An Introduction to Matlab. A Refresher Course in Mathematics. Introduction to statistical data.
A number of approaches for data fusion have been proposed which are based on probability, artificial intelligence, or evidence theory to efficiently aggregate the : David L. Hall. A sheaf is a combinatorial, high-dimensional data structure on a topological space. You can use it to stitch together images, spatio-temporal target tracking, inference, data fusion, IoT time-series, continuous functions -- all kinds of things. Crazy cool stuff! Applied sheaf theory is pretty new, and not yet widely known.
Books about all areas of mathematics including the history of mathematics, recreational mathematics, and any other book exploring the wonders of math. Score A book’s total score is based on multiple factors, including the number of people who have voted for it and how highly those voters ranked the book. Since the publication of the first edition of this book, advances in algorithms, logic and software tools have transformed the field of data fusion. The latest edition covers these areas as well as smart agents, human computer interaction, cognitive aides to analysis and data system fusion control. data fusion system, this book guides you through the process of determining the trade 3/5(2).
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Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion, and provides a proper and adequate understanding of the basic mathematics directly related to DF. The material covered can be used for evaluation of the performances of any designed and developed DF systems.
It tries to answer whether unified data 4/5(1). The purpose of this book is twofold: First, to point out present gaps in the way data fusion problems are conceptually treated.
Second, to address this issue by exhibiting mathematical tools which treat combination of evidence in the presence of uncertainty in a more systematic and comprehensive way.
Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion, and provides a proper and adequate understanding of the basic mathematics directly related to DF. The material covered can be used for evaluation of the performances of any designed and developed DF systems.
Book Description. Fills the Existing Gap of Mathematics for Data Fusion. Data fusion (DF) combines large amounts of information from a variety of sources and fuses this data algorithmically, logically and, if required intelligently, using artificial intelligence (AI).
Get this from a library. Mathematics of data fusion. [Irwin R Goodman; Ronald P S Mahler; Hung T Nguyen] -- The purpose of this book is twofold: First, to point out present gaps in the way data fusion problems are conceptually treated.
Second, to address this issue by exhibiting mathematical tools which. Mathematics of Data Fusion by Irwin R. Goodman,available at Book Depository with free delivery worldwide. Mathematics of Data Fusion | Data fusion or information fusion are names which have been primarily assigned to military-oriented problems.
In military applications, typical data fusion problems are: multisensor, multitarget detection, object identification, tracking, threat assessment, mission assessment and mission planning, among many others.
Get this from a library. Mathematics of Data Fusion. [I R Goodman; Ronald P S Mahler; Hung T Nguyen] -- Data fusion or information fusion are names which have been primarily assigned to military-oriented problems.
In military applications, typical data. Data Fusion Mathematics: Theory and Practice incorporates concepts, processes, methods, and approaches in data fusion that can help you with integrating DF mathematics and achieving higher levels of fusion activity, and clarity of performance.
This text is geared toward researchers, scientists, teachers and practicing engineers interested and. Fills the Existing Gap of Mathematics for Data FusionData fusion (DF) combines large amounts of information from a variety of sources and fuses this data algorithmically, logically and, if required intelligently, using artificial intelligence (AI).
Also, known as sensor data fusion (SDF), the DF fusion system is an important component for use in various applications that include the. Data Fusion Mathematics: Theory and Practice incorporates concepts, processes, methods, and approaches in data fusion that can help you with integrating DF mathematics and achieving higher levels of fusion activity, and clarity of performance.
This text is geared toward researchers, scientists, teachers and practicing engineers interested and Price: $ Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion, and provides a proper and adequate understanding of the basic mathematics directly related to.
Problem: Heterogeneous data fusion has been stymied by the lack of effective quantitative – qualitative fusion techniques Key points: Sheaf-based heterogeneous fusion is the sole canonical mathematical framework for multi-modal, multi-INT integration systems Demonstrate the framework by showing cross.
Open Library is an open, editable library catalog, building towards a web page for every book ever published. Mathematics ofdata fusion by Irwin R.
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Some Key Features. Points out gaps in the way Data fusion problems are conceptually treated and addresses this issue by exhibiting mathematical tools which treat combination of evidence in the presence of uncertainty in a more systematic way. This book offers research progress on Data fusion and serves as an introduction to powerful techniques.
SIAM Journal on Mathematics of Data Science (SIMODS) publishes work that advances mathematical, statistical, and computational methods in the context of data and information invite papers that present significant advances in this context, including applications to science, engineering, business, and medicine.
Data Fusion Mathematics: Theory and Practice incorporates concepts, processes, methods, and approaches in data fusion that can help you with integrating DF mathematics and achieving higher levels of fusion activity, and clarity of performance.
This text is geared toward researchers, scientists, teachers and practicing engineers interested and Brand: Jitendra R Raol. Data Fusion Mathematics: Theory and Practice Raol, Jiten R. Year: Publisher: CRC Press LLC. Language: english. Pages: ISBN ISBN Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them.Fills the Existing Gap of Mathematics for Data Fusion Data fusion (DF) combines large amounts of information from a variety of sources and fuses this data algorithmically, logically and, if required intelligently, using artificial intelligence (AI).
Also, known as sensor data fusion (SDF), the DF fusion system is an important component for use in various applications that include the.Also, known as sensor data fusion (SDF), the DF fusion system is an important component for use in various applications that include the monitoring of vehicles, aerospace systems, large-scale structures, and large industrial automation plants.
Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion, and.
