data fusion methodology and applications

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Data Fusion Methodology and Applications
Author : Marina Cocchi
Publisher : Elsevier
Release Date : 2019-05-11
ISBN 10 : 0444639853
Pages : 396 pages
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Data Fusion Methodology and Applications explores the data-driven discovery paradigm in science and the need to handle large amounts of diverse data. Drivers of this change include the increased availability and accessibility of hyphenated analytical platforms, imaging techniques, the explosion of omics data, and the development of information technology. As data-driven research deals with an inductive attitude that aims to extract information and build models capable of inferring the underlying phenomena from the data itself, this book explores the challenges and methodologies used to integrate data from multiple sources, analytical platforms, different modalities, and varying timescales. Presents the first comprehensive textbook on data fusion, focusing on all aspects of data-driven discovery Includes comprehensible, theoretical chapters written for large and diverse audiences Provides a wealth of selected application to the topics included

Exam Prep for: Data Fusion Methodology and Applications
Author : N.A
Publisher : N.A
Release Date :
ISBN 10 :
Pages : 329 pages
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Multisensor Data Fusion
Author : Hassen Fourati
Publisher : CRC Press
Release Date : 2017-12-19
ISBN 10 : 1482263750
Pages : 639 pages
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Multisensor Data Fusion: From Algorithms and Architectural Design to Applications covers the contemporary theory and practice of multisensor data fusion, from fundamental concepts to cutting-edge techniques drawn from a broad array of disciplines. Featuring contributions from the world’s leading data fusion researchers and academicians, this authoritative book: Presents state-of-the-art advances in the design of multisensor data fusion algorithms, addressing issues related to the nature, location, and computational ability of the sensors Describes new materials and achievements in optimal fusion and multisensor filters Discusses the advantages and challenges associated with multisensor data fusion, from extended spatial and temporal coverage to imperfection and diversity in sensor technologies Explores the topology, communication structure, computational resources, fusion level, goals, and optimization of multisensor data fusion system architectures Showcases applications of multisensor data fusion in fields such as medicine, transportation's traffic, defense, and navigation Multisensor Data Fusion: From Algorithms and Architectural Design to Applications is a robust collection of modern multisensor data fusion methodologies. The book instills a deeper understanding of the basics of multisensor data fusion as well as a practical knowledge of the problems that can be faced during its execution.

NDT Data Fusion
Author : Xavier Gros
Publisher : Elsevier
Release Date : 1996-11-01
ISBN 10 : 0080524044
Pages : 205 pages
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Data fusion is a rapidly developing technology which involves the combination of information supplied by several NDT (Non-Destructive Testing) sensors to provide a more complete and understandable picture of structural integrity. This text is the first to be devoted exclusively to the concept of multisensor integration and data fusion applied to NDT. The advantages of this methodology are widely acknowledged and the author presents an excellent introduction to data fusion processes. Problems are approached progressively through detailed case studies, offering practical guidance for those wishing to develop and explore NDT data fusion further. This book will prove invaluable to inspectors, students and researchers concerned with NDT signal processing measurements and testing. It shows the great value and major benefits which can be achieved by implementing multisensor data fusion, not only in NDT but also in any discipline where measurements and testing are key activities.

Data Fusion and Data Mining for Power System Monitoring
Author : Arturo Román Messina
Publisher : CRC Press
Release Date : 2020-05-05
ISBN 10 : 1000065898
Pages : 250 pages
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Data Fusion and Data Mining for Power System Monitoring provides a comprehensive treatment of advanced data fusion and data mining techniques for power system monitoring with focus on use of synchronized phasor networks. Relevant statistical data mining techniques are given, and efficient methods to cluster and visualize data collected from multiple sensors are discussed. Both linear and nonlinear data-driven mining and fusion techniques are reviewed, with emphasis on the analysis and visualization of massive distributed data sets. Challenges involved in realistic monitoring, visualization, and analysis of observation data from actual events are also emphasized, supported by examples of relevant applications. Features Focuses on systematic illustration of data mining and fusion in power systems Covers issues of standards used in the power industry for data mining and data analytics Applications to a wide range of power networks are provided including distribution and transmission networks Provides holistic approach to the problem of data mining and data fusion using cutting-edge methodologies and technologies Includes applications to massive spatiotemporal data from simulations and actual events

Multi-Sensor Data Fusion
Author : H.B. Mitchell
Publisher : Springer Science & Business Media
Release Date : 2007-07-13
ISBN 10 : 3540715592
Pages : 282 pages
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This textbook provides a comprehensive introduction to the theories and techniques of multi-sensor data fusion. It is aimed at advanced undergraduate and first-year graduate students in electrical engineering and computer science, as well as researchers and professional engineers. The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is recommended.

Multisensor Data Fusion
Author : David Hall,James Llinas
Publisher : CRC Press
Release Date : 2001-06-20
ISBN 10 : 1420038540
Pages : 568 pages
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The emerging technology of multisensor data fusion has a wide range of applications, both in Department of Defense (DoD) areas and in the civilian arena. The techniques of multisensor data fusion draw from an equally broad range of disciplines, including artificial intelligence, pattern recognition, and statistical estimation. With the rapid evolut

Data Fusion: Concepts and Ideas
Author : H B Mitchell
Publisher : Springer Science & Business Media
Release Date : 2012-02-09
ISBN 10 : 3642272223
Pages : 346 pages
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This textbook provides a comprehensive introduction to the concepts and idea of multisensor data fusion. It is an extensively revised second edition of the author's successful book: "Multi-Sensor Data Fusion: An Introduction" which was originally published by Springer-Verlag in 2007. The main changes in the new book are: New Material: Apart from one new chapter there are approximately 30 new sections, 50 new examples and 100 new references. At the same time, material which is out-of-date has been eliminated and the remaining text has been rewritten for added clarity. Altogether, the new book is nearly 70 pages longer than the original book. Matlab code: Where appropriate we have given details of Matlab code which may be downloaded from the worldwide web. In a few places, where such code is not readily available, we have included Matlab code in the body of the text. Layout. The layout and typography has been revised. Examples and Matlab code now appear on a gray background for easy identification and advancd material is marked with an asterisk. The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familarity with the basic tools of linear algebra, calculus and simple probability is recommended. Although conceptually simple, the study of mult-sensor data fusion presents challenges that are unique within the education of the electrical engineer or computer scientist. To become competent in the field the student must become familiar with tools taken from a wide range of diverse subjects including: neural networks, signal processing, statistical estimation, tracking algorithms, computer vision and control theory. All too often, the student views multi-sensor data fusion as a miscellaneous assortment of different processes which bear no relationship to each other. In contrast, in this book the processes are unified by using a common statistical framework. As a consequence, the underlying pattern of relationships that exists between the different methodologies is made evident. The book is illustrated with many real-life examples taken from a diverse range of applications and contains an extensive list of modern references.

Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing
Author : Ni-Bin Chang,Kaixu Bai
Publisher : CRC Press
Release Date : 2018-02-21
ISBN 10 : 1351650637
Pages : 528 pages
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Combining versatile data sets from multiple satellite sensors with advanced thematic information retrieval is a powerful way for studying complex earth systems. The book Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing offers complete understanding of the basic scientific principles needed to perform image processing, gap filling, data merging, data fusion, machine learning, and feature extraction. Written by two experts in remote sensing, the book presents the required basic concepts, tools, algorithms, platforms, and technology hubs toward advanced integration. By merging and fusing data sets collected from different satellite sensors with common features, we are enabled to utilize the strength of each satellite sensor to the maximum extent. The inclusion of machine learning or data mining techniques to aid in feature extraction after gap filling, data merging and/or data fusion further empowers earth observation, leading to confirm the whole is greater than the sum of its parts. Contemporary applications discussed in this book make all essential knowledge seamlessly integrated by an interdisciplinary manner. These case-based engineering practices uniquely illustrate how to improve such an emerging field of importance to cope with the most challenging real-world environmental monitoring issues.

Applications of NDT Data Fusion
Author : Xavier E. Gros
Publisher : Springer Science & Business Media
Release Date : 2013-11-27
ISBN 10 : 1461514118
Pages : 277 pages
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Non-destructive testing (NDT) systems can generate incomplete, incorrect or conflicting information about a flaw or a defect. Therefore, the use of more than one NDT system is usually required for accurate defect detection and/or quantification. In addition to a reduction in inspection time, important cost savings could be achieved if a data fusion process is developed to combine signals from multisensor systems for manual and remotely operated inspections. This gathering of data from multiple sources and an efficient processing of information help in decision making, reduce signal uncertainty and increase the overall performance of a non-destructive examination. This book gathers, for the first time, essays from leading NDT experts involved in data fusion. It explores the concept of data fusion by providing a comprehensive review and analysis of the applications of NDT data fusion. This publication concentrates on NDT data fusion for industrial applications and highlights progress and applications in the field of data fusion in areas ranging from materials testing in the aerospace industry to medical applications. Each chapter contains a specific case study with a theoretical part but also presents experimental results from a practical point of view. The book should be considered more as a pragmatic introduction to the applications of NDT data fusion rather than a rigorous basis for theoretical studies.

Sensor and Data Fusion Concepts and Applications
Author : Lawrence A. Klein
Publisher : Society of Photo Optical
Release Date : 1999
ISBN 10 :
Pages : 226 pages
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First published in 1993, this Tutorial Text has been revised and updated to provide explanations and examples of data fusion algorithms in areas not covered in the first edition. These include Bayesian inference, artificial neural networks and fuzzy logic. All of the chapters in the first edition have been revised and updated and new material is included on the FASCODE and MODTRAN atmospheric models, and EOSAEL, which analyzes physical processes that affect the performance of millimeter-wave and IR sensors.

NDT Data Fusion
Author : Xavier Gros
Publisher : Elsevier
Release Date : 1996-11-01
ISBN 10 : 0080524044
Pages : 205 pages
GET BOOK!

Data fusion is a rapidly developing technology which involves the combination of information supplied by several NDT (Non-Destructive Testing) sensors to provide a more complete and understandable picture of structural integrity. This text is the first to be devoted exclusively to the concept of multisensor integration and data fusion applied to NDT. The advantages of this methodology are widely acknowledged and the author presents an excellent introduction to data fusion processes. Problems are approached progressively through detailed case studies, offering practical guidance for those wishing to develop and explore NDT data fusion further. This book will prove invaluable to inspectors, students and researchers concerned with NDT signal processing measurements and testing. It shows the great value and major benefits which can be achieved by implementing multisensor data fusion, not only in NDT but also in any discipline where measurements and testing are key activities.

Data Fusion for Situation Monitoring, Incident Detection, Alert and Response Management
Author : E. Shahbazian,G. Rogova,P. Valin
Publisher : IOS Press
Release Date : 2006-03-02
ISBN 10 : 1607501244
Pages : 832 pages
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Data Fusion is a very broad interdisciplinary technology domain. It provides techniques and methods for; integrating information from multiple sources and using the complementarities of these detections to derive maximum information about the phenomenon being observed; analyzing and deriving the meaning of these observations and predicting possible consequences of the observed state of the environment; selecting the best course of action; and controlling the actions. Here, the focus is on the more mature phase of data fusion, namely the detection and identification / classification of phenomena being observed and exploitation of the related methods for Security-Related Civil Science and Technology (SST) applications. It is necessary to; expand on the data fusion methodology pertinent to Situation Monitoring, Incident Detection, Alert and Response Management; discuss some related Cognitive Engineering and visualization issues; provide an insight into the architectures and methodologies for building a data fusion system; discuss fusion approaches to image exploitation with emphasis on security applications; discuss novel distributed tracking approaches as a necessary step of situation monitoring and incident detection; and provide examples of real situations, in which data fusion can enhance incident detection, prevention and response capability. In order to give a logical presentation of the data fusion material, first the general concepts are highlighted (Fusion Methodology, Human Computer Interactions and Systems and Architectures), closing with several applications (Data Fusion for Imagery, Tracking and Sensor Fusion and Applications and Opportunities for Fusion).

Intelligent Data Mining and Fusion Systems in Agriculture
Author : Xanthoula-Eirini Pantazi,Dimitrios Moshou,Dionysis Bochtis
Publisher : Academic Press
Release Date : 2019-10-08
ISBN 10 : 0128143924
Pages : 330 pages
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Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms. Covers crop protection, automation in agriculture, artificial intelligence in agriculture, sensing and Internet of Things (IoTs) in agriculture Addresses AI use in weed management, disease detection, yield prediction and crop production Utilizes case studies to provide real-world insights and direction

Data Fusion for Sensory Information Processing Systems
Author : James J. Clark,Alan L. Yuille
Publisher : Springer Science & Business Media
Release Date : 2013-03-09
ISBN 10 : 1475720769
Pages : 244 pages
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The science associated with the development of artificial sen sory systems is occupied primarily with determining how information about the world can be extracted from sensory data. For example, computational vision is, for the most part, concerned with the de velopment of algorithms for distilling information about the world and recognition of various objects in the environ (e. g. localization ment) from visual images (e. g. photographs or video frames). There are often a multitude of ways in which a specific piece of informa tion about the world can be obtained from sensory data. A subarea of research into sensory systems has arisen which is concerned with methods for combining these various information sources. This field is known as data fusion, or sensor fusion. The literature on data fusion is extensive, indicating the intense interest in this topic, but is quite chaotic. There are no accepted approaches, save for a few special cases, and many of the best methods are ad hoc. This book represents our attempt at providing a mathematical foundation upon which data fusion algorithms can be constructed and analyzed. The methodology that we present in this text is mo tivated by a strong belief in the importance of constraints in sensory information processing systems. In our view, data fusion is best un derstood as the embedding of multiple constraints on the solution to a sensory information processing problem into the solution pro cess.

Remote Sensing Image Fusion
Author : Christine Pohl,John van Genderen
Publisher : CRC Press
Release Date : 2016-10-03
ISBN 10 : 1315353067
Pages : 266 pages
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Remote Sensing Image Fusion: A Practical Guide gives an introduction to remote sensing image fusion providing an overview on the sensors and applications. It describes data selection, application requirements and the choice of a suitable image fusion technique. It comprises a diverse selection of successful image fusion cases that are relevant to other users and other areas of interest around the world.?The book helps newcomers to obtain a quick start into the practical value and benefits of multi-sensor image fusion. Experts will find this book useful to obtain an overview on the state of the art and understand current constraints that need to be solved in future research efforts. For industry professionals the book can be a great introduction and basis to understand multisensor remote sensing image exploitation and the development of commercialized image fusion software from a practical perspective. The book concludes with a chapter on current trends and future developments in remote sensing image fusion. Along with the book, RSIF website provides additional up-to-date information in the field.

Multisensor Attitude Estimation
Author : Hassen Fourati,Djamel Eddine Chouaib Belkhiat
Publisher : CRC Press
Release Date : 2016-11-03
ISBN 10 : 1498745806
Pages : 580 pages
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There has been an increasing interest in multi-disciplinary research on multisensor attitude estimation technology driven by its versatility and diverse areas of application, such as sensor networks, robotics, navigation, video, biomedicine, etc. Attitude estimation consists of the determination of rigid bodies’ orientation in 3D space. This research area is a multilevel, multifaceted process handling the automatic association, correlation, estimation, and combination of data and information from several sources. Data fusion for attitude estimation is motivated by several issues and problems, such as data imperfection, data multi-modality, data dimensionality, processing framework, etc. While many of these problems have been identified and heavily investigated, no single data fusion algorithm is capable of addressing all the aforementioned challenges. The variety of methods in the literature focus on a subset of these issues to solve, which would be determined based on the application in hand. Historically, the problem of attitude estimation has been introduced by Grace Wahba in 1965 within the estimate of satellite attitude and aerospace applications. This book intends to provide the reader with both a generic and comprehensive view of contemporary data fusion methodologies for attitude estimation, as well as the most recent researches and novel advances on multisensor attitude estimation task. It explores the design of algorithms and architectures, benefits, and challenging aspects, as well as a broad array of disciplines, including: navigation, robotics, biomedicine, motion analysis, etc. A number of issues that make data fusion for attitude estimation a challenging task, and which will be discussed through the different chapters of the book, are related to: 1) The nature of sensors and information sources (accelerometer, gyroscope, magnetometer, GPS, inclinometer, etc.); 2) The computational ability at the sensors; 3) The theoretical developments and convergence proofs; 4) The system architecture, computational resources, fusion level.

Multi-Sensor Data Fusion with MATLAB®
Author : Jitendra R. Raol
Publisher : CRC Press
Release Date : 2009-12-16
ISBN 10 : 9781439800058
Pages : 568 pages
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Using MATLAB® examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and performance evaluation mainly for aerospace applications, although the methods can also be applied to systems in other areas, such as biomedicine, military defense, and environmental engineering. After presenting several useful strategies and algorithms for DF and tracking performance, the book evaluates DF algorithms, software, and systems. It next covers fuzzy logic, fuzzy sets and their properties, fuzzy logic operators, fuzzy propositions/rule-based systems, an inference engine, and defuzzification methods. It develops a new MATLAB graphical user interface for evaluating fuzzy implication functions, before using fuzzy logic to estimate the unknown states of a dynamic system by processing sensor data. The book then employs principal component analysis, spatial frequency, and wavelet-based image fusion algorithms for the fusion of image data from sensors. It also presents procedures for combing tracks obtained from imaging sensor and ground-based radar. The final chapters discuss how DF is applied to mobile intelligent autonomous systems and intelligent monitoring systems. Fusing sensors’ data can lead to numerous benefits in a system’s performance. Through real-world examples and the evaluation of algorithmic results, this detailed book provides an understanding of MSDF concepts and methods from a practical point of view. Select MATLAB programs are available for download on www.crcpress.com

Distributed Detection and Data Fusion
Author : Pramod K. Varshney
Publisher : Springer Science & Business Media
Release Date : 2012-12-06
ISBN 10 : 1461219043
Pages : 276 pages
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This book provides an introductory treatment of the fundamentals of decision-making in a distributed framework. Classical detection theory assumes that complete observations are available at a central processor for decision-making. More recently, many applications have been identified in which observations are processed in a distributed manner and decisions are made at the distributed processors, or processed data (compressed observations) are conveyed to a fusion center that makes the global decision. Conventional detection theory has been extended so that it can deal with such distributed detection problems. A unified treatment of recent advances in this new branch of statistical decision theory is presented. Distributed detection under different formulations and for a variety of detection network topologies is discussed. This material is not available in any other book and has appeared relatively recently in technical journals. The level of presentation is such that the hook can be used as a graduate-level textbook. Numerous examples are presented throughout the book. It is assumed that the reader has been exposed to detection theory. The book will also serve as a useful reference for practicing engineers and researchers. I have actively pursued research on distributed detection and data fusion over the last decade, which ultimately interested me in writing this book. Many individuals have played a key role in the completion of this book.

High-Level Data Fusion
Author : Subrata Das
Publisher : Artech House
Release Date : 2008-01-01
ISBN 10 : 1596932821
Pages : 393 pages
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The book explores object and situation fusion processes with an appropriate handling of uncertainties, and applies cutting-edge artificial intelligence and emerging technologies like particle filtering, spatiotemporal clustering, net-centricity, agent formalism, and distributed fusion together with essential Level 1 techniques and Level 1/2 interactions.