Relevant bibliographies by topics / Distributed Data Fusion (DDF)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Distributed Data Fusion (DDF)'

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Published: 6 September 2023

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Journal articles on the topic "Distributed Data Fusion (DDF)"

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Lu, Zongqing, Su-Lim Tan, and Jit Biswas. "D2F: A Routing Protocol for Distributed Data Fusion in Wireless Sensor Networks." Wireless Personal Communications 70, no. 1 (June 13, 2012): 391–410. http://dx.doi.org/10.1007/s11277-012-0700-9.

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Joelson, Anders, and Freyr Gauti Sigmundsson. "Additional operation rates after surgery for degenerative spine diseases: minimum 10 years follow-up of 4705 patients in the national Swedish spine register." BMJ Open 12, no. 12 (December 2022): e067571. http://dx.doi.org/10.1136/bmjopen-2022-067571.

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ObjectivesTo identify rates of additional operation after the index operation for degenerative lumbar spine diseases.DesignRetrospective register study.SettingNational outcome data from Swespine, the National Swedish spine register.ParticipantsA total of 4705 patients who underwent one-level surgery for degenerative disk disease (DDD) or lumbar spinal stenosis (LSS) with or without degenerative spondylolisthesis (DS) between 1 January 2007 and 31 December 2010 were followed from 1 January 2007 to 31 December 2020 to record all cases of additional lumbar spine operations.InterventionsOne-level spinal decompression and/or posterolateral fusion for degenerative spine diseases.Primary outcome measuresNumber of additional operations.ResultsAdditional operations were more common at adjacent levels for patients with LSS with DS treated with decompression and fusion whereas additional operations were more evenly distributed between the index level and the adjacent levels for DDD treated with fusion and LSS with and without DS treated with decompression only. For patients younger than 60 years, treated with decompression and fusion for LSS with DS, the additional operations were evenly distributed between the index level and the adjacent levels.ConclusionsThere are different patterns of additional operations following the index procedure after surgery for degenerative spine diseases. Rigidity across previously mobile segments is not the only important factor in the development of adjacent segment disease (ASD) after spinal fusion, also the underlying disease and age may play parts in ASD development. The findings of this study can be used in the shared decision-making process when surgery is a treatment option for patients with degenerative lumbar spine diseases as the first operation may be the start of a series of additional spinal operations for other degenerative spinal conditions, either at the index level or at other spinal levels.
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Peyman, Setoodeh, Khayatian Alireza, and Farjah Ebrahim. "Attitude Estimation By Divided Difference Filter-Based Sensor Fusion." Journal of Navigation 60, no. 1 (December 15, 2006): 119–28. http://dx.doi.org/10.1017/s037346330600405x.

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Strapdown inertial navigation systems (INS) often employ aiding sensors to increase accuracy. Nonlinear filtering algorithms are then needed to fuse the collected data from these aiding sensors with measurements of strapdown rate gyros. Aiding sensors usually have slower dynamics compared to gyros and therefore collect data at lower rates. Thus the system will be unobservable between aiding sensors' sampling instants, and the error covariance, which shows the uncertainty in the estimation, grows during the sampling period. This paper presents a divided difference filter (DDF)-based data fusion algorithm, which utilizes the complementary noise profile of rate gyros and gravimetric inclinometers to extend their limits and achieve more accurate attitude estimates. It is confirmed experimentally that DDF achieves better covariance estimates compared to the extended Kalman filter (EKF) because the uncertainty in the state estimate is taken care of in the DDF polynomial approximation formulation.
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Chair, Z., and P. K. Varshney. "Distributed Bayesian hypothesis testing with distributed data fusion." IEEE Transactions on Systems, Man, and Cybernetics 18, no. 5 (1988): 695–99. http://dx.doi.org/10.1109/21.21597.

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Xue, Ying Hua, and Jing Li. "Distributed Information Fusion Structure Based on Data Fusion Tree." Advanced Materials Research 225-226 (April 2011): 488–91. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.488.

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A distributed information fusion structure based on data fusion tree is built to realize precise localization and efficient navigation for the mobile robot. The multi-class, multi-level information from robot and environment is fused using different algorithms in different levels, and make the robot have a deeper understanding to the whole environment. Experiments demonstrate that the new model proposed in the paper can improve the positioning precision of robot greatly, and the search efficiency and success rate are also better than traditional mode.
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Hollinger, Geoffrey A., Srinivas Yerramalli, Sanjiv Singh, Urbashi Mitra, and Gaurav S. Sukhatme. "Distributed Data Fusion for Multirobot Search." IEEE Transactions on Robotics 31, no. 1 (February 2015): 55–66. http://dx.doi.org/10.1109/tro.2014.2378411.

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Fan, Lingling. "Data fusion-based distributed Prony analysis." Electric Power Systems Research 143 (February 2017): 634–42. http://dx.doi.org/10.1016/j.epsr.2016.10.052.

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Mcgrath, Michael, and Yuan Zheng. "Distributed contextual data fusion with ACIPL." IEEE Aerospace and Electronic Systems Magazine 24, no. 8 (August 2009): 31–36. http://dx.doi.org/10.1109/maes.2009.5256385.

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Park, Gyu-Dong, and Young-Tae Byun. "Improving the Distributed Data Fusion Ability of the JDL Data Fusion Model." Journal of the Korea Institute of Military Science and Technology 15, no. 2 (April 5, 2012): 147–54. http://dx.doi.org/10.9766/kimst.2012.15.2.147.

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Hang, Guo, and Yu Min. "Data fusion in distributed multi-sensor system." Geo-spatial Information Science 7, no. 3 (January 2004): 214–17. http://dx.doi.org/10.1007/bf02826294.

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Dissertations / Theses on the topic "Distributed Data Fusion (DDF)"

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Aziz, Ashraf Mamdouh Abdel. "New data fusion algorithms for distributed multi-sensor multi-target environments." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA369780.

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Dissertation (Ph.D. in Electrical Engineering) Naval Postgraduate School, September 1999.
"September 1999". Dissertation supervisor(s): Robert Cristi, Murali Tummala. Includes bibliographical references (p. 199-214). Also avaliable online.
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Wallace, Christopher John. "Distributed data fusion for condition monitoring of graphite nuclear reactor cores." Thesis, University of Strathclyde, 2013. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=20607.

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Nuclear power stations worldwide are exceeding their originally specified design lives and with only limited construction of new generation underway, there is a desire to continue the operation of existing stations to ensure electricity supply. Continued operation of nuclear power stations with degrading and life-limiting components necessitates increased monitoring and inspection, particularly of the reactor cores, to ensure they are safe to operate. The monitoring of a large number of components and their related data sources is a distributed and time consuming process for the engineer given the lack of infrastructure available for collecting, managing and analysing monitoring data. This thesis describes the issues associated with nuclear Condition Monitoring (CM) and investigates the suitability of a distributed framework utilising intelligent software agents to collect, manage and analyse data autonomously. The application of data fusion techniques is examined to estimate unre corded parameters, provide contextualisation for anomalies in order to quickly identify true faults from explainable anomalies and to extract more detail from existing CM data. A generalised framework is described for nuclear CM of any type of reactor, specifying the required components and capabilites based on the design of a suitable Multi Agent System, including the interaction of the framework with existing CM systems and human users. A high level ontology for nuclear CM is proposed and is emphasised as a crucial aspect of the data management and extendability of the framework to incorporate further data sources and analyses. A prototype system, based on the generalised framework is developed for the case of the Advanced Gas-cooled Reactor, with new and existing CM analyses formalised within intelligent agents. Using real station data and simulated fault data, the prototype system was shown to be capable of performing the existing monitoring tasks considerably faster than a human user while retaining all data and analyses for justification and traceability of decisions based on the analyses.
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Vilsmaier, Christian. "Contextualized access to distributed and heterogeneous multimedia data sources." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0094/document.

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Rendre les données multimédias disponibles en ligne devient moins cher et plus pratique sur une base quotidienne, par exemple par les utilisateurs eux-mêmes. Des phénomènes du Web comme Facebook, Twitter et Flickr bénéficient de cette évolution. Ces phénomènes et leur acceptation accrue conduisent à une multiplication du nombre d’images disponibles en ligne. La taille cumulée de ces images souvent publiques et donc consultables, est de l’ordre de plusieurs zettaoctets. L’exécution d’une requête de similarité sur de tels volumes est un défi que la communauté scientifique commence à cibler. Une approche envisagée pour faire face à ce problème propose d’utiliser un système distribué et hétérogène de recherche d’images basé sur leur contenu (CBIRs). De nombreux problèmes émergent d’un tel scénario. Un exemple est l’utilisation de formats de métadonnées distincts pour décrire le contenu des images; un autre exemple est l’information technique et structurelle inégale. Les métriques individuelles qui sont utilisées par les CBIRs pour calculer la similarité entre les images constituent un autre exemple. Le calcul de bons résultats dans ce contexte s’avère ainsi une tàche très laborieuse qui n’est pas encore scientifiquement résolue. Le problème principalement abordé dans cette thèse est la recherche de photos de CBIRs similaires à une image donnée comme réponse à une requête multimédia distribuée. La contribution principale de cette thèse est la construction d’un réseau de CBIRs sensible à la sémantique des contenus (CBIRn). Ce CBIRn sémantique est capable de collecter et fusionner les résultats issus de sources externes spécialisées. Afin d’être en mesure d’intégrer de telles sources extérieures, prêtes à rejoindre le réseau, mais pas à divulguer leur configuration, un algorithme a été développé capable d’estimer la configuration d’un CBIRS. En classant les CBIRs et en analysant les requêtes entrantes, les requêtes d’image sont exclusivement transmises aux CBIRs les plus appropriés. De cette fac ̧on, les images sans intérêt pour l’utilisateur peuvent être omises à l’avance. Les images retournées cells sont considérées comme similaires par rapport à l’image donnée pour la requête. La faisabilité de l’approche et l’amélioration obtenue par le processus de recherche sont démontrées par un développement prototypique et son évaluation utilisant des images d’ImageNet. Le nombre d’images pertinentes renvoyées par l’approche de cette thèse en réponse à une requête image est supérieur d’un facteur 4.75 par rapport au résultat obtenu par un réseau de CBIRs predéfini
Making multimedia data available online becomes less expensive and more convenient on a daily basis. This development promotes web phenomenons such as Facebook, Twitter, and Flickr. These phenomena and their increased acceptance in society in turn leads to a multiplication of the amount of available images online. This vast amount of, frequently public and therefore searchable, images already exceeds the zettabyte bound. Executing a similarity search on the magnitude of images that are publicly available and receiving a top quality result is a challenge that the scientific community has recently attempted to rise to. One approach to cope with this problem assumes the use of distributed heterogeneous Content Based Image Retrieval system (CBIRs). Following from this anticipation, the problems that emerge from a distributed query scenario must be dealt with. For example the involved CBIRs’ usage of distinct metadata formats for describing their content, as well as their unequal technical and structural information. An addition issue is the individual metrics that are used by the CBIRs to calculate the similarity between pictures, as well as their specific way of being combined. Overall, receiving good results in this environment is a very labor intensive task which has been scientifically but not yet comprehensively explored. The problem primarily addressed in this work is the collection of pictures from CBIRs, that are similar to a given picture, as a response to a distributed multimedia query. The main contribution of this thesis is the construction of a network of Content Based Image Retrieval systems that are able to extract and exploit the information about an input image’s semantic concept. This so called semantic CBIRn is mainly composed of CBIRs that are configured by the semantic CBIRn itself. Complementarily, there is a possibility that allows the integration of specialized external sources. The semantic CBIRn is able to collect and merge results of all of these attached CBIRs. In order to be able to integrate external sources that are willing to join the network, but are not willing to disclose their configuration, an algorithm was developed that approximates these configurations. By categorizing existing as well as external CBIRs and analyzing incoming queries, image queries are exclusively forwarded to the most suitable CBIRs. In this way, images that are not of any use for the user can be omitted beforehand. The hereafter returned images are rendered comparable in order to be able to merge them to one single result list of images, that are similar to the input image. The feasibility of the approach and the hereby obtained improvement of the search process is demonstrated by a prototypical implementation. Using this prototypical implementation an augmentation of the number of returned images that are of the same semantic concept as the input images is achieved by a factor of 4.75 with respect to a predefined non-semantic CBIRn
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Lin, Erwei Kam Moshe. "Detection in distributed sensor networks /." Philadelphia, Pa. : Drexel University, 2005. http://hdl.handle.net/1860/1303.

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Gnanapandithan, Nithya. "Data detection and fusion in decentralized sensor networks." Thesis, Manhattan, Kan. : Kansas State University, 2005. http://hdl.handle.net/2097/132.

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Palaniappan, Ravishankar. "A SELF-ORGANIZING HYBRID SENSOR SYSTEM WITH DISTRIBUTED DATA FUSION FOR INTRUDER TRACKING AND SURVEILLANCE." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2407.

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A wireless sensor network is a network of distributed nodes each equipped with its own sensors, computational resources and transceivers. These sensors are designed to be able to sense specific phenomenon over a large geographic area and communicate this information to the user. Most sensor networks are designed to be stand-alone systems that can operate without user intervention for long periods of time. While the use of wireless sensor networks have been demonstrated in various military and commercial applications, their full potential has not been realized primarily due to the lack of efficient methods to self organize and cover the entire area of interest. Techniques currently available focus solely on homogeneous wireless sensor networks either in terms of static networks or mobile networks and suffers from device specific inadequacies such as lack of coverage, power and fault tolerance. Failing nodes result in coverage loss and breakage in communication connectivity and hence there is a pressing need for a fault tolerant system to allow replacing of the failed nodes. In this dissertation, a unique hybrid sensor network is demonstrated that includes a host of mobile sensor platforms. It is shown that the coverage area of the static sensor network can be improved by self-organizing the mobile sensor platforms to allow interaction with the static sensor nodes and thereby increase the coverage area. The performance of the hybrid sensor network is analyzed for a set of N mobile sensors to determine and optimize parameters such as the position of the mobile nodes for maximum coverage of the sensing area without loss of signal between the mobile sensors, static nodes and the central control station. A novel approach to tracking dynamic targets is also presented. Unlike other tracking methods that are based on computationally complex methods, the strategy adopted in this work is based on a computationally simple but effective technique of received signal strength indicator measurements. The algorithms developed in this dissertation are based on a number of reasonable assumptions that are easily verified in a densely distributed sensor network and require simple computations that efficiently tracks the target in the sensor field. False alarm rate, probability of detection and latency are computed and compared with other published techniques. The performance analysis of the tracking system is done on an experimental testbed and also through simulation and the improvement in accuracy over other methods is demonstrated.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Modeling and Simulation PhD
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Cadell, Philip. "BabelFuse data fusion unit with precision wireless clock synchronisation." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/55225/1/Philip_Cadell_Thesis.pdf.

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A significant issue encountered when fusing data received from multiple sensors is the accuracy of the timestamp associated with each piece of data. This is particularly important in applications such as Simultaneous Localisation and Mapping (SLAM) where vehicle velocity forms an important part of the mapping algorithms; on fastmoving vehicles, even millisecond inconsistencies in data timestamping can produce errors which need to be compensated for. The timestamping problem is compounded in a robot swarm environment due to the use of non-deterministic readily-available hardware (such as 802.11-based wireless) and inaccurate clock synchronisation protocols (such as Network Time Protocol (NTP)). As a result, the synchronisation of the clocks between robots can be out by tens-to-hundreds of milliseconds making correlation of data difficult and preventing the possibility of the units performing synchronised actions such as triggering cameras or intricate swarm manoeuvres. In this thesis, a complete data fusion unit is designed, implemented and tested. The unit, named BabelFuse, is able to accept sensor data from a number of low-speed communication buses (such as RS232, RS485 and CAN Bus) and also timestamp events that occur on General Purpose Input/Output (GPIO) pins referencing a submillisecondaccurate wirelessly-distributed "global" clock signal. In addition to its timestamping capabilities, it can also be used to trigger an attached camera at a predefined start time and frame rate. This functionality enables the creation of a wirelessly-synchronised distributed image acquisition system over a large geographic area; a real world application for this functionality is the creation of a platform to facilitate wirelessly-distributed 3D stereoscopic vision. A ‘best-practice’ design methodology is adopted within the project to ensure the final system operates according to its requirements. Initially, requirements are generated from which a high-level architecture is distilled. This architecture is then converted into a hardware specification and low-level design, which is then manufactured. The manufactured hardware is then verified to ensure it operates as designed and firmware and Linux Operating System (OS) drivers are written to provide the features and connectivity required of the system. Finally, integration testing is performed to ensure the unit functions as per its requirements. The BabelFuse System comprises of a single Grand Master unit which is responsible for maintaining the absolute value of the "global" clock. Slave nodes then determine their local clock o.set from that of the Grand Master via synchronisation events which occur multiple times per-second. The mechanism used for synchronising the clocks between the boards wirelessly makes use of specific hardware and a firmware protocol based on elements of the IEEE-1588 Precision Time Protocol (PTP). With the key requirement of the system being submillisecond-accurate clock synchronisation (as a basis for timestamping and camera triggering), automated testing is carried out to monitor the o.sets between each Slave and the Grand Master over time. A common strobe pulse is also sent to each unit for timestamping; the correlation between the timestamps of the di.erent units is used to validate the clock o.set results. Analysis of the automated test results show that the BabelFuse units are almost threemagnitudes more accurate than their requirement; clocks of the Slave and Grand Master units do not di.er by more than three microseconds over a running time of six hours and the mean clock o.set of Slaves to the Grand Master is less-than one microsecond. The common strobe pulse used to verify the clock o.set data yields a positive result with a maximum variation between units of less-than two microseconds and a mean value of less-than one microsecond. The camera triggering functionality is verified by connecting the trigger pulse output of each board to a four-channel digital oscilloscope and setting each unit to output a 100Hz periodic pulse with a common start time. The resulting waveform shows a maximum variation between the rising-edges of the pulses of approximately 39¥ìs, well below its target of 1ms.
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Gallagher, Jonathan G. "Likelihood as a Method of Multi Sensor Data Fusion for Target Tracking." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1244041862.

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Borkar, Milind. "A distributed Monte Carlo method for initializing state vector distributions in heterogeneous smart sensor networks." Diss., Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/22680.

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The objective of this research is to demonstrate how an underlying system's state vector distribution can be determined in a distributed heterogeneous sensor network with reduced subspace observability at the individual nodes. We show how the network, as a whole, is capable of observing the target state vector even if the individual nodes are not capable of observing it locally. The initialization algorithm presented in this work can generate the initial state vector distribution for networks with a variety of sensor types as long as the measurements at the individual nodes are known functions of the target state vector. Initialization is accomplished through a novel distributed implementation of the particle filter that involves serial particle proposal and weighting strategies, which can be accomplished without sharing raw data between individual nodes in the network. The algorithm is capable of handling missed detections and clutter as well as compensating for delays introduced by processing, communication and finite signal propagation velocities. If multiple events of interest occur, their individual states can be initialized simultaneously without requiring explicit data association across nodes. The resulting distributions can be used to initialize a variety of distributed joint tracking algorithms. In such applications, the initialization algorithm can initialize additional target tracks as targets come and go during the operation of the system with multiple targets under track.
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Jones, Malachi Gabriel. "Design and implementation of a multi-agent systems laboratory." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29617.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Jeff Shamma; Committee Member: Eric Feron; Committee Member: Magnus Egerstedt. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Books on the topic "Distributed Data Fusion (DDF)"

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Varshney, Pramod K. Distributed Detection and Data Fusion. New York, NY: Springer New York, 1997.

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Varshney, Pramod K. Distributed Detection and Data Fusion. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1904-0.

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Varshney, Pramod K. Distributed detection and data fusion. Edited by Burrus C. S. Berlin: Springer, 1996.

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Varshney, Pramod K. Distributed detection and data fusion. Edited by Burrus C. S. New York: Springer, 1997.

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Zuidgeest, R. G. Multi-sensor data fusion in a distributed environment - architectural solutions -. Amsterdam: National Aerospace Laboratory, 1992.

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Iyengar, S. S. Advances in distributed sensor integration: Application and theory. Upper Saddle River, N.J: Prentice Hall PTR, 1995.

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Distributed sensor networks: Sensor networking and applications. 2nd ed. Boca Raton: Chapman & Hall/CRC, 2013.

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S, Schenker Paul, McKee G. T, and Society of Photo-optical Instrumentation Engineers., eds. Sensor fusion and distributed robotic agents: 21-22 November 1996, Boston, Massachusetts. Bellingham, Wash: SPIE, 1996.

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Victor, Lesser, Ortiz Charles L, and Tambe Milind 1965-, eds. Distributed sensor networks: A multiagent perspective. Boston: Kluwer Academic Publishers, 2003.

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Michael, Gastpar, ed. Distributed source coding: Theory, algorithms, and applications. Amsterdam: Academic Press, 2009.

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Book chapters on the topic "Distributed Data Fusion (DDF)"

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Varshney, Pramod K. "Distributed Sequential Detection." In Distributed Detection and Data Fusion, 216–32. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1904-0_6.

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Ducourthial, Bertrand, Véronique Cherfaoui, and Thierry Denoeux. "Self-stabilizing Distributed Data Fusion." In Lecture Notes in Computer Science, 148–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33536-5_15.

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Regazzoni, C. "Distributed Knowledge-based Systems for Integration of Image Processing Modules." In Data Fusion Applications, 133–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84990-9_14.

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Varshney, Pramod K. "Introduction." In Distributed Detection and Data Fusion, 1–5. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1904-0_1.

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Varshney, Pramod K. "Elements of Detection Theory." In Distributed Detection and Data Fusion, 6–35. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1904-0_2.

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Varshney, Pramod K. "Distributed Bayesian Detection: Parallel Fusion Network." In Distributed Detection and Data Fusion, 36–118. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1904-0_3.

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Varshney, Pramod K. "Distributed Bayesian Detection: Other Network Topologies." In Distributed Detection and Data Fusion, 119–78. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1904-0_4.

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Varshney, Pramod K. "Distributed Detection with False Alarm Rate Constraints." In Distributed Detection and Data Fusion, 179–215. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1904-0_5.

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Varshney, Pramod K. "Information Theory and Distributed Hypothesis Testing." In Distributed Detection and Data Fusion, 233–50. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1904-0_7.

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Yan, Liping, Lu Jiang, and Yuanqing Xia. "Distributed Data Fusion for Multirate Sensor Networks." In Multisensor Fusion Estimation Theory and Application, 53–68. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9426-7_4.

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Conference papers on the topic "Distributed Data Fusion (DDF)"

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Easthope, P. F. "Track degradation as a consequence of distributed sensor fusion." In 9th IET Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications. IET, 2012. http://dx.doi.org/10.1049/cp.2012.0408.

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Ahmed, Nisar. "Conditionally factorized DDF for general distributed Bayesian estimation." In 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI). IEEE, 2014. http://dx.doi.org/10.1109/mfi.2014.6997717.

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Coraluppi, Stefano, Laura Vertatschitsch, and Craig Carthel. "Decision Sequencing and Distributed Data Association." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011334.

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Rendas, Maria-Joao, and Jose Manuel Leitao. "Rumor-robust distributed data fusion." In 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010). IEEE, 2010. http://dx.doi.org/10.1109/mfi.2010.5604462.

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Yan Jin, Lin Sun, and Jing Song. "Research on distributed data fusion model." In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2016. http://dx.doi.org/10.1109/imcec.2016.7867218.

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Fong, Li-Wei. "Distributed Data Fusion via Hybrid Approach." In IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2007. http://dx.doi.org/10.1109/iecon.2007.4459985.

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Chang, Edward Y., Yuan-Fang Wang, and Volkan Rodoplu. "Distributed video data fusion and mining." In Defense and Security, edited by Edward M. Carapezza. SPIE, 2004. http://dx.doi.org/10.1117/12.540115.

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Nicholson, David, and Valerie Leung. "Managing a distributed data fusion network." In Defense and Security, edited by Ivan Kadar. SPIE, 2004. http://dx.doi.org/10.1117/12.543612.

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McGrath, Michael A., and Yuan F. Zheng. "Distributed Contextual Data Fusion with ACIPL." In 2008 IEEE National Aerospace and Electronics Conference. IEEE, 2008. http://dx.doi.org/10.1109/naecon.2008.4806568.

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Parra-Loera, Ramon, Wiley E. Thompson, and Syed A. Akbar. "Multilevel distributed fusion of multisensors data." In Aerospace Sensing, edited by Vibeke Libby and Ivan Kadar. SPIE, 1992. http://dx.doi.org/10.1117/12.138217.

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Reports on the topic "Distributed Data Fusion (DDF)"

1

Varshney, Pramod K. Distributed Detection Theory and Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, December 1999. http://dx.doi.org/10.21236/ada374837.

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Varshney, Pramod K. Distributed Detection Theory and Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada280410.

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Varshney, Pramod K. Distributed Detection Theory and Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada301116.

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Broman, V., and J. Pack. Effectiveness Measurements for the Distributed Data Fusion Problem. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada283084.

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Blum, Rick S. A Theory for Distributed Signal Detection and Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada377472.

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Mori, Shozo, Bruce D'Ambrosio, and Doug Hart. Autonomous Distributed Systems. Multiple-Level Distributed Data Fusion for Future DADS Using Bayesian Network Technology. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada399999.

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Singh, Aarti. Resource-constrained Data Collection and Fusion for Identifying Weak Distributed Patterns in Networks. Fort Belvoir, VA: Defense Technical Information Center, October 2013. http://dx.doi.org/10.21236/ada591821.

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Ramchandran, Kannan, and Kristofer Pister. Sensor Webs of SmartDust: Distributed Signal Processing/Data Fusion/Inferencing in Large Microsensor Arrays. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada422190.

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Llinas, James, Kedar Sambhoos, and Christopher Bowman. Research in Evaluation Methods for Data Fusion-Capable Tactical Platforms and Distributed Multi-platform Systems in Electronic Warfare and Information-Warfare Related Missions. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada499058.

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