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Books on the topic 'Data fusion algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

service), ScienceDirect (Online, ed. Image fusion: Algorithms and applications. Amsterdam: Academic Press/Elsevier, 2008.

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2

Antony, Richard T. Principles of data fusion automation. Boston: Artech House, 1995.

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3

Abdelgawad, Ahmed, and Magdy Bayoumi. Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1350-9.

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4

Abdelgawad, Ahmed. Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Boston, MA: Springer US, 2012.

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5

Klein, Lawrence A. Sensor and data fusion concepts and applications. Bellingham, Wash., USA: SPIE Optical Engineering Press, 1993.

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6

Klein, Lawrence A. Sensor and data fusion concepts and applications. 2nd ed. Bellingham, Wash: SPIE, 1999.

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7

Braun, Jerome J. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2011 : 27-28 April 2011, Orlando, Florida, United States. Bellingham, Wash: SPIE, 2011.

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8

Braun, Jerome J. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2010 : 7-8 April 2010, Orlando, Florida, United States. Bellingham, Wash: SPIE, 2010.

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9

(Society), SPIE, ed. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2009 : 16-17 April 2009, Orlando, Florida, United States. Bellingham, Wash: SPIE, 2009.

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10

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

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11

Efficient one-, two-, and multidimensional high resolution array signal processing. Aachen: Shaker, 1997.

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12

Resourceaware Data Fusion Algorithms For Wireless Sensor Networks. Springer, 2012.

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13

Bayoumi, Magdy, and Ahmed Abdelgawad. Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Springer, 2014.

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14

Bayoumi, Magdy, and Ahmed Abdelgawad. Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Springer, 2012.

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15

New Data Fusion Algorithms for Distributed Multi-Sensor Multi-Target Environments. Storming Media, 1999.

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16

V, Dasarathy Belur, and Society of Photo-Optical Instrumentation Engineers., eds. Sensor fusion--architectures, algorithms, and applications: 24-25 April 1997, Orlando, Florida. Bellingham, Wash: SPIE, 1997.

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17

V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Sensor fusion--architectures, algorithms, and applications III: 7-9 April 1999, Orlando, Florida. Bellingham, Wash: SPIE, 1999.

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18

V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Sensor fusion--architectures, algorithms, and applications V: 18-20 April, 2001, Orlando, USA. Bellingham, Wash: SPIE, 2001.

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19

V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Sensor fusion--architectures, algorithms, and applications IV: 25-28 April 2000, Orlando, Florida. Bellingham, Wash: SPIE, 2000.

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20

V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Sensor fusion--architectures, algorithms, and applications VI: 3-5 April 2002, Orlando, [Florida] USA. Bellingham, Wash., USA: SPIE, 2002.

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21

V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Sensor fusion--architectures, algorithms, and applications II: 16-17 April 1998, Orlando, Florida. Bellingham, Wash: SPIE, 1998.

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22

V, Dasarathy Belur, Society of Photo-optical Instrumentation Engineers., and Ball Aerospace & Technologies Corporation (USA), eds. Multisensor, multisource information fusion : architectures, algorithms, and applications 2005: 30-31 March, 2005, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2005.

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23

Multisensor, multisource information fusion: Architectures, algorithms, and applications 2004 : 14-15 April 2004, Orlando, Florida, USA. Bellingham, WA: SPIE, 2004.

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24

V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion--architectures, algorithms, and applications 2004: 14-15 April 2004, Orlando, Florida, USA. Bellingham, Wash., USA: SPIE, 2004.

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25

V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2006 : 19-20 April 2006, Kissimmee, Florida, USA. Bellingham, Wash: SPIE, 2006.

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26

V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion--architectures, algorithms, and applications 2003: 23-25 April 2003, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2003.

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27

Multisensor, multisource information fusion: Architectures, algorithms, and applications 2007 : 11-12 April, 2007, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2007.

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28

V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2007 : 11-12 April, 2007, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2007.

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29

Florentin, Smarandache, and Dezert Jean, eds. Advances and applications of DSmT for information fusion: Collected works. Rehoboth, N.M: American Research Press, 2004.

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30

Advances and Applications of DSmT for Information Fusion (Collected works). Am. Res. Press, 2006.

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31

Multisensor Data Fusion: From Algorithm and Architecture Design to Applications. Taylor & Francis Group, 2015.

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32

Low Choy, Samantha, Justine Murray, Allan James, and Kerrie Mengersen. Combining monitoring data and computer model output in assessing environmental exposure. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.18.

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This article discusses an approach that combines monitoring data and computer model outputs for environmental exposure assessment. It describes the application of Bayesian data fusion methods using spatial Gaussian process models in studies of weekly wet deposition data for 2001 from 120 sites monitored by the US National Atmospheric Deposition Program (NADP) in the eastern United States. The article first provides an overview of environmental computer models, with a focus on the CMAQ (Community Multi-Scale Air Quality) Eta forecast model, before considering some algorithmic and pseudo-statistical approaches in weather prediction. It then reviews current state of the art fusion methods for environmental data analysis and introduces a non-dynamic downscaling approach. The static version of the dynamic spatial model is used to analyse the NADP weekly wet deposition data.
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33

A Computationally Efficient and Cost Effective Multisensor Data Fusion Algorithm for the United States Coast Guard Vessel Traffic Services System. Storming Media, 1997.

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