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Libros sobre el tema "Particle filter"

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Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 8 de junio de 2024

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1

Particle filter retrofit for all diesel engines: With 40 tables. Renningen: expert-Verl., 2008.

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2

Dubuisson, Séverine. Tracking with Particle Filter for High-Dimensional Observation and State Spaces. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119004868.

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3

Jonathan, Brandt, AWWA Research Foundation y United States. Environmental Protection Agency., eds. Characterization of particles in filter effluents. Denver, CO: Awwa Research Foundation, 2007.

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4

Jonathan, Brandt, AWWA Research Foundation y United States. Environmental Protection Agency., eds. Characterization of particles in filter effluents. Denver, CO: Awwa Research Foundation, 2007.

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5

Jonathan, Brandt, AWWA Research Foundation y United States. Environmental Protection Agency., eds. Characterization of particles in filter effluents. Denver, CO: Awwa Research Foundation, 2007.

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6

Jonathan, Brandt, AWWA Research Foundation y United States. Environmental Protection Agency., eds. Characterization of particles in filter effluents. Denver, CO: Awwa Research Foundation, 2007.

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7

Ristic, Branko. Particle Filters for Random Set Models. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6316-0.

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8

Ristic, Branko. Particle Filters for Random Set Models. New York, NY: Springer New York, 2013.

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9

Stone, Lawrence D., Roy L. Streit y Stephen L. Anderson. Introduction to Bayesian Tracking and Particle Filters. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32242-6.

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10

Kolaru, Olukunle. An investigation into soil particle bridging in geotextile filters. [London]: Queen Mary and Westfield College, 1994.

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11

Lee, Rubow Kenneth, American Filtration Society y American Filtration Society. Regional Meeting, eds. Fine particle filtration and separation. Houston: Gulf Pub. Co., 1991.

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12

Fine particles in gaseous media. 2a ed. Chelsea, Mich: Lewis Publishers, 1986.

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13

Weickert, Joachim. Anisotropic diffusion in image processing. Stuttgart: B.G. Teubner, 1998.

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14

Oja, Marja. Pressure filtration of mineral slurries: Modelling and particle shape characterization. Lappeenranta, Finland: Lappeenranta University of Technology, 1996.

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15

Rule, D. L. Thermal conductivity of a polymide film between 4.2 and 300 K, with and without alumina particles as filler. Boulder, Colo: U.S. Dept. of Commerce, National Institute of Standards and Technology, 1990.

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16

Hinot, Karelle. Catalytic soot oxidation by platinum on sintered metal filters: Influence of the platinum quantity, particle size and location, and investigation of the platinum soot contact. Karlsruhe: Univ.-Verl. Karlsruhe, 2007.

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17

Sowers, R. B. Short-time geometry of random heat kernels. Providence, R.I: American Mathematical Society, 1998.

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18

Sowers, R. B. Short-time geometry of random heat kernels. Providence, R.I: American Mathematical Society, 1998.

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19

Gruntovich, Nadezhda. Technical diagnostics of electrical equipment. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1891041.

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The textbook presents a modified theory of vibration diagnostics of rolling bearings. The methods of technical diagnostics of electrical equipment insulation without high-voltage tests are given. The physical nature of the electric arc inside the tank of an oil-filled transformer is revealed for the first time. The methods of complex technical diagnostics of electric motors and power oil-filled transformers are described. Expert systems of technical diagnostics of electrical equipment are considered. Conditions of damage to cables by partial discharges are revealed. Meets the requirements of the federal state standards of higher education of the latest generation. The textbook is intended for students, undergraduates and postgraduates of energy specialties of universities, as well as for specialists in technical diagnostics of equipment of thermal and nuclear power plants, industrial enterprises.
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20

Kythe, Prem K. Partial differential equations and Mathematica. Boca Raton, Fla: CRC Press, 1997.

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21

Ross, Paul W. Using Enable: An introduction to integrated software. Boston: Boyd & Fraser, 1988.

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22

Junichiro, Makino y Hut Piet 1952-, eds. Astrophysical supercomputing using particle simulations: Proceedings of the 208th Symposium of the International Astronomical Union held in Tokyo, Japan, 10-13 July 2001. San Francisco, Calif: Published on behalf of IAU by the Astronomical Society of the Pacific, 2003.

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23

Partial differential equations with Mathematica. Wokingham, England: Addison-Wesley Pub. Co., 1993.

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24

Partial differential equations and boundary value problems with Maple V. San Diego: Academic Press, 1998.

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25

Computational partial differential equations: Numerical methods and Diffpack programming. Berlin: Springer, 1999.

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26

1962-, Langtangen Hans Petter y Tveito Aslak 1961-, eds. Advanced topics in computational partial differential equations: Numerical methods and Diffpack programming. Berlin: Springer, 2003.

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27

Computational partial differential equations: Numerical methods and Diffpack programming. 2a ed. Berlin: Springer, 2003.

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28

Partial differential equations for computational science: With Maple and vector analysis. New York: TELOS, 1998.

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29

Nonlinear filtering and optimal phase tracking. New York: Springer, 2012.

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30

Articolo, George A. Partial differential equations and boundary value problems with Maple. 2a ed. Boston: Academic Press, 2009.

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31

Carlos, Lizárraga-Celaya, ed. Solving nonlinear partial differential equations with Maple and Mathematica. Wien: Springer, 2011.

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32

Griffiths, Graham W. Traveling wave analysis of partial differential equations: Numerical and analytical methods with MATLAB and Maple. Amsterdam: Academic Press, 2011.

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33

Tikhonenko, A. V. Integrirovanie uravneniĭ dvizhenii︠a︡ zari︠a︡zhennykh chastit︠s︡ v MAPLE: Monografii︠a︡. Obninsk: IATĖ, 2007.

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34

1938-, Puri Pratap, Schäferkotter Michael R. 1955- y Kythe Prem K, eds. Partial differential equations and boundary value problems with Mathematica. 2a ed. Boca Raton, Fla: Chapman & Hall/CRC, 2003.

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35

1946-, Vorozhtsov E. V., ed. Numerical solutions for partial differential equations: Problem solving using Mathematica. Boca Raton, Fla: CRC Press, 1996.

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36

N, Bellomo, ed. Generalized collocations methods: Solutions to nonlinear problems using Mathematica. Boston, Mass: Birkhäuser, 2007.

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37

Ristic, Branko, Sanjeev Arulampalam y Neil Gordon. Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House Radar Library). Artech House Publishers, 2004.

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38

Using Oxidants to Enhance Filter Performance. American Water Works Research Foundation, 2004.

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39

Dubuisson, Séverine. Tracking with Particle Filter for High-Dimensional Observation and State Spaces. Wiley & Sons, Incorporated, John, 2015.

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40

Dubuisson, Séverine. Tracking with Particle Filter for High-Dimensional Observation and State Spaces. Wiley & Sons, Incorporated, John, 2015.

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41

Dubuisson, Séverine. Tracking with Particle Filter for High-Dimensional Observation and State Spaces. Wiley & Sons, Incorporated, John, 2015.

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42

Dubuisson, S. y Séverine Dubuisson. Tracking with Particle Filter for High-Dimensional Observation and State Spaces. Wiley & Sons, Incorporated, John, 2015.

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43

Dubuisson, Séverine. Tracking with Particle Filter for High-dimensional Observation and State Spaces. Wiley-Interscience, 2015.

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44

Parsons, J. N. Ultrasonic Resuspension of Collected Dust on Filter Papers for Particle Size Analysis. Atomic Weapons Establishment, 1988.

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45

Bao, Yun, Carl Chiarella y Boda Kang. Particle Filters for Markov-Switching Stochastic Volatility Models. Editado por Shu-Heng Chen, Mak Kaboudan y Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.9.

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This chapter proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain. It proposes an ongoing updated Dirichlet distribution to estimate the transition probabilities of the Markov chain in the auxiliary particle filter. A simulation-based algorithm is presented for the method that demonstrates the ability to estimate a class of models in which the probability that the system state transits from one regime to a different regime is relatively high. The methodology is implemented in order to analyze a real-time series, namely, the foreign exchange rate between the Australian dollar and the South Korean won.
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46

Particle filters for robot navigation. now publishers Inc, 2014.

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47

Characterization of particles in filter effluents. Denver, CO: Awwa Research Foundation, 2007.

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48

Brandt, Jonathan, Helene Lecoanet, Ismail Koyuncu, Mark R. Wiesner, Srinivas (Vasu) Veeraaneni, Jessica Edwards-Brandt y Gary Logs. Characterization of Particles in Filter Effluents. American Water Works Research Foundation, 2007.

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49

Sklar, Larry A., ed. Flow Cytometry for Biotechnology. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195183146.001.0001.

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Flow cytometry is a sensitive and quantitative platform for the measurement of particle fluorescence. In flow cytometry, the particles in a sample flow in single file through a focused laser beam at rates of hundreds to thousands of particles per second. During the time each particle is in the laser beam, on the order of ten microseconds, one or more fluorescent dyes associated with that particle are excited. The fluorescence emitted from each particle is collected through a microscope objective, spectrally filtered, and detected with photomultiplier tubes. Flow cytometry is uniquely capable of the precise and quantitative molecular analysis of genomic sequence information, interactions between purified biomolecules and cellular function. Combined with automated sample handling for increased sample throughput, these features make flow cytometry a versatile platform with applications at many stages of drug discovery. Traditionally, the particles studied are cells, especially blood cells; flow cytometry is used extensively in immunology. This volume shows how flow cytometry is integrated into modern biotechnology, dealing with issues of throughput, content, sensitivity, and high throughput informatics with applications in genomics, proteomics and protein-protein interactions, drug discovery, vaccine development, plant and reproductive biology, pharmacology and toxicology, cell-cell interactions and protein engineering.
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50

Particle Filters For Random Set Models. Springer-Verlag New York Inc., 2013.

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