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Books on the topic 'Estimation of the process noise'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Kesavan, R. Process planning and cost estimation. 2nd ed. New Age International Ltd., 2009.

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2

Johnson, A. Process dynamics, estimation and control. Peregrinus on behalf of the Institution of Electrical Engineers, 1985.

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3

Adithan, M. Process planning and cost estimation. New Age International (P) Ltd., Publishers, 2007.

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4

Engineers, Institution of Electrical, ed. Process dynamics estimation and control. P. Peregrinus Ltd. on behalf of the Institution of Electrical Engineers, 1985.

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5

Gershon, Eli. Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems. Springer London, 2013.

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6

Standardization, International Organization for, ed. Acoustics: Determination of occupational noise exposure and estimation of noise induced hearing impairment. 2nd ed. International Organization for Standardization, 1990.

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7

Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. National Bureau of Economic Research, 2005.

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8

Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. National Bureau of Economic Research, 2005.

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9

Ferguson, Ian. Dust and noise in the construction process. HSE Books, 1995.

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10

Ruhmer, W. T. Handbook on the estimation of metallurgical process costs. 2nd ed. Mintek, 1996.

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11

Wijers, B. J. Nonparametric estimation for a windowed line-segment process. Centrum voor Wiskunde en Informatica, 1997.

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12

Ruhmer, W. T. Handbook on the estimation of metallurgical process costs. 2nd ed. Mintek, 1991.

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13

Bastin, G. On-line estimation and adaptive control of bioreactors. Elsevier, 1990.

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14

Dehling, Herold. Empirical Process Techniques for Dependent Data. Birkhäuser Boston, 2002.

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15

M, Kontogeorgis Georgios, and Gani R, eds. Computer aided property estimation for process and product design. Elsevier, 2004.

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16

Page, Juliet, Chris Hobbs, and Phil Gliebe. Enhanced Modeling of Aircraft Taxiway Noise, Volume 2: Aircraft Taxi Noise Database and Development Process. Transportation Research Board, 2013. http://dx.doi.org/10.17226/22606.

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17

Colomo-Palacios, Ricardo. Agile estimation techniques and innovative approaches to software process improvement. Information Science Reference, 2014.

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18

Hawkins, Neal R. Development of a new process for determining design year traffic demands. Center for Transportation Research and Education, Iowa State University, 2007.

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19

Hawkins, Neal R. Development of a new process for determining design year traffic demands. Center for Transportation Research and Education, Iowa State University, 2007.

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20

Christianus Bernardus Maria Te Stroet. Calibration of stochastic groundwater flow models: Estimation of noise statistics and model parameters. Eburon], 1995.

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21

Polites, Michael E. The estimation error covariance matrix for the ideal state reconstructor with measurement noise. National Aeronautics and Space Administration, Scientific and Technical Information Division, 1988.

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22

Kelly, Patrick Aloysius. Robustness and stability in the detection and estimation of parameterized signals in noise. University of Michigan, 1985.

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23

Trees, Harry L. Van. Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise. John Wiley & Sons, Inc., 2004.

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24

Center, Langley Research, ed. Comparing parameter estimation techniques for an electrical power transformer oil temperature prediction model. National Aeronautics and Space Administration, Langley Research Center, 1999.

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25

Robinson, D. W. Tables for the estimation of hearing impairment due to noise for otologically normal and typical unscreened populations of males and females. HMSO Publications Centre, 1991.

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26

United States. National Telecommunications and Information Administration., ed. Estimation of system gain and bias using noisy observations with known noise power ratio. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 2002.

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27

Savaux, Vincent, and Yves Louët. MMSE-Based Algorithm for Joint Signal Detection, Channel and Noise Variance Estimation for OFDM Systems. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119005087.

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28

Cheung, Yin-Wong. On maximum-likelihood estimation of the differencing parameter of fractionally integrated noise with unknown mean. Federal Reserve Bank of Philadelphia, Economic Research Division, 1993.

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29

Pezeshki, Peyman. Use of cell cycle analysis in early state estimation and process control. University of Birmingham, 2002.

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30

Manning, Robert Michael. Real-time in situ signal-to-noise ratio estimation for the assessment of operational communications links. National Aeronautics and Space Administration, Glenn Research Center, 2002.

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31

Manning, Robert Michael. Real-time in situ signal-to-noise ratio estimation for the assessment of operational communications links. National Aeronautics and Space Administration, Glenn Research Center, 2002.

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32

Manning, Robert Michael. Real-time in situ signal-to-noise ratio estimation for the assessment of operational communications links. National Aeronautics and Space Administration, Glenn Research Center, 2002.

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33

Manning, Robert Michael. Real-time in situ signal-to-noise ratio estimation for the assessment of operational communications links. National Aeronautics and Space Administration, Glenn Research Center, 2002.

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34

Maine, Trindel A. A preliminary evaluation of an F100 engine parameter estimation process using flight data. Ames Research Center, 1990.

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35

B, Gilyard Glenn, Lambert Heather H, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. A preliminary evaluation of an F100 engine parameter estimation process using flight data. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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36

Pitard, Francis F. Pierre Gy's sampling theory and sampling practice: Heterogeneity, sampling correctness, and statistical process control. 2nd ed. CRC Press, 1993.

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37

Blackledge, J. M. Pattern recognition using artificial neural networks with white noise for process control engineering. SERCentre, De Montfort University, 1997.

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38

Orme, John S. Subsonic flight test evaluation of a propulsion system parameter estimation process for the F100 engine. Dryden Flight Research Facility, 1992.

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39

Milanese, James J. Process industry contingency estimation: A study of the ability to account for unforeseen costs. Rand, 1987.

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40

Zhang, Ke. Observer-Based Fault Estimation and Accomodation for Dynamic Systems. Springer Berlin Heidelberg, 2013.

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41

S. A. van de Geer. Applications of empirical process theory. Cambridge University Press, 2000.

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42

Aït-Sahalia, Yacine. How often to sample a continuous-time process in the presence of market microstructure noise. National Bureau of Economic Research, 2003.

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43

B, Gilyard Glenn, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Subsonic flight test evaluation of a propulsion system parameter estimation process for the F-10 engine. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1992.

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44

Taxation, United States Congress Joint Committee on. Discussion of revenue estimation methodology and process: Prepared by the staff of the Joint Committee on Taxation. U.S. G.P.O., 1992.

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45

Fermüller, Cornelia. Motion Illusions in Man and Machine. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0006.

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Abstract:

At the level of mathematical abstraction, computing image motion amounts to an estimation problem and can be analyzed using the tools of statistics and signal processing. As shown in this chapter, intrinsic limitations to the estimation processes make it impossible to derive veridical estimates for all images. Image motion is estimated erroneously, and as a result higher level processes compute erroneous three-dimensional motion and moving scenes. Specifically, two limitations are discussed: (a) due to noise in image data, there is statistical bias that affects anisotropic patterns and (2) the

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46

Solev, Valentin. Estimation of Function with Stationary Noise. Wiley & Sons, Incorporated, John, 2022.

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47

Process Planning and Cost Estimation. PRENTICE HALL, 2021.

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48

Process Planning and Cost Estimation. New Age International, 2008.

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49

Gershon, Eli, and Uri Shaked. Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems. Springer, 2013.

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50

Ilow, Jacek. Signal processing in alpha-stable noise environments: Noise modeling, detection and estimation. 1996.

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