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1

Identification of dynamical systems with small noise. Dordrecht: Kluwer Academic Publishers, 1994.

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2

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

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3

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

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4

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

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5

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

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6

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

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7

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

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8

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

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9

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

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10

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

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11

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

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12

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

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13

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

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14

Random signals, estimation, and identification: Analysis and applications. New York: Van Nostrand Reinhold, 1986.

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15

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

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16

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

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17

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

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18

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

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19

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

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20

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

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21

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

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22

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

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23

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

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24

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

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25

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

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26

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

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27

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

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28

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

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29

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

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32

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

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33

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

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34

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

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35

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

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36

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

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37

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

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38

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

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39

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

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40

R, Kumar. A novel multistage estimation of the signal parameters of a possibly data-modulated sinusoid under very high dynamics. Pasadena, Calif: National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1989.

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41

Robinson, D. W. Tables for the estimation of hearing impairement due to noise for otologically normal persons and for a typical unscreened population, as a function of age and duration of exposure. [Bootle]: Health and Safety Executive, 1988.

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42

New Jersey. Legislature. General Assembly. Task Force on Fiscal Responsibility. Task Force meeting of Assembly Task Force on Fiscal Responsibility: A review of the state revenue estimation and certification process : [August 26, 2002, Trenton, New Jersey]. Trenton, N.J: The Unit, 2002.

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43

New Jersey. Legislature. General Assembly. Task Force on Fiscal Responsibility. Task Force Meeting of Assembly Task Force on Fiscal Responsibility: A review of the state revenue estimation and certification process : [July 31, 2002, Trenton, New Jersey]. Trenton, N.J: The Unit, 2002.

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44

Magrab, Edward B. Computer Integrated Experimentation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991.

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45

Satdarova, Faina. DIFFRACTION ANALYSIS OF DEFORMED METALS: Theory, Methods, Programs. xxu: Academus Publishing, 2019. http://dx.doi.org/10.31519/monography_1598.

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General analysis of the distribution of crystals orientation and dislocation density in the polycrystalline system is presented. Recovered information in diffraction of X-rays adopting is new to structure states of polycrystal. Shear phase transformations in metals — at the macroscopic and microscopic levels — become a clear process. Visualizing the advances is produced by program included in package delivered. Mathematical models developing, experimental design, optimal statistical estimation, simulation the system under study and evolution process on loading serves as instrumentation. To reduce advanced methods to research and studies problem-oriented software will promote when installed. Automation programs passed a testing in the National University of Science and Technology “MISIS” (The Russian Federation, Moscow). You score an advantage in theoretical and experimental research in the field of physics of metals.
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46

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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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 filters computing changes in time are asymmetric (causal), using data only from the past but not the future, and misestimate on locally asymmetric intensity signals of certain spatial frequencies. Since these limitations are not an artifact of the hardware but are inherent to the computations, they will affect any system and thus create illusions in man and machine.
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47

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

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48

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

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49

Noise Control in the Process Industries. Professional Engineering Publishing, 1993.

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50

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

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