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Libros sobre el tema "Numerical Synthesis"

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Publicado: 14 de marzo de 2023

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1

Sriwattanathamma, Jen, ed. Robot kinematics: Symbolic automation and numerical synthesis. Norwood, N.J: Ablex Pub. Corp., 1990.

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2

Michiel, Hazewinkel, Jurkovich Ray, Paelinck Jean H. P y Erasmus Universiteit Rotterdam. Steering Committee on Interdisciplinary Studies., eds. Bifurcation analysis: Principles, applications, and synthesis. Dordrecht: D. Reidel Pub. Co., 1985.

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3

Meister, Neil E. Application of numerical optimization techniques to surface combatant design synthesis. Monterey, Calif: Naval Postgraduate School, 1998.

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4

Bilbao, Stefan D. Numerical sound synthesis: Finite difference schemes and simulation in musical acoustics. [Hoboken]: John Wiley & Sons, 2009.

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5

Meta-analysis, decision analysis, and cost-effectiveness analysis: Methods for quantitative synthesis in medicine. New York: Oxford University Press, 1994.

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6

Wilson, R. Gale. Numerical aperture limits on efficient ball lens coupling of laser diodes to single-mode fibers with defocus to balance spherical aberration. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.

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7

Wilson, R. Gale. Numerical aperture limits on efficient ball lens coupling of laser diodes to single-mode fibers with defocus to balance spherical aberration. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.

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8

Lorenzo, Pareschi y Russo Giovanni, eds. Modelling and numerics of kinetic dissipative systems. Hauppauge, N.Y: Nova Science Publishers, 2005.

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9

Bilbao, Stefan. Numerical Sound Synthesis. Wiley & Sons, Incorporated, John, 2009.

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10

Holloway, Ian D. y Daniel Ansari. Numerical Symbols. Editado por Roi Cohen Kadosh y Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.56.

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Human culture is founded upon an understanding of mathematics. Mathematics, in turn, is built upon a basic competency for abstracting and symbolizing numerical information. Although cognitive science has made great strides in the characterization of human knowledge, the understanding and use of numerical symbols remains largely unexplored. In this chapter we synthesize the current scientific understanding of numerical symbol processing using a synthesis of behavioral and neuroscientific findings. In particular, the chapter focuses on two interrelated topics: the processing of the semantics of numerical symbols and the processing of numerical symbols as audio-visual percepts. We conclude by proposing several avenues of inquiry about numerical symbols that can be traversed by future researchers.
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11

Hazewinkel, Michiel, R. Jurkovich y J. H. P. Pealinck. Bifurcation Analysis: Principles, Applications and Synthesis. Springer, 2011.

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12

Application of Numerical Optimization Techniques to Surface Combatant Design Synthesis. Storming Media, 1998.

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13

Sarris, Costas D. Adaptive Mesh Refinement for Time-Domain Numerical Electromagnetics (Synthesis Lectures on Computational Electromagnetics). Morgan and Claypool Publishers, 2007.

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14

Bilbao, Stefan. Numerical Sound Synthesis: Finite Difference Schemes and Simulation in Musical Acoustics. Wiley & Sons, Incorporated, John, 2009.

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15

Bilbao, Stefan. Numerical Sound Synthesis: Finite Difference Schemes and Simulation in Musical Acoustics. Wiley & Sons, Limited, John, 2009.

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16

Johnson, Joel. Numerical Methods in Electromagnetics for Remote Sensing (Synthesis Lectures on Computational Electromagnetics). Morgan & Claypool Publishers, 2007.

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17

Introduction to the Finite Element Method in Electromagnetics (Synthesis Lectures on Computational Electromagnetics). Morgan & Claypool Publishers, 2006.

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18

Kantartzis, Nikolaos V. Time-domain Modeling in Modern Emc Computational Electromagnetics I: Numerical Techniques (Synthesis Lectures on Computational Electromagnetics). Morgan & Claypool Publishers, 2007.

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19

Madan, Deepak S. y Iver E. Anderson. Computational and Numerical Techniques in Powder Metallurgy: Proceedings of a Symposium Sponsored by Powder Metallurgy Committee and Synthesis and A. Minerals, Metals, & Materials Society, 1993.

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20

(Editor), Constantine Balanis, ed. Recent Advances in Integral Equation Solvers in Electromagnetics (Synthesis Lectures on Computational Electromagnetics). Morgan & Claypool, 2007.

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21

Watts, Robert. Essentials of Applied Mathematics for Scientists and Engineers (Synthesis Lectures on Engineering). Morgan and Claypool Publishers, 2007.

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22

Petitti, Diana B. Meta-Analysis, Decision Analysis, and Cost-Effectiveness Analysis: Methods for Quantitative Synthesis in Medicine. Oxford University Press, 1999.

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23

Meta-Analysis, Decision Analysis, and Cost-Effectiveness Analysis: Methods for Quantitative Synthesis in Medicine. Oxford University Press, USA, 2000.

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24

Huffaker, Ray, Marco Bittelli y Rodolfo Rosa. Empirically Detecting Causality. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0008.

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Phenomenological models mathematically describe relationships among empirically observed phenomena without attempting to explain underlying mechanisms. Within the context of NLTS, phenomenological modeling goes beyond phase space reconstruction to extract equations governing real-world system dynamics from a single or multiple observed time series. Phenomenological models provide several benefits. They can be used to characterize the dynamics of variable interactions; for example, whether an incremental increase in one variable drives a marginal increase/decrease in the growth rate of another, and whether these dynamic interactions follow systematic patterns over time. They provide an analytical framework for data driven science still searching for credible theoretical explanation. They set a descriptive standard for how the real world operates so that theory is not misdirected in explaining fanciful behavior. The success of phenomenological modeling depends critically on selection of governing parameters. Model dimensionality, and the time delays used to synthesize dynamic variables, are guided by statistical tests run for phase space reconstruction. Other regression and numerical integration parameters can be set on a trial and error basis within ranges providing numerical stability and successful reproduction of empirically-detected dynamics. We illustrate phenomenological modeling with solutions of the Lorenz model so that we can recognize the dynamics that need to be reproduced.
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25

Modeling And Numerics of Kinetic Dissipative Systems. Nova Science Publishers, 2006.

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