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Books on the topic 'Nonlinear reconstruction'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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1

Rahimi Tabar, M. Reza. Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18472-8.

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2

Schultz, Gerrit. Magnetic Resonance Imaging with Nonlinear Gradient Fields: Signal Encoding and Image Reconstruction. Springer Fachmedien Wiesbaden, 2013.

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3

Tsang, K. M. Reconstruction of linear and nonlinear continuous time models from discrete time sampled-data systems. University of Sheffield, Dept. of Control Engineering, 1990.

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4

Billings, S. A. Reconstruction of MIMO nonlinear differential equation models from the generalised frequency response function matrix. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

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5

R, Dougherty Edward, ed. Enhancement and restoration of digital documents: Statistical design of nonlinear algorithms. SPIE Optical Engineering Press, 1997.

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6

Tsang, K. M. A prediction-error estimation algorithm for the reconstruction of linear and nonlinear continuous time models from frequency response data. University of Sheffield, Dept. of Control Engineering, 1991.

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7

Polites, Michael E. A nonlinear estimator for reconstructing the angular velocity of a spacecraft without rate gyros. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1991.

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8

Magnetic Resonance Imaging With Nonlinear Gradient Fields Signal Encoding And Image Reconstruction. Springer-Verlag Berlin and Heidelberg GmbH &, 2013.

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9

Tabar, M. Reza Rahimi. Analysis and Data-Based Reconstruction of Complex Nonlinear Dynamical Systems: Using the Methods of Stochastic Processes. Springer, 2019.

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10

Scholl, Marija Strojnik, Victor Gamiz, and Paul Idell. Unconventional Imaging II. SPIE, 2006.

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11

SPIE. Unconventional Imaging: 31 July-1 August, 2005, San Diego, California, USA. SPIE-International Society for Optical Engine, 2005.

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12

Unconventional Imaging IV: 11 August 2008, San Diego, California, USA. SPIE, 2008.

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13

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Data Preprocessing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0006.

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Successful reconstruction of a shadow attractor provides preliminary empirical evidence that a signal isolated from observed time series data may be generated by deterministic dynamics. However, because we cannot reasonably expect signal processing to purge the signal of all noise in practice, and because noisy linear behavior can be visually indistinguishable from nonlinear behavior, the possibility remains that noticeable regularity detected in a shadow attractor may be fortuitously reconstructed from data generated by a linear-stochastic process. This chapter investigates how we can test th
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14

Limebeer, D. J. N., and Matteo Massaro. Dynamics and Optimal Control of Road Vehicles. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198825715.001.0001.

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The broad aim of this book is to provide a comprehensive coverage of the modelling and optimal control of both two‐ and four‐wheeled road vehicles. The first focus of this book is a review of classical mechanics and its use in building vehicle and tyre dynamic models. The second is nonlinear optimal control, which is used to solve a range of minimum‐time, minimum‐fuel, and track curvature reconstruction problems. As is known classically, all thismaterial is bound together by the calculus of variations and stationary principles. The treatment of this material is supplemented with a large number
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15

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Entropy and Surrogate Testing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0005.

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Reconstructing real-world system dynamics from time series data on a single variable is challenging because real-world data often exhibit a highly volatile and irregular appearance potentially driven by several diverse factors. NLTS methods help eliminate less likely drivers of dynamic irregularity. We set a benchmark for regular behavior by investigating how linear systems of ODEs are restricted to exponential and periodic dynamics, and illustrating how irregular behavior can arise if regular linear dynamics are corrupted with noise or shift over time (i.e., nonstationarity). We investigate h
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16

D, Lightsey W., and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. A nonlinear estimator for reconstructing the angular velocity of a spacecraft without rate gyros. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1991.

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