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Books on the topic 'Linear Convolution'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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1

O'Halloran, Patrick. A convolution based approach for simulating linear circuit blocks defined in the frequency domain within a nonlinear time domain simulator. University College Dublin, 1997.

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2

Bozhinov, Nikolai. Convolutional representations of commutants and multipliers. Publishing House of the Bulgarian Academy of Sciences, 1988.

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3

Bozhinov, Nikolai. Convolutional representations of commutants and multipliers. Pub. House of the Bulgarian Academy of Sciences, 1988.

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4

United States. National Aeronautics and Space Administration., ed. Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer: Final report. National Aeronautics and Space Administration, 1985.

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5

United States. National Aeronautics and Space Administration., ed. Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer: Final report, appendix 5. National Aeronautics and Space Administration, 1985.

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6

United States. National Aeronautics and Space Administration., ed. Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer: Final report, appendix 5. National Aeronautics and Space Administration, 1985.

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7

United States. National Aeronautics and Space Administration., ed. Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer: Final report : [appendices]. National Aeronautics and Space Administration, 1985.

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8

Böttcher, Albrecht. Convolution Operators and Factorization of Almost Periodic Matrix Functions. Springer, 2012.

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9

Böttcher, Albrecht, Yuri I. Karlovich, and Ilya M. Spitkovsky. Convolution Operators and Factorization of Almost Periodic Matrix Functions. Birkhauser Verlag, 2012.

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10

Böttcher, Albrecht, Yuri I. Karlovich, and Ilya M. Spitkovsky. Convolution Operators and Factorization of Almost Periodic Matrix Functions (Operator Theory: Advances and Applications). Birkhäuser Basel, 2002.

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11

Karlovich, Yuri I., Albrecht Bottcher, and Ilya M. Spitkovskii. Convolution Operators and Factorization of Almost Periodic Matrix Functions (Operator Theory: Advances and Applications, 131). Birkhauser, 2002.

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12

Ferraty, Frédéric, and Philippe Vieu. Kernel Regression Estimation for Functional Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.4.

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This article provides an overview of recent nonparametric and semiparametric advances in kernel regression estimation for functional data. In particular, it considers the various statistical techniques based on kernel smoothing ideas that have recently been developed for functional regression estimation problems. The article first examines nonparametric functional regression modelling before discussing three popular functional regression estimates constructed by means of kernel ideas, namely: the Nadaraya-Watson convolution kernel estimate, the kNN functional estimate, and the local linear fun
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13

Convolutional calculus. 2nd ed. Kluwer Academic, 1990.

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14

Recovery of Unknown Constraint Length and Encoder Polynomials for Rate 1/2 Linear Convolutional Encoders. Storming Media, 1999.

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15

Chance, Kelly, and Randall V. Martin. Line Shapes. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199662104.003.0006.

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Line shapes describe how absorption and emission are spectrally distributed around the line positions formed by rotational, vibrational, and electronic transitions. Line shapes arise from the different processes that spectrally broaden the absorption and emission of radiation. Optical thickness and equivalent width are shown to be fundamentally related to line shape. The fundamental line shape functions for atmospheres including the Gaussian line shape due to molecular motion and the Lorentzian line shape from lifetime broadening, including collision (pressure) broadening are described. Their
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16

Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer: Final report : [appendices]. National Aeronautics and Space Administration, 1985.

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17

Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer: Final report. National Aeronautics and Space Administration, 1985.

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18

Butz, Martin V., and Esther F. Kutter. Primary Visual Perception from the Bottom Up. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0008.

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This chapter addresses primary visual perception, detailing how visual information comes about and, as a consequence, which visual properties provide particularly useful information about the environment. The brain extracts this information systematically, and also separates redundant and complementary visual information aspects to improve the effectiveness of visual processing. Computationally, image smoothing, edge detectors, and motion detectors must be at work. These need to be applied in a convolutional manner over the fixated area, which are computations that are predestined to be solved
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