Books on the topic 'Kernel'

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1

Krista, Sands, and Sands Krista, eds. Kernel. Windhoek, Namibia: Namibia Scientific Society, 1999.

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2

Owhadi, Houman, Clint Scovel, and Gene Ryan Yoo. Kernel Mode Decomposition and the Programming of Kernels. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82171-5.

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3

Wand, M. P., and M. C. Jones. Kernel Smoothing. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4493-1.

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4

C, Jones M., ed. Kernel smoothing. London: Chapman & Hall, 1995.

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5

Salzman, Peter Jay. The Linux Kernel Module programming guide ; Kernel 2.6. London: SoHo Books, 2009.

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6

Larkins, B., ed. Maize kernel development. Wallingford: CABI, 2017. http://dx.doi.org/10.1079/9781786391216.0000.

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7

Hirukawa, Masayuki. Asymmetric Kernel Smoothing. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5466-2.

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8

Rosen, Rami. Linux Kernel Networking. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6197-1.

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9

Love, Robert. Linux Kernel Development. Indianapolis, Ind: Sams, 2004.

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10

Love, Robert. Linux Kernel Development. Upper Saddle River: Pearson Education, 2005.

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11

Linux kernel development. 3rd ed. Upper Saddle River, NJ: Addison-Wesley, 2010.

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12

Michael, Beck, ed. Linux kernel internals. Harlow, England: Addison-Wesley, 1996.

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13

Michael, Beck, ed. Linux kernel internals. 2nd ed. Harlow, England: Addison-Wesley, 1998.

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14

Love, Robert. Linux Kernel Development. Upper Saddle River: Pearson Education, 2005.

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15

Linux Kernel Development. 2nd ed. Indianapolis, Ind: Novell Press, 2005.

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16

Horová, Ivana. Kernel smoothing in MATLAB: Theory and practice of Kernel smoothing. Singapore: World Scientific Pub. Co., 2012.

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17

Jorgenson, Jay, and Lynne Walling, eds. The Ubiquitous Heat Kernel. Providence, Rhode Island: American Mathematical Society, 2006. http://dx.doi.org/10.1090/conm/398.

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18

Mauerer, Wolfgang. Professional Linux Kernel Architecture. New York: John Wiley & Sons, Ltd., 2008.

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19

The Linux Kernel book. Chichester, [Eng.]: Wiley, 1998.

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20

Graphical Kernel System (GKS). Gaithersburg, MD: Computer Systems Laboratory, National Institute of Standards and Technology, 1991.

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21

Léon, Bottou, ed. Large-scale kernel machines. Cambridge, Mass: The MIT Press, 2007.

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22

Hartley, Michael J. Neoclassical econometrics: The kernel. [s.l.]: World Bank, 1985.

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23

From kernel to corn. Minneapolis: Lerner Publications Company, 2012.

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24

Marco, Cesati, ed. Understanding the Linux kernel. Beijing: O'Reilly, 2001.

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25

Marco, Cesati, ed. Understanding the Linux kernel. 2nd ed. Beijing: O'Reilly, 2003.

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26

Robin, Nelson. From kernel to corn. Minneapolis: Lerner, 2003.

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27

Mauerer, Wolfgang. Professional Linux kernel architecture. Indianapolis, Ind: Wiley Pub., 2008.

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28

The little kernel cookbook. New York: Crown, 1991.

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29

National Bureau of Standards. Graphical Kernel System (GKS). [Gaithersburg, MD]: U.S. Dept. of Commerce, National Bureau of Standards, 1986.

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30

Bayan, Rami et al. Requirements for a Real-Time Ada Runtime Kernel and Proposed Kernel Interface. Darmstadt: GMD, 1986.

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31

Linux Kernel Programming: A Comprehensive Guide to Kernel Internals, Writing Kernel Modules, and Kernel Synchronization. Packt Publishing, Limited, 2021.

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32

Owhadi, Houman, Clint Scovel, and Gene Ryan Yoo. Kernel Mode Decomposition and the Programming of Kernels. Springer International Publishing AG, 2021.

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33

Xu, Yuesheng, and Qi Ye. Generalized Mercer Kernels and Reproducing Kernel Banach Spaces. American Mathematical Society, 2019.

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34

Wand, M. P., and M. C. Jones. Kernel Smoothing. Chapman and Hall/CRC, 1994. http://dx.doi.org/10.1201/b14876.

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35

Mashhoon, Bahram. Acceleration Kernel. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803805.003.0003.

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The phenomenon of spin-rotation coupling provides the key to the determination of the kernel. Imagine an observer rotating in the positive sense about the direction of propagation of an incident plane monochromatic electromagnetic wave of positive helicity. Using the locality postulate, the field as measured by the rotating observer can be determined. If the observer rotates with the same frequency as the wave, the measured radiation field loses its temporal dependence. By a mere rotation, observers could in principle stay at rest with respect to an incident positive-helicity wave. To avoid this possibility, we assume that a basic radiation field cannot stand completely still with respect to an accelerated observer. This basic principle eventually leads to the determination of the kernel and a nonlocal theory of accelerated systems that is in better agreement with quantum mechanics than the standard theory based on the hypothesis of locality.
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36

Jones, M. C., and M. P. Wand. Kernel Smoothing. Taylor & Francis Group, 1994.

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37

Fares, Hend, Maha Eladwi, and Abdo a. Husseiny. Wheat Kernel. Independently Published, 2019.

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38

Collective, The Reboot. Kernel Magazine. The Reboot Collective, 2022.

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39

Jones, M. C., and M. P. Wand. Kernel Smoothing. Taylor & Francis Group, 1994.

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40

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 functional estimate. Uniform asymptotic results are then presented. The article proceeds by reviewing kernel methods in semiparametric functional regression such as single functional index regression and partial linear functional regression. It also looks at the use of kernels for additive functional regression and concludes by assessing the impact of kernel methods on practical real-data analysis involving functional (curves) datasets.
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41

Anger, Larry, Robert O'Neill, and Karen Davy. American Kernel Lessons: Beginning Level (American Kernel Lessons). Longman Publishing Group, 1989.

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42

Linux Kernel Debugging: Leverage Proven Tools and Advanced Techniques to Effectively Debug Linux Kernels and Kernel Modules. Packt Publishing, Limited, 2022.

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43

O'Neill, Robert, Edwin T. Cornelius, and Gay N. Washburn. American Kernel Lessons: Advanced Student Book (American Kernel Lessons). Longman Publishing Group, 1989.

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44

Mastering Linux Kernel Development: A kernel developer's reference manual. Packt Publishing - ebooks Account, 2017.

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45

SAMUEL, Davidson. Kernel of Wheat. Roperpenberthy Publishing, Limited, 2015.

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46

Rusling. Linux Kernel + CDROM. International Thomson Computer Press, 1998.

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47

Gieser, Suzanne. The Innermost Kernel. Springer, 2005.

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48

Kernel of Truth. Penguin Publishing Group, 2016.

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49

The Innermost Kernel. Berlin/Heidelberg: Springer-Verlag, 2005. http://dx.doi.org/10.1007/b138419.

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50

Herbrich, Ralf. Learning Kernel Classifiers. The MIT Press, 2001. http://dx.doi.org/10.7551/mitpress/4170.001.0001.

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