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Books on the topic 'Gaussian noise'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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1

Kassam, Saleem A. Signal Detection in Non-Gaussian Noise. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3834-8.

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2

Kassam, Saleem A. Signal Detection in Non-Gaussian Noise. Springer New York, 1988.

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3

1925-, Thomas John Bowman, ed. Signal detection in non-Gaussian noise. Springer-Verlag, 1988.

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4

White noise distribution theory. CRC Press, 1996.

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5

Obata, Nobuaki. White noise calculus and Fock space. Springer-Verlag, 1994.

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6

Hida, Takeyuki. Lectures on white noise functionals. World Scientific, 2008.

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7

Gualtierotti, Antonio F. Detection of Random Signals in Dependent Gaussian Noise. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22315-5.

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8

Middleton, D. First-order non-gaussian class C interference models and their associated threshold detection algorithms. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1987.

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9

Middleton, D. First-order non-gaussian class C interference models and their associated threshold detection algorithms. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1987.

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10

Middleton, D. First-order non-gaussian class C interference models and their associated threshold detection algorithms. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1987.

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11

Middleton, D. First-order non-gaussian class C interference models and their associated threshold detection algorithms. U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1987.

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12

Hippenstiel, Ralph Dieter. Analysis using Bi-spectral related technique. Naval Postgraduate School, 1993.

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13

Mandelbrot, Benoit B. Gaussian self-affinity and fractals: Globality, the earth, 1/f noise, and R/S. Springer, 2002.

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14

Kallianpur, G. White noise theory of prediction, filtering, and smoothing. Gordon and Breach Science Publishers, 1988.

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15

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

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16

White noise: An infinite dimensional calculus. Kluwer Academic Publishers, 1993.

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17

Introduction to Hida distributions. World Scientific, 2012.

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18

Si, Si, and Takeyuki Hida. White Noise: Functionals of Gaussian and Other Noises. World Scientific Publishing Co Pte Ltd, 2016.

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19

Lectures On White Noise Functions. World Scientific Publishing Company, 2008.

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20

Gualtierotti, Antonio F. Detection of Random Signals in Dependent Gaussian Noise. Springer, 2015.

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21

Gualtierotti, Antonio F. Detection of Random Signals in Dependent Gaussian Noise. Springer, 2015.

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22

Gualtierotti, Antonio F. Detection of Random Signals in Dependent Gaussian Noise. Springer, 2017.

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23

Detection of a chi-square fluctuating target in Gaussian noise. Naval Research Laboratory, 1994.

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24

Modelling stock market excess returns by Markov modulated Gaussian noise. University of Glasgow, Department of Economics, 1998.

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25

The Utility of Higher-Order Statistics in Gaussian Noise Suppression. Storming Media, 2003.

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26

Kanazawa, Kiyoshi. Statistical Mechanics for Athermal Fluctuation: Non-Gaussian Noise in Physics. Springer, 2018.

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27

Kanazawa, Kiyoshi. Statistical Mechanics for Athermal Fluctuation: Non-Gaussian Noise in Physics. Springer, 2017.

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28

Kim, Young Joo. Performance of digital communication receivers operating in the presence of white Gaussian nonstationary noise. 1986.

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29

Trees, Harry L. Van. Detection, Estimation, and Modulation Theory, Part III: Radar-Sonar Signal Processing and Gaussian Signals in Noise. Wiley & Sons, Limited, John, 2002.

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30

Trees, Harry L. Van. Radar-Sonar Signal Processing and Gaussian Signals in Noise (Detection, Estimation, and Modulation Theory, Part III). Wiley-Interscience, 2001.

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31

Trees, Harry L. Van. Detection, Estimation, and Modulation Theory, Part III: Radar-Sonar Signal Processing and Gaussian Signals in Noise. Wiley & Sons, Incorporated, John, 2008.

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32

Si, Si. Introduction to Hida Distributions. World Scientific Publishing Co Pte Ltd, 2011.

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33

1948-, Farina A., and Institution of Electrical Engineers, eds. Optimised radar processors. Peregrinus, 1987.

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34

Berber, Stevan. Discrete Communication Systems. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198860792.001.0001.

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The book present essential theory and practice of the discrete communication systems design, based on the theory of discrete time stochastic processes, and their relation to the existing theory of digital communication systems. Using the notion of stochastic linear time invariant systems, in addition to the orhogonality principles, a general structure of the discrete communication system is constructed in terms of mathematical operators. Based on this structure, the MPSK, MFSK, QAM, OFDM and CDMA systems, using discrete modulation methods, are deduced as special cases. The signals are processe

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35

Fyodorov, Yan, and Dmitry Savin. Condensed matter physics. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.35.

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This article discusses some applications of concepts from random matrix theory (RMT) to condensed matter physics, with emphasis on phenomena, predicted or explained by RMT, that have actually been observed in experiments on quantum wires and quantum dots. These observations range from universal conductance fluctuations (UCF) to weak localization, non-Gaussian thermopower distributions, and sub-Poissonian shot noise. The article first considers the UCF phenomenon, nonlogarithmic eigenvalue repulsion, and sub-Poissonian shot noise in quantum wires before analysing level and wave function statist

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36

Probability of Symbol Error for Coherent and Non-Coherent Detection of M-ary Frequency-Shift Keyed (MFSK) Signals Affected by Co-Channel Interference and Additive White Gaussian Noise (AWGN) in a. Storming Media, 2000.

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37

Nakura, Gou. An Approach to Hybrid Smoothing for Linear Discrete-Time Systems with Non-Gaussian Noises. INTECH Open Access Publisher, 2012.

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