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Quantifying Decoherence of Gaussian Noise Channels
Journal of Statistical Physics, 2021The authors review the basic properties of Gaussian Noise channels. These play an important role in continuous variable quantum information. They introduce an information- theoretic measure for the decoherence of optical states caused by these channels in terms of averaged Wigner-Yanase skew information, and derive a complementarity relation between ...
Yue Zhang, Shunlong Luo
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Coupled detection- estimation of Gaussian processes in Gaussian noise
IEEE Transactions on Information Theory, 1972This paper considers the joint detection and estimation of Gauss-Markov processes in white Gaussian noise, where the operations of detection and estimation are strongly coupled. Explicit Bayes' optimal recursive estimation and detection rules are derived, and it is shown that the resulting optimal receiver is amenable to a causal estimator-correlator ...
Jaffer, Amin G., Gupta, Someshwar C.
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SPIE Proceedings, 2003
In semiclassical theory, light is a classical electromagnetic wave and the fundamental source of photodetection noise is the shot effect arising from the discreteness of the electron charge. In quantum theory, light is a quantum-mechanical entity and the fundamental source of photodetection noise comes from measuring the photon-flux operator.
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In semiclassical theory, light is a classical electromagnetic wave and the fundamental source of photodetection noise is the shot effect arising from the discreteness of the electron charge. In quantum theory, light is a quantum-mechanical entity and the fundamental source of photodetection noise comes from measuring the photon-flux operator.
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Revisiting fractional Gaussian noise
Physica A: Statistical Mechanics and its Applications, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Ming, Sun, Xichao, Xiao, Xi
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Are Impulse Responses Gaussian Noises?
The Journal of the Acoustical Society of America, 2011Many years ago, Manfred Schroeder proved that transfer functions in rooms follow complex Gaussian distributions. This property was extended to impulse responses by the author, following a suggestion by Moorer for simulating impulse responses. More recently, several authors have checked again the later property with modern signal analysis tools.
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Is non-Gaussian noise preferred over Gaussian noise in signal detection?
The Journal of the Acoustical Society of America, 1982The generalized Gaussian class of noise densities is considered relative to detection probabilities for a recently proposed locally optimum discrete-time stochastic signal detector [H. V. Poor and J. B. Thomas, J. Acoust. Soc. Am. 63, 75–80 (1978)].
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Complex Gaussian noise moments
IEEE Transactions on Information Theory, 1971The problem of the computation of moments of nonzero mean circularly complex Gaussian noise is treated. The noise need not be symmetric about the carrier frequency. In particular, the second-order moments are computed, and expansions are given. The necessary univariate and bivariate complex Hermite polynomials are discussed.
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IEEE Transactions on Information Theory, 1970
The first-passage time problem for a continuous one-dimensional Markov process is reviewed, and upper bounds are obtained for both the probability of failure (or passage and the moments of the time to failure, in terms of the mean time to failure.
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The first-passage time problem for a continuous one-dimensional Markov process is reviewed, and upper bounds are obtained for both the probability of failure (or passage and the moments of the time to failure, in terms of the mean time to failure.
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DETECTING SINUSOIDS IN NON‐GAUSSIAN NOISE
Journal of Time Series Analysis, 1992Abstract.Spectral analysis is a well‐established procedure for detecting harmonic signals in a noisy environment. Much research has been done on methods that use second‐order statistics (i.e. the autocovariance function and power spectrum) such as Whittle's test, Bartlett's test, Hannan's test and the PriestleyP(Λ) test. When the noise is non‐Gaussian,
Lii, K.-S., Tsou, T.-H.
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IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989
Algorithm description, block diagram, and complete C source code for a fast Gaussian noise generator which uses only integer operations and table lookups are presented. The algorithm achieves its speed of computation by using table lookups to eliminate the need for evaluating transcendental functions.
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Algorithm description, block diagram, and complete C source code for a fast Gaussian noise generator which uses only integer operations and table lookups are presented. The algorithm achieves its speed of computation by using table lookups to eliminate the need for evaluating transcendental functions.
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