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In signal processing theory, Gaussian noise, named after Carl Friedrich Gauss, is a kind of signal noise that has a probability density function (pdf) equal to that of the normal distribution (which is also known as the Gaussian distribution). [1] [2] In other words, the values that the noise can take are Gaussian-distributed.
An example of a random vector that is Gaussian white noise in the weak but not in the strong sense is = [,] where is a normal random variable with zero mean, and is equal to + or to , with equal probability. These two variables are uncorrelated and individually normally distributed, but they are not jointly normally distributed and are not ...
Gaussian because it has a normal distribution in the time domain with an average time domain value of zero (Gaussian process). Wideband noise comes from many natural noise sources, such as the thermal vibrations of atoms in conductors (referred to as thermal noise or Johnson–Nyquist noise), shot noise, black-body radiation from the earth and ...
Thermal noise is approximately white, meaning that its power spectral density is nearly equal throughout the frequency spectrum. The amplitude of the signal has very nearly a Gaussian probability density function. A communication system affected by thermal noise is often modelled as an additive white Gaussian noise (AWGN) channel.
When these assumptions are satisfied, the following covariance matrix K applies for the 1D profile parameters , , and under i.i.d. Gaussian noise and under Poisson noise: [8] = , = , where is the width of the pixels used to sample the function, is the quantum efficiency of the detector, and indicates the standard deviation of the measurement noise.
In the simple version above, the signal and noise are fully uncorrelated, in which case + is the total power of the received signal and noise together. A generalization of the above equation for the case where the additive noise is not white (or that the / is not constant with frequency over the bandwidth) is obtained by treating the channel as many narrow, independent Gaussian ...
Adding controlled noise from predetermined distributions is a way of designing differentially private mechanisms. This technique is useful for designing private mechanisms for real-valued functions on sensitive data. Some commonly used distributions for adding noise include Laplace and Gaussian distributions.
Noise that has a frequency spectrum of predominantly zero power level over all frequencies except for a few narrow bands or spikes. Note: An example of black noise in a facsimile transmission system is the spectrum that might be obtained when scanning a black area in which there are a few random white spots. Thus, in the time domain, a few ...