Explore: Gaussian Noise

In signal processing theory, Gaussian noise, named after Carl Friedrich Gauss, is a kind of signal noise that has a probability density function (pdf)

Additive white Gaussian noise (AWGN) is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature

of white noise). In particular, if each sample has a normal distribution with zero mean, the signal is said to be additive white Gaussian noise. The samples

actually a set of parameters. For example, if the mixture components are Gaussian distributions, there will be a mean and variance for each component. If

same for any s). The increment process X(t) is known as fractional Gaussian noise. There is also a generalization of fractional Brownian motion: n-th

In mathematics, a Gaussian function, often simply referred to as a Gaussian, is a function of the base form f ( x ) = exp ⁡ ( − x 2 ) {\displaystyle f(x)=\exp(-x^{2})}

x\geq 0} . The error rates quoted here are those in additive white Gaussian noise (AWGN). These error rates are lower than those computed in fading channels

Active noise control (ANC), also known as noise cancellation (NC), or active noise reduction (ANR), is a method for reducing unwanted sound by the addition

In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function (named after mathematician

bandwidth in the presence of the noise interference, assuming that the signal power is bounded, and that the Gaussian noise process is characterized by a