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

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

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})}

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 probability theory and statistics, a Gaussian process is a stochastic process (a collection of random variables indexed by time or space), such that

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