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A random vector (that is, a random variable with values in R n) is said to be a white noise vector or white random vector if its components each have a probability distribution with zero mean and finite variance, [clarification needed] and are statistically independent: that is, their joint probability distribution must be the product of the ...
First, white noise is a generalized stochastic process with independent values at each time. [12] Hence it plays the role of a generalized system of independent coordinates, in the sense that in various contexts it has been fruitful to express more general processes occurring e.g. in engineering or mathematical finance, in terms of white noise.
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. The modifiers denote specific characteristics: Additive because it is added to any noise that might be intrinsic to the information system.
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.
Created Date: 8/30/2012 4:52:52 PM
It is also known as differentiated white noise, due to its being the result of the differentiation of a white noise signal. Due to the diminished sensitivity of the human ear to high-frequency hiss and the ease with which white noise can be electronically differentiated (high-pass filtered at first order), many early adaptations of dither to ...
Smart White Noise Machine. With 10 volume settings and thousands of positive reviews, this white noise machine from SNOOZ is anything but a snoozefest—and it comes with an easy-to-use app for ...
Kuo’s book White Noise Distribution Theory provided an introduction to the fundamentals of white noise theory and offered insights into its mathematical foundations and practical applications. The book showed the relevance of white noise analysis in a series of stochastic cable equations. [23]