Gaussian White Noise (Not in Base Package)

Generates a Gaussian-distributed, pseudorandom pattern whose statistical profile is (µ,sigma) = (0,s), where s is the absolute value of the specified standard deviation. Details

samples is the number of samples of the Gaussian Noise Pattern. samples must be greater than or equal to 0. The default is 128. If samples is less than zero, the VI sets Gaussian Noise Pattern to an empty array but does not return an error.
standard deviation is the standard deviation of the Gaussian probability density function. The default is 1.0.
seed, when greater than 0, causes reseeding of the noise sample generator. The default is -1. If seed is less than or equal to 0, the noise generator is not reseeded and resumes producing noise samples as a continuation of the previous noise sequence. This is the default behavior.
Gaussian Noise Pattern returns the Gaussian-distributed, pseudorandom pattern. The largest Gaussian Noise Pattern that the VI can generate depends upon the amount of memory in your system and is theoretically limited to 2,147,483,647 (2^31 - 1) elements.
error returns any error or warning from the VI. Refer to Signal Processing Error Codes for more information about these conditions.

Gaussian White Noise Details

The Gaussian White Noise VI generates the Gaussian-distributed pseudorandom sequence using a modified version of the Very-Long-Cycle random number generator algorithm based upon the Central Limit Theorem. The pseudorandom number generator implements a triple-seeded linear congruential algorithm. Given that the probability density function, f(x), of the Gaussian-distributed Gaussian Noise Pattern is

,

where s is the absolute value of the specified standard deviation, and that you can compute the expected values, E{·}, using the formula

,

then the expected mean value, µ, and the expected standard deviation value, , of the pseudorandom sequence are

The pseudorandom sequence produces approximately samples before the pattern repeats itself.