Owning Palette: Signal Generation VIs
Requires: Full Development System
Generates a pseudorandom pattern of ones and zeros. LabVIEW computes each element of bernoulli noise as if flipping a coin weighted by ones probability.
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initialize? controls the reseeding of the noise sample generator after the first call of the VI. If initialize? is TRUE, accepts a new state or new seed value and begins producing noise samples based on the new state or new seed value. If initialize? is FALSE, this VI maintains the initial internal seed state and resumes producing noise samples as a continuation of the previous noise sequence. The default is TRUE. |
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samples specifies the number of samples contained in the output array. samples must be greater than 0. The default is 128. |
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ones probability specifies the probability of a given element of bernoulli noise being true (1). ones probability must be in the range [0, 1]. The default is 0.5. |
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seed determines how to generate the internal seed state when initialize? is TRUE. If seed is greater than 0, this VI uses seed to generate the internal state directly. If seed is less than or equal to 0, this VI uses a random number to generate the internal state. seed must not be a multiple of 16384. If initialize? is FALSE, this VI ignores seed. The default is -1. |
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bernoulli noise contains the Bernoulli-distributed, pseudorandom pattern. |
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error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster. |
The Bernoulli Noise VI generates a pseudorandom pattern of ones and zeros that guarantees the Bernoulli distribution. The following equation defines the probability density function of the bernoulli noise:
P(X = i) = pi(1 – p)1 – i (i = 0, 1)
where p is the ones probability. If ones probability is 0.7, each element of bernoulli noise has a 70% chance of being one and a 30% chance of being zero. Bernoulli noise is a special case of Binomial noise.
The following equations define the mean value, µ, and the standard deviation value, 
, of the pseudorandom sequence:
µ = E{x} = p
You can use the initialize? input to generate a long random noise sequence block by block. The following block diagram shows two ways to generate identical 300-sample Bernoulli noise sequences with a seed of 2.
You also can use the Bernoulli Noise Waveform VI to generate a Bernoulli noise signal or the Discrete Random VI to generate random values from a bernoulli-distributed variate.