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  2. Autoregressive model - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_model

    An AR(1) process is given by: = + where is a white noise process with zero mean and constant variance . (Note: The subscript on φ 1 {\displaystyle \varphi _{1}} has been dropped.) The process is weak-sense stationary if | φ | < 1 {\displaystyle |\varphi |<1} since it is obtained as the output of a stable filter whose input is white noise.

  3. Signal-to-noise ratio - Wikipedia

    en.wikipedia.org/wiki/Signal-to-noise_ratio

    If the noise has expected value of zero, as is common, the denominator is its variance, the square of its standard deviation σ N. The signal and the noise must be measured the same way, for example as voltages across the same impedance. Their root mean squares can alternatively be used according to:

  4. Signal averaging - Wikipedia

    en.wikipedia.org/wiki/Signal_averaging

    Signal averaging is a signal processing technique applied in the time domain, intended to increase the strength of a signal relative to noise that is obscuring it. By averaging a set of replicate measurements, the signal-to-noise ratio (SNR) will be increased, ideally in proportion to the square root of the number of measurements.

  5. Noise spectral density - Wikipedia

    en.wikipedia.org/wiki/Noise_spectral_density

    For thermal noise, its spectral density is given by N 0 = kT, where k is the Boltzmann constant in joules per kelvin (J/K), and T is the receiver system noise temperature in kelvins. The noise amplitude spectral density is the square root of the noise power spectral density, and is given in units such as volts per square root of hertz, V / H z ...

  6. Gaussian noise - Wikipedia

    en.wikipedia.org/wiki/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) 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.

  7. Additive white Gaussian noise - Wikipedia

    en.wikipedia.org/wiki/Additive_white_Gaussian_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.

  8. New York to test gun-detection systems for city's subway - AOL

    www.aol.com/news/york-test-gun-detection-systems...

    New York City will begin testing gun-detecting technology in a pilot program at several subway stations, Mayor Eric Adams said on Thursday. The announcement comes after several shootings on the ...

  9. White noise analysis - Wikipedia

    en.wikipedia.org/wiki/White_noise_analysis

    In probability theory, a branch of mathematics, white noise analysis, otherwise known as Hida calculus, is a framework for infinite-dimensional and stochastic calculus, based on the Gaussian white noise probability space, to be compared with Malliavin calculus based on the Wiener process. [1]