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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.
A use variance is a variance that authorizes a land use not normally permitted by the zoning ordinance. [2] Such a variance has much in common with a special-use permit (sometimes known as a conditional use permit). Some municipalities do not offer this process, opting to handle such situations under special use permits instead.
Schmidt, in particular, accomplished this by first deriving a complete geometric solution in the absence of noise, then cleverly extending the geometric concepts to obtain a reasonable approximate solution in the presence of noise. The resulting algorithm was called MUSIC (MUltiple SIgnal Classification) and has been widely studied.
A typical noise ordinance sets forth clear definitions of acoustic nomenclature and defines categories of noise generation; then numerical standards are established, so that enforcement personnel can take the necessary steps of warnings, fines or other municipal police power to rectify unacceptable noise generation.
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:
It computes a windowed periodogram of each one, and computes an array average, i.e. an array where each element is an average of the corresponding elements of all the periodograms. For stationary processes, this reduces the noise variance of each element by approximately a factor equal to the reciprocal of the number of periodograms.
The noise factor (a linear term) is more often expressed as the noise figure (in decibels) using the conversion: = The noise figure can also be seen as the decrease in signal-to-noise ratio (SNR) caused by passing a signal through a system if the original signal had a noise temperature of 290 K. This is a common way of expressing the noise ...
Also, the gain factor, +, depends on our confidence in the new data sample, as measured by the noise variance, versus that in the previous data. The initial values of x ^ {\displaystyle {\hat {x}}} and C e {\displaystyle C_{e}} are taken to be the mean and covariance of the aprior probability density function of x {\displaystyle x} .