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measurements and statistics -凯发k8网页登录

bistate waveform measurements, variance, histogram, autocorrelation

you can use dsp system toolbox™ blocks and system objects to measure the moving statistics and stationary statistics of signals in matlab® and simulink®. moving statistics refer to the statistics of streaming signals that change with time. in the sliding window method for computing moving statistics, a window of specified length moves over the data sample by sample as the new data comes in. the objects and blocks compute the statistics of the data within this window. the exponential weighting method applies a set of weights to the data samples and processes the weighted data. these weights are computed recursively based on the age of the data. for stationary statistics, the blocks and objects compute the statistics of all the data that is available in a batch.

objects

median filter
moving average
moving maximum
moving minimum
moving root mean square
moving standard deviation
moving variance
powermetermeasure power and ccdf of the power of voltage signal
peak-to-root-mean-square value of vector

blocks

median filter
moving average
moving maximum
moving minimum
moving rms
moving standard deviation
moving variance
measure power and the ccdf of the power of voltage signal in simulink
autocorrelation of n-d array
cross-correlation of two inputs
maximum values of input or sequence of inputs
find mean value of input or sequence of inputs
median value of input
minimum values of input or sequence of inputs
root mean square value of input or sequence of inputs
sort input elements by value
standard deviation of input or sequence of inputs
variance of input or sequence of inputs
histogram of input or sequence of inputs

topics

moving statistics


  • learn how moving statistics are calculated.

  • learn the differences between the sliding window method and exponential weighting method.

  • moving average filter is a special case of the fir filter.

  • compute the moving average of streaming signals using matlab functions and system objects.

  • compare the sliding window averaging method and the exponentially weighted averaging method in simulink using the moving average block.

  • compute moving rms using both the sliding window method and the exponential weighting method.

  • compare the sliding window standard deviation method and the exponentially weighted standard deviation method in simulink using the moving standard deviation block.

  • compare the sliding window variance method and the exponentially weighted variance method in simulink using the moving variance block.

stationary statistics


  • simulink model example to compute the mean using the mean block.

  • model a sliding window using the buffer block. the mean block use this window to compute the mean.

  • simulink model example to compute the running mean using the mean block.

  • simulink model example to compute the maximum using the maximum block.

  • simulink model example to compute the running maximum using the maximum block.

  • simulink model example to compute the minimum using the minimum block.

  • simulink model example to compute the running minimum using the minimum block.

  • use the rms block to compute the rms of a noisy square wave signal.

  • simulink model example that explains how the histogram bin boundaries are calculated based on the input.

  • use the standard deviation block to compute the standard deviation.

  • use the standard deviation block to compute the running standard deviation.

  • use the variance block to compute the variance.

power measurements


  • compute average power, peak power, and peak-to-average power ratio of voltage signal.

  • compute relative power and probability, and plot the ccdf curve in array plot.

applications


  • remove high-frequency noise using a median filter.

  • detect the event when the signal energy crosses a particular threshold value.

variable-size signal support


  • list of system objects that support variable-sized signals in dsp system toolbox.
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