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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.

In addition to measuring statistics, you can also measure the pulse
metrics and transition metrics of signals by using the `dsp.PulseMetrics`

and
`dsp.TransitionMetrics`

System
objects.

Learn how moving statistics are calculated.

**Sliding Window Method and Exponential Weighting Method**

Learn the differences between the sliding window method and exponential weighting method.

**How Is a Moving Average Filter Different from an FIR Filter?**

Moving average filter is a special case of the FIR filter.

**Measure Statistics of Streaming Signals**

Compute the moving average of streaming signals using MATLAB functions and System objects.

**Remove High-Frequency Noise from Gyroscope Data**

Remove high-frequency noise using a median filter.

**Energy Detection in the Time Domain**

Detect the event when the signal energy crosses a particular threshold value.

**Measure Pulse and Transition Characteristics of Streaming Signals**

Learn how to compute the basic pulse and transition metrics of streaming signals.

**Variable-Size Signal Support DSP System Objects**

List of System objects which support variable-sized signals in DSP System Toolbox.