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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
histogram, pulse metrics, and transition metrics of signals by using
the `dsp.Histogram`

, `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.

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