Moving Minimum
Moving minimum.
Description
The Moving Minimum block determines the moving minimum of the input signal on each channel regardless of time. The block uses the sliding window method to determine the moving minimum. In this method, the block determines the minimum from the data in a window of a specified length that moves through each channel sample by sample.
Ports
Input
Port_1 - data input
vector-row | vector-column | matrix
Input data on which the moving minimum is determined by the sliding window method. The block accepts multichannel input data of dimension to , where and , with real or complex values. The block also accepts variable dimension input data. The size of each input channel can be changed during the simulation, but the number of channels cannot be changed.
Data types: Float16, Float32, Float64, Int8, Int16, Int32, Int64, Int128, UInt8, UInt16, UInt32, UInt64, UInt128.
Support for complex numbers: Yes
Output
Port_2 - moving minimum output
vector-line | vector-column | matrix
A moving minimum determined by the sliding window method. The output data size is the same as the input data size. The window slides along the columns along each channel, and the block determines the minimum of the data in the window.
Data types: Float16, Float32, Float64, Int8, Int16, Int32, Int64, Int128, UInt8, UInt16, UInt32, UInt64, UInt128.
Support for complex numbers: Yes
Parameters
Main
Specify window length - specify window length
enabled (by default) | enabled
If this checkbox is selected, the sliding window length will be equal to the value specified in the Window length parameter. If unchecked, the sliding window length will be infinite. In this mode, the block calculates the minimum value of the current sample and all previous samples in the channel.
Window length - sliding window length
4 (by default) | positive integer.
Specifies the length of the sliding window in the samples.
Dependencies
To enable this parameter, select the Specify window length checkbox.
Algorithms
Sliding window method
In the sliding window method, the output for each input sample is the minimum value of the current sample and previous samples, where is the window length in samples. When the algorithm computes the first of the output data, the window length is equal to the length of the available data.
If the window length is not given, the algorithm chooses an infinite window length. In this mode, the output is the minimum value of the current sample and all previous samples in the channel.
The figure below shows an example of computing the moving minimum of streaming input data using the sliding window method. The algorithm uses a window of length 4. With each incoming input sample, the window of length 4 is moved along the data.
