Create Composite System Object
This example shows how to create a System object™ composed of other System objects. This System object uses two moving average System objects to find the cross-correlation of two independent samples. The Create Moving Average System Object example explains in detail how to create a System object. This example focuses on how to use a System object within another System object.
System Objects as Private Properties
The ability to create more than one instance of a System object and having each instance manage its own state is one of the biggest advantages of using System objects over functions. The private properties
MovingAverageFilter2 are used to store the two moving average filter objects.
properties (Access=private) % This example class contains two moving average filters (more can be added % in the same way) MovingAverageFilter1 MovingAverageFilter2 end
Set Up the Moving Average Filter
setupImpl method, create the two moving average System objects and initialize their public properties.
function setupImpl(obj,~) % Set up moving average objects with default values obj.MovingAverageFilter1 = movingAverageFilter('WindowLength',obj.WindowLength1); obj.MovingAverageFilter2 = movingAverageFilter('WindowLength',obj.WindowLength2); end
Work with Dependent Properties
WindowLength public property from the movingAverage
Filter System object is implemented as a dependent property in this example.
properties(Nontunable,Dependent) % WindowLength Moving window length WindowLength1; WindowLength2; end
Whenever you assign a value to one of the dependent properties, the value is set in the corresponding moving average filter. When you read one of the dependent properties, the value is read from the corresponding moving average filter.
function set.WindowLength1(obj,WindowLength1) % Set the window length of one moving average filter obj.MovingAverageFilter1.WindowLength = WindowLength1; end function WindowLength = get.WindowLength1(obj) % Read window length from one of the moving average filters WindowLength = obj.MovingAverageFilter1.WindowLength; end function set.WindowLength2(obj,WindowLength2) % Set the window length of one moving average filter obj.MovingAverageFilter2.WindowLength = WindowLength2; end function WindowLength = get.WindowLength2(obj) % Read window length from one of the moving average filters WindowLength = obj.MovingAverageFilter2.WindowLength; end
Use the Cross-Correlation Object in MATLAB
Create random variables to calculate the cross-correlation of their moving averages, then view the results in a stem plot.
x = rand(20,1); y = rand(20,1); crossCorr = crossCorrelationMovingAverages('WindowLength1',1,'WindowLength2',5); for iter = 1:100 x = rand(20,1); y = rand(20,1); [corr,lags] = crossCorr(x,y); stem(lags,corr) end