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This example uses a variable-size vector to store the values of a white noise signal. The size of the vector can vary at run time because the signal values get pruned by functions that:

Filter out signal values that are not unique within a specified tolerance of each other.

Average every two signal values and output only the resulting means.

In this model, a Band-Limited White Noise block generates a set of normally distributed random values as the source of a white noise signal. The MATLAB Function `uniquify`

filters out signal values that are not unique to within a specified tolerance of each other. Then, the MATLAB Function `avg`

outputs the average of a specified number of unique signal values. The Scope blocks display the output from the `uniquify`

function and the `avg`

function.

The band-limited white noise signal has these properties:

The size of the noise power value defines the size of the array that holds the signal values. This array is a 1-by-9 vector of double values.

This block filters out signal values that are not within a tolerance of 0.2 of each other. Here is the code:

function y = uniquify(u) %#codegen y = emldemo_uniquetol(u,0.2);

The `uniquify`

function calls an external MATLAB^{®} function `emldemo_uniquetol`

to filter the signal values.
`uniquify`

passes the 1-by-9 vector of white noise signal values as the
first argument and the tolerance value as the second argument. Here is the code for
`emldemo_uniquetol`

:

function B = emldemo_uniquetol(A,tol) %#codegen A = sort(A); coder.varsize('B',[1 100]); B = A(1); k = 1; for i = 2:length(A) if abs(A(k) - A(i)) > tol B = [B A(i)]; k = i; end end

`emldemo_uniquetol`

returns the filtered values of `A`

in an output vector `B`

so that ```
abs(B(i) - B(j)) >
tol
```

for all `i`

and `j`

. Every time
Simulink^{®} samples the Band-Limited White Noise block, it generates a different set of
random values for `A`

. As a result, `emldemo_uniquetol`

may produce a different number of output signals in `B`

each time it is
called. To allow `B`

to accommodate a variable number of elements,
`emldemo_uniquetol`

declares it as variable-size data with an explicit
upper
bound:

`coder.varsize('B',[1 100]);`

In this statement, `coder.varsize`

declares `B`

as a vector whose first dimension is fixed at 1 and whose second dimension can grow to a
maximum size of 100. Accordingly, output `y`

of the
`uniquify`

block must also be variable-size so that it can pass the
values returned from `emldemo_uniquetol`

to the **Unique values** scope. Here are the properties of `y`

:

For variable-size outputs, you must specify an explicit size and upper bound, shown here
as `[1 9]`

.

This block averages signal values filtered by the `uniquify`

block
using the conditions outlined in this table.

Condition | Result |
---|---|

The number of signals is greater than 1 and divisible by 2. | The MATLAB Function block averages every consecutive pair of values. |

The number of signals is greater than 1 but not divisible
by 2. | The MATLAB Function block drops the first (smallest) value and averages the remaining consecutive pairs. |

There is exactly one signal. | The MATLAB Function block returns the value unchanged. |

The `avg`

function outputs the results to the **Average values** scope. Here is the
code:

function y = avg(u) %#codegen if numel(u) == 1 y = u; else k = numel(u)/2; if k ~= floor(k) u = u(2:numel(u)); end y = emldemo_navg(u,2); end

Both input `u`

and output `y`

of
`avg`

are declared as variable-size vectors because the number of
elements varies depending on how the `uniquify`

function block filters the
signal values. Input `u`

inherits its size from the output of
`uniquify`

.

The `avg`

function calls an external MATLAB function `emldemo_navg`

to calculate the average of every two
consecutive signal values. Here is the code for `emldemo_navg`

:

function B = emldemo_navg(A,n) %#codegen assert(n>=1 && n<=numel(A)); B = zeros(1,numel(A)/n); k = 1; for i = 1 : numel(A)/n B(i) = mean(A(k + (0:n-1))); k = k + n; end

Simulating the model produces the following results:

The

`uniquify`

block outputs a variable number of signal values each time it executes:The

`avg`

block outputs a variable number of signal values each time it executes — approximately half the number of the unique values: