/**
  \page tutorial-spc Tutorial: Using Statistical Process Control to monitor your signal
  \tableofcontents

\section tuto-spc-intro Introduction

Statistical Process Control (SPC) is defined as the use of statistical methods to monitor
if a signal is "in control".

In this tutorial, we will use a Statistical Process Control method to monitor if a
random signal following a normal distribution is "in control".

\subsection tuto-spc-intro-methods Available methods

The different methods available in ViSP aim at detecting if the mean of a signal is
changing, either due to an abrupt jump or due to a slow drift.

The different methods that are available are the following:
- *Exponentially Weighted Moving Average* (EWMA), implementent in the vpStatisticalTestEWMA class.
- *Hinkley's test*, implemented in the vpStatisticalTestHinkley class.
- *Mean Adjusted Cumulative Sum* (mean adjusted CUSUM), implemented in the vpStatisticalTestMeanAdjustedCUSUM class.
- *Shewhart's test*, implemented in the vpStatisticalTestShewhart class.
- *Sigma test*, implemented in the vpStatisticalTestSigma class.

We refer the reader to the documentation of each class to have more detailed information on
each method.

\section tuto-spc-tutorial Explanations about the tutorial

\subsection tuto-spc-tutorial-howtorun How to run the tutorial

To see the different options of the tutorial, please run the following commands:

```
$ cd $VISP_WS/visp-build/tutorial/mean-drift
$ ./tutorial-meandrift -h
```

If you run the program without argument, you should see something similar to the following image:

\image html img-tutorial-spc-run.jpg

A Gaussian signal of mean equal to 6 and of standard deviation equal to 2 is generated,
without any mean drift. The program tells you which method has been chosen in the
console, and which are its parameters. A monitoring loop stops once an alarm
is raised. When the alarm is raised, some information about the alarm and the
test signal(s) + limits of the SPC method are given. Press `Return` to leave the program.

\subsection tuto-spc-tutorial-explained Detailed explanations about the SPC tutorial

For this tutorial, we use the main program tutorial-meandrift.cpp .

It uses the following enumeration to let the user choose which SPC method to use to monitor
the signal:

\snippet tutorial-meandrift.cpp Enum_For_Test_Choice

The program arguments are parsed and fill the following structure that stores the SPC methods
parameters:

\snippet tutorial-meandrift.cpp Structure_Parameters

It is possible to choose to monitor only upward mean drifts, only downward mean drifts or both.
To do so, use the `--alarms` option with the name of the alarm(s) you want to monitor.

First, the plot that will show the data is created in the following section of code:

\snippet tutorial-meandrift.cpp Plot_Init

Then, the desired method is created in the following section of code, with the desired parameters:

\snippet tutorial-meandrift.cpp Test_Creat

Then, the method is filled with "in control" signals to initialize the expected mean and the standard deviation:

\snippet tutorial-meandrift.cpp Test_Init

Then, the monitoring loop is run, where the signal is randomly generated with a potential mean drift
(if desired). This random signal is then tested, and the loop is stopped if an alarm we
want to monitor is raised:

\snippet tutorial-meandrift.cpp Loop_Monitor

Finally, some information about why the loop was stopped is displayed in the console:

\snippet tutorial-meandrift.cpp Failure_Debrief

The program stops once the `Return` key is pressed.
*/
