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In multitasking applications there is a problem when concurrent access to resources, such as files or memory, when you launch several tasks at once. Two tasks cannot write at the same time in the same memory address, it is required to make sure that some data is not modified while we are reading them or things like that. In this article I will review the tools provided by .NET Framework to deal with these issues.

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In this article I will show how, through a very simple and totally deterministic process, we can move from a stationary system to a completely random one, going through periodic and chaotic dynamics. For this, I will generate several time series with these characteristics using the program R and several packages that can help us in the analysis of them.

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In this new article of the series dedicated to the graphic characterization of complex time series I will talk about two other graphical tools that can be useful, the power spectrum of the signal, which will be obtained through the Fourier transform, and the graph of the distribution of values of the series, a simple histogram with the frequency of the different values that also can provide us information about the series dynamics.

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In many applications it is necessary to generate random numbers. To do so, the .NET framework provides the Random class, which can generate uniformly distributed pseudo-random values, which means that all numbers are equally likely to appear in the result. But in some cases we need to generate random values that follow other different types of distribution. In this article we will see how to generate random numbers that follow a normal distribution, with a system that can be extended to any other type of distribution.

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Usually, when you perform a data analysis, you suppose that they come from a normal distribution. In fact, you perform a battery of tests to verify that this assumption is met and, otherwise, you try to modify the data so that it is satisfied. This is because most analysis techniques only work properly on normally distributed data. But there are a number of systems that present a complex dynamics where is not valid to apply this hypothesis and wherein adjusting the data only leads to distortions that invalidate the results.

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