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All massive data processing applications can benefit from the ever-increasing processing capacity of modern computers, which is now affordable for anyone's pocket. I will show in this article a basic performance comparison among various CPU / GPU platforms, based on the well-known Mandelbrot set and its surprising graphical representation.

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This article is the last in the series dedicated to building the Octopussy martial arts and self-defence training sparring robot. Here, I will explain the use of the software to control the device, which allows you to design a virtually unlimited range of training routines within the robot's features.

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This article is the second one on the Octopussy training sparring robot for self-defence and martial arts. Here, I will explain how to build the electronics of the invention. The task consists of little more than connecting a few wires, although a tin soldering iron would also be convenient to have at hand to finish the job better.

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I will show in this series of articles how to build a self-defence and martial arts training robot. I have named Octopussy to the invention since you can put several limbs to simulate different types of punches and kicks directed to all of the body parts. It is controlled by a computer, an Arduino board, and an application. I also provide the application source code for you to make modifications as you want.

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With this article I conclude the series dedicated to the application of genetic algorithms to the design of neural networks. I will explain the most relevant code of the sample application given with these articles, mainly the classes dedicated to the genes management and the selection process. You can find more information in the previous articles of the series.

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In this second article on the application of evolutionary algorithms to the optimization of the design of neural networks, I am going to provide a small sample application that allows you to build and train networks, in addition to using this type of algorithms to find the best configuration for a given data set. The application allows generating artificial test data, and I provide the source code for you to be able to modify it, as you want.

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One of the most popular algorithms for training multilayer artificial neural networks is the back propagation algorithm, or retro-propagation algorithm. In this article I will try to explain its fundamentals, through a simplified implementation of a neural network that allows testing with different configurations of the network.

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This article closes the series on posture recognition using the Microsoft Kinect sensor. To finish, I will simply show you the implementation of a basic form that uses the class libraries explained in the previous articles.

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We continue with the series about posture recognition with the Microsoft Kinect sensor. This time I will comment the classes responsible of posture normalization of the body in the way commented in the first article, so that we obtain a series of data that constitute a simplified version of the skeleton most appropriate for analysis using pattern recognition techniques.

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In the previous article of this series I showed the structures, enumerations and classes that the application uses to be independent of the sensor version and the Kinect SDK. In this third article I will show a possible implementation of a class that deals with reading and converting the skeletons using version 2.0 of the SDK, for the Xbox One sensor.

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In this second article in the series about recognition of postures using the Microsoft Kinect sensor I will show generic classes and structs used to isolate the application from the different versions of the SDK. The version of the SDK that we must use depends on the sensor model that we have. In the example code I have used version 2.0, for the Xbox One sensor.

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In previous articles I presented a home video surveillance system, the ThiefWatcher application. It is an extensible application that works combining several protocols, such as cameras, triggers, communication channels and storage systems. In this article I will show how to implement some of these protocols using the Microsoft Kinect sensor.

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The Microsoft Kinect sensor is a very powerful device that provides image capturing, distance measurement and body postures and facial expressions recognition services, which makes it suitable for infinity of applications. In this introductory article I will show how to use it to capture different types of images.

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Solving any Sudoku is a problem that can seem complicated if brute force methods are used, testing each and every one of the possible combinations. But, with the right approach, any of these problems can be solved in a few milliseconds. In this article, I will show you a way to achieve it through an optimized method.

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To conclude this series about programming multitasking applications, I will show the use of a simple mechanism that allows implementing asynchronous methods whose waiting times are used for the execution of other parallel tasks or events triggered by user interface controls.

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So far I have shown examples of multitasking that block the application until they finished. This is not very useful in practice. Usually, we want that the user can continue interacting with the application while the tasks are running in the background; we could want also the threads to interact with the user interface.

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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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With this article begins a series in which I will review the basic mechanisms provided by the .NET Framework for the implementation of multitasking applications. First of all I will show the basic classes that allow you to launch multiple processes and make a performance comparison between them.

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This is the last article of the series dedicated to the WinCA application. This application allows the edition and to execution of cellular automata. To finish, I will review the code that implements the automaton itself, using all the classes and interfaces explained in the previous articles.

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I continue explaining the basics of the WinCA application code, dedicated to the designing and execution of cellular automata. This time I will tell you about the implementation of the cells and other auxiliary classes needed to build and execute automata.

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