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This work defines a new nonlinear adaptive filter based on a feed-forward neural network with the capacity of significantly reducing the additive noise of an image. Even though measurements have been carried out using X-ray images with additive white Gaussian noise, it is possible to extend the results to other type of images. Comparisons have been carried… (More)
Incremental evolution has proved to be an extremely useful mechanism in complex actions sequence learning. Its performance is based on the decomposition of the original problem into increasingly complex stages whose learning is carried out sequentially, starting from the simplest stage and thus increasing its generality and difficulty. The present work… (More)
The detection of regions and objects in digital images is a topic of utmost importance for solving several problems related to the area of pattern recognition. In this direction, skeletonization algorithms are a widely used tool since they allow us to reduce the quantity of available data, easing the detection of characteristics for their recognition and… (More)
Unstructured Peer-to-Peer (P2P) networks as Gnutella are dynamic, distributed systems without any centralizing point favoring failure tolerance and strength. However, resource search in these systems is an important problem. Gnutella's breadth-first search algorithm is flooding-based and generates a large amount of traffic thus making scalability difficult.… (More)
Genetic algorithms have been used successfully to solve continuous optimization problems. However, an early convergence to low-quality solutions is one of the most common difficulties encountered when using these strategies. In this paper, a method that combines multiple auxiliary populations with the main population of the algorithm is proposed. The role… (More)
Mobile devices have changed how we conceive software. There is a great range of development alternatives. In this paper, four different multi-platform development approaches (mobile web applications, hybrid, interpreted, and cross-compiled) are analyzed, and their most significant features through a case study are discussed.
Evolving neural arrays (ENA) have proved to be capable of learning complex behaviors, i.e., problems whose solution requires strategy learning. For this reason, they present many applications in various areas such as robotics and process control. Unlike conventional methods –based on a single neural network– ENAs are made up of a set of networks organized… (More)