Publicado en Expert Systems with Application Abstract This work presents an ensemble of Attractor Neural Networks (ANN) modules, that increases the patterns’ storage, at similar computational cost when compared with a single-module ANN system. We build the ensemble of ANN components, and divide the uniform random patterns’ set into disjoint subsets during the learning stage, such that each subset is assigned to a different component. In this way, a larger overall number of patterns can be stored by the ANN ensemble, where each of its modules has a moderate pattern load, being able to retrieve its corresponding assigned subset with the desired quality. Allowing some noise in the retrieval, we are able to recall a larger number of patterns while discriminating between pattern subsets assigned to each component in the ensemble. We showed that the ANN ensemble system with units is able to approximately triple the maximal capacity of the single ANN, with similar wiring costs. We test

Publicado en 12th International Conference on Natural Computation and 13th International Conference on Fuzzy Systems and Knowledge Discovery Abstract The Computational Theory of Perceptions (CTP) provides capabilities for linguistic summarization of data and it aims the description of patterns emerging from these data by means of linguistic expressions. This technique is particularly well suited in applications where there is the need of understanding the information at different levels of expertise and/or when intense human-computer interaction is required. In this paper, we present a CTP-based system able to generate valuable linguistic reports from findings in breast image mammograms using the BI-RADS radiology standard. The implemented framework uses data obtained through the fusion of information provided by different medical experts on the same mammography. Then, our system automatically produces a collection of valid sentences describing: the breast lesion

Publicado en International Conference on Systems, Signals, and Image Processing Abstract: This paper describes an implemented solution to automatically detect and recognize in real-time both speed limit warning signs and speed limit signs in railway travel videos recorded from the driver's cab. The lack of available high-quality videos to train this kind of systems and the involved complexity of the rail scenes, with non-controlled illumination conditions, make it challenging the considered problem. Our framework achieved interesting recognition results of around 95% for both signs types and digits recognition.

Integrated Computer-Aided Engineering