César Martínez

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Speech-input translation can be properly approached as a pattern recognition problem by means of statistical alignment models and stochastic finite-state transducers. Under this general framework, some specific models are presented. One of the features of such models is their capability of automatically learning from training examples. Moreover, the(More)
Recurrent connectionist models provide a method to represent dynamic patterns in a neural network. In this work we present a method for chromosome classification based on an almost unexplored neural network technique for this task. A partially recurrent con-nectionist model, the Elman network, is managed to capture the dark and light band patterns of the(More)
This paper presents the configuration and control strategy for an input-series- and output-parallel-(ISOP) connected isolated DC/DC converter. The constituent modules are voltage-fed quasi-Z-source inverters with a single-phase isolation transformer and a voltage doubler rectifier. Experimental measurements with common duty cycle in open loop operation mode(More)
Computer-Assisted Translation systems can be used by human translators to increase their productivity. In these systems, the computer suggests portions of target sentence that can be accepted or amended by a human translator. In the present work we will introduce speech as a novel way to interact with these systems. The rational behind this approach is to(More)
In this paper we are going to show some specific extensions of the AHA! system developed at Córdoba University. AHA! is an open source general-purpose adaptive hypermedia system. We have developed some extensions in two different AHA! versions in order to increase its adaptation power in e-learning. In AHA! version 1.0 we added facilities for managing(More)
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