Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Technological solution for the automatic replacement of the catalytic filaments in HWCVD

The degradation of the catalytic filaments is the main factor limiting the industrial implementation of the hot wire chemical vapor deposition (HWCVD) technique. Up to now, no solution has been found to protect the catalytic filaments used in HWCVD without compromising their catalytic activity. Probably, the definitive solution relies on the automatic replacement of the catalytic filaments. In this work, the results of the validation tests of a new apparatus for the automatic replacement of the catalytic filaments are reported. The functionalities of the different parts have been validated using a 0.2 mm diameter tungsten filament under uc-Si:H deposition conditions.

Brain dynamics sustaining rapid rule extraction from speech

Language acquisition is a complex process that requires the synergic involvement of different cognitive functions, which include extracting and storing the words of the language and their embedded rules for progressive acquisition of grammatical information. As has been shown in other fields that study learning processes, synchronization mechanisms between neuronal assemblies might have a key role during language learning. In particular, studying these dynamics may help uncover whether different oscillatory patterns sustain more item-based learning of words and rule-based learning from speech input. Therefore, we tracked the modulation of oscillatory neural activity during the initial exposure to an artificial language, which contained embedded rules. We analyzed both spectral power variations, as a measure of local neuronal ensemble synchronization, as well as phase coherence patterns, as an index of the long-range coordination of these local groups of neurons. Synchronized activity in the gamma band (2040 Hz), previously reported to be related to the engagement of selective attention, showed a clear dissociation of local power and phase coherence between distant regions. In this frequency range, local synchrony characterized the subjects who were focused on word identification and was accompanied by increased coherence in the theta band (48 Hz). Only those subjects who were able to learn the embedded rules showed increased gamma band phase coherence between frontal, temporal, and parietal regions.

Melodic Analysis of Speech Method applied to Spanish and Catalan

In this paper, we present the Melodic Analysis of Speech method (MAS) that enables us to carry out complete and objective descriptions of a language's intonation, from a phonetic (melodic) point of view as well as from a phonological point of view. It is based on the acoustic-perceptive method by Cantero (2002), which has already been used in research on prosody in different languages. In this case, we present the results of its application in Spanish and Catalan.