Copyright © 2016 Claudia Bănescu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Oxidative stress might contribute

Abstract: In this paper a robust speech recognizer is presented based on features obtained from the speech signal and also from the image of the speaker. The features were combined by simple concatenation, resulting composed feature vectors to train the models corresponding to each class. For recognition, the classification process relies on a very effective algorithm, namely the multiclass SVM. Under additive noise conditions the bimodal system based on combined features acts better than the unimodal system, based only on the speech features, the added information obtained from the image playing an important role in robustness improvement.

Hidden Markov Models (HMMs) are a class of statistical models used to characterize the observable properties of a signal. HMMs consist of two interrelated processes: (i) an underlying, unobservable Markov chain with a finite number of states governed by a state transition probability matrix and an initial state probability distribution, and (ii) a set of

Abstract. To obtain good yields, the highbush blueberry (Vaccinium corymbosum) needs special soil conditions with high organic matter content and optimal pH values, ranging from 4.8 up to 5.5. To obtain such properties which are close to those from above, on an alluvial soil with low humus content, poorly gleyed, situated on 3rd terrace of the Doamnei River, an experiment was organized during 2006-2008 at the Research Institute for Fruit Growing Pitesti –Maracineni. The experiment had the following scheme: A Factor- cultivar with seven graduations: a1- Simultan, a2-Delicia, a3-Lax, a4-

1.2 Goals of this Research........................ 5

The scientific activities of the ECT * that are summarized in this report concern the international projects (workshops and collaboration meetings), the doctoral training programs, the

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From 2009 the journal is classified in the B+ Category (code 23) by the

de totalizare a activităţii de cercetare a cadrelor didactice

I Jurnalul de chirurgie îşi propune să devină în scurt timp o publicaţie cu impact în activitatea de cercetare chirurgicală şi de pregătire profesională continuă. Jurnalul apare ca o necesitate în condiţiile cerute de noile forme de pregătire a rezidenţilor în chirurgie şi se angajează să pună la dispoziţia tinerilor chirurgi din diverse specialităţi, cunoştinţele şi modelele de bază a pregătirii lor ca specialişti pentru noul mileniu. Editori onorifici