Polytechnic University of Valencia

About: Polytechnic University of Valencia is a education organization based out in Valencia, Spain. It is known for research contribution in the topics: Catalysis & Population. The organization has 16282 authors who have published 40162 publications receiving 850234 citations.

A monolithic integrated photonic microwave filter

Abstract: Meeting the increasing demand for capacity in wireless networks requires the harnessing of higher regions in the radiofrequency spectrum, reducing cell size, as well as more compact, agile and power-efficient base stations that are capable of smoothly interfacing the radio and fibre segments. Fully functional microwave photonic chips are promising candidates in attempts to meet these goals. In recent years, many integrated microwave photonic chips have been reported in different technologies. To the best of our knowledge, none has monolithically integrated all the main active and passive optoelectronic components. Here, we report the first demonstration of a tunable microwave photonics filter that is monolithically integrated into an indium phosphide chip. The reconfigurable radiofrequency photonic filter includes all the necessary elements (for example, lasers, modulators and photodetectors), and its response can be tuned by means of control electric currents. This is an important step in demonstrating the feasibility of integrated and programmable microwave photonic processors. A tunable photonic microwave filter is monolithically integrated in an InP chip. The filter includes all of the required elements — a laser, a modulator and a photodetector — and its response can be tuned by controlling the electric currents.

Machine Translation with Inferred Stochastic Finite-State Transducers

Abstract: Finite-state transducers are models that are being used in different areas of pattern recognition and computational linguistics. One of these areas is machine translation, in which the approaches that are based on building models automatically from training examples are becoming more and more attractive. Finite-state transducers are very adequate for use in constrained tasks in which training samples of pairs of sentences are available. A technique for inferring finite-state transducers is proposed in this article. This technique is based on formal relations between finite-state transducers and rational grammars. Given a training corpus of source-target pairs of sentences, the proposed approach uses statistical alignment methods to produce a set of conventional strings from which a stochastic rational grammar (e.g., an n -gram) is inferred. This grammar is finally converted into a finite-state transducer. The proposed methods are assessed through a series of machine translation experiments within the framework of the EuTrans project.