Abstract
Integrated polarization beam splitters based on birefringent directional couplers are demonstrated. The devices are fabricated in bulk fused silica glass by femtosecond laser writing (300 fs, 150 nJ at 500 kHz, 522 nm). The birefringence was measured from the spectral splitting of the Bragg grating resonances associated with the vertically and horizontally polarized modes. Polarization splitting directional couplers were designed and demonstrated with 0.5 dB/cm propagation losses and -19 dB and -24 dB extinction ratios for the polarization splitting. (C) 2011 Optical Society of America

Abstract

Abstract

Abstract
The design of safe industrial controllers is one of the most important domains related to Automation Systems research. To support it, synthesis and analysis techniques are available. Among the analysis techniques, two of the most important are Simulation and Formal Verification. In this paper these two techniques are used together in a complementary way. Understanding plant behaviour is essential for obtaining safe industrial systems controllers: hence, plant modelling is crucial to the success of these techniques. A two step approach is presented: first, the use of Simu ation and, second, the use of Formal Verification of Industrial Systems Specifications. The specification and plant models used for each technique are described. Simulation and Formal Verification results are presented and discussed. The approach presented in the paper can be applied to real industrial systems, and obtain safe controllers for hybrid plants. The Modelica modelling language and Dymola simulation environment are used for Simulation purposes, and Timed Automata formalism and the UPPAAL real-time model-checker are used for Formal Verification purposes.

Abstract
Agribusiness, as many other activities, produces huge amounts of spatio-temporal data. We need a system in order to store, analyze, and mine this data. In a previous work, we developed data warehouse tools to store, organize and query Brazilian agribusiness data from several regions along 10 years. In this paper, we go a step ahead, and propose specific data mining techniques to discover marks and evolution patterns from Agribusiness data. We propose the use of Tucker decomposition to automatically detect short time windows that exhibit large changes in the correlation structure between the time-series of prices from the Brazil Grain market.

Abstract