Abstract
A novel self-referencing parameter for electro-optical WDM networks in reflective configuration for remote fiber-optic intensity sensors is reported and experimentally tested. In this configuration electrical delay lines are deployed at the processing unit, instead of long delay fiber coils in each measuring point; providing a more compact sensor-head and an easy-reconfigurable operation point. The measurement parameter is defined as the ratio between voltage values at the reception stage for different electrical phase-shifts and it is analyzed following the Z-transform formalism. Measurements validating the theoretical model are reported and self-referencing and crosstalk properties of the network are analyzed. © 2009 SPIE.

Abstract
In this work, interferometric sensors based on antiresonance reflecting waveguide (ARROW) fibers were developed and their performances were characterized for measurement of physical parameters, particularly strain and temperature. Two types of ARROW fibers were considered and signal demodulation was achieved using the white light interferometric technique. Application issues of sensing heads based on these fibers are also addressed. © 2009 SPIE.

Abstract
The detection of a trend in a time series and the evaluation of its magnitude and statistical significance is an important task in geophysical research. This importance is amplified in climate change contexts, since trends are often used to characterize long-term climate variability and to quantify the magnitude and the statistical significance of changes in climate time series, both at global and local scales. Recent studies have demonstrated that the stochastic behavior of a time series can change the statistical significance of a trend, especially if the time series exhibits long-range dependence. The present study examines the trends in time series of daily average temperature recorded in 26 stations in the Tuscany region (Italy). In this study a new framework for trend detection is proposed. First two parametric statistical tests, the Phillips-Perron test and the Kwiatkowski-Phillips-Schmidt-Shin test, are applied in order to test for trend stationary and difference stationary behavior in the temperature time series. Then long-range dependence is assessed using different approaches, including wavelet analysis, heuristic methods and by fitting fractionally integrated autoregressive moving average models. The trend detection results are further compared with the results obtained using nonparametric trend detection methods: Mann-Kendall, Cox-Stuart and Spearman's rho tests. This study confirms an increase in uncertainty when pronounced stochastic behaviors are present in the data. Nevertheless, for approximately one third of the analyzed records, the stochastic behavior itself cannot explain the long-term features of the time series, and a deterministic positive trend is the most likely explanation.

Abstract
Simulating ecological models is always a difficult task, not only because of its complexity but also due to the slowness associated with each simulation run as more variables and processes are incorporated into the complex ecosystem model. The computational overhead becomes a very important limitation for model calibration and scenario analysis, due to the large number of model runs generally required. This paper presents a framework for ecological simulations that intends to increase system performance through the ability to do parallel simulations, allowing the joint analysis of different scenarios. This framework evolved from the usage of one simulator and several agents, that configure the simulator to run specific scenarios, related to possible ecosystem management options, one at a time, to the use of several simulators, each one simulating a different scenario concurrently, speeding up the process and reducing the time for decision between the alternative scenarios proposed by the agents. This approach was tested with a farmer agent that seeks optimal combinations of bivalve seeding areas in a large mariculture region, maximizing the production without exceeding the total allowed seeding area. Results obtained showed that the time needed to acquire a "near" optimal solution decreases proportionally with the number of simulators in the network, improving the performance of the agent's optimization process, without compromising its rationality. This work is a step forward towards an agent based decision support system to optimize complex environmental problems.

Abstract
Integration of equipment in industrial robot cells is to an increasing part involved with interfacing modern Ethernet technologies and low-cost mass produced devices, such as vision systems, laser cameras, force-torque sensors, soft-PLCs, digital pens, pocket-PCs, etc. This scenario enables integrators to offer powerful and smarter solutions, more adapted to small and medium enterprises (SMEs), capable of integrating process knowledge and interface better with humans. Nevertheless, programming all these devices efficiently requires too much specific knowledge about the devices, their hardware architectures and specific programming languages, details about system communication low-level protocols, and other tricky details at the system level. To address these issues, this paper describes and analyses two of the most interesting service-oriented architectures (SOA) available, which exhibit characteristics that are well adapted to industrial robotics cells. To compare, discuss and evaluate their programming features and applicability a test bed was specially designed, and the two SOA are fully implemented to program the test bed. Special focus is given to the way services are specified and to the orchestration tools used to manage system logic. The obtained results show clearly that using integrations schemes based on SOA reduces system integration time and are more adapted to industrial robotic cell system integrators.

Abstract