Developed within RD51 Collaboration for the Development of Micro-Pattern Gas Detectors Technologies, the Scalable Readout System (SRS) is intended as a general purpose multichannel readout solution for a wide range of detector types and detector complexities. The scalable architecture, achieved using multi-Gbps point-to-point links with no buses involved, allows the user to tailor the system size to his needs. The modular topology enables the integration of different front-end ASICs, giving the user the possibility to use the most appropriate front-end for his purpose or to build a heterogeneous experimental apparatus which integrates different front-ends into the same DAQ system. Current applications include LHC upgrade activities, geophysics or homeland security applications as well as detector R&D. The system architecture, development and running experience will be presented, together with future prospects, ATCA implementation options and application possibilities.
Conventional readout systems exist in many variants since the usual approach is to build readout electronics for one given type of detector. The Scalable Readout System (SRS) developed within the RD51 collaboration relaxes this situation considerably by providing a choice of frontends which are connected over a customizable interface to a common SRS DAQ architecture. This allows sharing development and production costs among a large base of users as well as support from a wide base of developers.The Front-end Concentrator card (FEC), a RD51 common project between CERN and the NEXT Collaboration, is a reconfigurable interface between the SRS online system and a wide range of frontends. This is accomplished by using application-specific adapter cards between the FEC and the frontends. The ensemble (FEC and adapter card are edge mounted) forms a 6U×220 mm Eurocard combo that fits on a 19" subchassis. Adapter cards exist already for the first applications and more are in development.
a b s t r a c tIn this paper, a computational technique to deal with uncertainty in dynamic continuous models in Social Sciences is presented. Considering data from surveys, the method consists of determining the probability distribution of the survey output and this allows to sample data and fit the model to the sampled data using a goodness-of-fit criterion based on the χ 2 -test. Taking the fitted parameters that were not rejected by the χ 2 -test, substituting them into the model and computing their outputs, 95% confidence intervals in each time instant capturing the uncertainty of the survey data (probabilistic estimation) is built. Using the same set of obtained model parameters, a prediction over the next few years with 95% confidence intervals (probabilistic prediction) is also provided. This technique is applied to a dynamic social model describing the evolution of the attitude of the Basque Country population towards the revolutionary organisation ETA.
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