Abstract
The analysis of trends in air temperature observations is one of the most common activities in climate change studies. This work examines the changes in daily mean air temperature over Central Europe using quantile regression, which allows the estimation of trends, not only in the mean but in all parts of the data distribution. A bootstrap procedure is applied for assessing uncertainty on the derived slopes and the resulting distributions are summarised via clustering. The results show considerable spatial diversity over the central European region. A distinct behaviour is found for lower (5%) and upper (95%) quantiles, with higher trends around 0.15 degrees C decade(-1) at the 5% quantile and around 0.20 degrees C decade(-1) at the 95% quantile, the largest trends (>0.2 degrees C decade(-1)) occurring in the Alps.

Abstract
Seven soil-gas radon monitoring stations were placed along the active front of a granite quarry in Canas de senhorim, Central Portugal, recording continuously for 81 days. Important differences in the radon concentration were found between stations, with average values comprised between 102 and 2982 Bq m(-3), which can be explained by the local presence of uranium anomalies in the regional late-orogenic Hercynian granite, usually associated with faults. One of the boreholes exhibits large radon anomalies lasting for several days, and two, contrary to the others, show a clear daily periodic behaviour, with minima around 19:00 LT and maxima around 07:00 LT. The different patterns observed in stations placed at such a short distance (< 100 m) has no clear explanation and deserves further investigation. Data analysis shows no evidence of soil-gas radon concentration changes during explosions carried out at the quarry. This is likely to result from the absence of a progressive stress field affecting the rock, as typically occurs before an earthquake.

Abstract
The behavior of alpha silicon diodes, gamma crystal scintillators and ionization chamber detectors employed for long-term radon monitoring in geological media was studied and a comparison of the efficiency and sensitivity, the capability to resolve signal to noise, background, stability, and reliability of their long-term measurements is presented. An understanding of the qualities of monitoring techniques is necessary for determining suitability to the characteristics of the individual monitoring site and what exactly they will measure: radon in an air cavity, in porous media or in water. The experimental layout was located inside the Amram Mountain research tunnel near Elat (Gulf of Aqaba), within a closed room in the tunnel core. This enabled monitoring natural temporal radon variations under fairly stable internal conditions, at a high-resolution sampling rate of once every several minutes. In an interval of several days, all the sensors responded simultaneously to the same eventual radon variations. An ionization chamber device, the AlphaGUARD designed with a long-time stable calibration factor and an inherent QA-System, was used as reference calibration of the different radon detectors. The results indicate that the higher sensitivity of 2-4 orders of magnitude exhibited by gamma sensors even with narrow dimensions (1 '' x 3 '' BGO detector) are preferred for long-term radon monitoring in comparison to the solid-state alpha detectors and ionization chambers.

Abstract
The successive approximation Linear Parameter Varying systems subspace identification algorithm for discrete-time systems is based on a convergent sequence of linear time invariant deterministic-stochastic state-space approximations. In this chapter, this method is modified to cope with continuous-time LPV state-space models. To do this, the LPV system is discretised, the discrete-time model is identified by the successive approximations algorithm and then converted to a continuous-time model. Since affine dependence is preserved only for fast sampling, a subspace downsampling approach is used to estimate the linear time invariant deterministic-stochastic state-space approximations. A second order simulation example, with complex poles, illustrates the effectiveness of the new algorithm. © 2012 by World Scientific Publishing Co. Pte. Ltd.

Abstract

Abstract
The fitting of a causal dynamic model to an image is a fundamental problem in image processing, pattern recognition, and computer vision. There are numerous other applications that require a causal dynamic model, such as in scene analysis, machined parts inspection, and biometric analysis, to name only a few. There are many types of causal dynamic models that have been proposed in the literature, among which the autoregressive moving average (ARMA) and state-space models are the most widely known. In this paper we introduce a 2-D stochastic state-space system identification algorithm for obtaining stochastic 2-D, causal, recursive, and separable-in-denominator (CRSD) models in the Roesser state-space form. The algorithm is tested with a real image and the reconstructed image is shown to be almost indistinguishable to the true image.