Abstract
Monitoring of environmental radioactivity for the purpose of earthquake prediction requires the discrimination of anomalies of non-tectonic origin from seismically-induced anomalies. This is a challenging task as time series of environmental radioactivity display a complex temporal pattern reflecting a wide range of different physical processes, including meteorological and surface effects. The present study is based on the detailed time series of gamma radiation from the Eastern North Atlantic (ENA) site in the Azores, and on very high resolution precipitation intensity and soil moisture time series. The results show that an abrupt shift in the average level of the gamma radiation time series previously reported as a potential earthquake precursor can also be explained by a corresponding abrupt change in soil moisture. It was concluded that the reduction of false positive earthquake precursors requires the detailed assessment of both precipitation and soil moisture conditions at high temporal resolution.

Abstract

Abstract
A remote sensor for radon continuous measurement using polymeric scintillation optical fibers was developed and evaluated. Successful preliminary results showed detection of natural occurring radon from a container with rocks rich in uranium oxides. © OSA 2018 © 2018 The Author(s)

Abstract
This article revisits the inverted pendulum-in particular, analyses a simplified model of a Segway, with a view to exploring its capabilities in Control Systems Engineering education. The integration between the theoretic and practical side is achieved through simulation, and in particular by using MathWorks software. We also present a structure for the work to be done in the Laboratory class and propose a solution for the problem.

Abstract
Linear parameter varying models (LPV) have proven to be effective to describe non-linearities and time-varying behaviors. In this work, a new non-parametric estimation algorithm for state-space LPV models based on support vector machines is presented. This technique allows the functional dependence between the model coefficients and the scheduling signal to be "learned" from the input and output data. The proposed algorithm is formulated in the context of instrumental (IV) estimators, in order to obtain consistent estimates for general noise conditions. The method is based on a canonical state space representation and admits a predictor form that has shown to be suitable for system identification, as it leads to a convenient regression form. In addition, this predictor has an inherent filtering feature. In the context of vector support machines, such filtering mechanism leads to two-dimensional data processing, which can be used to decrease the variance of estimates due to noisy data. The performance of the proposed approach is evaluated from simulated data subject to different noise scenarios. The technique was able to reduce the error due to the variance of the estimator in most of the analyzed scenarios.

Abstract
In this paper, a new system identification algorithm is proposed for linear and time invariant systems with multiple input and single output. The system is described by a state-space model in the canonical observable form and represented by a Luenberger observer with a known state matrix. Thence, the identification problem is reduced to the estimation of the system input matrix and the observer gain which can be performed by a simple Least Square Estimator. The quality of the estimator depends on the observer state matrix. In the proposed algorithm, this matrix is found by an iterative process where, in each iteration, a state matrix called curiosity is generated. A weight depending on the value of the Least Square Cost is associated to each curiosity. The optimal state matrix is the barycenter of the curiosities. This iterative process is a free derivative optimization algorithm with its roots in non-iterative barycenter methods previously introduced to solve adaptive control and system identification problems. Although the Barycenter iterative version was recently proposed as an optimization method, here it will be implemented in an identification algorithm for the first time.