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
Gesture recognition is very important for Human-Robot Interfaces. In this paper, we present a novel depth based method for gesture recognition to improve the interaction of a service robot autonomous shopping cart, mostly used by reduced mobility people. In the proposed solution, the identification of the user is already implemented by the software present on the robot where a bounding box focusing on the user is extracted. Based on the analysis of the depth histogram, the distance from the user to the robot is calculated and the user is segmented using from the background. Then, a region growing algorithm is applied to delete all other objects in the image. We apply again a threshold technique to the original image, to obtain all the objects in front of the user. Intercepting the threshold based segmentation result with the region growing resulting image, we obtain candidate objects to be arms of the user. By applying a labelling algorithm to obtain each object individually, a Principal Component Analysis is computed to each one to obtain its center and orientation. Using that information, we intercept the silhouette of the arm with a line obtaining the upper point of the interception which indicates the hand position. A Kalman filter is then applied to track the hand and based on state machines to describe gestures (Start, Stop, Pause) we perform gesture recognition. We tested the proposed approach in a real case scenario with different users and we obtained an accuracy around 89,7%.

Abstract

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.