Abstract

Abstract
Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book's web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining.

Abstract
This letter presents a novel architecture for evaluating the success of picking operations that are executed by industrial robots. It is formed by a cascade of machine learning algorithms (kNN and SVM) and uses information obtained by a 6 axis force/torque sensor and, if available, information from the built-in sensors of the robotic gripper. Beyond measuring the success or failure of the entire operation, this architecture makes it possible to detect in real-time when an object is slipping during the picking. Therefore, force and torque signatures are collected during the picking movement of the robot, which is decomposed into five different stages that allows to characterize distinct levels of success over time. Several trials were performed using an industrial robot with two different grippers for picking a long and flexible object. The experiments demonstrate the reliability of the proposed approach under different picking scenarios since, it obtained a testing performance (in terms of accuracy) up to 99.5% of successful identification of the result of the picking operations, considering an universe of 400 attempts.

Abstract
Purpose: To study the impact of shielding elements in the proximity of Intra-Operative Radiation Therapy (IORT) irradiation fields, and to generate graphical and quantitative information to assist radiation oncologists in the design of optimal shielding during pelvic and abdominal IORT. Method: An IORT system was modeled with BEAMnrc and EGS++ Monte Carlo codes. The model was validated in reference conditions by gamma index analysis against an experimental data set of different beam energies, applicator diameters, and bevel angles. The reliability of the IORT model was further tested considering shielding layers inserted in the radiation beam. Further simulations were performed introducing a bone-like layer embedded in the water phantom. The dose distributions were calculated as 3D dose maps. Results: The analysis of the resulting 2D dose maps parallel to the clinical axis shows that the bevel angle of the applicator and its position relative to the shielding have a major influence on the dose distribution. When insufficient shielding is used, a hotspot nearby the shield appears near the surface. At greater depths, lateral scatter limits the dose reduction attainable with shielding, although the presence of bone-like structures in the phantom reduces the impact of this effect. Conclusions: Dose distributions in shielded IORT procedures are affected by distinct contributions when considering the regions near the shielding and deeper in tissue: insufficient shielding may lead to residual dose and hotspots, and the scattering effects may enlarge the beam in depth. These effects must be carefully considered when planning an IORT treatment with shielding.

Abstract
A curvature sensor based on an Fabry-Perot (FP) interferometer was proposed. A capillary silica tube was fusion spliced between two single mode fibers, producing an FP cavity. Two FP sensors with different cavity lengths were developed and subjected to curvature and temperature. The FP sensor with longer cavity showed three distinct operating regions for the curvature measurement. Namely, a linear response was shown for an intermediate curvature radius range, presenting a maximum sensitivity of 68.52 pm/m(-1). When subjected to temperature, the sensing head produced a similar response for different curvature radii, with a sensitivity varying from 0.84 pm/degrees C to 0.89 pm/degrees C, which resulted in a small cross-sensitivity to temperature when the FP sensor was subjected to curvature. The FP cavity with shorter length presented low sensitivity to curvature.

Abstract
In this paper, we propose and experimentally investigate an optical sensor based on a novel combination of a long-period fiber grating (LPFG) with a permanent magnet to measure electrical current in unmanned aerial vehicles (UAVs). The proposed device uses a neodymium magnet attached to the grating structure, which suffers from an electromagnetic force produced when the current flows in the wire of the UAV engine. Therefore, it causes deformation on the sensor and thus, different shifts occur in the resonant bands of the transmission spectrum of the LPFG. Finally, the results show that it is possible to monitor electrical current throughout the entire operating range of the UAV engine from 0 A to 10 A in an effective and practical way with good linearity, reliability and response time, which are desirable characteristics in electrical current sensing. © 2016 by the authors; licensee MDPI, Basel, Switzerland.