Abstract
Tuberculosis is one of the major public health concerns. This highly contagious disease affects more than 10.4 million people, being a leading cause of morbidity by infection. Tuberculosis is diagnosed at the point-of-care by the Ziehl-Neelsen sputum smear microscopy test. Ziehl-Neelsen is laborious, prone to human error and infection risk, with a limit of detection of 10(4) cells/mL. In resource-poor nations, a more practical test, with lower detection limit, is paramount. This work uses a magnetoresistive biosensor to detect BCG bacteria for tuberculosis diagnosis. Herein we report: i) nanoparticle assembly method and specificity for tuberculosis detection; ii) demonstration of proportionality between BCG cell concentration and magnetoresistive voltage signal; application of multiplicative signal correction for systematic effects removal; iv) investigation of calibration effectiveness using chemometrics methods; and v) comparison with state-of-the-art point-of-care tuberculosis biosensors. Results present a clear correspondence between voltage signal and cell concentration. Multiplicative signal correction removes baseline shifts within and between biochip sensors, allowing accurate and precise voltage signal between different biochips. The corrected signal was used for multivariate regression models, which significantly decreased the calibration standard error from 0.50 to 0.03 log(10) (cells/mL). Results show that Ziehl-Neelsen detection limits and below are achievable with the magnetoresistive biochip, when pre-processing and chemometrics are used.

Abstract
This paper presents the positioning, navigation and awareness (PNA) system developed for the Underwater Robotic Mining System of the VAMOS! project [1]. It describes the main components of the VAMOS! system, the PNA sensors in each of those components, the global architecture of the PNA system, and its main subsystems: Position and Navigation, Real-time Mine Modeling, 3D Virtual reality HMI and Real-time grade system. General results and lessons learn during the first mining field trial in Lee Moor, Devon, UK during the months of September and October 2017 are presented.

Abstract
The paper describes the use of computer applications on education for industrial robotic systems. Robotics evolved as a central issue in teaching for scientific and engineering courses and inherently encompasses a spectrum of sciences and technologies and qualification levels. However, most current teaching approaches, related to robotics, concentrate on individual aspects or small student groups and do not involve dangerous situations. With a mixed-reality robotics teaching, a true interdisciplinary setup can be reached, this approach has been used in a robotics course at higher education, to prepare students for future activities in industry and also in research. This active learning experience approach in the field of Industrial Robotics was implemented in a Master degree on Electrical Engineering.

Abstract
This paper presents a Pattern Recognition System, which can be used in classification applications for hand gestures for control of robotic arms. The system based in three steps, uses feature matching for extracting objects from a scene, edge detector and deep learning. The use of extraction of the region of interest and edges segmentation reduces the amount of processing required to recognize signals, thus speeding up the recognition process. Experimental classification results were positive with good statistical results. The presented data were tested considering four different types of segmentation implementations.

Abstract
This paper describes a two-month summer collective internship conceived to provide a unique hands-on experience in robotics. The objective of the Robotics Craftsmanship International Academy, or RobotCraft for short, is to introduce higher education students in the full design cycle of a mobile robotic platform, providing training in computer-aided design (CAD), mechatronics, low-level programming of embedded systems, high-level development using the Robot Operating System (ROS), and artificial intelligence. This non-academic teaching, which successfully completed its second edition, already encompassed around 150 students and 100 universities, being evaluated by participants as challenging, engaging, and beneficial not only to their overall understanding of robotics, but also guiding them through their future academic and professional endeavors. © 2019, Springer Nature Switzerland AG.

Abstract
This paper describes a two-month summer intensive course designed to introduce participants with a hands-on technical craft on robotics and to acquire experience in the low-level details of embedded systems. Attendants started this course with a brief introduction to robotics; learned to draw, design and create a personalized 3D structure for their mobile robotic platform and developed skills in embedded systems. They were familiarize with the practices used in robotics, learning to connect all sensors and actuator, developing a typical application on differential kinematic using Arduino, exploring ROS features under Raspberry Pi environment and Arduino - Raspberry Pi communication. Different paradigms and some real applications and programming were addressed on the topic of Artificial Intelligence. This paper describes not just the concept, layout and methodology used on RobotCraft 2017 but also presents the participants knowledge background and their overall opinions, leading to focus on lessons learned and suggestions for future editions.