Abstract
There are currently several techniques for automatic calibration of inertial sensors. This paper describes a subset of these algorithms that could be used in a manipulator and should allow for its prompt use. A robotic manipulator specifically developed for the study of over-sensored systems is used to realistically test the performance of the implemented methods. The results of these methods show that the accelerometers and the gyroscopes were properly calibrated. However, the magnetometers suffer from variable interferences and therefore could not be calibrated.

Abstract
The recent growth in the use of 3D printers by independent users has contributed to a rise in interest in 3D scanners. Current 3D scanning solutions are commonly expensive due to the inherent complexity of the process. A previously proposed low-cost scanner disregarded uncertainties intrinsic to the system, associated with the measurements, such as angles and offsets. This work considers an approach to estimate these optimal values that minimize the error during the acquisition. The Particle Swarm Optimization algorithm was used to obtain the parameters to optimally fit the final point cloud to the surfaces. Three tests were performed where the Particle Swarm Optimization successfully converged to zero, generating the optimal parameters, validating the proposed methodology.

Abstract
In this paper it is presented an educational experiment, that consists of a mechatronic system applied to demonstrate concepts such as prototyping, control, haptic feedback and the use of different sensors and actuators. The described mechatronic system is based on the use of two identical manipulators, being physical devices commonly used in the industry. The physical components of the manipulators were 3D printed, being the original model of each manipulator the EEZYBotArm MK2. This, already existent prototype was modified, with the necessary changes, to fulfill the requisites of the proposed system, being included load cells to provide measurement of the applied forces, and the robot gripper was also modified, being applied an electromagnetic actuator.

Abstract
The COVID-19 virus outbreak led to the need of developing smart disinfection systems, not only to protect the people that usually frequent public spaces but also to protect those who have to subject themselves to the contaminated areas. In this paper it is developed a human detector smart sensor for autonomous disinfection mobile robot that use Ultra Violet C type light for the disinfection task and stops the disinfection system when a human is detected around the robot in all directions. UVC light is dangerous for humans and thus the need for a human detection system that will protect them by disabling the disinfection process, as soon as a person is detected. This system uses a Raspberry Pi Camera with a Single Shot Detector (SSD) Mobilenet neural network to identify and detect persons. It also has a FLIR 3.5 Thermal camera that measures temperatures that are used to detect humans when within a certain range of temperatures. The normal human skin temperature is the reference value for the range definition. The results show that the fusion of both sensors data improves the system performance, compared to when the sensors are used individually. One of the tests performed proves that the system is able to distinguish a person in a picture from a real person by fusing the thermal camera and the visible light camera data. The detection results validate the proposed system.

Abstract
Internet of Things, IoT, is a promising methodology that has been increasing over the last years. It can be used to allow the connection and exchange data with other devices and systems over the Internet. One of the IoT connection protocols is the LoRaWAN, which has several advantages but has a low bandwidth and limited data transfer. There is a necessity of optimising the data transfer between devices. Some sensors have a 10 or 12 bits resolution, while LoRaWAN owns 8 bits or multiples slots of transmission remaining unused bits. This paper addresses a communication optimisation for wireless sensors resorting to encoding and decoding procedures. This approach is applied and validated on the real scenario of a wildfire detection system.

Abstract
Nowadays, with the availability of 3D printers, the scanners for objects are becoming increasingly present since they allow to replicate objects by 3D printing, especially for small scale sizes. However, the majority of these technologies are expensive, due to the complexity of this task. Therefore, this work presents a prototype of a low-cost 3D scanning system for small objects using a point cloud to stereolithography approach where it was already validated in simulation in previous work. This concept has a restriction that the objects must have a uniform shape, i.e, without discontinuities. The architecture is composed of two stepper motors, due to their precision, a rotating plate to allow 360 degrees scans and another rotating structure that allows the infrared distance sensor to scan the object from bottom to top (90 degrees). The prototype was validated in the real scenario with good results.