Abstract
In view of the actual climate change scenario felt across the globe, resource management is crucial, especially with regard to water. In this sense, continuous monitoring of plant water status is essential to optimise not only crop management but also water resources. Currently, monitoring of vine water status is done through expensive and time-consuming methods that do not allow continuous monitoring, which is especially inconvenient in places with difficult access. The aim of the developed work was to install three groups of sensors (Environmental, Plant and Soil) in a vineyard and connect them through LoRaWAN protocol for data transmission. The results demonstrate that the implemented system is capable of continuous data communication without data loss. The reduced cost and superior range of LoRaWAN compared to WiFi or Bluetooth is especially important for applications in remote areas where cellular networks have little coverage. Altogether, this methodology provides a remote, continuous and more effective method to monitor plant water status and is capable of supporting producers in more efficient management of their farms and water resources.

Abstract
Rehabilitation robotics aims to facilitate the rehabilitation procedure for patients and physical therapists. This field has a relatively long history dating back to the 1990s; however, their implementation and the standardisation of their application in the medical field does not follow the same pace, mainly due to their complexity of reproduction and the need for their approval by the authorities. This paper aims to describe architecture that can be applied to industrial robots and promote their application in healthcare ecosystems. The control of the robotic arm is performed using the software called SmartHealth, offering a 2 Degree of Autonomy (DOA). Data are gathered through electromyography (EMG) and force sensors at a frequency of 45 Hz. It also proves the capabilities of such small robots in performing such medical procedures. Four exercises focused on shoulder rehabilitation (passive, restricted active-assisted, free active-assisted and Activities of Daily Living (ADL)) were carried out and confirmed the viability of the proposed architecture and the potential of small robots (i.e., the UR3) in rehabilitation procedure accomplishment. This robot can perform the majority of the default exercises in addition to ADLs but, nevertheless, their limits were also uncovered, mainly due to their limited Range of Motion (ROM) and cost.

Abstract
Unmanned Aerial Systems (UAS) are becoming more attractive in diverse applications due to their efficiency in performing tasks with a reduced time execution, covering a larger area, and lowering human risks at harmful tasks. In the context of Oil & Gas (O&G), the scenario is even more attractive for the application of UAS for inspection activities due to the large extension of these facilities and the operational risks involved in the processes. Many authors proposed solutions to detect gas leaks regarding the onshore unburied pipeline structures. However, only a few addressed the navigation and tracking problem for the autonomous navigation of UAS over these structures. Most proposed solutions rely on traditional computer vision strategies for tracking. As a drawback, depending on lighting conditions, the obtained path line may be inaccurate, making a strategy to force the UAS to continue on the path necessary. Therefore, this research describes the potential of an autonomous UAS based on image processing technique and Convolutional Neural Network (CNN) strategy to navigate appropriately in complex unburied pipeline networks contributing to the monitoring procedure of the Oil & Gas Industry structures. A CNN is used to detect the pipe, while image processing techniques such as Canny edge detection and Hough Transform are used to detect the pipe line reference, which is used by a line following algorithm to guide the UAS along the pipe. The framework is assessed by a PX4 flight controller Software-in-The-Loop (SITL) simulations performed with the Robot Operating System (ROS) along with the Gazebo platform to simulate the proposed operational environment and verify the approach's functionality as a proof of concept. Real tests were also conducted. The results showed that the solution is robust and feasible to deploy in this proposed task, achieving 72% of mean average precision on detecting different types of pipes and 0.0111 m of mean squared error on the path following with a drone 2 m away from a tube.

Abstract
This article focuses on a sensorial glove prototype capable of acquiring hand motion and estimating its pose. The presented solution features twelve inertial measurement units (IMUs) to track hand orientation. The sensors are attached to a glove to decrease the project cost. The system also focuses on sensor fusion algorithms for the IMUs and further implementations, presenting the algebraic quaternion algorithm (AQUA), used because of its modularity and intuitive implementation. An adaptation of a human hand model is proposed, explaining its advantages and its limitations. Considering that the calibration is a very important process in gyroscope performance, the online and offline calibration data was analyzed, pointing out its challenges and improvements. To better visualize the model and sensors a simulation was conducted in Unity.

Abstract
The identification of olive-tree cultivars is a lengthy and expensive process, therefore, the proposed work presents a new strategy for identifying different cultivars of olive trees using their leaf and machine learning algorithms. In this initial case, four autochthonous cultivars of the Tras-os-Montes region in Portugal are identified (Cobrancosa, Madural, Negrinha e Verdeal). With the use of this type of algorithm, it is expected to replace the previous techniques, saving time and resources for farmers. Three different machine learning algorithms (Decision Tree, SVM, Random Forest) were also compared and the results show an overall accuracy rate of the best algorithm (Random Forest) of approximately 93%.

Abstract
Assessing the different factors that contribute to accidents in the workplace is essential to ensure the safety and well-being of employees. Given the importance of risk identification in hazard prediction, this work proposes a comparative study between different feature selection techniques (.2 test and Forward Feature Selection) combined with learning algorithms (Support VectorMachine, Random Forest, and Naive Bayes), both applied to a database of a leading company in the retail sector, in Portugal. The goal is to conclude which factors of each database have the most significant impact on the occurrence of accidents. Initial databases include accident records, ergonomic workplace analysis, hazard intervention and risk assessment, climate databases, and holiday records. Each method was evaluated based on its accuracy in the forecast of the occurrence of the accident. The results showed that the Forward Feature Selection-Random Forest pair performed better among the assessed combinations, considering the case study database. In addition, data from accident records and ergonomic workplace analysis have the largest number of features with the most significant predictive impact on accident prediction. Future studies will be carried out to evaluate factors from other databases that may have meaningful information for predicting accidents.