Abstract
Municipal Solid Waste Management Systems (MSWMS) worldwide are currently facing pressure due to the rapid growth of the population in cities. One of the biggest challenges in this system is the inefficient expenditure of time and fuel in waste collection. In this regard, cities/municipalities in charge of MSWMS could take advantage of information and communication technologies to improve the overall quality of their infrastructure. One particular strategy that has been explored and is showing interesting results is using a Wireless Sensors Network (WSN) to monitor waste levels in real-time and help decision-making regarding the need for collection. The WSN is equipped with sensing devices that should be carefully chosen considering the real scenario in which they will work. Therefore, in this work, three sets of sensors were studied to evaluate which is the best to be used in the future WSN assembled in Braganca, Portugal. Sets tested were HC-SR04 (S1), HC-SR04 + DHT11 (S2), and US-100 (S3). Tests considered for this work were air temperature and several distances. In the first, the performance of each set to measure a fixed target (metal and plastic box) was evaluated under different temperatures (1.7-37 degrees C). From these results, two best sets were further used to assess distance measurement at a fixed temperature. This test revealed low absolute errors measuring the distances of interest in this work, ranging from 0.18% to 1.27%.

Abstract
Robotic competitions are an excellent way to promote innovative solutions for the current industries' challenges and entrepreneurial spirit, acquire technical and transversal skills through active teaching, and promote this area to the public. In other words, since robotics is a multidisciplinary field, its competitions address several knowledge topics, especially in the STEM (Science, Technology, Engineering, and Mathematics) category, that are shared among the students and researchers, driving further technology and science. A new competition encompassed in the Portuguese Robotics Open was created according to the Industry 4.0 concept in the production chain. In this competition, RobotAtFactory 4.0, a shop floor, is used to mimic a fully automated industrial logistics warehouse and the challenges it brings. Autonomous Mobile Robots (AMRs) must be used to operate without supervision and perform the tasks that the warehouse requests. There are different types of boxes which dictate their partial and definitive destinations. In this reasoning, AMRs should identify each and transport them to their destinations. This paper describes an approach to the indoor localization system for the competition based on the Extended Kalman Filter (EKF) and ArUco markers. Different innovation methods for the obtained observations were tested and compared in the EKF. A real robot was designed and assembled to act as a test bed for the localization system's validation. Thus, the approach was validated in the real scenario using a factory floor with the official specifications provided by the competition organization.

Abstract
Life in society has initiated a search for comfort and security in social centers. This search generated revolutions within the knowledge about the technologies involved, making the environments automated and integrated. Along with this increase, ecological concerns have also arisen, which have been involved since the design of intelligent buildings, remaining through the years of their use. Based on these two pillars, the present study aims to monitor three central systems inside the apartments of the Apolo Building (Braganca city, Portugal). The electrical energy consumption, water flow, and waste disposal systems are integrated through a single database. The data is sent remotely via WiFi through the microcontroller. For better visualization and analytics of the data, a web application is also developed, which allows for real-time monitoring. The obtained results demonstrate to the consumer his behavior regarding household expenses. The idea of showing the consumer their expenditure is to create an ecological awareness. Through the data collected and the environmental alternatives found, it is possible to observe whether there was a behavior change when receiving this data, either in the short or long term.

Abstract
Prediction of solar irradiation and wind speed are essential for enhancing the renewable energy integration into the existing power system grids. However, the deficiencies caused to the network operations provided by their intermittent effects need to be investigated. Regarding reserves management, regulation, scheduling, and dispatching, the intermittency in power output become a challenge for the system operator. This had given the interest of researchers for developing techniques to predict wind speeds and solar irradiation over a large or short-range of temporal and spatial perspectives to accurately deal with the variable power output. Before, several statistical, and even physics, approaches have been applied for prediction. Nowadays, machine learning is widely applied to do it and especially regression models to assess them. Tuning these models is usually done following manual approaches by changing the minimum leaf size of a decision tree, or the box constraint of a support vector machine, for example, that can affect its performance. Instead of performing it manually, this paper proposes to combine optimization methods including the bayesian optimization, grid search, and random search with regression models to extract the best hyper parameters of the model. Finally, the results are compared with the manually tuned models. The Bayesian gives the best results in terms of extracting hyper-parameters by giving more accurate models.

Abstract
This paper presents a cost-effective approach of a mecanum wheel robotic platform for educational propose on the development of an autonomous or remote controlled mobile robot with a four-wheel mecanum drive train. The main structure of the mobile robot was developed in Solidworks and it was built using additive manufacturing to validate in a real scenario. The main objective of developing this type of mobile platform was the ability to transport different types of cargo or robotic arm on industrial spaces or on rough terrain, since the implemented suspension mechanism allows the wheels contact to the floor. Another important objective is the maneuverability and the capacity to be guided in various environments, a great advantage in this type of mobile platform. An additional advantage of the developed mobile robot is the easy way to reconfigure the structure for new acquired parts.

Abstract
This research proposes positioning obstacle detection sensors by multirotor unmanned aerial vehicles (UAVs) dedicated to detailed inspections in high voltage towers. Different obstacle detection sensors are analyzed to compose a multisensory architecture in a multirotor UAV. The representation of the beam pattern of the sensors is modeled in the CoppeliaSim simulator to analyze the sensors' coverage and detection performance in simulation. A multirotor UAV is designed to carry the same sensor architecture modeled in the simulation. The aircraft is used to perform flights over a deactivated electrical tower, aiming to evaluate the detection performance of the sensory architecture embedded in the aircraft. The results obtained in the simulation were compared with those obtained in a real scenario of electrical inspections. The proposed method achieved its goals as a mechanism to early evaluate the detection capability of different previously characterized sensor architectures used in multirotor UAV for electrical inspections.