Vitor Manuel Filipe

Abstract
Climate change affects global temperature and precipitation patterns. These effects, in turn, influence the intensity and, in some cases, the frequency of extreme environmental events, such as forest fires, hurricanes, heat waves, floods, droughts, and storms. In general, these events can be particularly conducive to the appearance of plant pests and diseases. The availability of models and a data collection system is crucial to manage pests and diseases in sustainable agricultural ecosystems. Agricultural ecosystems are known to be complex, multivariable, and unpredictable. It is important to anticipate crop pests and diseases in order to improve its control in a more ecological and economical way (e.g., precision in the use of pesticides). The development of an intelligent monitoring and management platform for the prevention of pests and diseases in olive groves at Trás-os- Montes region will be very beneficial. This platform must: a) integrate data from multiple data sources such as sensory data (e.g., temperature), biological observations (e.g., insect counts), georeferenced data (e.g., altitude) or digital images (e.g., plant images); b) systematize these data into a regional repository; c) provide relevant forecasts for pest and diseases. Convolutional Neural Networks (CNNs) can be a valuable tool for the identification and classification of images acquired by Internet of Things (IoT).

Abstract
Recommender systems form a class of Artificial Intelligence systems that aim to recommend relevant items to the users. Due to their utility, it has gained attention in several applications domains and is high demanded for research. In order to obtain successful models in the recommendation problem in non-prohibitive computational time, different heuristics, architectures and information filtering techniques are studied with different datasets. More recently, machine learning, especially through the use of deep learning, has driven growth and expanded the sequential recommender systems development. This research focuses on models for managing sequential recommendation supported by session-based recommendation. This paper presents the characterization in the specific theme and the state-of-the-art towards study object of the thesis: the adaptive recommendation to mitigate the information overload in online environments.

Abstract
This research project consists of bringing innovation to the shop floor in such a way that it will allow its approach to the Industry 4.0 concept. The main aim includes integrating the present installed systems in order to provide its user with data as if it was a unique system. More concretely, this study intends to unify the information that comes from different systems: Manufacturing Execution System (MES); Enterprise Resource Planning (ERP); Supervisory Control and Data Acquisition (SCADA); Product Lifecycle Management (PLM); Computerized Maintenance Management Systems (CMMS); Quality Management System (QMS). Integrating this data will enable the creation of automatic procedures which can eliminate the existing gaps within the communication among the different systems. Furthermore, this will allow a real-time view of the whole plant so that immediate decisions can be made in case of any occurrence. In order to provide data fusion from the distinct systems previously mentioned, machine learning (ML) methodology will be applied. This document presents the research done and the reviewed literature, as well as the technologies and methodologies needed in this project. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
Autonomous driving is one of the fastest developing fields of robotics. With the ever-growing interest in autonomous driving, the ability to provide robots with both efficient and safe navigation capabilities is of paramount significance. With the continuous development of automation technology, higher levels of autonomous driving can be achieved with vision-based methodologies. Moreover, materials handling in industrial assembly lines can be performed efficiently using automated guided vehicles (AGVs). However, the visual perception of industrial environments is complex due to the existence of many obstacles in pre-defined routes. With the INDTECH 4.0 project, we aim to develop an autonomous navigation system, allowing the AGV to detect and avoid obstacles based in the processing of depth data acquired with a frontal depth camera mounted on the AGV. Applying the RANSAC (random sample consensus) and Euclidean clustering algorithms to the 3D point clouds captured by the camera, we can isolate obstacles from the ground plane and separate them into clusters. The clusters give information about the location of obstacles with respect to the AGV position. In experiments conducted outdoors and indoors, the results revealed that the method is very effective, returning high percentages of detection for most tests.