ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus
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Percorrer ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus por Objetivos de Desenvolvimento Sustentável (ODS) "04:Educação de Qualidade"
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- A prototype to enhance academic attendance using BLE beaconsPublication . Simas, Sily; Oliveira, Pedro Filipe; Matos, PauloThis project addresses the prototyping and application of an attendance control solution in an academic context, using Beacons. The introduction highlights the importance of effectively managing student attendance to ensure active participation in educational activities and provide insights into student engagement. BLE Beacons technology is compared to other RF signaling technologies, highlighting its advantages in terms of range, power consumption, cost, frequency, and data rate. BLE Beacon is chosen as the preferred technology due to its combination of adequate range, low power consumption, affordable cost, compatibility, security, and flexibility. The results section presents Estimote Beacons as devices used, highlighting their advanced features and techniques used, such as proximity and triangulation, to determine student presence. A visual summary of the project is presented in a flowchart that illustrates the operating processes of each stage. This solution promises to significantly improve the efficiency, safety, and quality of the academic environment, while simplifying the attendance recording process and providing valuable insights into student behavior.
- Actuators with Force Feedback: A Literature Review in the Scope of Educational, Academic, and Industrial ApplicationsPublication . Alvarez, Mariano José; Brancalião, Laiany Suganuma; Coelho, João Paulo; Carneiro, Jorge; Lopes, Ricardo; Costa, Paulo; Gonçalves, JoséForce sensors are essential elements of actuator systems, providing measurement and force control in different domains. This literature review discusses its applications in the industry, academic research, and educational domains. In an industrial setup, force sensors enhance efficiency, safety, and reliability within automation systems, predominantly robotic arms and assembly lines. In the academic environment, using such sensors fosters innovation within robotics and biomechanical studies, allowing for testing theoretical models and new methodologies. In education, force sensors help students understand basic concepts about mechanics and robotics from practical work. Understanding this diverse application allows one to design effective actuator systems, promoting technological advances and improved learning experiences. With this literary review, the aim is to gain an understanding of the state of the art in force sensor actuators applied in various areas, such as academia, education, and industry.
- Advance Reconnaissance of UGV Path Planning Using Unmanned Aerial Vehicle to Carry Our Mission in Unknown EnvironmentPublication . Nowakowski, Marek; Berger, Guido S.; Braun, João A.; Mendes, João; Bonzatto Junior, Luciano; Lima, JoséThe utilization of unmanned vehicles for specialized tasks has gained significant attention in both military and civilian domains. This article explores the application of commercial unmanned aerial vehicles (UAVs) for reconnaissance purposes, specifically to verify autonomous driving missions assigned to the developed TAERO manned-unmanned vehicle in field operations. The paper introduces the TAERO vehicle, highlighting its functionality and capabilities for unmanned missions. The architecture of the unmanned ground vehicle (UGV) system is discussed taking into consideration the autonomy subsystem and used location data. The limitations associated with terrain and potential obstacles are addressed as well as importance of acquiring accurate terrain information for successful autonomous operation. The solution proposed in our study involves the use of a commercially available UAV applied to the visual tracking of potential targets in an engagement scenario. Details related to flight route planning system, geolocation, target tracking, and data transmission between robotic platforms are discussed and presented in this work. The acquired real-time data plays a crucial role in confirming the mission, making necessary adjustments, or altering the planned route. The UAV platform, known for its maneuverability and operational capabilities, can operate ahead as a reconnaissance element, improving the overall reconnaissance capabilities of the system. Upon completion of the mission, the UAV can return to the base or land on a moving vehicle platform. The authors proposed integration of a UAV that significantly enhances the autonomous mode capabilities of unmanned ground platform, improving operation in unknown environment during special mission.
- Advancing Sustainability and Productivity: The Role of Precision Agriculture in Vineyards and Olive GrovesPublication . Fernandes, Fernanda Mara; Santos, Murillo Ferreira dos; Morais, Maurício Herche Fófano de; Lima, José; Pereira, Ana I.; Mercorelli, PaoloPrecision agriculture has emerged as a vital approach to modern agricultural management, addressing the dual challenge of increasing food production while preserving the environment. Its importance lies in its ability to leverage advanced technologies to optimize productivity, reduce waste, and ensure sustainability, particularly in high-value crops such as vineyards and olive groves. This study explores the application of precision agriculture tools, such as sensors, drones, geolocation systems, and data analytics, in these crops to enhance productivity, improve product quality, and minimize environmental impact. In vineyards, precision viticulture focuses on managing spatial and temporal variability within plots to increase economic performance through higher productivity, superior fruit quality, and reduced production costs. The targeted application of inputs and precise management practices result in resource savings and uniform fruit quality, crucial for producing premium wines. Similarly, in olive groves, technologies enable effective plant health monitoring and early disease detection. At the same time, drones assist in evaluating plant vigor and planning optimal harvest times, ultimately maximizing yield and olive oil quality. By integrating traditional agricultural practices with modern technological advancements, this study anticipates a range of positive outcomes, including reduced resource waste, improved competitiveness in global markets, and strengthened sustainability in production networks. The findings underscore the transformative potential of precision agriculture, offering valuable insights into sustainable agricultural development and setting a pathway for further innovation in the sector.
- An intelligent environment application case to manage comfort preferences, at an University residencePublication . Oliveira, Pedro Filipe; Matos, PauloThis paper presents a novel application of intelligent environmental management within a university residence, aiming to enhance the overall well-being and satisfaction of residents by dynamically addressing their comfort preferences. The proposed system leverages cutting-edge technologies such as Internet of Things (IoT) sensors, machine learning algorithms, and smart devices to create an adaptive and responsive living environment. Through real-time data collection and analysis, the system learns individual and collective comfort patterns, allowing for personalized adjustments to temperature, and other environmental factors. The study focuses on the development and implementation of the intelligent environment application, emphasizing user-centric design and seamless integration into daily life. Residents are empowered to set and modify their comfort preferences through a user-friendly interface, while the system continuously refines its understanding of these preferences over time. Additionally, the application considers energy e!ciency and sustainability, contributing to a greener and more resource-conscious university residence. The paper discusses the technical architecture of the intelligent environment application, including the deployment of sensors, data processing pipelines, and the communication infrastructure. Furthermore, it addresses privacy concerns by outlining robust security measures and anonymization techniques to protect user data. In conclusion, this paper contributes to the growing body of research on intelligent environments by showcasing a practical application tailored to university residence settings. The presented system not only prioritizes resident comfort but also aligns with the broader goals of sustainability and resource optimization, making it a valuable addition to smart living solutions in educational institutions.
- Application of programming cocktails identity cards to development complexity analysisPublication . Costa Neto, Alvaro; Pereira, Maria João; Henriques, Pedro RangelComplexity in software projects tends to grow considerably as resources and stakeholders raise in numbers. Several factors may contribute to this, ranging from miscommunication between developers, to excessive dependency on external libraries and frameworks. Consequently, managing both developers and the assets they use becomes increasingly hard as features are implemented and changes in linguistic characteristics and coding styles become necessary. This position paper presents Programming Cocktails, their Ingredients, and Resources, three basic software development management concepts. These three main concepts culminate in Programming Cocktails Identity Cards, an ontology-based modelling technique to aid in assessing, planning, and understanding how each development technology contributes—both positively and negatively—to several aspects of software development, such as cognitive burden, risk and cost.
- Architecture for efficient food management and waste reductionPublication . Pereira, Hélder; Oliveira, Pedro Filipe; Matos, PauloThis article presents a modular architecture for developing the ZeroWaste mobile application, designed to optimize food management in the home environment and reduce food waste through collaborative and scalable features. Food waste is a global issue with severe environmental, economic, and social repercussions, and ZeroWaste seeks to address this challenge by promoting conscious and sustainable consumption practices. Developed in React Native to support multiple platforms, the application integrates Firebase for authentication, notifications, and real-time data storage, enabling timely alerts on product expiration and facilitating user control over food inventory. Additionally, it incorporates an artificial intelligence module that suggests personalized recipes based on available products, encouraging food usage before spoilage. The proposed architecture also includes an automated product registration system using barcode scanning, supporting the creation of a community database that streamlines food item identification. Other features, such as shared shopping lists and multi-residence inventory management, expand the collaborative scope of the application, fostering the exchange and donation of food between users. With its flexible and expandable design, ZeroWaste is oriented towards continuous development and future improvements, meeting the growing need for technological solutions in sustainable and collaborative food management. This architectural proposal provides a robust foundation for developing an innovative solution.
- Artificial intelligence-based control of autonomous vehicles in simulation: a CNN vs. RL case studyPublication . Vasiljević, Ive; Musić, Josip; Lima, JoséThe article provides a comparison of Convolutional Neural Network (CNN) and Reinforcement Learning (RL) applied to the field of autonomous driving within the CARLA (CAr Learning to Act) simulator for training and evaluation. The analysis of results revealed CNNs better overall performance, as it demonstrated a more refined driving experience, shorter training durations, and a more straightforward learning curve and optimization process. However, it required data labelling. In contrast, RL relayed on an exhaustive (unsupervised) exploration of different models, ultimately selecting the model at timestep 600,000, which had the highest mean reward. Nevertheless, RL’s approach revealed its susceptibility to excessive oscillations and inconsistencies, necessitating additional optimization and tuning of hyperparameters and reward functions. This conclusion is further substantiated by a range of used performance metrics (objective and subjective), designed to assess the performance of each approach.
- Attitude Determination and Control System Open-Source Simulator of a 1U CubeSat NanosatellitePublication . Resende, Túlio Santos; Silva, Josué Lima da; Peixoto, Alessandro Jacoud; Santos, Murillo Ferreira dos; Pereira, Ana I.; Lima, José; Morais, Maurício Herche Fófano deThis work developed an open-source educational simulator to evaluate control schemes for small CubeSat-class satellites of 1U size (1 unit). Attitude (orientation) estimation was achieved using the Three-Axis Attitude Determination (TRIAD) algorithm, commonly employed in competitions and real-world CubeSats. A simple Proportional (P) controller was designed to regulate the CubeSat's angular velocity. Additionally, for pointing control (line-of-sight orientation), a Proportional-Derivative (PD) controller was implemented. Numerical simulations were conducted to assess the performance of the CubeSat's Attitude Determination and Control Subsystem (ADCS).
- Automated preprocessing of olive leaf images for cultivar classification using YOLO11Publication . Mendes, João; Lima, José; Rodrigues, Nuno; Pereira, Ana I.Olive cultivation is a pillar of Mediterranean agriculture, deeply rooted in both tradition and economic importance. This paper presents a novel two-phase methodology for the automated preprocessing of olive leaf images to facilitate accurate cultivar classification. Leveraging the state-of-the-art YOLO11 framework, two models (YOLO11n and YOLO11s) were employed for detection and segmentation tasks. A comprehensive dataset, combining in-situ captured images with publicly available data, was meticulously annotated using both manual and semi-automatic processes. The detection model identifies individual olive leaves, while the segmentation model isolates the leaves by replacing the background with a uniform white, thereby simulating laboratory conditions. Experimental results demonstrate that YOLO11n outperforms YOLO11s in terms of mean Average Precision and F1-score, confirming the feasibility of deploying the system on mobile devices for real-time, in-field classification.