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) "09:Indústria, Inovação e Infraestruturas"
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- A Practical Approach to Teaching Differential Robot Control Using a Goal-to-Goal InterfacePublication . Amorim, Johann S.J. C. C.; Moraes, Camile A.; Neto, Accacio F. dos; Amorim, Josef G. J. C. C.; Santos, Patricia S.; Haddad, Diego; Pinto, Milena F.; Lima, JoséThis paper presents an interface to assist students in performing robot controllers. This application is designed to stabilize the movement of a goal-to-goal robot. The primary objective is to enhance the learning experience by offering an easy application that showcases the robot’s behavior based on its kinematic model. The paper outlines the various stages involved in developing and testing this simple interface, including a study of the kinematics of differential robots. It discusses the discrete- time equations for goal-to-goal behavior, the data required to obtain the controller, the application of the controller in a discrete microcontroller, and the development of code to simulate the robot’s behavior. The results show the developed interface and the possible outcomes through the use of this application in control classes.
- 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.
- 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.
- Classical Versus Wellness Thermalism: The Case of Portuguese Thermal Establishments Before and After the COVID-19 PandemicPublication . Alves, Maria José; Nunes, Alcina; Alves, Jéssica; Gonçalves, Estelle SilvaThermal/mineral springs are one of the fastest-growing subcategories of wellness tourism. Indeed, it is an activity that has steadily increased in all of Europe’s developed economies over the last few decades. The pandemic has raised awareness of the importance of healthy lifestyles and has subsequently led to a surge in consumption of experiences and travel, somehow motivated by wellness. This study analyses the evolution of thermal users’ alternation between wellness and classical thermalism in Portugal. The objective is achieved by applying exploratory and cluster data analysis to a Portuguese administrative database containing the number of user registers and revenues generated from 2012 to 2022. During this period, the wellness registers increased in most thermal establishments compared to the classic records, even if service diversification may be found in most thermal establishments. Still, the financial value added by wellness consumers does not seem to follow the previously observed shift. The establishments with more classical registers are still the ones that are able to generate the highest income per person.
- Comparing RL policies for robotic pusherPublication . Bonjour, Pedro; Lopes, Rui PedroReinforcement learning (RL) has been consolidated as a promising approach to optimizing robotic tasks, allowing the improvement of performance and energy efficiency. This study investigates the effectiveness of five RL algorithms in the Pusher environment. Advantage Actor-Critic (A2C), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC) and Twin Delayed Deep Deterministic Policy Gradient (TD3). We evaluated training time, computational efficiency, and reward values to identify the most balanced solution between accuracy and energy consumption. The results indicate that the PPO offers the best compromise between performance and efficiency, with reduced training time and stability in learning. SAC achieves the best rewards but requires more training time, while A2C faces difficulties in continuous spaces. DDPG and TD3, despite t he good results, have high computational consumption, which limits their viability for real-time industrial applications. These findings highlight the importance of considering energy efficiency when choosing RL algorithms for robotic applications. As a future direction, we propose the implementation of these algorithms in a real-world environment, as well as the exploration of hybrid approaches that combine different strategies to improve accuracy and minimize energy consumption.
- D.R.E.A.M. App to Promote the Mental Health in Higher Education StudentsPublication . Vaz, Clara B.; Galvão, Ana Maria; Pais, Clarisse; Pinheiro, MarcoThis paper presents the development process of the mobile App D.R.E.A.M., Design-thinking to Reach-out, Embrace and Acknowledge Mental health, which is a tool for self-assessment and self-care in promoting the mental health of higher education students. In Portugal, the program for promoting Mental Health in higher education advocates the development and use of digital tools, such as apps and/or social networks and platforms, aimed at promoting wellbeing and with the potential for use to be more accessible to higher education students. The objective of this app is to promote the mental health and wellbeing of higher education students. Design Thinking was used as the methodology for building the app, which was developed using a combination of low-code/no-code tools, Flutter/Dart coding, and Google’s Firebase capabilities and database functionalities. In the first semester of the 2023/2024 academic year, 484 students downloaded the app, and 22 emails were received for psychological consultations. A dynamic update of the app is required, with modules on time management and study organization, structured physical activity programs, development of socio-ntrepreneurial skills, and vocational area.
- Development of a Simulation Tool for Electromagnetism EducationPublication . Auwarter, Bianca; Brandão, Diego; Ferreira, Ângela P.The widespread adoption in the educational system of information and communications technologies allows the use of interactive simulations, able to support a meaningful insight into the fundamental laws and concepts of electromagnetic theory, which, in a classical approach, would require a vector calculus background and three-dimensional geometrical resourcefulness, typically not maturated by the students in undergraduate engineering programmes. The use of simulation tools based on finite element analysis can facilitate the learning process by allowing users to create and/or exploit visual and more accurate models. ONELAB (Open Numerical Engineering LABoratory) is a simulation platform that integrates severalmodelling tools, including Gmsh, a three-dimensionalmesh modelling software. This simulation tool is able to provide interaction, accuracy, and visual interpretations of classical problems using the fundamental laws of electromagnetism. The application with the basic laws of electromagnetism has been developed to run on mobile devices, besides desktops, to improve its ease of access and dissemination.
- Forecasting COVID-19 in european countries using long short-term memoryPublication . Carvalho, Kathleen; Teixeira, Rita de Almeida; Reis, Luis Paulo; Teixeira, João PauloEffective time series forecasts are increasingly important in supporting judgment in various decisions. Various prediction models are available to support these projections based on how each area provides a diverse set of data with variable behavior. Artificial neural networks (ANNs) significantly contribute to medical research since using predictive ideas allows for the study of disease progression in the future, as well as the behavior of other variables. This study implemented the proposed model based on Long Short-Term Memory (LSTM) to forecast COVID-19 daily new cases, deaths, and ICU patients. The methodology uses quantitative and qualitative data from six European countries: Austria, France, Germany, Italy, Portugal, and Spain to predict the last 242 days of the COVID-19 pandemic. The dataset uses the healthcare parameters of the number of daily new cases, deaths, ICU patients, and mitigation procedures, such as the percentage of the population fully vaccinated, the mandatory use of masks, and the lockdown. Two approaches were used to evaluate the model’s performance: the mean absolute error (MAE) and the mean square error (MSE). The results demonstrate that the LSTM model efficiently captures general trends in COVID-19 metrics but shows limitations when predicting data with low values or high variability, such as daily deaths. The model reported the lowest errors for Spain and Portugal, while France and Germany exhibited higher error rates due to differences in data reporting and pandemic dynamics. These findings highlight the importance of contextualizing predictive models based on specific regional characteristics.
- A GPU implementation of the analog ensemble methodPublication . Crico, Ruben; Charles, Ines; Balsa, Carlos; Rufino, JoséThe Analog Ensemble (AnEn) method can be used to reconstruct incomplete time series using correlated series. Since the AnEn method may use data including several variables through long periods of time, its storage and computational cost may be substantial, slowing down reconstructions. This paper presents a full GPU implementation of the AnEn method, based on PyCUDA, that leads to a significant a cceleration of its execution. The implementation resorts to several techniques that seek to minimize the consumption of GPU global memory in the various steps of the AnEn algorithm, thus making room for larger input datasets. This is further reinforced by the use of batch processing as a way to automatically adapt the datasets size to the GPU memory available. The GPU implementation was tested on a meteorological dataset spanning 10 years, exhibiting a 30-fold speedup in the reconstruction time against a comparable CPU-based multicore version executed with up to 48 cores. The impact on the reconstruction error of changes on several important parameters of the implementation was also assessed.
- HaaS - a platform for password cracking in distributed heterogeneous systemsPublication . Lima, Carlos; Alves, Rui; Rufino, JoséTraditional passwords and respective cryptographic hashes are still widely used for user authentication. Breaking these hashes, to recover the original passwords, may be necessary for a variety of legitimate reasons. Hashcat, a widely used password auditing tool, is able to exploit the parallel processing power of many GPUs to accelerate the breaking of cryptographic hashes. Moreover, there are already ways of performing this task in a distributed environment, though they face several challenges, including the difficulty of assembling and managing distributed deployments, and using them in a user-friendly and resource-efficient way. This work presents Hashcat-as-a-Service (HaaS), a novel platform that targets these challenges. It combines Hashcat with web technologies, containerization and remote OpenCL middleware, to allow the user-friendly management of Hashcat instances that leverage the processing power of distributed GPUs, including support of instances migration in order to maximize GPU utilization. The evaluation of HaaS in different configurations demonstrated promising results, confirming its ability to handle intensive workloads and its flexibility to adapt to different usage scenarios and resources availability, making HaaS a relevant contribution in the password recovery field.