ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus
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- Nonlinear control of mecanum-wheeled robots applying H∞controllerPublication . Chellal, Arezki Abderrahim; Braun, João A.; Lima, José; Gonçalves, José; Valente, Antonio; Costa, PauloMecanum wheeled mobile robots have become relevant due to their excellent maneuverability, enabling omnidirectional motion in constrained environments as a requirement in industrial automation, logistics, and service robotics. This paper addresses a low-level controller based on the H-Infinity (H-infinity) control method for a four-wheel Mecanum mobile robot. The proposed controller ensures stability and performance despite model uncertainties and external disturbances. The dynamic model of the robot was developed and introduced in MATLAB to generate the controller. Further, the controller's performance is validated and compared to a traditional PID controller using the SimTwo simulator, a realistic physics-based simulator with dynamics of rigid bodies incorporating non-linearities such as motor dynamics and friction effects. The preliminary simulation results show that the H-infinity reached a time-independent Euclidean error of 0.0091 m, compared to 0.0154 m error for the PID in trajectory tracking. Demonstrating that the H-infinity controller handles nonlinear dynamics and disturbances, ensuring precise trajectory tracking and improved system performance. This research validates the proposed approach for advanced control of Mecanum wheeled robots.
- Hybrid wavefront and bidirectional A* coverage path planning for AGVs in photovoltaic farm inspectionPublication . Castro, Gabriel G.R.; Carvalho, Lucas L. M.; Marques, Diogo G.; Lima, José; Pinto, Milena F.This work presents a novel Coverage Path Planning (CPP) framework integrating theWavefront Algorithm with bidirectional A* to optimize path planning for Autonomous Ground Vehicles (AGVs) that inspect photovoltaic farms. The proposed framework combines the efficient propagation capabilities of the Wavefront Algorithm with the heuristic-driven optimization of bidirectional A*, comparing the results achieved to those from the individual algorithms. The framework was validated in a Software-In-The-Loop (SITL) simulation environment using Gazebo and the Robot Operating System (ROS), focusing on AGV-based inspection of ground infrastructure and cables beneath solar panels. The outcomes demonstrate significant coverage efficiency and overall robustness.
- Schizophrenia diagnosis support with spectral and cepstral features of speechPublication . Teixeira, Felipe; Mendes, João; Soares,Salviano F. P.; Abreu, J. L. Pio; Teixeira, João PauloSchizophrenia is a severe mental illness affecting over 20 million people worldwide, significantly impairing quality of life and daily functioning. Current diagnostic methods rely heavily on subjective assessments and interactions between doctors and patients, leaving room for potential misdiagnoses. Recent advancements in technology have introduced non-invasive, fast, and user-friendly approaches, such as machine learning, to support psychiatric diagnosis. In this study, spectral features extracted from speech samples of individuals with and without schizophrenia were analyzed. Using an ensemble bagged tree model, we achieved an accuracy of 96.3%, a sensitivity of 94.6%, and an F1-score of 95.4%. These results highlight the potential of speech-based machine learning models as effective tools for aiding schizophrenia diagnosis.
- Performance benchmarking of or-tools methods for capacitated vehicle routing problems with time windowsPublication . Sena, Inês; Ribeiro, Tiago B.; Silva, Adriano S.; Fernandes, Florbela P.; Costa, Lino A.; Pereira, Ana I.The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a significant challenge in combinatorial optimization, with extensive practical applications in logistics and transportation. This study aims to conduct a comparative analysis of the various methods available in OR-Tools for solving the CVRPTW across datasets of different sizes and types using the Solomon and the Gehring and Homberger benchmarks. The analysis provided insights into the relative strengths of each method, with a primary focus on Guided Local Search (GLS) and Tabu Search (TS), showing consistent performance and adaptability to different dataset characteristics. The results indicate that GLS is the most robust method overall, and TS can outperform it in specific scenarios. In conclusion, this study offers insights for selecting the most effective method to solve vehicle routing problems based on the characteristics and scale of the problem.
- Interconnection between lifestyle, health, and academic outcomes: an analysis on study habits and well-beingPublication . Azevedo, Beatriz Flamia; Bezerra, Ana J.B.; Sirmakessis, Spiros; Pereira, Ana I.Balancing academic demands with personal and professional responsibilities has become an increasingly challenging task, making it difficult to maintain well-being and potentially leading to serious health problems. The stress resulting from these multiple daily tasks, combined with the pressure to perform at high academic levels, directly impacts students’ mental and emotional health, significantly compromising their quality of life. In this work, statistical and clustering techniques are employed to analyze the dataset “Daily Lifestyle and Academic Performance of Students”. The objective of this work is to explore the relationship between students’ daily habits, level of stress, and the impact on academic performance. The results point out that many students have difficulty managing time and maintaining well-being (low-stress levels) with high academic performance since, according to the results, the higher the academic outcome, the higher the student’s stress level.
- Influence of habits and comorbidities on liver diseasePublication . Leite, Gabriel A.; Azevedo, Beatriz Flamia; Pacheco, Maria F.; Fernandes, Florbela P.; Pereira, Ana I.The prevalence of hepatocellular carcinoma is expected to continue increasing worldwide, and its difficulty in early detection highlights the need for advanced monitoring technologies. As the disease progresses, it has a serious impact on patients’ health, and in severe cases, liver transplantation becomes the only viable solution, reinforcing its importance as a global health problem. This study proposes the use of different artificial intelligence methods to compare and understand them related to liver disease. Well-known algorithms such as Random Forest and Multi-Layer Perceptron were tested, as well as ensemble methods that exploit different modeling structures. The results showed that AdaBoost, Random Forest, and Gradient Boosting performed best with Area Under the Curve of 0.89, 0.86, and 0.84 respectively. To analyze their influence on clinical results, the best-performing model was reapplied only to the non-biochemical features that compose the dataset. The results indicate that portal vein thrombosis, diabetes, and hypertension are the most influential variables, with contributions of 29.48%, 20.50%, and 16.60%, respectively.
- 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.
- 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.
- 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.
- An openmodelica package for BELBICPublication . Coelho, João Paulo; Coelho, J. A. B.; Braz-César, ManuelThis paper presents the development and implementation of a new package for OpenModelica that integrates the Brain Emotional Learning Based Intelligent Controller (BELBIC) approach into control system simulations. BELBIC, inspired by neurobiological models of emotional learning, has demonstrated effectiveness in handling complex, nonlinear, and adaptive control problems. The proposed package provides a modular and user-friendly framework for integrating BELBIC enabling researchers and engineers to design, simulate, and analyze intelligent control strategies within an open-source environment. Key features of the package include customizable emotional response parameters and compatibility with existing Modelica libraries. To validate the package, a set of examples are included which demonstrates its application to the control of common dynamic systems.