Abstract
The rise of Industry 4.0 has revolutionized manufacturing by integrating real-time data analysis, artificial intelligence (AI), automation, and interconnected systems, enabling adaptive and resilient smart factories. Autonomous Mobile Robots (AMRs), with their advanced mobility and navigation capabilities, are a pillar of this transformation. However, their deployment in job shop environments adds complexity to the already challenging Job Shop Scheduling Problem (JSSP), expanding it to include task allocation, robot scheduling, and travel time optimization, creating a multi-faceted, non-deterministic polynomial-time hardness (NP-hard) problem. Traditional approaches such as heuristics, meta-heuristics, and mixed integer linear programming (MILP) are commonly used. Recent AI advancements, particularly large language models (LLM), have shown potential in addressing these scheduling challenges due to significant improvements in reasoning and decision-making from textual data. This paper examines the application of LLM to tackle scheduling complexities in smart job shops with mobile robots. Guided by tailored prompts inserted manually, LLM are employed to generate scheduling solutions, being these compared to an heuristic-based method. The results indicate that LLM currently have limitations in solving complex combinatorial problems, such as task scheduling with mobile robots. Due to issues with consistency and repeatability, they are not yet reliable enough for practical implementation in industrial environments. However, they offer a promising foundation for augmenting traditional approaches in the future.

Abstract
Whispers are common and essential for secondary communication. Nonetheless, individuals with aphonia, including laryngectomees, rely on whispers as their primary means of communication. Due to the distinct features between whispered and regular speech, debates have emerged in the field of speech recognition, highlighting the challenge of effectively converting between them. This study investigates the characteristics of whispered speech and proposes a system for converting whispered vowels into normal ones. The system is developed using multilayer perceptron networks and two types of generative adversarial networks. Three metrics are analyzed to evaluate the performance of the system: mel-cepstral distortion, root mean square error of the fundamental frequency, and accuracy with f1-score of a vowel classifier. Overall, the perceptron networks demonstrated better results, with no significant differences observed between male and female voices or the presence/absence of speech silence, except for improved accuracy in estimating the fundamental frequency during the conversion process. © 2025, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract

Abstract
This paper presents the development of fleet management software for mobile robots, including AGV and AMR technologies, within the scope of a case study from the GreenAuto project. The system was designed to integrate position and status data from different robots, unifying this information into a single map. To achieve this, a web-based platform was developed to allow the simultaneous, real-time visualization of all robots in operation. However, the main challenge of this research lies in the heterogeneity of the fleet, which comprises robots of different makes and models from various manufacturers, each using distinct data formats. The proposed approach addresses this by facilitating fleet monitoring and management, ensuring a greater efficiency and coordination in the robot movement. The results demonstrate that the platform improves the traceability and operational supervision, promoting the optimized management of mobile robots. It is concluded that the proposed solution contributes to industrial automation by providing an intuitive and centralized interface, enabling future expansions for new functionalities and the integration with other emerging technologies. The proposed system demonstrated efficiency in updating and supervising operations, with an average latency of 120 ms for task status updates and an interface refresh rate of less than 1 s, enabling near real-time supervision and facilitating operational decision-making.

Abstract
There is a strong need to motivate students to learn science, technology, engineering, and mathematics (STEM) subjects. This is a problem not only at lower educational levels, but also at college institutions. With this idea in mind, the School of Engineering of the Porto Polytechnic (ISEP) Electrical Engineering Department decided, in 2021, to launch a robotics competition in order to foster students' interest in the areas of robotics and automation. This event, named Robotics@ISEP Open, aims to raise awareness of the area of electronics, computing, and robotics among students, involving them in the use of techniques and tools in this area, and encompasses three distinct robotics competitions covering both manipulator arms and mobile robots. It is based on two main points of interest: (i) robotic competitions and (ii) outside class training in robotics, aimed at students who want support to participate in competitions. Since its first edition, the event has grown and internationalized and has already become a milestone in the academic life of ISEP. This paper presents the motivations that led to the creation of this event, its main organizational aspects, and the competitions that are part of it, as well as some results gathered from the experience accumulated in organizing it.

Abstract
Segmentation of lung nodules in CT images is an important step during the clinical evaluation of patients with lung cancer. Furthermore, early assessment of the cancer is crucial to increase the overall survival chances of patients with such disease, and the segmentation of lung nodules can help detect the cancer in its early stages. Consequently, there are many works in the literature that explore the use of neural networks for the segmentation of lung nodules. However, these frameworks tend to rely on accurate labelling of the nodule centre to then crop the input image. Although such works are able to achieve remarkable results, they do not take into account that the healthcare professional may fail to correctly label the centre of the nodule. Therefore, in this work, we propose a new framework based on the U-Net model that allows to correct such inaccuracies in an interactive fashion. It is composed of two U-Net models in cascade, where the first model is used to predict a rough estimation of the lung nodule location and the second model refines the generated segmentation mask. Our results show that the proposed framework is able to be more robust than the studied baselines. Furthermore, it is able to achieve state-of-the-art performance, reaching a Dice of 91.12% when trained and tested on the LIDC-IDRI public dataset.