Abstract
This article introduces “Friday”, a Mobile Manipulator (MoMa) solution designed at iiLab - INESC TEC. Friday is versatile and applicable in various contexts, including warehouses, naval shipyards, aerospace industries, and production lines. The robot features an omnidirectional platform, multiple grippers, and sensors for localisation, safety, and object detection. Its modular hardware and software system enhances functionality across different industrial scenarios. The system provides a stable platform supporting scientific advancements and meeting modern industry demands, with results verified in the aerospace, automotive, naval, and logistics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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
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. © 2025 IEEE.

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.

Abstract
The present critical literature review describes the state-of-the-art innovative proximal (ground-based) solutions for plant disease diagnosis, suitable for promoting more precise and efficient phytosanitary measures. Research and development of new sensors for this purpose are currently a challenge. Present procedures and diagnosis techniques depend on visual characteristics and symptoms to be initiated and applied, compromising an early intervention. Also, these methods were designed to confirm the presence of pathogens, which did not have the required high throughput and speed to support real-time agronomic decisions in field extensions. Proximal sensor-based systems are a reasonable tool for an efficient and economic disease assessment. This work focused on identifying the application of optical and spectroscopic sensors as a tool for disease diagnosis. Biophoton emission, fluorescence spectroscopy, laser-induced breakdown spectroscopy, multi- and hyperspectral spectroscopy (HS), nuclear magnetic resonance spectroscopy, Raman spectroscopy, RGB imaging, thermography, volatile organic compounds assessment, and X-ray fluorescence were described due to their relevant potential. Nevertheless, some techniques revealed a low technology readiness level (TRL). The main conclusions identify HS, single and multi-spatial point observation, as the most applied methods for early plant disease diagnosis studies (88%), combined with distinct feature selection (FeS), dimensionality reduction (DR), and modeling techniques. Vegetation indices (28%) and principal component analysis (19%) were the most popular FeS and DR approaches, highlighting the most relevant wavelengths contributing to disease diagnosis. In modeling, classification was the most applied technique (80%), used mainly for binary and multi-class health status identification. Regression was used in the remaining (21%) scientific works screened. The data was collected primarily in laboratory conditions (62%), and a few works were performed in field conditions (21%). Regarding the study's etiological agent responsible for causing the disease, fungi (53%) and viruses (23%) were the most analyzed group of pathogens found in the literature. Overall, proximal sensors are suitable for early plant disease diagnosis before and after symptom appearance, presenting classification accuracies mostly superior to 71% and regression coefficients superior to 61%. Nevertheless, additional research regarding the study of specific host-pathogen interactions is necessary.