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- Adaptation to TV delays based on the user behavior towards a cheating-free second screen entertainmentPublication . Madeira, Rui Neves; Centineiro, Paulo; Correia, NunoRecent advances in technology created new opportunities to enhance TV personalization, providing viewers with individualized ways to watch TV and to interact with its content. Second screen applications are promising vehicles to enhance the viewers’ experiences, but researchers need to take into account the effect that the TV delay has on viewers, in particular when watching broadcasted live events. In this paper, we propose a software-based solution to deal with TV delays. It is mainly directed for a gaming context in which the user has the means to control the synchronisation between the second screen application and the TV content. Taking this scenario into account, we implemented a cheating-detection mechanism to cope with the potential exploitation of the system by its users.
- AI-powered solution for plant disease detection in viticulturePublication . Madeira, Miguel; Porfírio, Rui; Santos, Pedro Albuquerque; Madeira, Rui NevesIn an era dominated by the intersection of advanced technology and traditional industries, the domain of agriculture is on the verge of a revolutionary transformation. This article introduces a solution for vineyard producers, harnessing satellite imagery, weather data, and deep learning (DL) to identify vineyard diseases robustly. This solution, designed for proactive plant health management, stands as a transformative tool towards digital viticulture. Such tools transition from luxuries to essentials as vineyards confront evolving challenges like climate change and new pathogens. Our research builds on the hypothesis that customising deep learning architectures for specific tasks is crucial in enhancing their effectiveness. We contribute by introducing a tailored convolutional neural network (CNN) architecture, developed specifically for the classification of plant diseases using vineyard imagery. The experimental results demonstrate that our custom CNN architecture exhibits performance on par with established state-of-the-art models like ResNet50 and MobileNetV2, underscoring the value of specialized solutions in addressing the unique challenges of viticulture. This paper introduces an overview of the solution’s architecture, presents the implementation of DL modules with their corresponding results, and describes use case scenarios.
- An exploratory study for the implementation of the oxidative potential assessment of particulate matter in PortugalPublication . Vicente, C.; Gonçalves, S.; Gamelas ou Carla A. Gamelas, Carla; Almeida, S. M.; Cabo Verde, S.; Bartolomeo, A. R.; Guascito, M. R.; Contini, D.; Canha, N.Particulate matter (PM) is a harmful air pollutant that damages human health by inducing oxidative stress through the excessive generation of reactive oxygen species (ROS). Oxidative Potential (OP) is a proposed metric to measure PM's capacity to generate ROS (Almetwally et al., 2020; Jiang et al., 2019). This study aims to implement the OPDTT assessment methodology at C2TN (Portugal). A set of PM2.5 samples was evaluated using a widely used protocol (Chirizzi et al., 2017), and the results were compared to the values previously obtained at DISTEBA, where the protocol is well established. Moreover, the use of the reference material SRM 1648 (Urban Particulate) as a standard for determining OPDTT was also evaluated. Analyzing a 10 mg.L-1 solution of the reference material, it was possible to conclude that the standard solution presented an average DTT activity (normalized to the mass) value of 27.60 ± 3.79 pmol.min-1.μg-1.
- An innovative faster R-CNN-based framework for breast cancer detection in MRIPublication . Raimundo, João Nuno; Fontes, João Pedro; Magalhães, Luís; Guevara Lopez, Miguel AngelReplacing lung cancer as the most commonly diagnosed cancer globally, breast cancer (BC) today accounts for 1 in 8 cancer diagnoses and a total of 2.3 million new cases in both sexes combined. An estimated 685,000 women died from BC in 2020, corresponding to 16% or 1 in every 6 cancer deaths in women. BC represents a quarter of a total of cancer cases in females and by far the most commonly diagnosed cancer in women in 2020. However, when detected in the early stages of the disease, treatment methods have proven to be very effective in increasing life expectancy and, in many cases, patients fully recover. Several medical imaging modalities, such as X-rays Mammography (MG), Ultrasound (US), Computer Tomography (CT), Magnetic Resonance Imaging (MRI), and Digital Tomosynthesis (DT) have been explored to support radiologists/physicians in clinical decision-making workflows for the detection and diagnosis of BC. In this work, we propose a novel Faster R-CNN-based framework to automate the detection of BC pathological Lesions in MRI. As a main contribution, we have developed and experimentally (statistically) validated an innovative method improving the “breast MRI preprocessing phase” to select the patient’s slices (images) and associated bounding boxes representing pathological lesions. In this way, it is possible to create a more robust training (benchmarking) dataset to feed Deep Learning (DL) models, reducing the computation time and the dimension of the dataset, and more importantly, to identify with high accuracy the specific regions (bounding boxes) for each of the patient’s images, in which a possible pathological lesion (tumor) has been identified. As a result, in an experimental setting using a fully annotated dataset (released to the public domain) comprising a total of 922 MRI-based BC patient cases, we have achieved, as the most accurate trained model, an accuracy rate of 97.83%, and subsequently, applying a ten-fold cross-validation method, a mean accuracy on the trained models of 94.46% and an associated standard deviation of 2.43%.
- Aparelhos de medição de posição angularPublication . V. Dionísio, R.
- Aparelhos de medição de temperatura por radiaçãoPublication . V. Dionísio, R.
- Application of an Indenyl Molybdenum Dicarbonyl Complex in the Isomerisation of α-Pinene Oxide to Campholenic AldehydePublication . Bruno, S.; Gomes, A. C.; Gamelas ou Carla A. Gamelas, Carla; Abrantes, M.; Oliveira, M. C.; Valente, A.; Almeida-Paz, Filipe; Pillinger, M.; Romão, C.; Gonçalves, I.The complex [{(Z5-Ind)Mo(CO)2(m-Cl)}2] (1) has been tested for the industrially relevant catalytic isomerisation of a-pinene oxide (PinOx) to campholenic aldehyde (CPA) in the liquid phase. PinOx conversion and CPA selectivity are strongly influenced by the solvent employed. Complete conversion of PinOx was achieved within 1 min at 55 1C or 30 min at 35 1C using 1,2-dichloroethane as solvent, giving CPA in 68% yield. Other products included trans-carveol, iso-pinocamphone and trans-pinocarveol. The stability of 1 under the reaction conditions used was investigated by using FT-IR spectroscopy and electrospray ionisation mass spectrometry (ESI-MS) to characterise recovered solids. In the presence of air/moisture 1 undergoes oxidative decarbonylation upon dissolution to give oxomolybdenum species that are proposed to include a tetranuclear oxomolybdenum(V) complex. Conversely, ESI-MS studies of 1 dissolved in dry acetonitrile show mononuclear species of the type [IndMo(CO)2(CH3CN)n]+. The crystal structure of the ring-slipped dicarbonyl complex [(Z3-Ind)Mo(CO)2Cl(CH3CN)2] (2) (obtained after dissolution of 1 in acetonitrile) is reported.
- Application of force and inertial sensors to monitor gait on legacy walkersPublication . Viegas, Vitor; Pereira, José Miguel Costa Dias; Postolache, Octavian; Girão, Pedro SilvaWalker assistive devices play an important role in extending the autonomy of elderly people and in recovering the mobility of people affected by locomotion disabilities. The next generation of walkers are hoped to include embedded sensors and data processing capabilities that will allow for the extraction of objective metrics (about gait and body posture) to assist the work of physiotherapists and to enable the self-control nature of walker usage. The paper presents the Andante, our latest proposal of a smart walker intended to monitor and analyze gait in real time. The system makes use of e-textile electrodes to sense the heart rate of the user, load cells to measure the forces applied on the walker legs, and an inertial measurement unit to sense motion and orientation. These signals are sampled locally and transferred over a Bluetooth link to a remote host, where they are processed in real time. Data processing includes the detection, classification, and characterization of the steps. A rich set of parameters is presented for each step, including estimates of balance and motor coordination, step length and azimuth, and lift of the walker frame. This information can be used by physiotherapist to objectively assess the physical condition of the user and tune rehabilitation therapy if needed. The proposed solution can be easily integrated into any commercial walker without any loss of functionality.
- Applications of geographic information systemsPublication . Grueau, Cédric
- Assessment of oxidative potential of fine aerosols from different indoor and outdoor environmentsPublication . Gonçalves, S.; Gamelas ou Carla A. Gamelas, Carla; Mendez, S.; Cabo Verde, S.; Almeida, S. M.; Bartolomeo, A.R.; Guascito, M. R.; Contini, D.; Canha, N.