ESTCB - Artigos em revistas com arbitragem científica
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- A practical approach to industrial digitalization through data acquisition and systems integration for predictive maintenancePublication . Bocharov, Nikita; Torres, Pedro; Matos, JoãoThe digital transformation of industrial environments requires the ability to collect, process, and integrate data from production systems in real time. However, many manufacturing facilities operate with legacy equipment that is perfectly functional and operational but lacks native connectivity or standardized interfaces for data acquisition. This paper presentes an approach to enable industrial digitalization through the implementation of a network architecture at the Operational Technology (OT) level that facilitates the collection of structured data from legacy and modern machines. The proposed solution ensures integration between production systems and supervisory platforms, supporting real-time monitoring through SCADA systems and providing relevant information for predictive maintenance strategies. The proposal is based on the implementation of a standardized and secure communication infrastructure between the shop floor and higher-level Information Technology (IT) systems, aligning with the principles of Industry 4.0. The solution has been implemented in a real industrial scenario, is fully operational and the results demonstrate significant benefits of integrating heterogeneous industrial assets into a unified data ecosystem, improving process insight, operational efficiency and supporting maintenance decision-making.
- Recognition of food ingredients: Dataset analysisPublication . Louro, João; Fidalgo, Filipe; Oliveira, ÂngelaNowadays, food waste is seen as a complex problem with effects on the social, economic, and environmental domains. Even though this view is widely held, it is frequently believed that individual acts have little to no impact on the issue. But just like with recycling, there may be a significant impact if people start adopting more sustainable eating habits. We suggest using a cutting-edge convolutional neural network (CNN) model to identify food in light of these factors. To improve performance, this model makes use of several strategies, such as fine-tuning and transfer learning. Additionally, we suggest using the Selenium library to create a dataset by employing the web scraping technique. This strategy solves the problem that many open-source datasets have with the overrepresentation of foods from the Asian continent by enabling the addition of foods to the dataset in a customized way. First, using the PRISMA methodology, a thorough examination of recente research in this field will be carried out. We will talk about the shortcomings of the most widely used dataset (Food-101), which prevent the ResNet-50 model from performing well. Using this information, a smartphone app that can identify food and suggest recipes based on the ingredients it finds could be developed. This would prevent food waste that results from the lack of imagination and patience of most people. The food recognition model used was the ResNet-50 convolutional neural network, which achieved 90% accuracy for the validation set and roughly 97% accuracy in training.
- Gamification and emotional intelligence: Development of a digital application for childrenPublication . Nunes, Maria; Oliveira, Ângela; Fidalgo, FilipeIt is important to work on educating children’s emotional intelligence, namely the re-awareness and control of emotions, both their own and those around them so that they feel empathy for others, establish and maintain positive relationships and make conscious decisions. This document describes a proposal for a gamified solution, based on the development of a multimedia product, which aims to help children, parents and teachers in the education of emotional intelligence in children. The solution makes it possible to present everyday scenarios to children, allowing adults to find out how they feel and, based on this information, to work on feelings and social behaviour. The solution was based on research into studies available in scientific databases on children’s emotional intelligence, as well as research into exercises that can help work on this same issue. Once implemented, the solution was tested with children from a primary school, where it was possible to collect feedback from them and their teachers and make improvements. This study presentsthe design, development and evaluation of a gamified application for children focused on emotional intelligence. The methodology used is based on a systematic literature review following the PRISMA protocol and the development of an iterative multimediaproduct. The study sample included around 200 elementary school children, where it was possible to collect qualitative feedback to evaluate the effectiveness of the application. The results obtained made it possible to make improvements to the design of the application and to obtain feedback from the teachers, which was very positive, but transmitted by direct interview.
- Edge-computing smart irrigation controller using LoRaWAN and LSTM for predictive controlled deficit irrigationPublication . Baseca, Carlos Cambra; Dionísio, Rogério Pais; Ribeiro, Fernando Reinaldo; Metrôlho, J.C.M.M; Yu, GuandingEnhancing sustainability in agriculture has become a significant challenge today where in the current context of climate change, particularly in countries of the Mediterranean area, the amount of water available for irrigation is becoming increasingly limited. Automating irrigation processes using affordable sensors can help save irrigation water and produce almonds more sustainably. This work presents an IoT-enabled edge computing model for smart irrigation systems focused on precision agriculture. This model combines IoT sensors, hybrid machine learning algorithms, and edge computing to predict soil moisture and manage Controlled Deficit Irrigation (CDI) strategies in high density almond tree fields applying reductions of 35% ETc (crop evapotranspiration). By gathering and analyzing meteorological, humidity soil, and crop data, a soft ML (Machine Learning) model has been developed to enhance irrigation practices and identify crop anomalies in real-time without cloud computing. This methodology has the potential to transform agricultural practices by enabling precise and efficient water management, even in remote locations with lack of internet access. This study represents an initial step toward implementing ML algorithms for irrigation CDI strategies.
- Digital platforms to promote sustainable and authentic tourism in low-density territories of Southern Europe: Challenges and opportunitiesPublication . Nunes, Jorge; Mata, Diogo; Ribeiro, Fernando Reinaldo; Metrôlho, José CarlosTourism in low-density regions has gained increasing attention as travelers seek more sustainable and authentic tourism experiences. However, despite their cultural and environmental richness, these territories often face structural challenges such as limited visibility, fragmented promotion, and inadequate digital infrastructure. This study explores how digital platforms can support sustainable tourism development in such contexts, combining a systematic literature review with an exploratory analysis of commercial applications. The analysis focuses on academic initiatives that propose IT-based solutions for promoting tourism in sparsely populated areas of Southern Europe, while the platform analysis assesses functionalities and limitations of widely used applications. The findings reveal that most academic solutions remain at the prototype stage or have yet to be tested in real-world contexts, with limited evidence of large-scale implementation or practical validation. Accessibility for people with functional limitations is also largely neglected in both academic and commercial platforms, despite its importance for inclusive tourism. In addition, the digital landscape remains fragmented, with few solutions effectively designed to bring together diverse local stakeholders or to meaningfully enable user-generated content. The study concludes by identifying key challenges, such as fragmentation, lack of accessibility features, and limited deployment, and outlines future directions for developing scalable, inclusive, and culturally sensitive platforms tailored to the realities of low-density territories.
- A study on acoustic bird detection in the context of smart agriculturePublication . Cardoso, Fábio; Carvalho, Daniel; Gaspar, Pedro Dinis; Soares, V.N.G.J.; Caldeira, J.M.L.P.Bird attacks on crops represent one of the main challenges faced by farmers to make a farm profitable and sustainable. This challenge requires a paradigm shift from old, traditional, ineffective methods, towards the incorporation of smart farming technologies. Intelligent or precision agriculture contributes to the effective and efficient management of resources and, consequently, an increase in production. This work first reviews projects, techniques and methods that can be used for bird species detection and identification based on their sound. Then, a performance evaluation study is conducted on two different approaches that can be employed for the development of a smart bird deterrence solution. Their strengths and limitations are highlighted. The findings can be used as a foundation for future research in this area.
- Dynamic energy-aware anchor optimization for contact-based indoor localization in MANETsPublication . Jesús-Azabal, Manuel; Zheng, Meichun; Soares, V.N.G.J.Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or cellular towers, rendering GPS inoperative and requiring alternative positioning techniques. Despite numerous approaches, indoor contexts with resource limitations, energy constraints, or physical restrictions continue to suffer from unreliable localization. Many existing methods employ a fixed number of reference anchors, which sets a hard balance between localization accuracy and energy consumption, forcing designers to choose between precise location data and battery life. As a response to this challenge, this paper proposes an energy-aware indoor positioning strategy based on Mobile Ad Hoc Networks (MANETs). The core principle is a self-adaptive control loop that continuously monitors the network’s positioning accuracy. Based on this real-time feedback, the system dynamically adjusts the number of active anchors, increasing them only when accuracy degrades and reducing them to save energy once stability is achieved. The method dynamically estimates relative coordinates by analyzing node encounters and contact durations, from which relative distances are inferred. Generalized Multidimensional Scaling (GMDS) is applied to construct a relative spatial map of the network, which is then transformed into absolute coordinates using reference nodes, known as anchors. The proposal is evaluated in a realistic simulated indoor MANET, assessing positioning accuracy, adaptation dynamics, anchor sensitivity, and energy usage. Results show that the adaptive mechanism achieves higher accuracy than fixed-anchor configurations in most cases, while significantly reducing the average number of required anchors and their associated energy footprint. This makes it suitable for infrastructure-poor, resource-constrained indoor environments where both accuracy and energy efficiency are critical.
- Modular microservices architecture for generative music integration in digital audio workstations via VST pluginPublication . Raposo, Adriano N.; Soares, V.N.G.J.This paper presents the design and implementation of a modular cloud-based architecturethat enables generative music capabilities in Digital audioWorkstations through a MIDI microservices backend and a user-friendly VST plugin frontend. The system comprises a generative harmony engine deployed as a standalone service, a microservice layer that orchestrates communication and exposes an API, and a VST plugin that interacts with the backend to retrieve harmonic sequences and MIDI data. Among the microservices is a dedicated component that converts textual chord sequences into MIDI files. The VST plugin allows the user to drag and drop the generated chord progressions directly into a DAW’sMIDI track timeline. This architecture prioritizes modularity, cloud scalability, and seamless integration into existing music production workflows, while abstracting away technical complexity from end users. The proposed system demonstrates how microservice-based design and cross-platform plugin development can be effectively combined to Support generative music workflows, offering both researchers and practitioners a replicable and extensible framework.
- Phrase-oriented generative rhythmic patterns for jazz solosPublication . Raposo, Adriano N.; Soares, V.N.G.J.This study introduces a novel generative approach for crafting phrase-oriented rhythmic patterns in jazz solos, leveraging statistical analyses of a comprehensive corpus, the Weimar Jazz Database. Jazz solos, celebrated for their improvisational complexity, require a delicate interplay between rhythm and melody, making the generation of authentic rhythmic patterns a challenging task. This work systematically explores the relationships among rhythmic elements, including phrases, beats, divisions, and patterns. The generative method employs a Markov chain framework to synthesize rhythmic divisions and patterns, ensuring stylistic coherence and diversity. An extensive evaluation compares original and generated datasets through statistical and machine learning metrics, validating the generative model’s ability to replicate key rhythmic characteristics while fostering innovation. The findings underscore the potential of this approach to contribute significantly to the fields of computational creativity and algorithmic music composition, providing a robust tool for generating compelling jazz solos.
- Avaliação do nível de contaminação nos sedimentos de cursos de água do Sistema Mineiro de Caveira (Grândola)Publication . Silva, R. da; Fonseca1, R.; Araújo, J. F.; Silva, N.; Albuquerque, M.T.D.O presente trabalho, inserido no Projeto GeoMatRe, visa o levantamento das condições mais atualizadas no que toca aos parâmetros físico-químicos numa mina de drenagem ácida. Uma situação crítica que ocorre na área mineira de Caveira, em Grândola, Portugal, abandonada desde a década de 60 do século passado. O objetivo passa por analisar as condições de contaminação de Elementos Potencialmente Tóxicos (EPT) nos sedimentos que ocorrem quer em fases móveis e dissolvidas na água intersticial dos sedimentos, quer em fases mais imóveis. Neste trabalho é realizada uma caracterização geral da área do sistema mineiro, verificando-se um nível de contaminação da área, através da análise do Índice de Geoacumulação (IGEO) e do Fator de Enriquecimento (FE), onde os elementos Cu, Pb, Zn, As e Hg demonstram ser os principais EPT’s na região, apresentando valores, em alguns dos casos, milhares de vezes acima dos estipulados pelas normas nacionais.