ESTCB - Artigos em revistas com arbitragem científica
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Browsing ESTCB - Artigos em revistas com arbitragem científica by Field of Science and Technology (FOS) "Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática"
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- Computer vision for reducing food waste in an institutional canteen: A literature review and performance analysisPublication . Correia, Ana; Aidos, Clara; Caldeira, J.M.L.P.; Soares, V.N.G.J.Food waste in today's society has been the subject of growing interest and discussion, given its economic, environmental, social, and nutritional implications. Although food waste is present throughout the food supply chain, in developed countries it tends to be higher in the final stages of consumption (e.g., households and food services). This study focuses on institutional canteens, where food waste includes prepared meals that have not been sold (i.e., leftovers), as well as food served that is left on plates after the meal has been consumed (i.e., scraps). It presents a first step towards developing a prototype/solution based on computer vision techniques to identify and quantify food waste in an institutional canteen. It begins by introducing the related concepts. It then surveys the state-of-the-art and categorizes existing solutions, presenting their main characteristics, strengths, and limitations. Inception-V3 and ResNet-50 are identified as the most promising computer vision techniques, and their performance has been evaluated. Information is also provided on open questions and research directions in this area
- Generative jazz chord progressions: A statistical approach to harmonic creativityPublication . Raposo, Adriano; Soares, V.N.G.J.Jazz music has long been a subject of interest in the field of generative music. Traditional jazz chord progressions follow established patterns that contribute to the genre’s distinct sound. However, the demand for more innovative and diverse harmonic structures has led to the exploration of alternative approaches in music generation. This paper addresses the challenge of generating novel and engaging jazz chord sequences that go beyond traditional chord progressions. It proposes an unconventional statistical approach, leveraging a corpus of 1382 jazz standards, which includes key information, song structure, and chord sequences by section. The proposed method generates chord sequences based on statistical patterns extracted from the corpus, considering a tonal context while introducing a degree of unpredictability that enhances the results with elements of surprise and interest. The goal is to move beyond conventional and well-known jazz chord progressions, exploring new and inspiring harmonic possibilities. The evaluation of the generated dataset, which matches the size of the learning corpus, demonstrates a strong statistical alignment between distributions across multiple analysis parameters while also revealing opportunities for further exploration of novel harmonic pathways.
- Hybrid B5G-DTN architecture with federated learning for contextual communication offloadingPublication . Jesús-Azabal, Manuel; Zheng, Meichun; Soares, V.N.G.J.In dense urban environments and large-scale events, Internet infrastructure often becomes overloaded due to high communication demand. Many of these communications are local and short-lived, exchanged between users in close proximity but still relying on global infrastructure, leading to unnecessary network stress. In this context, delay-tolerant networks (DTNs) offer an alternative by enabling device-to-device (D2D) communication without requiring constant connectivity. However, DTNs face significant challenges in routing due to unpredictable node mobility and intermittent contacts, making reliable delivery difficult. Considering these challenges, this paper presents a hybrid Beyond 5G (B5G) DTN architecture to provide private context-aware routing in dense scenarios. In this proposal, dynamic contextual notifications are shared among relevant local nodes, combining federated learning (FL) and edge artificial intelligence (AI) to estimate the optimal relay paths based on variables such as mobility patterns and contact history. To keep the local FL models updated with the evolving context, edge nodes, integrated as part of the B5G architecture, act as coordinating entities for model aggregation and redistribution. The proposed architecture has been implemented and evaluated in simulation testbeds, studying its performance and sensibility to the node density in a realistic scenario. In high-density scenarios, the architecture outperforms state-of-the-art routing schemes, achieving an average delivery probability of 77%, with limited latency and overhead, demonstrating relevant technical viability.
- Indoor microclimate monitoring and forecasting: Public Sector building use casePublication . Sudniks, Ruslans; Ziemelis, Arturs; Nikitenko, Agris; Soares, V.N.G.J.; Supe, AndisThis research aims to demonstrate a machine learning (ML) algorithm-based indoor air quality (IAQ) monitoring and forecasting system for a public sector building use case. Such a system has the potential to automate existing heating/ventilation systems, therefore reducing energy consumption. One of Riga Technical University’s campus buildings, equipped with around 128 IAQ sensors, is used as a test bed to create a digital shadow including a comparison of five ML-based data prediction tools. We compare the IAQ data prediction loss using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) error metrics based on real sensor data. Gated Recurrent Unit (GRU) and Kolmogorov–Arnold Networks (KAN) prove to be the most accurate models regarding the prediction error. Also, GRU proved to be the most efficient model regarding the required computation time.