Publications
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D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on Transformer for Assessment of Patient Physical Rehabilitation
This paper tackles the challenge of automatically assessing physical rehabilitation exercises for patients who perform the exercises without clinician supervision. The objective is to provide a quality score to ensure correct performance and achieve desired results. To achieve this goal, a new graph-based model, the Dense Spatio-Temporal Graph Conv-GRU Network with Transformer, is introduced. This […]
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Fault Prediction in a Smart Building Lighting System
With the advances in many areas such as sensing technologies, new connectivity options and improved IoT architectures, predictive maintenance is considered as a promising solution for the maintenance of Smart Buildings (SBs). However, because of the lack of failure data for these systems, the approaches in the literature, which are mostly data-based approaches, are not […]
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Guaranteeing information integrity and access control in smart cities through blockchain
The distributed devices in a smart city are characterized by different degrees of sensitivity. Some of them can be accessed by everyone whereas others are limited to a specific class of users (subjects). Therefore, we created an access control system named (Subject-Object-Task System) supported by blockchains that sort processes applied by subjects on smart devices. […]
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Exploring human-machine collaboration in industry: a systematic literature review of digital twin and robotics interfaced with extended reality technologies
This systematic literature review presents the latest advancements and insights about digital twin technology and robotics interfaced with extended reality in the context of Industry 4.0. As the extended reality technologies emerge, it results in an increasing overlap between digital twins and human-robot interactions in industrial settings, promoting collaboration between operators and cobots in manufacturing […]
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Determinantes do Trabalho
Cuvelier, L., Nascimento, A., & Fourrièrre, J. (2023). Determinantes do Trabalho. In R. Rocha (Ed.), Dicionário de Ergonomia e Fatores Humanos.
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A SPARQL-based framework to preserve privacy of sensitive data on the semantic web
Over the last few years, the web of data has been evolved. Indeed, it allows sharing of a significant interconnection of a huge amount of data in several domains and it keeps increasing continuously. Due to the confidential nature of some data, sectors such as health, financial, and government, it have limited participation with fewer […]
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Solution de génération de formes 2D d’objets basée sur des connaissances préalables pour la cartographie sémantique
Dans ce travail, nous proposons une solution de cartographie sémantique en temps réel basée sur les données RGBD. Nous nous focalisons sur la proposition d’une approche d’association pour générer les formes 2D des objets sémantiques en utilisant des connaissances préalables. Notre approche est évaluée dans un environnements bureautique à l’aide du robot mobile MIR. Les […]
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Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems
The emergence of the Internet of Medical Things (IoMT) has brought together developers from the Industrial Internet of Things (IIoT) and healthcare providers to enable remote patient diagnosis and treatment using mobile-device-collected data. However, the utilization of traditional AI systems raises concerns about patient privacy. To address this issue, we present a privacy-enhanced approach for […]
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RUL Prediction with Encoding and Spatial-Temporal Deep Neural Networks
The objective of this paper is to design and develop an approach to estimate the Remaining Useful Life (RUL) of an industrial equipment evolving in a Cyber-Physical System (CPS). To do so, this work aims to predict failures and malfunctions of an industrial equipment, as well as evaluating all the main underlying causes. The system […]
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Réseau antagoniste génératif pour la fusion spatio-temporelle d’images satellitaires multi-spectrales
Résumé – Dans cet article, nous étudions la fusion spatio-temporelle d’une série temporelle d’images multi-spectrales avec une série temporelle d’images hyper-spectrales. Nous proposons pour cela une nouvelle approche fondée sur un réseau antagoniste génératif (GAN). Notre contribution principale réside dans le fait que le GAN prend en entrée des images satellitaires plutôt que du bruit. […]
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A systematic review of federated learning: Challenges, aggregation methods, and development tools
Since its inception in 2016, federated learning has evolved into a highly promising decentral-ized machine learning approach, facilitating collaborative model training across numerous devices while ensuring data privacy. This survey paper offers an exhaustive and systematic review of federated learning, emphasizing its categories, challenges, aggregation techniques, and associated development tools. To start, we outline our […]
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Variable Scale Pruning for Transformer Model Compression in End-to-End Speech Recognition
Transformer models are being increasingly used in end-to-end speech recognition systems for their performance. However, their substantial size poses challenges for deploying them in real-world applications. These models heavily rely on attention and feedforward layers, with the latter containing a vast number of parameters that significantly contribute to the model’s memory footprint. Consequently, it becomes […]
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