Articles dans des revues internationales ou nationales avec comité de lecture
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A Novel Integration Technique for Optimal Location & Sizing of DG Units With Reconfiguration in Radial Distribution Networks Considering Reliability
This paper introduces an advanced approach for optimizing the distribution network reconfiguration (DNR) with the placement and sizing of multiple types of distributed generators (DGs). The method employs the ant colony optimization algorithm (ACOA), which is an innovative adaptive optimization algorithm, while also considering the system’s reliability. The primary objectives of the optimization problem are […]
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ELSO: A Blockchain-Based Technique for a Reliable and Secure Healthcare Information Exchange
Nowadays, healthcare takes a great attention from people and governments due to the emergence of new diseases and viruses. Health Information Exchange (HIE) allows doctors, clinicians, and healthcare facilities (HCF) to exchange and share patient records according to patient permission. Unfortunately, the HIE systems suffer from several challenges such as security, privacy, latency, throughput, and […]
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Fully Residual Unet-based Semantic Segmentation of Automotive Fisheye Images: a Comparison of Rectangular and Deformable Convolutions
Semantic image segmentation is an essential task for autonomous vehicles and self-driving cars where a complete and real-time perception of the surroundings is mandatory. Convolutional Neural Network approaches for semantic segmentation standout over other state-of-the-art solutions due to their powerful generalization ability over unknown data and end-to-end training. Fisheye images are important due to their […]
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Particle filter meets hybrid octrees: an octree-based ground vehicle localization approach without learning
This paper proposes an accurate lidar-based outdoor localization method that requires few computational resources, is robust in challenging environments (urban, off-road, seasonal variations) and whose performances are equivalent for two different sensor technologies: scanning LiDAR and flash LiDAR. The method is based on the matching between a pre-built 3D map and the LiDAR measurements. Our […]
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SPDGAN: A Generative Adversarial Network based on SPD Manifold Learning for Automatic Image Colorization
This paper addresses the automatic colorization problem, which converts a gray-scale image to a colorized one. Recent deep-learning approaches can colorize automatically grayscale images. However, when it comes to different scenes which contain distinct color styles, it is difficult to accurately capture the color characteristics. In this work, we propose a fully automatic colorization approach […]
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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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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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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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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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Investigating the Optimal DOD and Battery Technology for Hybrid Energy Generation Models in Cement Industry Using HOMER Pro
The cement industry is a major energy consumer, with most of its costs associated with fuel and energy requirements. While traditional thermal power plants generate electricity, they are both harmful and inefficient. In this study, battery depth of discharge (DOD) is evaluated for four different battery technologies in the context of the cement industry. The […]
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