Artificial Intelligence

The striking progress of statistical learning over the past decade has led to wild expectations about the ability of machines to reproduce human behavior.

Labo bcom - intelligence artificielle
© Fred Pieau
These Artificial Intelligence techniques can apply equally to masses of data compiled in a computing center, or to data captured and processed by a smartphone. They are now entering a domestication phase, where technological, legal, and ethical questions are being asked in order to offer responsible AI approaches.

The Artificial Intelligence lab designs solutions to address issues in future networks, computer vision, cyberdefense, and e-health. Designing embedded imaging and radio communication applications, anomaly detection or classification, system resilience, human factor management, pathology prediction, and reliable diagnostics are the main challenges being tackled. To do so, the lab relies on traditional disciplines of automated language, signal, image processing, 3D vision, and dynamic programming, whose potential has been compounded by statistical learning. It can also count on its skills in hardware/software engineering and a legal team. Its methodological approach makes its algorithms explainable and robust so as to facilitate their certification.

Stéphane Paquelet - bcom
© Fred Pieau

Stéphane Paquelet

Artificial Intelligence lab Manager

With current learning techniques, we can automate tasks as varied as interpreting text, mapping space, generating realistic images, predicting behaviors, and planning actions.
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scientific publications


An Efficient Deep-Learning-Based Solution for the Recognition of Relative Changes in Mental Workload Using Wearable Sensors

In this work, a new solution for the automatic recognition of relative changes in mental workload is proposed. Wearable sensors were used to collect EEG, EDA, PPG and eyetracking data from 26 human subjects while performing the nback task with three difficulty levels n ∈ {1, 2, 3}. The objective is to recognize whether the mental workload is increasing, decreasing or stable by comparing the…

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Towards a closed loop recycling process of end-of-life lithium-ion batteries: Recovery of critical metals and electrochemical performance evaluation of a regenerated LiCoO2

The growing demand for lithium-ion battery technology emphasizes the critical need to establish effective recycling and proper disposal methods for used batteries, ensuring the long-term sustainability and security of the battery supply chain. This study addresses this need by exploring two hydrochemical routes, using sulfuric acid and nitric acid, respectively. The objective is to investigate…

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Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting

Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna system, and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially. Among the different methods of learning this mapping, some rely on a distance measure between…

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MES-loss: Mutually Equidistant Separation Metric Learning Loss Function

Deep metric learning has attracted much attention in recent years due to its extensive applications, such as clustering and image retrieval. Thanks to the success of deep learning (DL), many deep metric learning (DML) methods have been proposed. Neural networks (NNs) utilize DML loss functions to learn a mapping function that maps samples into a highly discriminative low-dimensional feature space…

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