Scientific Publications of the University of Toulouse II Le Mirail
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Soutenir la transition vers l’EHPAD : une prise en soin individualisée par protocole à cas unique
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Effects of transcranial alternating current stimulation (tACS) on neural oscillations in speech perception are replicable and predictable
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Polynomial slowdown in space-inhomogeneous branching Brownian motion
No full textWe consider a branching Brownian motion in in which particles independently diffuse as standard Brownian motions and branch at an inhomogeneous rate which depends only on the angle of the particle. We assume that is maximal when , which is the preferred direction for breeding. Furthermore we assume that b(\theta) = 1 - \beta \abs{\theta}^\alpha + O(\theta^2), as , for and \beta>0. We show that if is the maximum distance to the origin at time , then is tight whereand is explicit in terms of the first eigenvalue of a certain operator
Mandinka, langue venue d'un silence, jonglant avec les silences d'un exil non contraint
No full textInternational audienceDans le prolongement de la recherche-création, ce texte retrace et analyse une expérience singulière : un accident de vie à valeur de silence a doté l’auteur d’une deuxième langue maternelle : le mandinka. Des années après, loin de ce lieu d’apprentissage intime, en exil non contraint, il pratique le jonglage de langues, version artistique de la sociolinguistique qui consiste en performances sonores improvisées. L’analyse de trouvailles montre le rôle que joue cette langue dans le concert des autres, articulation des paroles et maîtresse des silences
Towards global stabilization of a hovercraft model using hybrid systems and discontinuous feedback laws
No full textIn this paper, we propose discontinuous control laws to globally stabilize a target position of a hovercraft. Equations of motion of the hovercraft are derived through a kinematic model approximation of a dynamic model taken from the literature. Discontinuous feedback laws for the kinematic and the dynamic model are derived and analyzed using a hybrid systems formulation of the closed-loop dynamics. Numerical simulations confirm the theoretical convergence guarantees and validate the kinematic model as a valid simplification of the dynamic model
Data-Driven Approaches for Indirect Aging Estimation in Power Converters
No full textNational audienceThis paper presents an indirect method to estimate the aging of active and passive components in a synchronous buck converter. By monitoring electrical indicators such as efficiency, current ripple, voltage overshoot, and settling time, we demonstrate the ability to detect parameter change resulting from component degradation, including ESR increase, inductance variation, and MOSFET on-resistance (R ds,on ) rise. A combination of simulation sensitivity analysis, principal component analysis (PCA), and regression models is used to evaluate indicator relevance and build aging estimators. Experimental results confirm that this approach can detect changes in capacitor parameters, validating the possibility of monitoring degradation simply from microcontroller measures
How Machine Learning can contribute to evaluate stress level
No full textInternational audiencesolution has been embedded or run in real-time. Finally, none of them uses a database combination that can be compared with the literature (public database) while being robust for real-life use (customized database).The Pulse system, introduced in [11], addresses embedded and real-time challenges by using Ballistocardiography [12] and includes a strain sensors matrix and dedicated circuits. A real-time embedded software called BCG-VARS [13] associated with Pulse performs contactless monitoring of vital parameters across various equipment types. Unlike ECG or PPG, this solution does not need to be worn, providing greater comfort and ease of use. The software features two algorithms and is designed to extract three vital parameters: the actigraphy which is a quantification of movement, Breath-to-Breath Interval (BBI) which is the distance between 2 breath cycles, and heart Inter-Beat Interval (IBI) which is the distance between 2 heart cycles. BCG-VARS and Pulse are low-cost and can be embedded.This paper introduces a novel approach for contactless stress detection using machine learning techniques. We propose an innovative algorithm, VESLA (Variable and Embedded Stress Level Algorithm), designed to monitor stress levels dynamically. While existing databases typically employ binary stress labels, VESLA enables the quantification of multiple stress levels, offering a flexible solution adaptable to various applications and surpassing many current models in the literature. Due to the unobtrusive design of the Pulse system, longterm stress evaluation becomes feasible, allowing for chronic stress monitoring without inducing a "white coat" effect. This solution can be effectively integrated into equipment in airplanes, emergency services, and teleconsultation settings to provide continuous, reliable stress monitoring.This article is organized as follows: Section II details the database generation required for ML training. Section III describes the training and evaluation of ML models for absolute stress measurement. Section IV highlights performance improvements achieved through relative stress measurement with the VESLA algorithm, which enhances the ML model. Finally, the paper concludes with a discussion of the models' performance and overall findings. II. DATABASE GENERATION BASED ON A PUBLIC DATASETCreating datasets for ML models is often the most timeconsuming stage, especially when involving human subjects.Abstract-Chronic stress, common in modern lifestyles, contributes to heart disease, hypertension, diabetes, and obesity, highlighting the need for effective stress management. In this article, stress detection using machine learning (ML) techniques has been explored. However, individuals have very different stress levels. Therefore, measuring the evolution of stress over time is more relevant. So, an innovative algorithm called VESLA (Variable and Embedded Stress Level Algorithm) has been developed to assess stress evolution. Despite binary nature labels in the dedicated database, VESLA can quantify multiple stress levels, adapting to the application's requirements. This algorithm outperforms literature models, achieving an average accuracy of 94.1 % with an open-access database
Graphic Organizers for Learning: Effects of Readymade and Self-Generated Formats in the Classroom
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Leveraging the chaotic regime of a MEMS oscillator for gas detection
No full textNational audienceIn harsh environments, measurement noise can severely limit the performance of traditional linear sensors. In this work, we propose exploiting the exponential sensitivity of chaotic systems to minute variations in system parameters to overcome these noise-related limitations1. Specifically, we employ a MEMS Duffing resonator operating in a chaotic regime. Chaos is triggered on demand through a modulated driving frequency2, resulting in a mechanical response in both amplitude and phase that displays chaotic behavior. Importantly, within a limited time window—assuming nearly identical initial conditions—the system dynamics remain reproducible. Within this window, any deviations in the trajectory directly reflect changes in the sensor’s environment, which are then amplified by the system's inherent exponential sensitivity to initial conditions. Crucially, because chaos is deterministic, distinct changes in the initial conditions consistently yield distinct chaotic evolutions, thereby enabling the chaotic patterns to reliably encode information about variations in the physical variable of interest. We demonstrate this concept for the detection of carbon dioxide (CO₂) in nitrogen (N₂): the variation in the gas composition (CO₂:N₂) alters the mixture density, which, in turn, affects the natural resonance frequency of the MEMS device3. Notably, our experiments reveal that the reproducibility time of the chaotic trajectories scales inversely—and monotonously—with the CO₂ concentration, marking a significant milestone for gas sensing using a chaotic regime
Pro-inflammatory processes and metabolic syndrome: combined risk of resistance to treatment in patients with schizophrenia from the FACE-SZ cohort
No full textInternational audienceSchizophrenia (SZ) is characterized by a variable clinical expression and course peppered/hampered by severe complications. In particular, resistance to treatment (overall-TRS, treatment resistant SZ including UTRS, ultra-TRS) and metabolic syndrome (MetS) are highly prevalent, and both demonstrated to be underpinned, at least partly, by pro-inflammatory processes. Given that such processes also underlie SZ per se , we hypothesized that potential inter-twinning between SZ-and MetS-related inflammatory processes may exert a combined effect on the risk of having overall-TRS/UTRS. A total of 419 outpatients with SZ underwent clinical assessments and blood sample collection. Using the values of circulating levels of eleven cytokines, we built a ratio between pro-and anti-inflammatory components respectively corresponding to the immunoinflammatory response system (IRS) and the compensatory immunoregulatory reflex system (CIRS) which overall reflect the underlying in-flammatory status. Such ratios allowed to categorize the patients according to Inflammation and MetS on four categories as follow: symbolscript symbolscript symbolscript and Inflam-mation(-)MetS(-). Multivariate logistic regression analysis showed that the combination of inflammation and MetS modulate the risk of having the overall-TRS symbolscript symbolscript 95 %CI [1.04-5.00], p symbolscript 0.039 and symbolscript symbolscript 95 % CI [1.76-11.97], p symbolscript 0.002 overall in comparison to Inflammation(-)MetS(-)]. Moreover, we observed that individuals with UTRS are those associated with both inflammation and/or MetS. Our results demonstrated the potential combined effect of MetS and inflammation towards resistance to treatment. Given that overall-TRS/UTRS are unpredictable while both inflammation and MetS can be early detected and managed, our findings shed new light on the possibility to better prevent treatment resistance in SZ