Fuzzy detection of events in driving for DriveSafe application

En los últimos años ha habido un creciente interés en monitorizar el comportamiento de los conductores usando para ello "smartphones" dada su alta tasa de penetración en el mercado. Los sensores inerciales embebidos en estos dispositivos son clave para realizar esta tarea de monitorización. La mayor parte de aplicaciones hoy en día hacen uso de umbrales fijos para detectar los eventos que se producen en la conducción a partir de los datos aportados por los sensores inerciales. Sin embargo, los valores dados por los sensores pueden ser distintos ya que dependen de muchos parámetros. En este documento presentamos un clasificador adaptativo basado en Lógica Borrosa para identificar repentinas acciones que se producen en la conducción (acelerones, frenazos, volantazos) así como baches e irregularidades presentes en la carretera, a partir únicamente de la información de los sensores inerciales y el GPS. En primer lugar, se propone un método de calibración continuo para ajustar los umbrales de decisión de las funciones miembro, para determinar la posición del teléfono y las características dinámicas del vehículo. En segundo lugar, se desarrolla una capa de alto nivel para hallar otras maniobras que realice el conductor y puedan ser útiles para evaluar su comportamiento. Para validar el clasificador usamos la base de datos UAH-Driveset que incluye más de 500 minutos de conducción naturalista, y comparamos los resultados con los obtenidos en la anterior versión de DriveSafe, basada en umbrales fijos. Los resultados muestran una notable mejora en la detección de eventos respecto a la anterior versión.In the last years there has been a rising interest in monitoring driver behaviours by using smartphones, due to their increasing market penetration. Inertial sensors embedded in these devices are key to carry out this task. Most of the state-of-the-art apps use fix thresholds to detect driving events from the inertial sensors. However, sensors output values can differ depending on many parameters. In this document, we present an Adaptative Fuzzy Classifier to identify sudden driving events (acceleration, steering, braking) and road bumps from the inertial and GPS sensors. Firstly, an on-line calibration method is proposed to adjust the decisión thresholds of the Membership Functions (MFs) to the specific phone pose and vehicle dynamics. Secondly, a high-level layer is developed to find other manoeuvers performed by the drivers and can be useful to assess driver behaviour. To validate our method, we use the UAH-Driveset database, which includes more tan 500 minutes of naturalistic driving, and we compare results with our previous DriveSafe app versión, based on fix thresholds. Results show a notable improvement in the events detection regarding our previous versión.Máster Universitario en Sistemas Electrónicos Avanzados. Sistemas Inteligentes (M128

Development and Field Validation of Low-Cost Metal Oxide Nanosensors for Tropospheric Ozone Monitoring in Rural Areas

International audienceThis work describes the technical features and the performance of two different types of metal-oxide semiconductor sensors, based on ZnO:Ga thin films and SnO2-G nanofibrous layers, for tropospheric ozone monitoring in ambient air. These nanostructures were tested and compared with commercial metal-oxide semiconductor sensors under controlled laboratory conditions and in a field campaign during summer 2021 in Monfragüe National Park (western Spain). The paper also details the design of the electronic device developed for this purpose. A machine learning algorithm based on Support Vector Regression (SVR) allowed the conversion of the resistive values into ozone concentration, which was evaluated afterward. The results showed that the manufactured sensors performed similarly to the commercial sensors in terms of R2 (0.94 and 0.95) and RMSE (5.21 and 4.83 μg·m−3). Moreover, a novel uncertainty calculation based on European guides for air quality sensor testing was conducted, in which the manufactured sensors outperformed the commercial ones

Presence of vacuoles in natural rubber-Cloisite 15A nanocomposites

We studied natural rubber (NR) filled with frequently used organoclay Cloisite 15A using transmission electron microscopy (TEM), cryoporosimetry, and electron spin resonance (ESR) spectroscopy. Quantitative analysis of the TEM micrographs showed a high level of dispersion without the formation of a rigid filler network. The presence of vacuoles was established on the surface of Cloisite 15A ; this indicated weak filler–matrix interactions. The mechanism of reinforcement is, therefore, discussed. The volume of vacuoles was found to be proportional to the crosslinking density ; this was confirmed with ESR spin-probe method. The shape of the ESR spectra was highly influenced by the presence of vacuoles. In the NR–Cloisite 10A nanocomposites, vacuoles were absent. The strong interactions implied by this result were confirmed by ESR measurements and are discussed further