Aprendizaje automático aplicado al procesamiento de imágenes para la clasificación de objetos reciclables

El presente proyecto se basa en la utilización de técnicas de Deep Learning, específicamente se realizó el modelado de redes neuronales convolucionales (CNN) capaces de clasificar distintas imágenes de objetos reciclables, estos modelos fueron probados con una clasificación binaria (reciclable-no_reciclable) y una clasificación multiclase (plástico-vidrio-metalpapel- carton, orgánico, no_reciclable). Además, se realizaron pruebas con modelos pre entrenados, utilizando aprendizaje por transferencia (Transfer Learning) para comparar resultados. Estos modelos fueron implementados utilizando como lenguaje de programación Python, apoyándose en el Framework de backend TensorFlow y la librería de alto nivel Keras. El modelo final se probó en una aplicación (beta) implementada también en Python sobre un mini computador Raspberry Pi y un módulo de cámara (picam) en donde se toman fotos y se aplica el modelo para realizar una clasificación en tiempo real.Workshop: WASI – Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informátic

Individual Particle Characteristics, Optical Properties and Evolution of an Extreme Long‐Range Transported Biomass Burning Event in the European Arctic (Ny‐Ålesund, Svalbard Islands)

This paper reports an exceptional biomass burning (BB) advection event from Alaska registered at Ny‐Ålesund from 10 to 17 July 2015 with particular interest on the influence of the airborne particle characteristics on the optical properties of the aerosol during the event. To this purpose we considered two DEKATI 12‐stage aerosol samples spanning the entire advection and analyzed them by scanning electron microscopy techniques. Aerosol chemical data and microphysical properties were also evaluated in order to correlate any change of individual particle characteristics with the bulk properties of the aerosol. The results of individual particle analysis depict a complex event characterized by a first phase (P1) of massive input of BB carbonaceous particles (i.e., tar balls, popcorn refractory particles, and organic particles), and by a second phase (P2) dominated by inorganic salts. The peculiar feature of this BB event is the exceptionally large grain size of the subspherical organic particles at the beginning of the event with respect to the background. At these conditions a significant increase of the scattering efficiency may occur even for a small increase of the size parameter. Results of the simulation of the complex refractive indices (n‐ik) confirm this evaluation. Aerosol evolution during the event resulted from the combination of three distinct occurrences: (a) progressive rotation of air mass circulation toward non‐BB source areas, (b) development of a thick fog layer in the planetary boundary layer, and (c) sea salt spray direct advection of local/regional provenance

Enhancing duration processing with parietal brain stimulation

Numerosity and duration are thought to share common magnitude-based mechanisms in brain regions including the right parietal and frontal cortices like the supplementary motor area, SMA. Numerosity and duration are, however, also different in several intrinsic features. For instance, in a quantification context, numerosity is known for being more automatically accessed than temporal events, and durations are by definition sequential whereas numerosity can be both sequential and simultaneous. Moreover, numerosity and duration processing diverge in terms of their neuronal correlates. Whether these observed neuronal specificities can be accounted for by differences in automaticity or presentation-mode is however not clear. To address this issue, we used brain stimulation (transcranial random noise stimulation, tRNS) to the right parietal cortex or the SMA combined with experimental stimuli differing in their level of automaticity (numerosity and duration) and presentation mode (sequential or simultaneous). Compared to a non-stimulation group, performance changed in duration but not in numerosity categorisation following right parietal but not SMA stimulation. These results indicate that the right parietal cortex is critical for duration processing, and suggest that tRNS has a stronger effect on less automatic processes such as duration