Negative Refraction Angular Characterization in One-Dimensional Photonic Crystals

Background: Photonic crystals are artificial structures that have periodic dielectric components with different refractive indices. Under certain conditions, they abnormally refract the light, a phenomenon called negative refraction. Here we experimentally characterize negative refraction in a one dimensional photonic crystal structure; near the low frequency edge of the fourth photonic bandgap. We compare the experimental results with current theory and a theory based on the group velocity developed here. We also analytically derived the negative refraction correctness condition that gives the angular region where negative refraction occurs. Methodology/Principal Findings: By using standard photonic techniques we experimentally determined the relationship between incidence and negative refraction angles and found the negative refraction range by applying the correctness condition. In order to compare both theories with experimental results an output refraction correction was utilized. The correction uses Snell’s law and an effective refractive index based on two effective dielectric constants. We found good agreement between experiment and both theories in the negative refraction zone. Conclusions/Significance: Since both theories and the experimental observations agreed well in the negative refraction region, we can use both negative refraction theories plus the output correction to predict negative refraction angles. This can be very useful from a practical point of view for space filtering applications such as a photonic demultiplexer or fo

Tecnologías de IoT y aprendizaje automático para la solución de problemas en el medio productivo y el cuidado del medioambiente

El presente proyecto se basa en la utilización de internet de las cosas (IoT) como herramienta fundamental para proveer soluciones a problemáticas de interés social, como lo es el cuidado del medioambiente y la innovación en el sector productivo, focalizando la investigación en las técnicas de visión por computadora y aprendizaje automático. Entre los temas de investigación que se desarrollarán, se incluye el diseño e implementación de técnicas de visión por computadora con el objeto de agregar funcionalidades a un robot móvil, de manera de proveer autonomía en ambientes con obstáculos, con el agregado de control y supervisión remota mediante IoT. En esta línea, también se implementarán técnicas de visión por computadora para la clasificación de residuos reciclables mediante algoritmos de aprendizaje automático. Está última aplicación se suma a las líneas relacionadas con el cuidado del medioambiente que se desarrollaron en el proyecto anterior. En esta propuesta se continuará con las líneas del proyecto anterior de procesamiento digital de imágenes con el agregado de técnicas de aprendizaje automático. Teniendo en cuenta que las técnicas de procesamiento de imágenes aplicadas a visión por computadora requieren alto poder de cómputo, se considera necesario determinar la tolerancia a fallos del sistema de procesamiento utilizado, para asegurar la correcta ejecución de dichos algoritmos. En relación a la detección de fallos, se propone el perfeccionamiento de la metodología desarrollada de tolerancia a fallos transitorios característicos de las arquitecturas multicore, con el objeto de aplicarlo al sistema de visión por computadora.Eje: Arquitectura, redes y sistemas operativos.Red de Universidades con Carreras en Informátic

Tecnologías de Smart IoT y aprendizaje automático para la solución de problemas en el medio productivo

El presente proyecto se basa en la utilización de internet de las cosas (IoT) como herramienta fundamental para proveer soluciones a problemáticas de interés social, como lo es el cuidado del medioambiente y la innovación en el sector productivo, focalizando la investigación en las técnicas de aprendizaje automático, es decir, Smart IoT. Entre los temas de investigación que se desarrollarán, se incluye el diseño e implementación de técnicas de visión por computadora con el objeto de agregar funcionalidades a dispositivos robóticos, de manera de proveer autonomía para determinadas tareas, con el agregado de control y supervisión remota mediante IoT. En esta línea, también se implementarán técnicas de visión por computadora para la clasificación de residuos reciclables mediante algoritmos de aprendizaje automático. Además, mediante técnicas de aprendizaje profundo y visión por computadora, se propone la clasificación de diferentes condiciones de cielo como consecuencia de la cobertura de nubes, lo cual será de suma utilidad para la optimización de sistemas que aprovechen la energía solar. En esta propuesta se continúa con algunas líneas de procesamiento digital de imágenes con el agregado de técnicas de aprendizaje automático. Por otro lado, Teniendo en cuenta que las técnicas de procesamiento de imágenes aplicadas a visión por computadora requieren alto poder de cómputo, se considera necesario investigar la tolerancia a fallos del sistema de procesamiento utilizado, para asegurar la correcta ejecución de dichos algoritmos. En la misma línea de Smart IoT, se incluye en la propuesta actual el procesamiento y análisis de datos obtenidos de una red de sensores basada en IoT, lo que permitirá mediante técnicas de aprendizaje automático la implementación de un sistema de ayuda a la toma de decisiones, para optimizar y mejorar el aprovechamiento de los recursos agrícolas.Red de Universidades con Carreras en Informátic

Ubiquitous Crossmodal Stochastic Resonance in Humans: Auditory Noise Facilitates Tactile, Visual and Proprioceptive Sensations

BACKGROUND: Stochastic resonance is a nonlinear phenomenon whereby the addition of noise can improve the detection of weak stimuli. An optimal amount of added noise results in the maximum enhancement, whereas further increases in noise intensity only degrade detection or information content. The phenomenon does not occur in linear systems, where the addition of noise to either the system or the stimulus only degrades the signal quality. Stochastic Resonance (SR) has been extensively studied in different physical systems. It has been extended to human sensory systems where it can be classified as unimodal, central, behavioral and recently crossmodal. However what has not been explored is the extension of this crossmodal SR in humans. For instance, if under the same auditory noise conditions the crossmodal SR persists among different sensory systems. METHODOLOGY/PRINCIPAL FINDINGS: Using physiological and psychophysical techniques we demonstrate that the same auditory noise can enhance the sensitivity of tactile, visual and propioceptive system responses to weak signals. Specifically, we show that the effective auditory noise significantly increased tactile sensations of the finger, decreased luminance and contrast visual thresholds and significantly changed EMG recordings of the leg muscles during posture maintenance. CONCLUSIONS/SIGNIFICANCE: We conclude that crossmodal SR is a ubiquitous phenomenon in humans that can be interpreted within an energy and frequency model of multisensory neurons spontaneous activity. Initially the energy and frequency content of the multisensory neurons' activity (supplied by the weak signals) is not enough to be detected but when the auditory noise enters the brain, it generates a general activation among multisensory neurons of different regions, modifying their original activity. The result is an integrated activation that promotes sensitivity transitions and the signals are then perceived. A physiologically plausible model for crossmodal stochastic resonance is presented

Planckian Power Spectral Densities from Human Calves during Posture Maintenance and Controlled Isometric Contractions.

The relationship between muscle anatomy and physiology and its corresponding electromyography activity (EMGA) is complex and not well understood. EMGA models may be broadly divided in stochastic and motor-unit-based models. For example, these models have successfully described many muscle physiological variables such as the value of the muscle fiber velocity and the linear relationship between median frequency and muscle fiber velocity. However they cannot explain the behavior of many of these variables with changes in intramuscular temperature, or muscle PH acidity, for instance. Here, we propose that the motor unit action potential can be treated as an electromagnetic resonant mode confined at thermal equilibrium inside the muscle. The motor units comprising the muscle form a system of standing waves or modes, where the energy of each mode is proportional to its frequency. Therefore, the power spectral density of the EMGA is well described and fit by Planck's law and from its distribution we developed theoretical relationships that explain the behavior of known physiological variables with changes in intramuscular temperature or muscle PH acidity, for instance.EMGA of the calf muscle was recorded during posture maintenance in seven participants and during controlled isometric contractions in two participants. The power spectral density of the EMGA was then fit with the Planckian distribution. Then, we inferred nine theoretical relationships from the distribution and compared the theoretically derived values with experimentally obtained values.The power spectral density of EMGA was fit by Planckian distributions and all the theoretical relationships were validated by experimental results.Only by considering the motor unit action potentials as electromagnetic resonant modes confined at thermal equilibrium inside the muscle suffices to predict known or new theoretical relationships for muscle physiological variables that other models have failed to do

Unimodal and crossmodal SR architecture.

<p>The scheme represents the physical paths through which the signals combine in the brain.</p

SR interactions between auditory noise and first order visual signals.

<p>Normalized visual threshold changes with the noise level in sixth subjects for luminance modulated (first order) stimuli. In all the graphs the no-noise condition is taken as baseline; the black dots indicate p-values (right y-axis) and the broken line represents the 5% significance level. Error bars correspond to one standard error.</p

SR interactions between auditory noise and tactile signals.

<p>(Left column) normalized tactile threshold changes with the noise level in three subjects. (Right column, top) normalized tactile thresholds of sixteen subjects when the 3D sound level was fixed at 69 dBSPL. (Right column, middle) normalized tactile thresholds of sixteen subjects when the noise level was fixed at 69 dBSPL. (Right column, bottom) Group average results for three conditions: baseline, 3D sound and noise. The average group threshold decreased significantly in the presence of noise (p<0.001) and no significant change was found for the 3D-like sound (p = 0.72). In all the graphs the no-noise condition is taken as baseline; the black dots indicate p-values (right y-axis) and the broken line represents the 5% significance level. Error bars correspond to one standard error.</p

Theoretical model results for crossmodal SR.

<p>(Left column) shows the neurons' spectrum amplitude as a function of the noise intensity <i>σ</i>⁻. The insert (left column, middle row) shows the well-known SR inverted u-shape function. The maximum peak is found when <i>P</i> = 70 <i>dB</i>. Right column shows neuronal firing histograms with their corresponding time histories. T is the signal period and N means the probability to have certain neuronal activity levels.</p

Crossmodal SR threshold minima in ceiling decibels.

<p>Shows the averaged SR minima for the four experiments discussed. For our visual experiments the minima are localized (below the noise ceiling levels) at 6±1 dBc (first order) and 5±1 dBc (second order). In the proprioception experiments the minima happened around 6±1 dBc and for the tactile experiments at 8±1 dBc.</p