Neurite, a finite difference large scale parallel program for the simulation of the electrical signal propagation in neurites under mechanical loading

With the growing body of research on traumatic brain injury and spinal cord injury, computational neuroscience has recently focused its modeling efforts on neuronal functional deficits following mechanical loading. However, in most of these efforts, cell damage is generally only characterized by purely mechanistic criteria, function of quantities such as stress, strain or their corresponding rates. The modeling of functional deficits in neurites as a consequence of macroscopic mechanical insults has been rarely explored. In particular, a quantitative mechanically based model of electrophysiological impairment in neuronal cells has only very recently been proposed (Jerusalem et al., 2013). In this paper, we present the implementation details of Neurite: the finite difference parallel program used in this reference. Following the application of a macroscopic strain at a given strain rate produced by a mechanical insult, Neurite is able to simulate the resulting neuronal electrical signal propagation, and thus the corresponding functional deficits. The simulation of the coupled mechanical and electrophysiological behaviors requires computational expensive calculations that increase in complexity as the network of the simulated cells grows. The solvers implemented in Neurite-explicit and implicit-were therefore parallelized using graphics processing units in order to reduce the burden of the simulation costs of large scale scenarios. Cable Theory and Hodgkin-Huxley models were implemented to account for the electrophysiological passive and active regions of a neurite, respectively, whereas a coupled mechanical model accounting for the neurite mechanical behavior within its surrounding medium was adopted as a link between lectrophysiology and mechanics (Jerusalem et al., 2013). This paper provides the details of the parallel implementation of Neurite, along with three different application examples: a long myelinated axon, a segmented dendritic tree, and a damaged axon. The capabilities of the program to deal with large scale scenarios, segmented neuronal structures, and functional deficits under mechanical loading are specifically highlighted.

Real-time Surveillance Application by Multiple Detectors and Compressive Trackers

A real-time surveillance system for IP network cameras is presented. Motion, part-body, and whole-body detectors are efficiently combined to generate robust and fast detections, which feed multiple compressive trackers. The generated trajectories are then improved using a reidentification strategy for long term operation.

Health monitoring of web plastifying dampers subjected to cyclic loading through vibration tests

This article investigates experimentally the application of health monitoring techniques to assess the damage on a particular kind of hysteretic (metallic) damper called web plastifying dampers, which are subjected to cyclic loading. In general terms, hysteretic dampers are increasingly used as passive control systems in advanced earthquake-resistant structures. Nonparametric statistical processing of the signals obtained from simple vibration tests of the web plastifying damper is used here to propose an area index damage. This area index damage is compared with an alternative energy-based index of damage proposed in past research that is based on the decomposition of the load?displacement curve experienced by the damper. Index of damage has been proven to accurately predict the level of damage and the proximity to failure of web plastifying damper, but obtaining the load?displacement curve for its direct calculation requires the use of costly instrumentation. For this reason, the aim of this study is to estimate index of damage indirectly from simple vibration tests, calling for much simpler and cheaper instrumentation, through an auxiliary index called area index damage. Web plastifying damper is a particular type of hysteretic damper that uses the out-of-plane plastic deformation of the web of I-section steel segments as a source of energy dissipation. Four I-section steel segments with similar geometry were subjected to the same pattern of cyclic loading, and the damage was evaluated with the index of damage and area index damage indexes at several stages of the loading process. A good correlation was found between area index damage and index of damage. Based on this correlation, simple formulae are proposed to estimate index of damage from the area index damage.

Induction of through-thickness compressive residual stress fields in thin Al2024-T351 plates by laser shock processing

Laser Shock Processing (LSP) has been demonstrated as an emerging technique for the induction of RS’s fields in subsurface layers of relatively thick specimens. However, the LSP treatment of relatively thin specimens brings, as an additional consequence, the possible bending in a process of laser shock forming. This effect poses a new class of problems regarding the attainment of specified RS’s depth profiles in the mentioned type of sheets, and, what can be more critical, an overall deformation of the treated component. The analysis of the problem of LSP treatment for induction of tentatively through-thickness RS’s fields for fatigue life enhancement in relatively thin sheets in a way compatible with reduced overall workpiece deformation due to spring-back self-equilibration is envisaged in this paper. The coupled theoretical-experimental predictive approach developed by the authors has been applied to the specification of LSP treatments for achievement of RS's fields tentatively able to retard crack propagation on normalized specimens. A convergence between numerical code results and experimental results coming from direct RS's measurement is presented as a first step for the treatment of the normalized specimens under optimized conditions and verification of the crack retardation properties virtually induced.

Development of a visual object tracking system using a compressive sensing framework for modeling the appearance

En el presente trabajo se aborda el problema del seguimiento de objetos, cuyo objetivo es encontrar la trayectoria de un objeto en una secuencia de video. Para ello, se ha desarrollado un método de seguimiento-por-detección que construye un modelo de apariencia en un dominio comprimido usando una nueva e innovadora técnica: “compressive sensing”. La única información necesaria es la situación del objeto a seguir en la primera imagen de la secuencia. El seguimiento de objetos es una aplicación típica del área de visión artificial con un desarrollo de bastantes años. Aun así, sigue siendo una tarea desafiante debido a varios factores: cambios de iluminación, oclusión parcial o total de los objetos y complejidad del fondo de la escena, los cuales deben ser considerados para conseguir un seguimiento robusto. Para lidiar lo más eficazmente posible con estos factores, hemos propuesto un algoritmo de tracking que entrena un clasificador Máquina Vector Soporte (“Support Vector Machine” o SVM en sus siglas en inglés) en modo online para separar los objetos del fondo de la escena. Con este fin, hemos generado nuestro modelo de apariencia por medio de un descriptor de características muy robusto que describe los objetos y el fondo devolviendo un vector de dimensiones muy altas. Por ello, se ha implementado seguidamente un paso para reducir la dimensionalidad de dichos vectores y así poder entrenar nuestro clasificador en un dominio mucho menor, al que denominamos domino comprimido. La reducción de la dimensionalidad de los vectores de características se basa en la teoría de “compressive sensing”, que dice que una señal con poca dispersión (pocos componentes distintos de cero) puede estar bien representada, e incluso puede ser reconstruida, a partir de un conjunto muy pequeño de muestras. La teoría de “compressive sensing” se ha aplicado satisfactoriamente en este trabajo y diferentes técnicas de medida y reconstrucción han sido probadas para evaluar nuestros vectores reducidos, de tal forma que se ha verificado que son capaces de preservar la información de los vectores originales. También incluimos una actualización del modelo de apariencia del objeto a seguir, mediante el reentrenamiento de nuestro clasificador en cada cuadro de la secuencia con muestras positivas y negativas, las cuales han sido obtenidas a partir de la posición predicha por el algoritmo de seguimiento en cada instante temporal. El algoritmo propuesto ha sido evaluado en distintas secuencias y comparado con otros algoritmos del estado del arte de seguimiento, para así demostrar el éxito de nuestro método.

Shear stress distribution on beam cross-sections under shear loading

In this work, some results obtained by Trabucho and Viaño for the shear stress distribution in beam cross sections using asymptotic expansions of the three-dimensional elasticity equations are compared with those calculated by the classical formulae of the Strength of Materials. We use beams with rectangular and circular cross section to compare the degree of accuracy reached by each method.