Sistema de seguridad basado en técnicas de procesamiento de imagen

El objetivo de este proyecto es el de realizar un sistema de videovigilancia de bajo coste utilizando técnicas de procesamiento de imagen para detectar a los posibles intrusos del sistema. Este sistema estará basado en la captación de imágenes a través de una cámara de red colocada en el entorno de vigilancia. Las imágenes serán enviadas a una interfaz Web y a un ordenador de control colocado fuera del entorno de vigilancia.Este ordenador de control llevará a cabo tareas de procesamiento de imagen con el fin de detectar posibles intrusos en el sistema.Ingeniería Electrónica Industrial y Automátic

Structural brain changes in patients with persistent headache after COVID-19 resolution

Producción CientíficaHeadache is among the most frequently reported symptoms after resolution of COVID-19. We assessed structural brain changes using T1- and diffusion-weighted MRI processed data from 167 subjects: 40 patients who recovered from COVID- 19 but suffered from persistent headache without prior history of headache (COV), 41 healthy controls, 43 patients with episodic migraine and 43 patients with chronic migraine. To evaluate gray matter and white matter changes, morphometry parameters and diffusion tensor imaging-based measures were employed, respectively. COV patients showed significant lower cortical gray matter volume and cortical thickness than healthy subjects (p < 0.05, false discovery rate corrected) in the inferior frontal and the fusiform cortex. Lower fractional anisotropy and higher radial diffusivity (p < 0.05, family-wise error corrected) were observed in COV patients compared to controls, mainly in the corpus callosum and left hemisphere. COV patients showed higher cortical volume and thickness than migraine patients in the cingulate and frontal gyri, paracentral lobule and superior temporal sulcus, lower volume in subcortical regions and lower curvature in the precuneus and cuneus. Lower diffusion metric values in COV patients compared to migraine were identified prominently in the right hemisphere. COV patients present diverse changes in the white matter and gray matter structure. White matter changes seem to be associ- ated with impairment of fiber bundles. Besides, the gray matter changes and other white matter modifications such as axonal integrity loss seemed subtle and less pronounced than those detected in migraine, showing that persistent headache after COVID-19 resolution could be an intermediate state between normality and migraine.Gerencia Regional de Salud (GRS) de Castilla y León, (GRS 2284/A/2020)Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Use of Artificial Neural Networks as a Predictive Method to Determine Moisture Resistance of Particle and Fiber Boards Under Cyclic Testing Conditions (UNE-EN 321)

Determining internal bond strength and thickness swelling after cyclic aging tests in humid conditions is essential to assess moisture resistance of particle and fiber boards. However, because operating procedures for these types of tests take at least 3 wk, their use in daily finished product control is impractical. To solve this problem, an artificial neural network was used as a predictive method to determine these values from the board properties of thickness, density, and moisture content in conjunction with thickness swelling and internal bond strength values obtained before the aging cycle. Using 113 boards, an artificial neural network was designed consisting of two separate feedforward multilayer perceptrons applying the hyperbolic tangent as the transfer function. Training was conducted through supervised learning after the input data had been normalized. In the testing group, the network attained a determination coefficient of 0.94 for internal bond strength and 0.92 for thickness swelling

Psychological symptoms of the outbreak of the COVID-19 confinement in Spain

We studied the short-term psychological effects of the COVID-19 crisis and the quarantine on 3550 adults from the Spanish population in a cross-sectional survey. Symptoms of anxiety, depression, and stress were analyzed using the 21-item version of the Depression Anxiety Stress Scale. Symptoms of posttraumatic stress disorder were analyzed using the Impact of Event Scale. Symptomatic scores of anxiety, depression, and stress were observed in 20% to 30% of respondents. Symptomatic scores indicating psychological stress were found in 47.5% of respondents. Similar to the findings of other multiple studies, confinement has been found to have significant emotional impact in the Spanish population

Variation throughout the tree stem in the physical-mechanical properties of the wood of Abies alba Mill. from the Spanish Pyrenees

This study analyses the variation of main physical-mechanical properties of wood along the longitudinal and radial directions of the tree for Abies alba Mill. growing in the Spanish Pyrenees. Small clear specimens were used to study the properties of volumetric shrinkage (VS), density (?), hardness (H), bending strength (MOR), modulus of elasticity (MOE), maximum compressive strength parallel to the grain (MCS) and impact strength (K). Several models of properties variation in the longitudinal and radial directions were analyzed. Main trends of variation of properties throughout the tree stem were identified although none of them could be fitted to predictive statistical models. Along the longitudinal direction, the properties studied followed a downward trend from the base to the crown, which was not significant in all cases, indicating that no differences in quality existed. Throughout the radial direction the trend is upward for the first 40-50 growth rings, after which it slopes downwards, more gently at first until rings 70-75 and then more steeply. This behaviour is related to variation in wood structure from the pith to the bark, depending on whether the wood is juvenile, sapwood or heartwood, and to wood maturity and microfibril angle. Authors encourage carrying further studies on other populations of A. alba in the Spanish Pyrenees to check if the trends found in this study apply to other provenances

Micro-structure diffusion scalar measures from reduced MRI acquisitions

In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant microstructural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like the Diffusion Tensor. The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space. Due to the huge amount of samples needed for an accurate reconstruction, more efficient alternative techniques have been proposed in the last decade. Even so, all of them imply acquiring a large number of diffusion gradients with different b-values. In order to use the EAP in practical studies, scalar measures must be directly derived, being the most common the return-to-origin probability (RTOP) and the return-to-plane and return-to-axis probabilities (RTPP, RTAP). In this work, we propose the so-called “Apparent Measures Using Reduced Acquisitions” (AMURA) to drastically reduce the number of samples needed for the estimation of diffusion properties. AMURA avoids the calculation of the whole EAP by assuming the diffusion anisotropy is roughly independent from the radial direction. With such an assumption, and as opposed to common multi-shell procedures based on iterative optimization, we achieve closed-form expressions for the measures using information from one single shell. This way, the new methodology remains compatible with standard acquisition protocols commonly used for HARDI (based on just one b-value). We report extensive results showing the potential of AMURA to reveal microstructural properties of the tissues compared to state of the art EAP estimators, and is well above that of Diffusion Tensor techniques. At the same time, the closed forms provided for RTOP, RTPP, and RTAP-like magnitudes make AMURA both computationally efficient and robust

Toward deep learning replacement of gadolinium in neuro-oncology: A review of contrast-enhanced synthetic MRI

Gadolinium-based contrast agents (GBCAs) have become a crucial part of MRI acquisitions in neuro-oncology for the detection, characterization and monitoring of brain tumors. However, contrast-enhanced (CE) acquisitions not only raise safety concerns, but also lead to patient discomfort, the need of more skilled manpower and cost increase. Recently, several proposed deep learning works intend to reduce, or even eliminate, the need of GBCAs. This study reviews the published works related to the synthesis of CE images from low-dose and/or their native —non CE— counterparts. The data, type of neural network, and number of input modalities for each method are summarized as well as the evaluation methods. Based on this analysis, we discuss the main issues that these methods need to overcome in order to become suitable for their clinical usage. We also hypothesize some future trends that research on this topic may follow

Electrical characterization of amorphous silicon MIS-based structures for HIT solar cell applications

A complete electrical characterization of hydrogenated amorphous silicon layers (a-Si:H) deposited on crystalline silicon (c-Si) substrates by electron cyclotron resonance chemical vapor deposition (ECR-CVD) was carried out. These structures are of interest for photovoltaic applications. Different growth temperatures between 30 and 200 °C were used. A rapid thermal annealing in forming gas atmosphere at 200 °C during 10 min was applied after the metallization process. The evolution of interfacial state density with the deposition temperature indicates a better interface passivation at higher growth temperatures. However, in these cases, an important contribution of slow states is detected as well. Thus, using intermediate growth temperatures (100–150 °C) might be the best choice

Moment-based representation of the diffusion inside the brain from reduced DMRI acquisitions: Generalized AMURA

Producción CientíficaAMURA (Apparent Measures Using Reduced Acquisitions) was originally proposed as a method to infer micro-structural information from single-shell acquisitions in diffusion MRI. It reduces the number of samples needed and the computational complexity of the estimation of diffusion properties of tissues by assuming the diffusion anisotropy is roughly independent on the b-value. This simplification allows the computation of simplified expressions and makes it compatible with standard acquisition protocols commonly used even in clinical practice. The present work proposes an extension of AMURA that allows the calculation of general moments of the diffusion signals that can be applied to describe the diffusion process with higher accuracy. We provide simplified expressions to analytically compute a set of scalar indices as moments of arbitrary orders over either the whole 3-D space, particular directions, or particular planes. The existing metrics previously proposed for AMURA (RTOP, RTPP and RTAP) are now special cases of this generalization. An extensive set of experiments is performed on public data and a clinical clase acquired with a standard type acquisition. The new metrics provide additional information about the diffusion processes inside the brain.Ministerio de Ciencia, Innovación y Universidades (grant RTI2018-094569-B-I00)Polish National Agency for Academic Exchange (grant PN/BEK/2019/1/00421)Ministry of Science and Higher Education of Poland (scholarship 692/STYP/13/2018)Junta de Castilla y León - Fondo Social Europeo (ID: 376062