Funcionamiento de una escala de intensidad de apoyos en niños y adolescentes con Tea en edad escolar

[ES] Los constructos de apoyo y necesidades de apoyo han constituido uno de los ejes de desarrollo en investigación y aplicación de acciones destinadas a mejorar la calidad de vida de las personas con discapacidad intelectual o del desarrollo. La evaluación de las necesidades de apoyo y de la intensidad de estos apoyos resulta una estrategia crucial tanto en personas adultas con discapacidad intelectual, como en escolares. En la investigación que se presenta, se exponen los resultados de una evaluación de las necesidades de apoyo y su intensidad, en escolares con Trastorno del Espectro del Autismo (TEA) del territorio español. Se comparan los resultados en intensidad de necesidad de apoyo con otros grupos con discapacidad intelectual, pero sin TEA, además de presentar unos primeros análisis de las necesidades de apoyo de escolares con TEA de alto funcionamiento o sin discapacidad intelectual. ´ El método utilizado ha sido la administración de una escala de intensidad de apoyos (SIS-C) en su primera adaptación al contexto español. La escala cuenta con dos secciones: (1) necesidades excepcionales de apoyo (médicas y conductuales) y (2) escala de necesidades de apoyo en actividades representativas, agrupadas en siete factores, vida en el hogar, vida en la comunidad, participación escolar, aprendizaje escolar, salud y seguridad, actividades sociales y defensa o autorrepresentación. Se evalúa la intensidad de apoyo a través de tres parámetros: tipo de apoyo, frecuencia de apoyo y tiempo de apoyo diario. Hemos evaluado 249 escolares con TEA y discapacidad intelectual, así como en 44 niños y adolescentes con TEA sin discapacidad intelectual de entre 5 a 16 años edad. Se han comparado los valores medios obtenidos con una muestra de 565 niños y adolescentes con discapacidad intelectual sin TEA de España. Las medias obtenidas han mostrado diferencias significativas en intensidad de de apoyo, entre los niños y adolescentes con discapacidad intelectual sin TEA y con TEA; y también cuando se contrastan los valores de los grupos de edad: 5-10 años y 11-16 años. En todas las actividades representativas de la escala, la muestra con TEA presenta necesidades de apoyo más intensas. La muestra con TEA sin discapacidad intelectual ha presentado necesidades de apoyo en diversas áreas de apoyo, especialmente en actividades sociales, defensa (autorrepresentación) y aprendizaje escolar. Algunas conclusiones presentadas en la tesis van en la línea de explicitar que, la escala SIS-C es un instrumento útil para evaluar las necesidades de apoyo en actividades de la vida diaria en escolares con TEA y discapacidad intelectual. Los datos sugieren que la intensidad de necesidades de apoyo de los escolares con TEA es mayor que la mostrada por otros escolares con discapacidad intelectual y que no presentan TEA. Existen actividades en las que la intensidad de apoyo es significativamente diferente para el grupo con TEA, lo que puede resultar de interés para los procesos de: (1) reflexión y planificación de apoyos individuales, (2) organización de recursos y servicios y (3) para el desarrollo de estrategias de la administración educativa

Multilayer Spiking Neural Network for Audio Samples Classification Using SpiNNaker

Audio classification has always been an interesting subject of research inside the neuromorphic engineering field. Tools like Nengo or Brian, and hardware platforms like the SpiNNaker board are rapidly increasing in popularity in the neuromorphic community due to the ease of modelling spiking neural networks with them. In this manuscript a multilayer spiking neural network for audio samples classification using SpiNNaker is presented. The network consists of different leaky integrate-and-fire neuron layers. The connections between them are trained using novel firing rate based algorithms and tested using sets of pure tones with frequencies that range from 130.813 to 1396.91 Hz. The hit rate percentage values are obtained after adding a random noise signal to the original pure tone signal. The results show very good classification results (above 85 % hit rate) for each class when the Signal-to-noise ratio is above 3 decibels, validating the robustness of the network configuration and the training step.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130

A Sensor Fusion Horse Gait Classification by a Spiking Neural Network on SpiNNaker

The study and monitoring of the behavior of wildlife has always been a subject of great interest. Although many systems can track animal positions using GPS systems, the behavior classification is not a common task. For this work, a multi-sensory wearable device has been designed and implemented to be used in the Doñana National Park in order to control and monitor wild and semiwild life animals. The data obtained with these sensors is processed using a Spiking Neural Network (SNN), with Address-Event-Representation (AER) coding, and it is classified between some fixed activity behaviors. This works presents the full infrastructure deployed in Doñana to collect the data, the wearable device, the SNN implementation in SpiNNaker and the classification results.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130

Event-based Row-by-Row Multi-convolution engine for Dynamic-Vision Feature Extraction on FPGA

Neural networks algorithms are commonly used to recognize patterns from different data sources such as audio or vision. In image recognition, Convolutional Neural Networks are one of the most effective techniques due to the high accuracy they achieve. This kind of algorithms require billions of addition and multiplication operations over all pixels of an image. However, it is possible to reduce the number of operations using other computer vision techniques rather than frame-based ones, e.g. neuromorphic frame-free techniques. There exists many neuromorphic vision sensors that detect pixels that have changed their luminosity. In this study, an event-based convolution engine for FPGA is presented. This engine models an array of leaky integrate and fire neurons. It is able to apply different kernel sizes, from 1x1 to 7x7, which are computed row by row, with a maximum number of 64 different convolution kernels. The design presented is able to process 64 feature maps of 7x7 with a latency of 8.98 s.Ministerio de Economía y Competitividad TEC2016-77785-

Accuracy Improvement of Neural Networks Through Self-Organizing-Maps over Training Datasets

Although it is not a novel topic, pattern recognition has become very popular and relevant in the last years. Different classification systems like neural networks, support vector machines or even complex statistical methods have been used for this purpose. Several works have used these systems to classify animal behavior, mainly in an offline way. Their main problem is usually the data pre-processing step, because the better input data are, the higher may be the accuracy of the classification system. In previous papers by the authors an embedded implementation of a neural network was deployed on a portable device that was placed on animals. This approach allows the classification to be done online and in real time. This is one of the aims of the research project MINERVA, which is focused on monitoring wildlife in Do˜nana National Park using low power devices. Many difficulties were faced when pre-processing methods quality needed to be evaluated. In this work, a novel pre-processing evaluation system based on self-organizing maps (SOM) to measure the quality of the neural network training dataset is presented. The paper is focused on a three different horse gaits classification study. Preliminary results show that a better SOM output map matches with the embedded ANN classification hit improvement.Junta de Andalucía P12-TIC-1300Ministerio de Economía y Competitividad TEC2016-77785-

Embedded neural network for real-time animal behavior classification

Recent biological studies have focused on understanding animal interactions and welfare. To help biolo- gists to obtain animals’ behavior information, resources like wireless sensor networks are needed. More- over, large amounts of obtained data have to be processed off-line in order to classify different behaviors.There are recent research projects focused on designing monitoring systems capable of measuring someanimals’ parameters in order to recognize and monitor their gaits or behaviors. However, network unre- liability and high power consumption have limited their applicability.In this work, we present an animal behavior recognition, classification and monitoring system based ona wireless sensor network and a smart collar device, provided with inertial sensors and an embeddedmulti-layer perceptron-based feed-forward neural network, to classify the different gaits or behaviorsbased on the collected information. In similar works, classification mechanisms are implemented in aserver (or base station). The main novelty of this work is the full implementation of a reconfigurableneural network embedded into the animal’s collar, which allows a real-time behavior classification andenables its local storage in SD memory. Moreover, this approach reduces the amount of data transmittedto the base station (and its periodicity), achieving a significantly improving battery life. The system hasbeen simulated and tested in a real scenario for three different horse gaits, using different heuristics andsensors to improve the accuracy of behavior recognition, achieving a maximum of 81%.Junta de Andalucía P12-TIC-130

Neuronal Specialization for Fine-Grained Distance Estimation using a Real-Time Bio-Inspired Stereo Vision System

The human binocular system performs very complex operations in real-time tasks thanks to neuronal specialization and several specialized processing layers. For a classic computer vision system, being able to perform the same operation requires high computational costs that, in many cases, causes it to not work in real time: this is the case regarding distance estimation. This work details the functionality of the biological processing system, as well as the neuromorphic engineering research branch—the main purpose of which is to mimic neuronal processing. A distance estimation system based on the calculation of the binocular disparities with specialized neuron populations is developed. This system is characterized by several tests and executed in a real-time environment. The response of the system proves the similarity between it and human binocular processing. Further, the results show that the implemented system can work in a real-time environment, with a distance estimation error of 15% (8% for the characterization tests).Ministerio de Ciencia, Innovación y Universidades TEC2016-77785-

Covid-19 y docencia online: impacto en los resultados académicos en las asignaturas de expresión gráfica arquitectónica de la Universidad Politécnica de Cartagena

La crisis sanitaria derivada de la pandemia (COVID-19) supuso una precipitada adaptación de la Universidad hacia metodologías docentes basadas en experiencias online. Como consecuencia, los métodos de enseñanza y de evaluación se adaptaron siguiendo diferentes fórmulas. La docencia en Arquitectura, en la que el dibujo es una herramienta fundamental en la comunicación, requirió de una adaptación particular dada la especificidad de las competencias y resultados del aprendizaje en este tipo de enseñanzas. El objetivo de esta comunicación es analizar los resultados académicos de los estudiantes tras el periodo de pandemia (cursos 2019/20 y 2020/21) con docencia y evaluación online y semipresencial respectivamente, y contrastarlos con los de cursos anteriores desarrollados con metodologías tradicionales de docencia y evaluación presencial. El interés se centra en las asignaturas básicas del Área de Expresión Gráfica del Grado en Fundamentos de Arquitectura en la Escuela Técnica Superior de Arquitectura y Edificación de la Universidad Politécnica de Cartagena. Las conclusiones abordan datos estadísticos relativos a la tasa de rendimiento de estudiantes y proporcionan una visión global del efecto que la docencia y la evaluación virtuales han tenido en los resultados académicos.Se agradece a la Oficina de Prospección y Análisis de Datos de la Universidad Politécnica de Cartagena (OPADA) los datos proporcionados sin los cuales no habría sido posible realizar el estudio

Disentangling the seasonal effects of agricultural intensification on birds and bats in Mediterranean olive groves

Assessing the spatio-temporal impact of agricultural intensification on species and communities is key for biodiversity conservation. Here, we investigated the seasonal effects of olive grove intensification at both local (farming practices and grove structural complexity) and landscape scale (land-cover diversity) on birds and bats, at species and community-level. Both groups were surveyed during spring, summer, and autumn in 60 sites representing varying levels of olive grove intensification throughout the Alentejo region (southern Portugal). At the local scale, the number of chemical applications was used as a proxy for the intensification of farming practices and a Structural Index, which accounted for within-grove variability in tree density and features, was used as a measure of grove structural complexity. At landscape scale, we quantified the proportion of the major land-cover types potentially affecting birds and bats. We found that the abundance of ca. 77% of the species analyzed (ca. 84% and 55% of birds and bats respectively) was negatively related to olive grove intensification in at least one season. The Structural Index was the most influential factor at both species and community-levels, especially for birds, with a consistent and strong effect across seasons. Chemical applications had a stronger negative effect on birds, whereas the amount of olive grove cover had a stronger detrimental effect on bats. Birds and bats showed a variable response to predictor variables depending on the season, particularly for the bat community. Our study shows differences in bird and bat responses associated with the spatio-temporal variability of the agricultural intensification components. On the one hand, birds and bats showed a seasonal pattern of association with the different components of olive grove intensification, probably due to their ecological and biological requirements. On the other hand, the responses of both groups also appear to be scale-dependent: while birds seem to respond to in-farm or local intensification more strongly, bats seem to be more influenced by landscape-scale simplification. Overall, we highlight the importance of the structural complexity of olive groves for birds and bats, an aspect that should be considered in the design of agricultural policies aiming to promote biodiversity conservation.11 página