2,272 research outputs found
Romance de la guerra, en el que se declara cómo empezó el Movimiento...
Copia digital. Valladolid : Junta de Castilla y León. Consejería de Cultura y Turismo, 2009-201
Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors
Auscultation is one of the most used techniques for
detecting cardiovascular diseases, which is one of the main causes
of death in the world. Heart murmurs are the most common abnormal
finding when a patient visits the physician for auscultation.
These heart sounds can either be innocent, which are harmless, or
abnormal, which may be a sign of a more serious heart condition.
However, the accuracy rate of primary care physicians and expert
cardiologists when auscultating is not good enough to avoid most
of both type-I (healthy patients are sent for echocardiogram) and
type-II (pathological patients are sent home without medication or
treatment) errors made. In this paper, the authors present a novel
convolutional neural network based tool for classifying between
healthy people and pathological patients using a neuromorphic
auditory sensor for FPGA that is able to decompose the audio into
frequency bands in real time. For this purpose, different networks
have been trained with the heart murmur information contained in
heart sound recordings obtained from nine different heart sound
databases sourced from multiple research groups. These samples
are segmented and preprocessed using the neuromorphic auditory
sensor to decompose their audio information into frequency
bands and, after that, sonogram images with the same size are
generated. These images have been used to train and test different
convolutional neural network architectures. The best results
have been obtained with a modified version of the AlexNet model,
achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%,
PhysioNet/CinC Challenge 2016 score: 0.9416). This tool could aid
cardiologists and primary care physicians in the auscultation process,
improving the decision making task and reducing type-I and
type-II errors.Ministerio de Economía y Competitividad TEC2016-77785-
NAVIS: Neuromorphic Auditory VISualizer Tool
This software presents diverse utilities to perform the first post-processing layer taking the neuromorphic auditory sensors (NAS) information. The used NAS implements in FPGA a cascade filters architecture, imitating the behavior of the basilar membrane and inner hair cells and working with the sound information decomposed into its frequency components as spike streams. The well-known neuromorphic hardware interface Address-Event-Representation (AER) is used to propagate auditory information out of the NAS, emulating the auditory vestibular nerve. Using the information packetized into aedat files, which are generated through the jAER software plus an AER to USB computer interface, NAVIS implements a set of graphs that allows to represent the auditory information as cochleograms, histograms, sonograms, etc. It can also split the auditory information into different sets depending on the activity level of the spike streams. The main contribution of this software tool is that it allows complex audio post-processing treatments and representations, which is a novelty for spike-based systems in the neuromorphic community and it will help neuromorphic engineers to build sets for training spiking neural networks (SNN).Ministerio de Economía y Competitividad TEC2012-37868-C04-0
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
Stereo Matching in Address-Event-Representation (AER) Bio-Inspired Binocular Systems in a Field-Programmable Gate Array (FPGA)
In stereo-vision processing, the image-matching step is essential for results, although it
involves a very high computational cost. Moreover, the more information is processed, the more time
is spent by the matching algorithm, and the more ine cient it is. Spike-based processing is a relatively
new approach that implements processing methods by manipulating spikes one by one at the time
they are transmitted, like a human brain. The mammal nervous system can solve much more complex
problems, such as visual recognition by manipulating neuron spikes. The spike-based philosophy
for visual information processing based on the neuro-inspired address-event-representation (AER)
is currently achieving very high performance. The aim of this work was to study the viability of a
matching mechanism in stereo-vision systems, using AER codification and its implementation in
a field-programmable gate array (FPGA). Some studies have been done before in an AER system
with monitored data using a computer; however, this kind of mechanism has not been implemented
directly on hardware. To this end, an epipolar geometry basis applied to AER systems was studied
and implemented, with other restrictions, in order to achieve good results in a real-time scenario.
The results and conclusions are shown, and the viability of its implementation is proven.Ministerio de Economía y Competitividad TEC2016-77785-
Delayed Leukoencephalopathy: Three Case Reports and a Literature Review
Background: Delayed leukoencephalopathy (DL) is a rare entity associated with cerebral hypoxia and heroin consumption. We describe the clinical course of three cases of DL due to non-heroin drug use.
Material and methods: We describe the cases of three DL patients admitted to our hospital in 2012.
Discussion: These cases contribute to the aetiological spectrum of DL since multifactorial causes could account for the clinical symptoms
Los equipos directivos de educación primaria ante la integración de las TICs
Ante la apuesta decidida de las distintas Administraciones Educativas acerca de la introducción de las
TICs en el aula los Equipos Directivos reflexionan sobre los factores condicionantes, la realidad de su
aplicación y el futuro próximo.Owing to the determined option adopted by different Educational Governments referred to the integration
of TICs in the school, the board of directors is reflecting on the main factors, the use's reality and the next
future
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
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-
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