942 research outputs found
A short curriculum of the robotics and technology of computer lab
Our research Lab is directed by Prof. Anton Civit. It is an interdisciplinary group of 23
researchers that carry out their teaching and researching labor at the Escuela
Politécnica Superior (Higher Polytechnic School) and the Escuela de Ingeniería
Informática (Computer Engineering School). The main research fields are: a)
Industrial and mobile Robotics, b) Neuro-inspired processing using electronic spikes,
c) Embedded and real-time systems, d) Parallel and massive processing computer
architecture, d) Information Technologies for rehabilitation, handicapped and elder
people, e) Web accessibility and usability
In this paper, the Lab history is presented and its main publications and research
projects over the last few years are summarized.Nuestro grupo de investigación está liderado por el profesor Civit. Somos un grupo
multidisciplinar de 23 investigadores que realizan su labor docente e investigadora
en la Escuela Politécnica Superior y en Escuela de Ingeniería Informática. Las
principales líneas de investigaciones son: a) Robótica industrial y móvil. b)
Procesamiento neuro-inspirado basado en pulsos electrónicos. c) Sistemas
empotrados y de tiempo real. d) Arquitecturas paralelas y de procesamiento masivo.
e) Tecnología de la información aplicada a la discapacidad, rehabilitación y a las
personas mayores. f) Usabilidad y accesibilidad Web.
En este artículo se reseña la historia del grupo y se resumen las principales
publicaciones y proyectos que ha conseguido en los últimos años
TPU Cloud-Based Generalized U-Net for Eye Fundus Image Segmentation
Medical images from different clinics are acquired with different instruments and settings.
To perform segmentation on these images as a cloud-based service we need to train with multiple datasets
to increase the segmentation independency from the source. We also require an ef cient and fast segmentation
network. In this work these two problems, which are essential for many practical medical imaging
applications, are studied. As a segmentation network, U-Net has been selected. U-Net is a class of deep
neural networks which have been shown to be effective for medical image segmentation. Many different
U-Net implementations have been proposed.With the recent development of tensor processing units (TPU),
the execution times of these algorithms can be drastically reduced. This makes them attractive for cloud
services. In this paper, we study, using Google's publicly available colab environment, a generalized fully
con gurable Keras U-Net implementation which uses Google TPU processors for training and prediction.
As our application problem, we use the segmentation of Optic Disc and Cup, which can be applied to
glaucoma detection. To obtain networks with a good performance, independently of the image acquisition
source, we combine multiple publicly available datasets (RIM-One V3, DRISHTI and DRIONS). As a result
of this study, we have developed a set of functions that allow the implementation of generalized U-Nets
adapted to TPU execution and are suitable for cloud-based service implementation.Ministerio de Economía y Competitividad TEC2016-77785-
Syntactic parsing of unrestricted Spanish text
This research focusses on the syntactical parsing of morphologycal
tagged corpora. A proposal for a corpus oriented Spanish grammar is
presented in this document. This work has been developed in the
framework of the ITEM project and its main goal is to provide
multilingual background for information extraction and retrieval
tasks. The main goal of Tacat analyser is to provide a way of
obtaining large amounts of bracketed and parsed corpora, both general land specific domain. Tacat uses context free grammars and has as input following categories of Parole specification.The incremental
methodology that we use allows us to recognise different levels of
complexity in the analysis and to produce compatible outputs of all
the grammars.Postprint (published version
Poniente
Fil: Civit, Cecilia
An Automated Fall Detection System Using Recurrent Neural Networks
Falls are the most common cause of fatal injuries in elderly
people, causing even death if there is no immediate assistance. Fall detection
systems can be used to alert and request help when this type of accident
happens. Certain types of these systems include wearable devices
that analyze bio-medical signals from the person carrying it in real time.
In this way, Deep Learning algorithms could automate and improve the
detection of unintentional falls by analyzing these signals. These algorithms
have proven to achieve high effectiveness with competitive performances
in many classification problems. This work aims to study 16
Recurrent Neural Networks architectures (using Long Short-Term Memory
and Gated Recurrent Units) for falls detection based on accelerometer
data, reducing computational requirements of previous research. The
architectures have been tested on a labeled version of the publicly available
SisFall dataset, achieving a mean F1-score above 0.73 and improving
state-of-the-art solutions in terms of network complexity.Ministerio de Economía y Competitivida TEC2016-77785-
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