7 research outputs found
GNSS signal detection based on first-order absolute statistics
Aquest projecte es centra principalment en el detector no coherent d'un GPS. Per tal de caracteritzar el procés de detecció d'un receptor, es necessita conèixer l'estadÃstica implicada. Pel cas dels detectors no coherents convencionals, l'estadÃstica de segon ordre intervé plenament. Les prestacions que ens dóna l'estadÃstica de segon ordre, plasmada en la ROC, són prou bons tot i que en diferents situacions poden no ser els millors. Aquest projecte intenta reproduir el procés de detecció mitjançant l'estadÃstica de primer ordre com a alternativa a la ja coneguda i implementada estadÃstica de segon ordre. Per tal d'aconseguir-ho, s'usen expressions basades en el Teorema Central del LÃmit i de les sèries Edgeworth com a bones aproximacions. Finalment, tant l'estadÃstica convencional com l'estadÃstica proposada són comparades, en termes de la ROC, per tal de determinar quin detector no coherent ofereix millor prestacions en cada situació.Este proyecto se centra básicamente en el receptor no coherente de un GPS. Con tal de caracterizar el proceso de detección de un receptor, es necesario conocer la estadÃstica implicada. En el caso de los detectores no coherentes convencionales, la estadÃstica de segundo orden interviene plenamente. Los resultados que nos da esta estadÃstica, plasmada en la curva ROC, son bastante satisfactorios aunque en distintos escenarios pueden no ser los mejores. Este proyecto intenta reproducir el proceso de detección mediante la estadÃstica de primer orden como alternativa a la ya conocida e implementada estadÃstica de segundo orden. Con tal de conseguirlo, se van a usar expresiones basadas en el Teorema Central del LÃmite y de las series Edgeworth como aproximaciones fiables. Finalmente, tanto la estadÃstica propuesta como la estadÃstica convencional son comparadas, en términos de la curva ROC, con tal de determinar cual detector no coherente ofrece mejores prestaciones en cada situación.This project focuses in GPS noncoherent detectors. In order characterize detection metrics from a receiver, statistics are demanded to be known. For the case of conventional noncoherent detectors, second-order statistics play an important role. The detection performance in terms of ROC curve gives satisfying results. However, using second-order statistics does not give the best performance in different environmental situations. This project tries to characterize detection metrics using first-order statistics as an alternative to conventional second-order statistics. To do so, some closed-form expressions based in the Central Limit Theorem and the Edgeworth series will be used as a good approximation. Finally, they are both compared to determine the best noncoherent detector for each situation
Privacy-Constrained Biometric System for Non-cooperative Users
With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject's hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance
GNSS signal detection based on first-order absolute statistics
Aquest projecte es centra principalment en el detector no coherent d’un GPS. Per tal de
caracteritzar el procés de detecció d’un receptor, es necessita conèixer l’estadÃstica implicada.
Pel cas dels detectors no coherents convencionals, l’estadÃstica de segon ordre intervé
plenament. Les prestacions que ens dóna l’estadÃstica de segon ordre, plasmada en la ROC, són
prou bons tot i que en diferents situacions poden no ser els millors. Aquest projecte intenta
reproduir el procés de detecció mitjançant l’estadÃstica de primer ordre com a alternativa a la ja
coneguda i implementada estadÃstica de segon ordre. Per tal d’aconseguir-ho, s’usen
expressions basades en el Teorema Central del LÃmit i de les sèries Edgeworth com a bones
aproximacions. Finalment, tant l’estadÃstica convencional com l’estadÃstica proposada són
comparades, en termes de la ROC, per tal de determinar quin detector no coherent ofereix millor
prestacions en cada situació.Este proyecto se centra básicamente en el receptor no coherente de un GPS. Con tal de
caracterizar el proceso de detección de un receptor, es necesario conocer la estadÃstica
implicada. En el caso de los detectores no coherentes convencionales, la estadÃstica de segundo
orden interviene plenamente. Los resultados que nos da esta estadÃstica, plasmada en la curva
ROC, son bastante satisfactorios aunque en distintos escenarios pueden no ser los mejores.
Este proyecto intenta reproducir el proceso de detección mediante la estadÃstica de primer orden
como alternativa a la ya conocida e implementada estadÃstica de segundo orden. Con tal de
conseguirlo, se van a usar expresiones basadas en el Teorema Central del LÃmite y de las series
Edgeworth como aproximaciones fiables. Finalmente, tanto la estadÃstica propuesta como la
estadÃstica convencional son comparadas, en términos de la curva ROC, con tal de determinar
cual detector no coherente ofrece mejores prestaciones en cada situación.This project focuses in GPS noncoherent detectors. In order characterize detection metrics from
a receiver, statistics are demanded to be known. For the case of conventional noncoherent
detectors, second-order statistics play an important role. The detection performance in terms of
ROC curve gives satisfying results. However, using second-order statistics does not give the best
performance in different environmental situations. This project tries to characterize detection
metrics using first-order statistics as an alternative to conventional second-order statistics. To do
so, some closed-form expressions based in the Central Limit Theorem and the Edgeworth series
will be used as a good approximation. Finally, they are both compared to determine the best
noncoherent detector for each situation
Mobile eHealth platform for home monitoring of bipolar disorder
Comunicació presentada al 27th International Conference on Multimedia Modeling (MMM), celebrat del 22 al 24 de juny de 2021 a Praga, República Txeca.People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur.
MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians.This work is part of the MYMPHA-MD project, which has been funded by the European Union under Grant Agreement Nº 610462. It has also been partially supported by the Spanish project PID2019-105093GB-I00 (MINECO/FEDER, UE) and CERCA Programme/Generalitat de Catalunya.), and by ICREA under the ICREA Academia programme. The last author has been funded by the Agencia Estatal de Investigación (AEI), Ministerio de Ciencia, Innovación y Universidades and the Fondo Social Europeo (FSE) under grant RYC-2015-17239 (AEI/FSE, UE)