76 research outputs found
Integrating state-of-the-art CNNs for multi-sensor 3D vehicle detection in real autonomous driving environments
2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 Oct. 2019This paper presents two new approaches to detect
surrounding vehicles in 3D urban driving scenes and their corresponding Bird’s Eye View (BEV). The proposals integrate two
state-of-the-art Convolutional Neural Networks (CNNs), such as
YOLOv3 and Mask-RCNN, in a framework presented by the
authors in [1] for 3D vehicles detection fusing semantic image
segmentation and LIDAR point cloud. Our proposals take
advantage of multimodal fusion, geometrical constrains, and
pre-trained modules inside our framework. The methods have
been tested using the KITTI object detection benchmark and
comparison is presented. Experiments show new approaches
improve results with respect to the baseline and are on par
with other competitive state-of-the-art proposals, being the only
ones that do not apply an end-to-end learning process. In this
way, they remove the need to train on a specific dataset and
show a good capability of generalization to any domain, a
key point for self-driving systems. Finally, we have tested our
best proposal in KITTI in our driving environment, without
any adaptation, obtaining results suitable for our autonomous
driving application.Ministerio de Economía y CompetitividadComunidad de Madri
Simulating use cases for the UAH autonomous electric car
2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 Oct. 2019This paper presents the simulation use cases for
the UAH Autonomous Electric Car, related with typical driving
scenarios in urban environments, focusing on the use of hierarchical interpreted binary Petri nets in order to implement the
decision making framework of an autonomous electric vehicle.
First, we describe our proposal of autonomous system architecture, which is based on the open source Robot Operating
System (ROS) framework that allows the fusion of multiple
sensors and the real-time processing and communication of
multiple processes in different embedded processors. Then, the
paper focuses on the study of some of the most interesting
driving scenarios such as: stop, pedestrian crossing, Adaptive
Cruise Control (ACC) and overtaking, illustrating both the
executive module that carries out each behaviour based on
Petri nets and the trajectory and linear velocity that allows
to quantify the accuracy and robustness of the architecture
proposal for environment perception, navigation and planning
on a university Campus.Ministerio de Economía y CompetitividadComunidad de Madri
The Experience of Robesafe Team in CARLA Autonomous Driving Challenge
Robótica e Inteligencia Artificial: Retos y nuevas oportunidades. 10 de diciembre de 2019, ETSII UPM (RoboCity2030)The future of the automotive is focused on achieving total
autonomous cars in realistic urban environments. To reach it,
many researching teams are working with 3D simulators such as
V-REP and Gazebo, due to an easy integration with ROS
platform. ROS is a middle-ware for robot code development. It
allows easy communication between different systems. It is multilanguage, admitting C++ and Python code programming. Those
simulators provide precise motion information, but they are
designed for smaller environments like robotic arms, providing
unrealistic appearance and very slow performance, being
unrecommended for real-time systems in rich worlds like urban
cities. CARLA simulator provides high detailed environments in
realistic urban situations, being able to train and test control and
perception algorithms in complex traffic scenarios, very close to
real situations.
CARLA Autonomous Driving Challenge was launched in Summer
2019, allowing everyone to test their own control techniques under
the same traffic scenarios, scoring its performance regarding
traffic rules. Robesafe researching group, from Universidad de
Alcalá, submitted to this challenge, with the aim of achieving high
results and compare our control and perception techniques with
others provided by other teams.Comunidad de Madri
Naturalistic driving study for older drivers based on the DriveSafe app
2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 Oct. 2019Elderly population is increasing year after year
in the developed countries. However, the knowledge of actual
mobility needs of senior drivers is scarce. In this paper,
we present a naturalistic driving study (NDS) focused on
older drivers through smartphone technology and using our
DriveSafe app. Our system automatically generates a driving
analysis report based on objective indicators. The proposal
supposes an improvement over the traditional surveys and
observers, and represents an advance over the current NDSs by
using smartphones instead of complex instrumented vehicles.
Our method avoids the problems of manual annotation by
using an automatic method for data reduction information.
Furthermore, a comparison between traditional questionnaires
and information provided by our system is carried out and
conclusions are presented.Ministerio de Economía y CompetitividadDGTComunidad de Madri
Coding Prony's method in MATLAB and applying it to biomedical signal filtering
Background:The response of many biomedical systems can be modelled using a linear combination of damped exponential functions. The approximation parameters, based on equally spaced samples, can be obtained using Prony's method and its variants (e.g. the matrix pencil method). This paper provides a tutorial on the main polynomial Prony and matrix pencil methods and their implementation in MATLAB and analyses how they perform with synthetic and multifocal visual-evoked potential (mfVEP) signals. This paper briefly describes the theoretical basis of four polynomial Prony approximation methods: classic, least squares (LS), total least squares (TLS) and matrix pencil method (MPM). In each of these cases, implementation uses general MATLAB functions. The features of the various options are tested by approximating a set of synthetic mathematical functions and evaluating filtering performance in the Prony domain when applied to mfVEP signals to improve diagnosis of patients with multiple sclerosis (MS). Results:The code implemented does not achieve 100%-correct signal approximation and, of the methods tested, LS and MPM perform best. When filtering mfVEP records in the Prony domain, the value of the area under the receiver-operating-characteristic (ROC) curve is 0.7055 compared with 0.6538 obtained with the usual filtering method used for this type of signal (discrete Fourier transform low-pass filter with a cut-off frequency of 35 Hz). Conclusions:This paper reviews Prony's method in relation to signal filtering and approximation, provides the MATLAB code needed to implement the classic, LS, TLS and MPM methods, and tests their performance in biomedical signal filtering and function approximation. It emphasizes the importance of improving the computational methods used to implement the various methods described above.Universidad de AlcaláSecretaría de Estado de Investigación, Desarrollo e Innovació
A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings.
Introduction: The aim of this study is to develop a computer-aided diagnosis system to identify subjects at differing stages of development of multiple sclerosis (MS) using multifocal visual-evoked potentials (mfVEPs). Using an automatic classifier, diagnosis is performed first on the eyes and then on the subjects.
Patients: MfVEP signals were obtained from patients with Radiologically Isolated Syndrome (RIS) (n = 30 eyes), patients with Clinically Isolated Syndrome (CIS) (n = 62 eyes), patients with definite MS (n = 56 eyes) and 22 control subjects (n = 44 eyes). The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON).
Methods: For individual eye diagnosis, a feature vector was formed with information about the intensity, latency and singular values of the mfVEP signals. A flat multiclass classifier (FMC) and a hierarchical classifier (HC) were tested and both were implemented using the k-Nearest Neighbour (k-NN) algorithm. The output of the best eye classifier was used to classify the subjects. In the event of divergence, the eye with the best mfVEP recording was selected.
Results: In the eye classifier, the HC performed better than the FMC (accuracy = 0.74 and extended Matthew Correlation Coefficient (MCC) = 0.68). In the subject classification, accuracy = 0.95 and MCC = 0.93, confirming that it may be a promising tool for MS diagnosis. Chirped-pulse φOTDR provides distributed strain measurement via a time-delay estimation process. We propose a lower bound for performance, after reducing sampling error and compensating phase-noise. We attempt to reach the limit, attaining unprecedented pε/√Hz sensitivities.
Conclusion: In addition to amplitude (axonal loss) and latency (demyelination), it has shown that the singular values of the mfVEP signals provide discriminatory information that may be used to identify subjects with differing degrees of the disease.Secretaría de Estado de Investigación, Desarrollo e InnovaciónInstituto de Salud Carlos II
Sustainable Production in the Food Industry Volume 6: Sustainable Primary Production (6): 128-149 (2021)
22 Páginas.-- 2 FigurasChallenges in the food industry relate not only
to demographic pressure and environment
protection, but also to new consumer demands
and specific health requirements.
Multidisciplinary studies on raw materials,
waste valorisation, efficient green technologies,
new products and biodegradable smart
packaging are needed. Real-time process
control, quality, safety and authenticity will rely
on powerful proces analytical technology,
multi-sensors and advanced computing and
digitalisation systemsPeer reviewe
Guia per desenvolupar la competència transversal. Comunicació escrita i oral
Les competències transversals són un aspecte clau en la formació dels estudiants de la Universitat. El Grup d'innovació docent de la Facultat de Veterinària ha desenvolupat unes guies amb l'objectiu d'estandarditzar l'ensenyament de les competències transversals al graus impartits a la Facultat, oferir eines i recursos a l'alumnat per ajudar-los a desenvolupar aquestes competències, i oferir eines i recursos al professorat per facilitar l'ensenyament i l'avaluació de les competències en el marc de les assignatures pròpies. La guia per desenvolupar la competència transversal de comunicació escrita i oral estableix tres nivells de domini i 7 indicadors d'aprenentatge que l'estudiant ha d'assolir de forma progressiva al llarg del grau. En acabar els estudis l'estudiant ha de demostrar que ha adquirit l'habilitat d'expressar-se oralment i per escrit en un registre científic
Cross-disease Meta-analysis of Genome-wide Association Studies for Systemic Sclerosis and Rheumatoid Arthritis Reveals IRF4 as a New Common Susceptibility Locus
Objectives: Systemic sclerosis (SSc) and rheumatoid arthritis (RA) are autoimmune diseases that share clinical and immunological characteristics. To date, several shared SSc- RA loci have been identified independently. In this study, we aimed to systematically search for new common SSc-RA loci through an inter-disease meta-GWAS strategy. Methods: We performed a meta-analysis combining GWAS datasets of SSc and RA using a strategy that allowed identification of loci with both same-direction and opposingdirection allelic effects. The top single-nucleotide polymorphisms (SNPs) were followed-up in independent SSc and RA case-control cohorts. This allowed us to increase the sample size to a total of 8,830 SSc patients, 16,870 RA patients and 43,393 controls. Results: The cross-disease meta-analysis of the GWAS datasets identified several loci with nominal association signals (P-value < 5 x 10-6), which also showed evidence of association in the disease-specific GWAS scan. These loci included several genomic regions not previously reported as shared loci, besides risk factors associated with both diseases in previous studies. The follow-up of the putatively new SSc-RA loci identified IRF4 as a shared risk factor for these two diseases (Pcombined = 3.29 x 10-12). In addition, the analysis of the biological relevance of the known SSc-RA shared loci pointed to the type I interferon and the interleukin 12 signaling pathways as the main common etiopathogenic factors. Conclusions: Our study has identified a novel shared locus, IRF4, for SSc and RA and highlighted the usefulness of cross-disease GWAS meta-analysis in the identification of common risk loci
Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2
The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality
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