HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence

The automation of railroad operations is a rapidly growing industry. In 2023, a new European standard for the automated Grade of Automation (GoA) 2 over European Train Control System (ETCS) driving is anticipated. Meanwhile, railway stakeholders are already planning their research initiatives for driverless and unattended autonomous driving systems. As a result, the industry is particularly active in research regarding perception technologies based on Computer Vision (CV) and Artificial Intelligence (AI), with outstanding results at the application level. However, executing high-performance and safety-critical applications on embedded systems and in real-time is a challenge. There are not many commercially available solutions, since High-Performance Computing (HPC) platforms are typically seen as being beyond the business of safety-critical systems. This work proposes a novel safety-critical and high-performance computing platform for CV- and AI-enhanced technology execution used for automatic accurate stopping and safe passenger transfer railway functionalities. The resulting computing platform is compatible with the majority of widely-used AI inference methodologies, AI model architectures, and AI model formats thanks to its design, which enables process separation, redundant execution, and HW acceleration in a transparent manner. The proposed technology increases the portability of railway applications into embedded systems, isolates crucial operations, and effectively and securely maintains system resources.The novel approach presented in this work is being developed as a specific railway use case for autonomous train operation into SELENE European research project. This project has received funding from RIA—Research and Innovation action under grant agreement No. 871467

Effects of leucine-enriched whey protein supplementation on physical function in post-hospitalized older adults participating in 12-weeks of resistance training program: a randomized controlled trial

Age-related strength and muscle mass loss is further increased after acute periods of inactivity. To avoid this, resistance training has been proposed as an effective countermeasure, but the additional effect of a protein supplement is not so clear. The aim of this study was to examine the effect of a whey protein supplement enriched with leucine after resistance training on muscle mass and strength gains in a post-hospitalized elderly population. A total of 28 participants were included and allocated to either protein supplementation or placebo supplementation following resistance training for 12 weeks (2 days/week). Physical function (lower and upper body strength, aerobic capacity and the Short Physical Performance Battery (SPPB) test), mini nutritional assessment (MNA) and body composition (Dual X-ray Absorptiometry) were assessed at baseline and after 12 weeks of resistance training. Both groups showed improvements in physical function after the intervention (p 0.05). Muscle mass did not improve after resistance training in either group (p > 0.05). In conclusion, 12 weeks of resistance training are enough to improve physical function in a post-hospitalized elderly population with no further benefits for the protein-supplemented group.This study was supported by the Basque Government (2016111138), and the European Regional Development Funds (ERDF), the University of Granada Plan Propio de Investigación 2016 (Excellence Actions: Unit of Excellence on Exercise and Health [UCEES]) and the Junta de Andalucía, Consejería de Conocimiento, Investigación y Universidades (ERDF: ref. SOMM17/6107/UGR). MA was supported by a grant from the University of the Basque Country (PIF17/186), IE by a grant from the University of the Basque Country in collaboration with the University of Bordeaux (Université of Bordeaux (UBX)) (PIFBUR16/07) and JRR by grants from the Spanish Ministry of Economy and Competitiveness (RYC 2010-05957; RYC-2011-09011 and BES-2014-068829).This work was also supported by grants from the Public University of Navarra, 'Plan de Promoción de Grupos de Investigación (2019)'

Determinants of Participation in a Post-Hospitalization Physical Exercise Program for Older Adults

Background: Older patients often experience a decline in physical function and cognitive status after hospitalization. Although interventions involving physical exercise are effective in improving functional performance, participation in physical exercise interventions among older individuals is low. We aimed to identify factors that contribute to exercise refusal among post-hospitalized older patients. Methods: A cross-sectional study of recruitment data from a randomized controlled trial was conducted involving 495 hospitalized people >= 70 years old. Sociodemographic and clinical data were obtained from the Basque Public Health System database. We determined physical function with the Short Physical Performance Battery (SPPB), nutritional status with the Mini-Nutritional Assessment, frailty according to the Fried phenotype criteria, and cognitive function with the Short Portable Mental Status Questionnaire (SPMSQ). Student's t, Mann-Whitney U, or chi-squared tests were applied for bivariate analysis. Parameters significantly associated with participation were introduced in a logistic multivariate regression model. Results: Among the analyzed patients, 88.8% declined participation in the physical exercise program. Multivariate regression revealed that older age (OR: 1.13; 95% CI: 1.07-1.19), poor nutritional status (OR: 0.81; 95% CI: 0.69-0.95), and reduced home accessibility (OR: 0.27; 95% CI: 0.08-0.94) were predictors of participation refusal. Moreover, patients who declined participation had worse performance on the SPPB (P < 0.05) and its tests of balance, leg strength, and walking speed (P < 0.05). No differences were found between groups in other variables. Conclusions: This study confirms low participation of older adults in a post-hospitalization physical exercise program. Non-participation was associated with increased age, poor nutritional status, and reduced home accessibility. Our findings support the need for intervention design that accounts for these factors to increase older patient participation in beneficial exercise programs.The study was funded by the Department of Education, Language Policy and Culture (2016111138) and a Programme Contract of the Department of Health, both departments of the Government of the Basque Country, which provided financial support during the research. The funders had no role in the study design, data collection and analysis and interpretation or writing the manuscript

SELENE: Self-Monitored Dependable Platform for High-Performance Safety-Critical Systems.

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[Otros] xisting HW/SW platforms for safety-critical systems suffer from limited performance and/or from lack of flexibility due to building on specific proprietary components. This jeopardizes their wide deployment across domains. While some research has been done to overcome these limitations, they have had limited success owing to missing flexibility and extensibility. Flexibility and extensibility are the cornerstones of industry adoption: industries dealing in capital goods need technologies on which they can rely on during decades (e.g. avionics, space, automotive). SELENE aims at covering this gap by proposing a new family of safety-critical computing platforms, which builds upon open source components such as the RISC-V instruction set architecture, GNU/Linux, and the Jailhouse hypervisor. SELENE will develop an advanced computing platform that is able to: (1) adapt the system to the specific requirements of different application domains, to changing environmental conditions, and to internal conditions of the system itself; (2) allow the integration of applications of different criticalities and performance demands in the same platform, guaranteeing functional and temporal isolation properties; (3) achieve flexible diverse redundancy by exploiting the inherent redundant capabilities of the multicore; and (4) efficiently execute compute-intensive applications by means of specific accelerators.This work has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement no. 871467.Hernández Luz, C.; Flich Cardo, J.; Paredes Palacios, R.; Lefebvre, C.; Allende, I.; Abella, J.; Trilla, D.... (2020). SELENE: Self-Monitored Dependable Platform for High-Performance Safety-Critical Systems. IEEE. 370-377. https://doi.org/10.1109/DSD51259.2020.00066S37037

Visual Odometry in Challenging Environments:An Urban Underground Railway Scenario Case

Localization is one of the most critical tasks for an autonomous vehicle, as position information is required to understand its surroundings and move accordingly. Visual Odometry (VO) has shown promising results in the last years. However, VO algorithms are usually evaluated in outdoor street scenarios and do not consider underground railway scenarios, with low lighting conditions in tunnels and significant lighting changes between tunnels and railway platforms. Besides, there is a lack of GPS, and it is not easy to access such infrastructures. This research proposes a method to create a ground truth of images and poses in underground railway scenarios. Second, the EnlightenGAN algorithm is proposed to face challenging lighting conditions, which can be coupled with any state-of-the-art VO techniques. Finally, the obtained ground truth and the EnlightenGAN have been tested in a real scenario. Two different VO approaches have been used: ORB-SLAM2 and DF-VO. The results show that the EnlightenGAN enhancement improves the performance of both approaches.</p