Real-time detection of uncalibrated sensors using Neural Networks

Nowadays, sensors play a major role in several contexts like science, industry and daily life which benefit of their use. However, the retrieved information must be reliable. Anomalies in the behavior of sensors can give rise to critical consequences such as ruining a scientific project or jeopardizing the quality of the production in industrial production lines. One of the more subtle kind of anomalies are uncalibrations. An uncalibration is said to take place when the sensor is not adjusted or standardized by calibration according to a ground truth value. In this work, an online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. This solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. Then, after trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively. This solution can be adapted to different contexts by means of transfer learning, whose application allows for the addition of new sensors, the deployment into new environments and the retraining of the model with minimum amounts of data

Abnormal expression of cerebrospinal fluid cation chloride cotransporters in patients with Rett Syndrome

Objective: Rett Syndrome is a progressive neurodevelopmental disorder caused mainly by mutations in the gene encoding methyl-CpG-binding protein 2. The relevance of MeCP2 for GABAergic function was previously documented in animal models. In these models, animals show deficits in brain-derived neurotrophic factor, which is thought to contribute to the pathogenesis of this disease. Neuronal Cation Chloride Cotransporters (CCCs) play a key role in GABAergic neuronal maturation, and brain-derived neurotrophic factor is implicated in the regulation of CCCs expression during development. Our aim was to analyse the expression of two relevant CCCs, NKCC1 and KCC2, in the cerebrospinal fluid of Rett syndrome patients and compare it with a normal control group. Methods: The presence of bumetanide sensitive NKCC1 and KCC2 was analysed in cerebrospinal fluid samples from a control pediatric population (1 day to 14 years of life) and from Rett syndrome patients (2 to 19 years of life), by immunoblot analysis. Results: Both proteins were detected in the cerebrospinal fluid and their levels are higher in the early postnatal period. However, Rett syndrome patients showed significantly reduced levels of KCC2 and KCC2/NKCC1 ratio when compared to the control group. Conclusions: Reduced KCC2/NKCC1 ratio in the cerebrospinal fluid of Rett Syndrome patients suggests a disturbed process of GABAergic neuronal maturation and open up a new therapeutic perspective

On Neural Networks Redundancy and Diversity for Their Use in Safety-Critical Systems

An increasing number of critical functionalities integrated in embedded critical systems rely on deep learning (DL) technology. This article summarizes certain key aspects of DL’s intrinsic stochastic and training-data-dependent nature that are at odds with current domain-specific functional safety standards. We exemplify how redundancy and diversity of neural networks can help to reconcile DL technology and functional safety requirements.The research leading to these results has received funding from the European Research Council (ERC) grant agreement No. 772773 (SuPerCom), the Horizon Europe Programme under the SAFEXPLAIN Project (www.safexplain.eu), grant agreement num.101069595, and the Spanish Ministry of Science and Innovation under grant PID2019-107255GBC21/AEI/10.13039/501100011033.Peer ReviewedPostprint (author's final draft

Multi-core devices for safety-critical systems: a survey

Multi-core devices are envisioned to support the development of next-generation safety-critical systems, enabling the on-chip integration of functions of different criticality. This integration provides multiple system-level potential benefits such as cost, size, power, and weight reduction. However, safety certification becomes a challenge and several fundamental safety technical requirements must be addressed, such as temporal and spatial independence, reliability, and diagnostic coverage. This survey provides a categorization and overview at different device abstraction levels (nanoscale, component, and device) of selected key research contributions that support the compliance with these fundamental safety requirements.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2015-65316-P, Basque Government under grant KK-2019-00035 and the HiPEAC Network of Excellence. The Spanish Ministry of Economy and Competitiveness has also partially supported Jaume Abella under Ramon y Cajal postdoctoral fellowship (RYC-2013-14717).Peer ReviewedPostprint (author's final draft

SAFEXPLAIN: Safe and Explainable Critical Embedded Systems Based on AI

Deep Learning (DL) techniques are at the heart of most future advanced software functions in Critical Autonomous AI-based Systems (CAIS), where they also represent a major competitive factor. Hence, the economic success of CAIS industries (e.g., automotive, space, railway) depends on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost. However, there is a fundamental gap between Functional Safety (FUSA) requirements on CAIS and the nature of DL solutions. This gap stems from the development process of DL libraries and affects high-level safety concepts such as (1) explainability and traceability, (2) suitability for varying safety requirements, (3) FUSA-compliant implementations, and (4) real-time constraints. As a matter of fact, the data-dependent and stochastic nature of DL algorithms clashes with current FUSA practice, which instead builds on deterministic, verifiable, and pass/fail test-based software. The SAFEXPLAIN project tackles these challenges and targets by providing a flexible approach to allow the certification - hence adoption - of DL-based solutions in CAIS building on: (1) DL solutions that provide end-to-end traceability, with specific approaches to explain whether predictions can be trusted and strategies to reach (and prove) correct operation, in accordance to certification standards; (2) alternative and increasingly sophisticated design safety patterns for DL with varying criticality and fault tolerance requirements; (3) DL library implementations that adhere to safety requirements; and (4) computing platform configurations, to regain determinism, and probabilistic timing analyses, to handle the remaining non-determinism.The research leading to these results has received funding from the Horizon Europe Programme under the SAFEXPLAIN Project (www.safexplain.eu), grant agreement num. 101069595. BSC authors have also been supported by the Spanish Ministry of Science and Innovation under grant PID2019- 107255GBC21/AEI/10.13039/501100011033.Peer Reviewed"Article signat per 22 autors/es: Jaume Abella, Jon Perez, Cristofer Englund, Bahram Zonooz, Gabriele Giordana, Carlo Donzella, Francisco J. Cazorla, Enrico Mezzetti, Isabel Serra, Axel Brando, Irune Agirre, Fernando Eizaguirre, Thanh Hai Bui, Elahe Arani, Fahad Sarfraz, Ajay Balasubramaniam, Ahmed BadarIlaria Bloise, Lorenzo Feruglio, Ilaria Cinelli, Davide Brighenti, Davide Cunial"Postprint (author's final draft

Chemical and Metabolic Aspects of Antimetabolite Toxins Produced by Pseudomonas syringae Pathovars

Pseudomonas syringae is a phytopathogenic bacterium present in a wide variety of host plants where it causes diseases with economic impact. The symptoms produced by Pseudomonas syringae include chlorosis and necrosis of plant tissues, which are caused, in part, by antimetabolite toxins. This category of toxins, which includes tabtoxin, phaseolotoxin and mangotoxin, is produced by different pathovars of Pseudomonas syringae. These toxins are small peptidic molecules that target enzymes of amino acids’ biosynthetic pathways, inhibiting their activity and interfering in the general nitrogen metabolism. A general overview of the toxins’ chemistry, biosynthesis, activity, virulence and potential applications will be reviewed in this work

Rickettsia slovaca Infection: DEBONEL/TIBOLA

Producción CientíficaThis study describes the epidemiological, clinical, and microbiological characteristics of a new tick-borne disease in Spain—Dermacentor-borne necrosis erythema lymphadenopathy (DEBONEL). The clinical presentations include an eschar at the site of the tick bite, surrounded by an erythema and painful regional lymphadenopathy. The disease appears during the colder months and its vector is Dermacentor marginatus (D. marginatus). From January 1990 to December 2004, 54 patients presented at Hospital of La Rioja with these clinical and epidemiological data. The ratio of females to males was 32/22. The average age was 37 years. In all cases tick bites were located on the upper body (90% on the scalp). The median incubation period was 4.7 days. Signs and symptoms were mild in all cases. Only a small number of patients presented mild and nonspecific abnormalities in a complete blood cell count and mild elevation of erythrocyte sedimentation rates and C-protein reactive and liver enzyme levels. Serological evidence of acute rickettsiosis was observed in 19 patients (61%). In 29% sera tested by polymerase chain reactions (PCRs) were positive. The sequence obtained from a PCR product revealed 98% identity with Rickettsia sp. strains RpA4, DnS14, and DnS28. All ticks removed from patients were PCR-positive. Sequencing showed 8 of them identified as R. slovaca and 2 as Rickettsia sp. strains RpA4, DnS14, and DnS28

A Survey of Ocean Simulation and Rendering Techniques in Computer Graphics

This paper presents a survey of ocean simulation and rendering methods in computer graphics. To model and animate the ocean's surface, these methods mainly rely on two main approaches: on the one hand, those which approximate ocean dynamics with parametric, spectral or hybrid models and use empirical laws from oceanographic research. We will see that this type of methods essentially allows the simulation of ocean scenes in the deep water domain, without breaking waves. On the other hand, physically-based methods use Navier-Stokes Equations (NSE) to represent breaking waves and more generally ocean surface near the shore. We also describe ocean rendering methods in computer graphics, with a special interest in the simulation of phenomena such as foam and spray, and light's interaction with the ocean surface

Dendrímeros antigénicos sobre partículas de sílica para determinar IgE específicas de antibióticos betalactámicos

Los inmunoensayos empleados para el diagnóstico in vitro de alergias a antibióticos betalactámicos actualmente presentan una limitada sensibilidad y pobre especificidad, además de no estar disponibles de forma comercial para todos los antibióticos implicados en procesos alérgicos. En este trabajo, se han diseñado nanopartículas de sílica altamente funcionalizadas con dendrímeros y conjugadas con amoxicilina y bencilpenicilina con objeto de mejorar el diagnóstico clínico in vitro de alergia a estos antibióticos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech