Real-time detection of uncalibrated sensors using neural networks

Nowadays, sensors play a major role in several fields, such as science, industry and everyday technology. Therefore, the information received from the sensors must be reliable. If the sensors present any anomalies, serious problems can arise, such as publishing wrong theories in scientific papers, or causing production delays in industry. One of the most common anomalies are uncalibrations. An uncalibration occurs 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 is presented. This development integrates an artificial neural network as the main component which learns from the behavior of the sensors under calibrated conditions. Then, after being trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed system is able to detect the 100% of the presented uncalibration events, although the time response in the detection depends on the resolution of the model for the specific location, i.e., the minimum statistically significant variation in the sensor behavior that the system is able to detect. This architecture can be adapted to different contexts by applying transfer learning, such as adding new sensors or having different environments by re-training the model with minimum amount of dataEuropean Union (UE). H2020 VIMS Grant ID: 878757Ministerio de Ciencia, Innovación y Universidades PID2019-105556GB-C33 (MIND-ROB)European Union H2020 CHIST-ERA SMALL (PCI2019-111841-2

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

Brain injury MRI simulator based on theoretical models of neuroanatomic damage

In order to improve the body of knowledge about brain injury impairment is essential to develop image database with different types of injuries. This paper proposes a new methodology to model three types of brain injury: stroke, tumor and traumatic brain injury; and implements a system to navigate among simulated MRI studies. These studies can be used on research studies, to validate new processing methods and as an educational tool, to show different types of brain injury and how they affect to neuroanatomic structures

The Intragenesis and Synthetic Biology Approach towards Accelerating Genetic Gains on Strawberry: Development of New Tools to Improve Fruit Quality and Resistance to Pathogens

Under climate change, the spread of pests and pathogens into new environments has a dramatic effect on crop protection control. Strawberry (Fragaria spp.) is one the most profitable crops of the Rosaceae family worldwide, but more than 50 different genera of pathogens affect this species. Therefore, accelerating the improvement of fruit quality and pathogen resistance in strawberry represents an important objective for breeding and reducing the usage of pesticides. New genome sequencing data and bioinformatics tools has provided important resources to expand the use of synthetic biology-assisted intragenesis strategies as a powerful tool to accelerate genetic gains in strawberry. In this paper, we took advantage of these innovative approaches to create four RNAi intragenic silencing cassettes by combining specific strawberry new promoters and pathogen defense-related candidate DNA sequences to increase strawberry fruit quality and resistance by silencing their corresponding endogenous genes, mainly during fruit ripening stages, thus avoiding any unwanted effect on plant growth and development. Using a fruit transient assay, GUS expression was detected by the two synthetic FvAAT2 and FvDOF2 promoters, both by histochemical assay and qPCR analysis of GUS transcript levels, thus ensuring the ability of the same to drive the expression of the silencing cassettes in this strawberry tissue. The approaches described here represent valuable new tools for the rapid development of improved strawberry lines

Sistema de enfoque basado en dos espejos elípticos y un espejo plano rotatorio para un radar a 300 GHz

A focusing system for a 300 GHz radar with two target distances (5m and 10m) is proposed, having 1cm resolution in both cases. The focusing system is based on a gaussian telescope scheme and it has been designed using gaussian beam quasi-optical propagation theory with a homemade Matlab analysis tool. It has been translated into a real focusing system based on two elliptical mirrors and a plane mirror in order to have scanning capabilities and validated using the commercial antenna software GRAS

Mimicking the bioelectrocatalytic function of recombinant CotA laccase through electrostatically self-assembled bioconjugates

Unprecedented 3D nanobiosystems composed of recombinant CotA laccases and citrate-stabilised gold nanoparticles have been successfully achieved by an electrostatic self-assembly strategy. The bioelectrochemical reduction of O2 driven by CotA laccase at the spore coat was mimicked. Consequently key insights into its bioelectrocatalytic function were unravelled

Adaptive robust control and admittance control for contact-driven robotic surface conditioning

[EN] This work presents a hybrid position/force control of robots for surface contact conditioning tasks such as polishing, profiling, deburring, etc. The robot force control is designed using sliding mode ideas to benefit from robustness. On the one hand, a set of equality constraints are defined to attain the desired tool pressure on the surface, as well as to keep the tool orientation perpendicular to the surface. On the other hand, inequality constraints are defined to adapt the tool position to unmodeled features present in the surface, e.g., a protruding window frame. Conventional and non-conventional sliding mode controls are used to fulfill the equality and inequality constraints, respectively. Furthermore, in order to deal with sudden changes of the material stiffness, which are forwarded to the robot tool and can produce instability and bad performance, adaptive switching gain laws are considered not only for the conventional sliding mode control but also for the non-conventional sliding mode control. A lower priority tracking controller is also defined to follow the desired reference trajectory on the target surface. Moreover, the classical admittance control typically used in force control tasks is adapted for the proposed surface contact application in order to experimentally compare the performance of both control approaches. The effectiveness of the proposed method is substantiated by experimental results using a redundant 7R manipulator, whereas its advantages over the classical admittance control approach are experimentally shown.This work was supported in part by the Spanish Government under the Project DPI2017-87656-C2-1-R and the Generalitat Valenciana under Grants VALi+d APOSTD/2016/044 and BEST/2017/029.Solanes Galbis, JE.; Gracia Calandin, LI.; Muñoz-Benavent, P.; Esparza Peidro, A.; Valls Miro, J.; Tornero Montserrat, J. (2018). Adaptive robust control and admittance control for contact-driven robotic surface conditioning. Robotics and Computer-Integrated Manufacturing. 54:115-132. https://doi.org/10.1016/j.rcim.2018.05.003S1151325

Dysfunctional 3D model based on structural and neuropsychological information

Acquired brain injury (ABI) 1-2 refers to any brain damage occurring after birth. It usually causes certain damage to portions of the brain. ABI may result in a significant impairment of an individuals physical, cognitive and/or psychosocial functioning. The main causes are traumatic brain injury (TBI), cerebrovascular accident (CVA) and brain tumors. The main consequence of ABI is a dramatic change in the individuals daily life. This change involves a disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges in neurorehabilitation is to obtain a dysfunctional profile of each patient in order to personalize the treatment. This paper proposes a system to generate a patient s dysfunctional profile by integrating theoretical, structural and neuropsychological information on a 3D brain imaging-based model. The main goal of this dysfunctional profile is to help therapists design the most suitable treatment for each patient. At the same time, the results obtained are a source of clinical evidence to improve the accuracy and quality of our rehabilitation system. Figure 1 shows the diagram of the system. This system is composed of four main modules: image-based extraction of parameters, theoretical modeling, classification and co-registration and visualization module

Neuroanatomic-based detection algorithm for automatic labeling of brain structures in brain injury

The number and grade of injured neuroanatomic structures and the type of injury determine the degree of impairment after a brain injury event and the recovery options of the patient. However, the body of knowledge and clinical intervention guides are basically focused on functional disorder and they still do not take into account the location of injuries. The prognostic value of location information is not known in detail either. This paper proposes a feature-based detection algorithm, named Neuroanatomic-Based Detection Algorithm (NBDA), based on SURF (Speeded Up Robust Feature) to label anatomical brain structures on cortical and sub-cortical areas. Themain goal is to register injured neuroanatomic structures to generate a database containing patient?s structural impairment profile. This kind of information permits to establish a relation with functional disorders and the prognostic evolution during neurorehabilitation procedures

Growth of nanocolumnar thin films on patterned substrates at oblique angles

The influence of one dimensional substrate patterns on the nanocolumnar growth of thin films deposited by magnetron sputtering at oblique angles is theoretically and experimentally studied. A well‐established growth model has been used to study the interplay between the substrate topography and the thin film morphology. A critical thickness has been defined, below which the columnar growth is modulated by the substrate topography, while for thicknesses above, the impact of substrate features is progressively lost in two stages; first columns grown on taller features take over neighboring ones, and later the film morphology evolves independently of substrate features. These results have been experimentally tested by analyzing the nanocolumnar growth of SiO2 thin films on ion‐induced patterned substrates.University of Seville: V PPIUSUniversity of Seville: VI PPIT-USEuropean Development Funds program (EU-FEDER) / Spanish Ministry of Economy and Competitiveness and Agencia Estatal de Investigación (AEI) : MAT2013-40852-REuropean Development Funds program (EU-FEDER) / Spanish Ministry of Economy and Competitiveness and Agencia Estatal de Investigación (AEI) : MAT2016- 79866-REuropean Development Funds program (EU-FEDER) / Spanish Ministry of Economy and Competitiveness and Agencia Estatal de Investigación (AEI) : MAT2015-69035-REuropean Development Funds program (EU-FEDER) / Spanish Ministry of Economy and Competitiveness and Agencia Estatal de Investigación (AEI) : MAT2015-69035-REDCEuropean Development Funds program (EU-FEDER) / Spanish Ministry of Economy and Competitiveness and Agencia Estatal de Investigación (AEI) : MAT2017-85089-C2-1- RComunidad Autónoma de Madrid S2013/MIT-3029Comunidad Autónoma de Madrid IND2017/IND766