Evaluation between methods for the color measurement in holograms by using a CMOS-RGB camera and a spectrometer

Many models and methods commonly used in colorimetry have been incorporated to the study and knowledge of the colorimetric properties in the reflection color holograms; these methods have reported the possibilities of color reproduction in holograms. One method is based in calculating the color differences between the CIE-L*a*b* coordinates of the original object compared to the same values obtained for the reconstructed hologram; these values are calculated through the measurement of the spectral composition of the light in the reproduced hologram which are made with spectrometers. Other methods are based in the use of cameras for the color measurement, although, they are not commonly used for that ending in holography. This work presents the results of a comparative study between the use of spectrometers and RGB digital cameras for the color measurement in holograms. The diffraction efficiency of the holograms for a GretagMacbeth Colorchecker samples is measured through a spectrometer and their CIE-L*a*b* coordinates are calculated; the color differences are also calculated by taking as theoretical values the coordinates of the original object. A similar procedure is made by capturing the reconstructed images of the hologram through a CMOSRGB camera, which requires a linearizing and characterizing procedure. The RGB coordinates of the original object are compared with the RGB coordinates of the reproduced hologram too. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.Universidad EAFI

Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps

Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.Postprint (published version

Deep matrix factorization approach for collaborative filtering recommender systems

Providing useful information to the users by recommending highly demanded products and services is a fundamental part of the business of many top tier companies. Recommender Systems make use of many sources of information to provide users with accurate predictions and novel recommendations of items. Here we propose, DeepMF, a novel collaborative filtering method that combines the Deep Learning paradigm with Matrix Factorization (MF) to improve the quality of both predictions and recommendations made to the user. Specifically, DeepMF performs successive refinements of a MF model with a layered architecture that uses the acquired knowledge in a layer as input for subsequent layers. Experimental results showed that the quality of both the predictions and recommendations of DeepMF overcome the baselines.This work has been supported by Spanish Ministry of Science and Education and Competitivity (MINECO) and European Regional Development Fund (FEDER) under grants TIN2017-85727-C4-3-P (DeepBio)

Multiple-fault detection methodology based on vibration and current analysis applied to bearings in induction motors and gearboxes on the kinematic chain

© 2016 Juan Jose Saucedo-Dorantes et al. Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral components of the faults. In this work, the influence of vibrations over the system is observed by performing motor current signal analysis to detect the presence of faults. The obtained results show the feasibility of detecting multiple faults in a kinematic chain, making the proposed methodology suitable to be used in the application of industrial machinery diagnosis.Postprint (published version

In Vitro Mutagenic and Genotoxic Assessment of a Mixture of the Cyanotoxins Microcystin-LR and Cylindrospermopsin

The co-occurrence of various cyanobacterial toxins can potentially induce toxic effects different than those observed for single cyanotoxins, as interaction phenomena cannot be discarded. Moreover, mixtures are a more probable exposure scenario. However, toxicological information on the topic is still scarce. Taking into account the important role of mutagenicity and genotoxicity in the risk evaluation framework, the objective of this study was to assess the mutagenic and genotoxic potential of mixtures of two of the most relevant cyanotoxins, Microcystin-LR (MC-LR) and Cylindrospermopsin (CYN), using the battery of in vitro tests recommended by the European Food Safety Authority (EFSA) for food contaminants. Mixtures of 1:10 CYN/MC-LR (CYN concentration in the range 0.04-2.5 µg/mL) were used to perform the bacterial reverse-mutation assay (Ames test) in Salmonella typhimurium, the mammalian cell micronucleus (MN) test and the mouse lymphoma thymidine-kinase assay (MLA) on L5178YTk± cells, while Caco-2 cells were used for the standard and enzyme-modified comet assays. The exposure periods ranged between 4 and 72 h depending on the assay. The genotoxicity of the mixture was observed only in the MN test with S9 metabolic fraction, similar to the results previously reported for CYN individually. These results indicate that cyanobacterial mixtures require a specific (geno)toxicity evaluation as their effects cannot be extrapolated from those of the individual cyanotoxins.España Ministerio de Economía y Competitividad AGL2015-64558-

Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions

The performance of speaker verification systems degrades when vocal effort conditions between enrollment and test (e.g., shouted vs. normal speech) are different. This is a potential situation in non-cooperative speaker verification tasks. In this paper, we present a study on different methods for linear compensation of embeddings making use of Gaussian mixture models to cluster shouted and normal speech domains. These compensation techniques are borrowed from the area of robustness for automatic speech recognition and, in this work, we apply them to compensate the mismatch between shouted and normal conditions in speaker verification. Before compensation, shouted condition is automatically detected by means of logistic regression. The process is computationally light and it is performed in the back-end of an x-vector system. Experimental results show that applying the proposed approach in the presence of vocal effort mismatch yields up to 13.8% equal error rate relative improvement with respect to a system that applies neither shouted speech detection nor compensation

Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis

This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.Postprint (published version

Multiscale modeling of prismatic heterogeneous structures based on a localized hyperreduced-order method

This work aims at deriving special types of one-dimensional Finite Elements (1D FE) for efficiently modeling heterogeneous prismatic structures, in the small strains regime, by means of reduced-order modeling (ROM) and domain decomposition techniques. The employed partitioning framework introduces “fictitious” interfaces between contiguous subdomains, leading to a formulation with both subdomain and interface fields. We propose a low-dimensional parameterization at both subdomain and interface levels by using reduced-order bases precomputed in an offline stage by applying the Singular Value Decomposition (SVD) on solution snapshots. In this parameterization, the amplitude of the fictitious interfaces play the role of coarse-scale displacement unknowns. We demonstrate that, with this partitioned framework, it is possible to arrive at a solution strategy that avoids solving the typical nested local/global problem of other similar methods (such as the FE method). Rather, in our approach, the coarse-grid cells can be regarded as special types of finite elements, whose nodes coincides with the centroids of the interfaces, and whose kinematics are dictated by the modes of the “fictitious” interfaces. This means that the kinematics of our coarse-scale FE are not pre-defined by the user, but extracted from the set of “training” computational experiments. Likewise, we demonstrate that the coarse-scale and fine-scale displacements are related by inter-scale operators that can be precomputed in the offline stage. Lastly, a hyperreduced scheme is considered for the evaluation of the internal forces, allowing us to deal with possible material nonlinearities.This work has received support from the Spanish Ministry of Economy and Competitiveness, through the “Severo Ochoa Programme for Centres of Excellence in R&D” (CEX2018-000797-S)”. A. Giuliodori also gratefully acknowledges the support of “Secretaria d’Universitats i Recerca de la Generalitat de Catalunya i del Fons Social Europeu” through the FI grant (00939/2020), and J.A. Hernández the support of, on the one hand, the European High-Performance Computing Joint Undertaking (JU) under grant agreement No. 955558 (the JU receives, in turn, support from the European Union’s Horizon 2020 research and innovation program and from Spain, Germany, France, Italy, Poland, Switzerland, Norway), and the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 952966 (project FIBREGY).Peer ReviewedPostprint (published version

Mutagénesis letal del virus de la hepatitis C

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de lectura: 23-10-2014RNA viruses exhibit high mutation rates during genome replication. Nucleotide analogues can increase the mutation rate of RNA viruses by acting as ambiguous substrates during replication. They have been explored as antiviral agents acting through lethal mutagenesis. Lethal mutagenesis, or virus extinction produced by enhanced mutation rates, is an antiviral strategy that aims at counteracting the adaptive capacity of viral quasispecies, avoiding selection of antiviral-escape mutants. Hepatitis C virus (HCV) is a RNA virus whose infections affect about 180 million people worldwide, and about 75% of newly infected patients progress towards a chronic infection, with a risk of severe liver disease. The main objective of this PhD thesis was to characterize the mechanism of anti-HCV activity produced by ribavirin in hepatoma Huh-7.5 in cell culture. The study led to the observation that guanosine can produce inhibition of HCV progeny production, and that inhibition is also related to a lethal mutagenic effect triggered by this nucleoside. Ribavirin is a recognised mutagenic agent for several other RNA viruses, but it is not clear whether it exerts its anti-HCV activity through mutagenesis or other mechanisms. In the present thesis we provide evidence of a mutagenic activity of ribavirin, documented by statistically significant increases of mutant spectrum complexity (as determinated by mutation frecuency, genetic distances and Shannon entropy), and a mutational bias in favor of G→A and C→U transitions. Both molecular cloning and Sanger sequencing, and ultra-deep pyrosequencing have been used for the analysis of HCV populations. Ribavirin treatment resulted in nucleotide imbalances (a reduction of intracellular GTP and an increase of UTP, ATP and CTP). Control experiments using mycophenolic acid and guanosine indicated that GTP depletion cannot explain ribavirin mutagenesis. Moreover, HCV extinction by ribavirin, but not by the non-mutagenic HCV inhibitor mycophenolic acid, occurred with decreases of specific infectivity, a feature typical of lethal mutagenesis. Thus, at least part of the antiviral activity of ribavirin on HCV in Huh-7.5 cells is exerted via lethal mutagenesis. Unexpectedly, guanosine which partially counteracted the inhibitory activity of ribavirin, inhibited HCV progeny production. We studied the effect of high concentrations of guanosine and other nucleotides on HCV and Huh-7.5 cells. Guanosine, but no the other nucleosides, produces a general decrease of the NTP/NDP ratios. Guanosine exerts its anti- HCV activity through mutagenesis, with a significant increase of the proportion of deletions and insertions, accompanied of a decrease of specific infectivity. This antiviral activity is not observed with other RNA viruses, and it constitutes the first example of a metabolite-induced lethal mutagenesis. The results open the way to study means to increase the efficacy of lethal mutagenesisbased treatments for HCV infections