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One of the problems that most affect hospitals is infections by pathogenic microorganisms. Rapid identification and adequate, timely treatment can avoid fatal consequences and the development of antibiotic resistance, so it is crucial to use fast, reliable, and not too laborious techniques to obtain quick results. Raman spectroscopy has proven to be a powerful tool for molecular analysis, meeting these requirements better than traditional techniques. In this work, we have used Raman spectroscopy combined with machine learning algorithms to explore the automatic identification of eleven species of the genus Candida, the most common cause of fungal infections worldwide. The Raman spectra were obtained from more than 220 different measurements of dried drops from pure cultures of each Candida species using a Raman Confocal Microscope with a 532 nm laser excitation source. After developing a spectral preprocessing methodology, a study of the quality and variability of the measured spectra at the isolate and species level, and the spectral features contributing to inter-class variations, showed the potential to discriminate between those pathogenic yeasts. Several machine learning and deep learning algorithms were trained using hyperparameter optimization techniques to find the best possible classifier for this spectral data, in terms of accuracy and lowest possible overfitting. We found that a one-dimensional Convolutional Neural Network (1-D CNN) could achieve above 80 % overall accuracy for the eleven classes spectral dataset, with good generalization capabilities. This work was supported by the R + D projects INNVAL19/17 (funded by Instituto de Investigación Valdecilla-IDIVAL), PID2019-107270RB-C21 (funded by MCIN/ AEI /10.13039/501100011033) and by Plan Nacional de I + D + and Instituto de Salud Carlos III (ISCIII), Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Ciencia, Innovación y Universidades, Spanish Network for Research in Infectious Diseases (REIPI RD16/0016/0007), CIBERINFEC (CB21/13/00068), CIBER-BBN (BBNGC1601), cofinanced by European Development Regional Fund “A way to achieve Europe”. A. A. O.-S was financially supported by the Miguel Servet II program (ISCIII-CPII17-00011).

The Capbreton Canyon System is an area currently under study for its proposal as a Site of Community Importance under the EU Habitats Directive in the context of the LIFE IP INTEMARES project. Identifying and mapping benthic Vulnerable Marine Ecosystems (VMEs) plays a key role in this process. Although obtaining information on species distribution in deep sea rocky habitats has traditionally been a complicated task, the development of underwater remote sensing techniques resulted in a massive increase in the collection of digital imagery; however, processing all this information has led to another bottleneck due to the time-consuming nature of biota manual annotation. At this point, the use of computer vision and deep learning to automate image processing has substantial benefits but has rarely been adopted within the field of marine ecology. This study presents the integration of deep learning techniques for benthic fauna identification, high resolution multibeam echosounder (MBES) data and Species Distribution Models (SDMs), to map the potential habitat of the yellow coral Dendrophyllia cornigera, a representative species of the VME 1170 Reef habitat, on the circalitoral area of the Capbreton Canyon System. The localization and identification of the coral colonies was based on more than 7500 photographs taken during the INTEMARES-CapBreton 0619 and 0620 surveys using the photogrammetric ROTV Politolana. For the automatic annotation of the image set a deep learning based framework was developed by testing two different deep neural networks architectures; a FasterRCNN+Resnet101 model, accomplishing a precision of 100% over human expert annotation for presence/absence discrimination, was selected. Environmental data included different quantitative terrain attributes derived from high resolution MBES bathymetry data. A presence-only species distribution model, Maximum Entropy (MaxEnt), was used to infer the spatial distribution of D. cornigera over the study area. Predicted occurrences corresponded mainly to relevant topographic structures with significant slope, mainly associated to the edge of the continental shelf. These results are consistent with the ecological knowledge on the species and validate the use of deep learning tools to assist in the identification and mapping of VME for management and conservation purposes. This study provides a baseline for the protection of vulnerable habitats of the Capbreton Canyon System in the context of the Natura 2000 Network. The authors would like to thank the crew and scientific team aboard the R/V Ramón Margalef from the Spanish Institute of Oceanography and also appreciate the helpful assistance of the technicians of the ROTV Politolana for their skill in executing the dangerous visual transects very close to the rough bottoms of the study area. This research has been performed in the scope of the INTEMARES project. INTEMARES was partially funded by the European Commission LIFE + “Nature and Biodiversity” call (LIFE15 IPE ES 012). The Biodiversity Foundation of the Spanish Ministry for Ecological Transition, was the institution responsible for coordination this project. Deep-learning advances presented here are part of Deep-RAMP (Deep learning to improve the management of marine protected area network in the North Atlantic region) project funded in the frame of the Pleamar Program of the Biodiversity Foundation and is co-financed by the European Maritime and Fisheries Fund (EMFF).

The appearance of defects during the manufacture of tanker trucks via arc-welding is a significant problem in the industry. A reliable low-cost and non-destructive on-line method could aid the discovery of solutions to overcome productivity problems. Plasma optical spectroscopy was employed in this study to correlate the presence of several elemental emission lines with certain quality-related events, such as the appearance of porosities. Results obtained through field trials and also during in-line production show that the convenient processing of acquired process signals facilitates not only the detection of defects, but also the identification of their causes. Output monitoring signals have been compared with X-ray inspections of the seam welds performed. It has been demonstrated that the spectroscopic monitoring variables obtained are good indicators for evaluating contamination in the process and therefore the occurrence of welding defects. This work has been co-supported by the project PID2019-107270RB-C21 funded by MCIN/AEI/10.13039/501100011033. Ministerio de Ciencia e Innovación (Spanish Government).

Recovery of heavy metals from industrial waste streams is becoming an important challenge due to increasing wastewater production. Chelating resins provide an effective treatment for selective adsorption of metals. Several analytical techniques can be used to assess the adsorption performance, but LIBS offers the unique advantage of in-process continuous monitoring. In this work, LIBS measurements of copper and nickel spectra were performed on single and bi-component resin samples. Single component calibration curves for copper and nickel were obtained with high correlation (R2 = 0.99 and R2 = 0.98 respectively). Bi-component resin measurements reveal good prediction accuracy for nickel and copper concentrations, the mean relative error in concentration prediction being 4.69% and 7.98% respectively. From the calibration curves, the prediction of concentration shows a high correlation with the real values (R2 = 0.99 for copper and 0.98 for nickel). This research was developed in the framework of the projects TEC2016-76021-C2-2-R (AEI/FEDER, UE), PID2019-107270RB-C21/AEI/10.13039/501100011033, CTM2017-87740-R, RTI2018-093310-B-I00 and grant BES-2017-080076, all financed by the Spanish Ministry of Science, Innovation and Universities.

3D reconstruction and virtual reality (VR) technology provide many opportunities for the documentation and dissemination of underwater cultural heritage. Advances in the development of underwater exploration technology have allowed for the first time to accurately reconstruct a complete 3D model of the cargo Río Miera in the Cantabrian Sea. Sunk on December 6, 1951 after a strong collision, the cargo ship Río Miera rests on a sandy bottom about 40 meters deep, very close to the Cantabrian coast. Located in an area of strong currents is a classic objective of the region for the most experienced divers. The survey was carried out this summer in R/V Ramón Margalef of the IEO, acquiring acoustic data with multibeam echo sounders and hundreds of images acquired by a remotely piloted underwater vehicle. The campaign is part of the PhotoMARE project - Underwater Photogrammetry for MArine Renewable Energy. This work describes the workflow regarding the survey, images and acoustic data acquisition, data processing, optic 3D point cloud color enhancement and acoustic and optic dataset merging procedure to obtain a complete 3D model of wreck Río Miera in Cantabrian Sea. Through this project, Spanish Institute of Oceanography - IEO have advanced - combining acoustic and image methods - in the generation of 3D models of archaeological sites and submerged structures. Survey funded by PhotoMARE Project. PhotoMARE - Underwater Photogrammetry for MArine Renewable Energy, was selected as part of the call for grants of the Program I + C = + C 2016, Support for R & D projects in cooperation in Marine Renewable Energies by SODERCAN (Sociedad para el Desarrollo Regional de Cantabria). The authors would like to thank the crew and scientific team aboard the RV Ramón Margalef from the Spanish Institute of Oceanography, for their help with the objectives of the PhotoMARE2018 survey. Thanks to COMPLUTIG for its contribution to the achievement of project objectives.

The management of spent acids containing heavy metals has been traditionally carried out by neutralization-precipitation technologies that often fail to provide the required selectivity for metal recovery. This work proposed a split elution process using H2SO4 and NH4OH solutions to regenerate bispicolylamine-based chelating resins (Puromet™ MTS9600) employed in a previous work to separate nickel and copper from spent sulfuric acid effluents. Operation variables namely S/L ratio, concentration of the regeneration agents and process configuration were analysed to select the best conditions to optimize the metal unloading and their selective recovery in independent solutions. It was found that 43% of nickel was eluted from the resin by H2SO4 2.0 M and recovered with purities of 98% while 47% of copper was desorbed with NH4OH 2.0 M and recovered with purities of 97%. The long-term assessment evidenced that copper removal and metal recovery were not affected after 10 adsorption-regeneration cycles, while the nickel adsorption efficacy decreased about 10%. This research was developed in the framework of the projects CTM2017-87740-R (Spanish Ministry of Science, Innovation and Universities), RTI2018-093310-B-I00 (Spanish Ministry of Science, Innovation and Universities), TEC2016-76021-C2-2-R) (AEI/FEDER, UE & PID2019-107270RB-C21/AEI/10.13039/501100011033), and grant BES-2017-080076.

This works deals with the synthesis of Cobalt doped MoO 3 thin films which were grown on glass substrates by the spray pyrolysis at 460 °C. First, X-ray diffraction analysis shows an orthorhombic structure related to α-MoO 3 allotropic variety with (020) and (040) preferred orientations. The surface topography performed by atomic force microscopy (AFM) shows that the grain size varies from 150 to 280 nm. Second, optical parameters, such as optical band gap, Urbach energy, refractive index and dielectric constants were studied in terms of Co content. This optical study shows a direct transition of all prepared thin films and normal dispersion of the refractive index showing both Cauchy and Wemple & Di-dominico variations. On the other hand, PL measurements show transition bands mainly in blue and green domains related to band-to-band transitions as well as to oxygen vacancy in all films. Finally, magnetic measurements at room temperature using vibrating sample magnetometer (VSM) technique reveal a ferromagnetic behavior of such doped films.

Combined shell growth pattern and oxygen isotope analysis has become a powerful approach in palaeoclimate and archaeological studies for reconstructing palaeoclimate conditions and littoral exploitation patterns, respectively. Recent investigations have shown that the gastropod Phorcus lineatus (da Costa, 1778) forms its shell in conditions of near equilibrium with the oxygen isotope signature of the seawater environment, demonstrating the utility of this species for reconstruction of sea surface temperature and determination of the season of harvest in archaeological studies. In contrast, the shell growth patterns of this species have received virtually no attention despite providing information on the rate and timing of shell growth that is crucial for correctly interpreting environmental proxies derived from shell geochemistry. In this paper, we compare microgrowth patterns and isotopic profiles of four modern individuals of the gastropod P. lineatus from northern Iberia to determine the timing and periodicity of subannual growth markers within the shells. Results of this sclerochronological study showed the presence of two types of growth lines/increments: i) large-scale accretionary units formed with variable periodicity, and ii) small-scale accretionary units formed by micro growth lines and increments determined by semidiurnal tidal cycles. Results suggest that shells grew uninterruptedly during early ontogeny. However, older specimens exhibited growth cessation/slowdown during summer and winter/spring. Therefore, shell growth rate is not only controlled by environmental conditions, but also by ontogenetic age and/or endogenous rhythms. A high correlation was found between seawater temperature derived from shell oxygen isotopes and instrumental seawater temperature (r2 = 0.88–0.98; p-values

The presence of gorgonian forests and deep-sea sponge aggregations in the Le Danois Bank promoted its declaration as the “El Cachucho” Marine Protected Area (MPA) by the Spanish Ministry of Environment, and its inclusion in the Natura 2000 network. Both habitats are considered vulnerable, so follow-up surveys are being performed to monitor their conservation in compliance with the EU Habitats Directive. The use of a non-invasive methodology, which does not cause damage or alterations on benthic communities, is particularly necessary in vulnerable ecosystem studies and MPA monitoring. This study analyzed the assemblage structure of a Placogorgia sp. population using a 3D photogrammetry-based method. The study was carried out through the analysis of the video transects obtained at the Le Danois Bank, using the Politolana underwater towed vehicle during the July 2017 ECOMARG survey. Recent developments in specific software of photogrammetric image analysis allowed extracting valuable information from these video transects. Using the Pix4D Mapper Pro software, 3D point clouds were obtained, and the size and morphometry of yellow fan-shaped gorgonian population structure could be evaluated. Due to gorgonian's high structural complexity, the use of length (i.e. height) as the morphometric descriptor of the real size of the colonies is not appropriate. Instead of length, the fan surface area covered by each gorgonian colony was selected as a suitable parameter of size. The direct measurement of this parameter was possible through a complete 3D reconstruction of the gorgonian forest. A total of 426 colonies of Placogorgia sp. were digitalized to obtain surface measurements and fan spread orientation calculations in 3D models. The results show that gorgonian populations were mostly composed of a high proportion of small colonies (0–0.10 m2). The population structure distribution shows a high proportion (~27%) of recruits (0.5 m2). In 78% of the gorgonian colonies, facing angles were grouped inside the first quadrant (0°-90°), in accordance with the main current direction in this zone. Colony distribution and fan orientation inside the gorgonian forest can be used as data sources to improve monitoring and management programs of these unique habitats in MPAs., Sí

Biogenic carbonate mollusc shells have the unique property of being a durable material found in many archaeological and geological sites, recording in their shell chemical composition the ambient environmental conditions during the mollusc's lifespan. In particular, mollusc shell Mg/Ca ratios have been suggested to be related to seawater temperature, although such a relationship is controversial and appears to be species- and even location-specific. This study investigates the use of Laser-Induced Breakdown Spectroscopy (LIBS) for the rapid measurement of Mg/Ca profiles within Patella vulgata shells, via comparison with one established analytical technique that is most often used for this purpose, ICP-OES. LIBS offers some advantages over other spectrometric techniques, including ICP-OES, the latter requiring initial micromilling of sample powders. LIBS offers faster measurement, reduced sample preparation, easier automation and less complex and lower cost instrumentation. A high correlation is evident between LIBS and ICP-OES Mg/Ca profiles within four archaeological P. vulgata shells, as well as strong similarities between LIBS measurements made in two different areas of each P. vulgata shell (i.e. the apex and a more conventional transect along the axis of shell growth). Validation of the LIBS technique for determination of Mg/Ca profiles within P. vulgata shells has implications for archaeological studies, because a greater number of shell specimens sampled from each archaeological site and chronological level can be measured, thereby improving the statistical robustness of data interpretation and conclusions. One example archaeological application that would benefit from application of the LIBS technique is identification of the season-of-capture of marine molluscs as a food resource for prehistoric societies. This research was performed as part of the projects “TRACECHANGE: Tracing Climatic Abrupt Change Events and its Social Impact during the Late Pleistocene and Early Holocene (15-7 ky cal BP) (HAR2013-46802-P)”, and “Photonics Sensors for Safety & Security (SENSA)” (TEC2016-76021-C2-2-R), both funded by the Spanish Ministry of Economy and Competitiveness. A. García-Escárzaga’s work was funded by the University of Cantabria through a predoctoral grant and I. Gutiérrez-Zugasti’s work was funded by the Spanish Ministry of Economy and Competitiveness through a Juan de la Cierva grant (grant JCI-2012-12094).

A new optical fiber sensor for monitoring the compaction pressure of a composite material during the molding process is designed and experimentally demonstrated. The sensor is based on a Fiber Bragg Grating embedded into a composite material, and it is able to measure the compaction pressure during the manufacturing process and the strain information while the structure is in operation. The proposed design has been characterized before its embodiment, showing a logarithmic response to the pressure. The sensor also exhibits a linear response to both strain and temperature, making it suitable for structural health monitoring applications.

Human populations exploited coastal areas with intensity during the Mesolithic in Atlantic Europe, resulting in the accumulation of large shell middens. Northern Spain is one of the most prolific regions, and especially the so-called Asturian area. Large accumulations of shellfish led some scholars to propose the existence of intensification in the exploitation of coastal resources in the region during the Mesolithic. In this paper, shell remains (molluscs, crustaceans and echinoderms) from stratigraphic units 114 and 115 (dated to the early Mesolithic c. 9 kys cal BP) at El Mazo cave (Asturias, northern Spain) were studied in order to establish resource exploitation patterns and environmental conditions. Species representation showed that limpets, top shells and sea urchins were preferentially exploited. One-millimetre mesh screens were crucial in establishing an accurate minimum number of individuals for sea urchins and to determine their importance in exploitation patterns. Environmental conditions deduced from shell assemblages indicated that temperate conditions prevailed at the time of the occupation and the morphology of the coastline was similar to today (rocky exposed shores). Information recovered relating to species representation, collection areas and shell biometry reflected some evidence of intensification (reduced shell size, collection in lower areas of exposed shores, no size selection in some units and species) in the exploitation of coastal resources through time. However, the results suggested the existence of changes in collection strategies and resource management, and periods of intense shell collection may have alternated with times of shell stock recovery throughout the Mesolithic. This research was performed as part of the project “The human response to the global climatic change in a littoral zone: the case of the transition to the Holocene in the Cantabrian coast (10,000–5000 cal BC) (HAR2010-22115-C02-01)” funded by the Spanish Ministry of Economy and Competitiveness. AGE was funded by the University of Cantabria through a predoctoral grant and IGZ was funded by the Spanish Ministry of Economy and Competitiveness through a Juan de la Cierva grant. We also would like to thank the University of Cantabria and the IIIPC for providing support, David Cuenca-Solana, Alejandro García Moreno and Lucia Agudo Pérez for their help. We also thank Jennifer Jones for correcting the English. Comments from two anonymous reviewers helped to improve the paper.

The chemical composition of mollusk shells offers information about environmental conditions present during the lifespan of the organism. Shells found in geological deposits and in many archeological sites can help to reconstruct past climatic conditions. For example, a correlation has been found between seawater temperature and the amount of some substituent elements (e.g., magnesium, strontium) in the biogenerated calcium carbonate matrix of the shell, although it is very species-specific. Here we propose the use laser-induced breakdown spectroscopy (LIBS) to estimate Mg/Ca ratios in modern specimens of the common limpet Patella vulgata. An automated setup was used to obtain a sequence of Mg/Ca ratios across a sampling path that could be compared with the seawater temperatures recorded during the organism's lifespan. Results using four shells collected in different months of the year showed a direct relationship between the Mg/Ca ratios and the seawater temperature, although the sequences also revealed small-scale (short-term) variability and an irregular growth rate. Nevertheless, it was possible to infer the season of capture and the minimum and maximum seawater temperatures from the LIBS sequences. This fact, along with the reduction in sampling and measurement time compared with other spectrometric techniques (such as inductively coupled plasma mass spectrometry [ICP-MS]), makes LIBS useful in paleoclimatic studies. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. This research was part of the projects TEC2013-47264-C2-1-R and HAR2013-46802-P, both funded by the Spanish Ministry of Economy and Competitiveness. Asier García-Escázaga was funded by the University of Cantabria through a predoctoral grant and Igor Gutierrez-Zugasti was supported by the Juan de la Cierva programme (JCI-2012-12094) funded by the Spanish Ministry of Economy and Competitiveness.

A technique to enhance the spatial resolution of typical fiber Bragg grating–based strain gauges has been experimentally verified in this study. Just analyzing the intensity of the reflection spectrum of a sampled fiber Bragg grating, its inner deformation profile has been obtained with a spatial resolution of Formula L

Defect classification in on-line welding quality monitoring systems is an active area of research with a significant relevance to several industrial sectors where welding processes are extensively employed. Approaches based on some artificial intelligence implementations, like Artificial Neural Networks or Fuzzy Logic have been attempted, but their impact in real industrial scenarios is nowadays rather modest. In this paper a new approach based on Hierarchical Temporal Memories and the acquired plasma spectra is explored and analyzed by means of several arc-welding experimental tests. Results show the ability of the proposed solution to perform a suitable classification among several weld perturbations. The search for an optimal configuration of the algorithm and the usefulness of both spatial (spectral) and temporal identification of patterns will be also discussed, and the results will be compared with those provided by a solution based on feature selection and neural networks, exhibiting the better performance of the HTM model in terms of performance and handling of the input data.

In this paper, a processing scheme based on the morphological similarities of speckle patterns is proposed to extend the dynamic range of Fiber Specklegram Sensors (FSS). The method has been applied to a low cost FSS demonstrating its good performance to extend the dynamic range of strain measurements. The scheme analyzes the pattern energy distribution to establish the current modal state of the FSS and employs the correlation value with the reference modal state specklegram to determine the fine strain measurements. The achieved results exhibit a very high performance, making this low cost technology applicable to structural monitoring.

A new bonding sensor for composite material manufacturing based on a single uniform Fiber Bragg Grating (FBG) is proposed and demonstrated in this paper. The sensor design includes two edge sections attached to the bonded parts and a central free region, all made of an epoxy resin block of constant thickness, where the FBG is embedded. The quality of this bond is quantified by measuring the FBG spectral evolution. The strain transfer from the sensor to the embedded fiber allows to distinguish between two typical bonding defects in composite structures: asymmetric deformation and slide of bonded parts. Both cases have been experimentally checked and the results are in good agreement with simulations based on a simplified model using the Finite Elements Analysis (FEA) and Transfer Matrix methods, even under complex deformations.

The identification and intensity estimation of some aluminium emission lines have been proposed to perform an on-line quantification of the Al contribution to the laser-welding process of Usibor blanks. This boron steel is protected by an Al-Si coating that is removed by laser ablation before welding. If this process fails to remove Al from the joint surface, its contribution may affect the final properties of the resulting seams, therefore compromising their quality. Experimental tests have been performed, some of them in a real production scenario. They have been analysed and compared to the results of welding test specimens, analysis of the associated tensile properties and fracture locations and seam macrographs. These studies have indicated that on-line quantification of the Al contribution to the process is feasible and that a correlation can be established between the Al content estimated in real-time and the results derived from the off-line tests considered. The authors would like to thank the staff of Autotech Engineering and Solblank (both Gestamp companies) for their valuable help during the design, implementation and test of the monitoring system. This work has been supported by the project TEC2013- 47264-C2-1-R

In this paper, a Fiber Specklegram Sensor (FSS) based on Plastic Optical Fiber (POF) has been proposed with a reflection-based configuration. A HeNe laser is launched through a coupler, reflected by a mirrored fiber end and detect the specklegram sequence using a commercial camera. Different sensor performance metrics have been analyzed in terms of final sensitivity to external vibration applied to the fiber. The results confirm the performance reduction of the reflection-based scheme in comparison with the transmission-based approach but it can be also employed as sensing system where only a single fiber end is available. This work has been supported by the project TEC2013-47264-C2-1-R.

In this paper an analysis on the suitability of employing a colorimetric analysis of the acquired plasma spectra to perform on-line arc-welding quality monitoring will be discussed. Different colorimetric parameters like the color temperature or the parameters associated with the hue, saturation, luminance color space will be evaluated in comparison with the standard approach based on the estimation of the plasma electronic temperature. This approach does not require the identification of the emission lines involved in the analysis, thus giving rise to a more efficient solution in terms of computational efficiency, and avoiding unambiguous identifications that may give rise to incorrect results. In particular, experimental tests performed with a tungsten inert gas arc-welding process will show the feasibility of using the proposed solution and its ability to perform an online detection of different welding perturbations. This work has been supported by the project TEC2013-47264-C2-1-R.

The recently proposed hierarchical temporal memory (HTM) paradigm of soft computing is applied to the detection and classification of foreign materials in a conveyor belt carrying tobacco leaves in a cigarette manufacturing industry. The HTM has been exposed to hyperspectral imaging data from 10 types of unwanted materials intermingled with tobacco leaves. The impact of the HTM architecture and the configuration of internal parameters on its classification performance have been explored. Classification results match or surpass those attained with other methods, such as Artificial Neural Networks (ANNs), with the advantage that HTM are able to handle raw spectral data and no preprocessing, spectral compression, or reflectance correction is required. It is also demonstrated that an optimized configuration of the HTM architecture and internal values can be derived from the statistical properties of the hyperspectral data, allowing the extension of the approach to other classification problems.

In a previous contribution, a welding diagnostics approach based on plasma optical spectroscopy was presented. It consisted of the employment of optimization algorithms and synthetic spectra to obtain the participation profiles of the species participating in the plasma. A modification of the model is discussed here: on the one hand the controlled random search algorithm has been substituted by a particle swarm optimization implementation. On the other hand a feature selection stage has been included to determine those spectral windows where the optimization process will take place. Both experimental and field tests will be shown to illustrate the performance of the solution that improves the results of the previous work. This work has been supported by the TEC2010-20224-C02-02 and OPENAER CENIT 2007–2010 projects.

Welding processes are one of the most widespread industrial activities, and their quality control, in both online and offline methods, is an important area of research. In the particular process of laser welding of boron steel with aluminium-silicon covering in the automotive industry, one problem is the presence of residual traces from the protective antioxidant coating, an aluminium-silicon alloy, which can result in a significant reduction of the welding seam strength. This work proposes a sensor system based on a laser induced breakdown spectroscopy (LIBS) setup to detect and discriminate aluminium residues in the welding area without destroying the sample before the welding procedure. A spectral algorithm based on support vector machines (SVMs) is used as a classifier to automatically identify areas with aluminum presence.

In this paper a detailed study on the ability of a system based on photodiodes to detect a wide variety of weld defects is presented. Narrow optical filters have been included within the setup to analyse their influence in the resulting monitoring signal and a direct comparison to the performance of the spectrometer has been also considered. The results obtained during the experimental tests indicate that the use of these filters clearly improves the resulting monitoring signals provided by the photodiodes, suggesting that a photodiode-based setup could become an alternative to the employment of spectrometers in some particular scenarios.

Structural Health Monitoring (SHM) can be understood as the integration of sensing and intelligence to enable the structure loading and damage-provoking conditions to be recorded, analyzed, localized, and predicted in such a way that nondestructive testing becomes an integral part of them. In addition, SHM systems can include actuation devices to take proper reaction or correction actions. SHM sensing requirements are very well suited for the application of optical fiber sensors (OFS), in particular, to provide integrated, quasi-distributed or fully distributed technologies. In this tutorial, after a brief introduction of the basic SHM concepts, the main fiber optic techniques available for this application are reviewed, emphasizing the four most successful ones. Then, several examples of the use of OFS in real structures are also addressed, including those from the renewable energy, transportation, civil engineering and the oil and gas industry sectors. Finally, the most relevant current technical challenges and the key sector markets are identified. This paper provides a tutorial introduction, a comprehensive background on this subject and also a forecast of the future of OFS for SHM. In addition, some of the challenges to be faced in the near future are addressed.

Plasma optical spectroscopy is widely employed in on-line welding diagnostics. The determination of the plasma electron temperature, which is typically selected as the output monitoring parameter, implies the identification of the atomic emission lines. As a consequence, additional processing stages are required with a direct impact on the real time performance of the technique. The line-to-continuum method is a feasible alternative spectroscopic approach and it is particularly interesting in terms of its computational efficiency. However, the monitoring signal highly depends on the chosen emission line. In this paper, a feature selection methodology is proposed to solve the uncertainty regarding the selection of the optimum spectral band, which allows the employment of the line-to-continuum method for on-line welding diagnostics. Field test results have been conducted to demonstrate the feasibility of the solution.

The validity of a processing technique, based on feature selection and artificial neural networks, when applied to arc-welding on-line quality monitoring is analyzed. An optical fiber embedded within the welding torch captures the plasma radiation, which is delivered to a CCD spectrometer. In the proposed solution the spectral analysis is performed in two stages: dimensionality reduction is accomplished using a feature selection algorithm and, after that, an artificial neural network carries out the spectral identification task. The validity of the technique has been successfully demonstrated by means of several experimental welding tests.

A non-intrusive and non-contact system for the real time detection of spurious elements in raw material processing chains for industrial environments is presented. Observation line spectrographs, obtained from the visible–near infrared (Vis–NIR) reflectance of the material under study, are obtained using a dual spatial-spectral technique. Therefore, a huge amount of information is involved in the process and an efficient classification algorithm would be a great asset. In this paper, the validity of Fisher's linear discriminant for this application is investigated. The technique has been successfully tested on the tobacco industry. However, the technique is not limited to tobacco leaves, but other materials can additionally be discriminated or classified.

In a previous paper a new technique was proposed to allow real-time, online operation for arc-welding quality assurance based on plasma spectroscopy. In this paper, the proposed system has been used to determine the appearance of weld defects in the arc-welding nuclear steam generator tube-to-tubesheet process. The system was implemented in the facilities of Equipos Nucleares S.A. (ENSA), where several welding tests were performed on weld test coupons. Results will show the feasibility of the proposed system to be used in a real industrial scenario, presenting successful examples of weld defect detections.

A novel system which allows arc-welding defect detection and classification is presented in this paper. The spectroscopic analysis of the plasma spectra produced during the welding process is a well-known technique to monitor the quality of the resulting weld seams. The analysis of specific emission lines and the subsequent estimation of the electronic temperature T e profile offers a direct correlation between this parameter and the corresponding weld seams. However, the automatic identification and classification of weld defects has proven to be difficult, and it is usually performed by means of statistical studies of the electronic temperature profile. In this paper, a new approach that allows automatic weld defect detection and classification based in the combined use of principal component analysis (PCA) and an artificial neural network (ANN) is proposed. The plasma spectra captured from the welding process is processed with PCA, which reduces the processing complexity, by performing a data compression in the spectral dimension. The designed ANN, after the selection of a proper data training set, allows automatic detection of weld defects. The proposed technique has been successfully checked. Arc-weld tests on stainless steel are reported, showing a good correlation between the ANN outputs and the classical interpretation of the electronic temperature profile.

After a mention of what it must be understood as Photonics field we will go into Optical Sensing area including the particular cases of the sensors using fiber technologies. Four significant cases will be presented and discussed

The ability of two different machine learning approaches to map non-linear problems from experimental data is evaluated under controlled experiments. A well-known machine learning algorithm (Artificial Neural Network) is compared against a new computing paradigm (Hierarchical Temporal Memory) under a controlled scenario. The chosen scenario is the detection of impacts in a cantilever beam under vibration instrumented with fiber Bragg gratings. The main characteristics of both of the machine learning approaches are analyzed while varying environmental parameters such as the number of sensing points and their location. From the achieved results some clues can be extracted regarding dealing with noisy or partial data using different machine learning approaches.

An optimized technique for real-time spectral analysis of thermal plasmas, with application in the monitoring and defect detection of industrial welding processes, particularly arc-welding, is presented in this paper. The calculation of the plasma electronic temperature by means of a sub-pixel algorithm permits on-line quality assessment of the welds, allowing the detection of common defects to be found in the welding seam, such as oxidation due to insufficient shielding gas flux or lack of penetration caused by current fluctuations of the welding power source. The proposed technique has been successfully checked in a real-time arc-welding monitoring system, and experimental results of stainless-steel welds are also reported.

Although the laser welding process has many advantages over other techniques, especially for the welding of complex parts, it requires a precise focusing of the laser beam onto the workpiece to achieve the proper penetration over the entire weld seam. However, the unavoidable set-up tolerances and thermal distortion of the workpiece can result in focus errors that should be minimized. In this paper, an optoelectronic device for real-time measurement and control of the focal position is presented. It is based on the non-intrusive capture of the light emitted by the welding process by means of an optical fibre inside the laser head, and the estimation and correction of the focal error from the analysis of the light at two different spectral bands. The reported system has been optimized for use in a real environment: it is robust, compact, easy to operate and able to adjust itself to different welding conditions. Details of the design, figures of performance obtained from lab testing and results from recent field trials on complex aerospace parts are provided.

The recent research and development results of the Photonic Engineering Group in the photonic sensing field are reported. This article includes a sample of contributions in several of the ongoing RD gas sensing, high temperature monitoring; optoelectronic instrumentation for laser welding monitoring; fiber active devices; and, finally, on sensing technology using plastic optical fiber.