Actividades del LCMM-UB

Peer Reviewe

Actividades del LCMM-UB

Peer Reviewe

Environmental chemical sensing using small drones: A review

Recent advances in miniaturization of chemical instrumentation and in low-cost small drones are catalyzing exponential growth in the use of such platforms for environmental chemical sensing applications. The versatility of chemically sensitive drones is reflected by their rapid adoption in scientific, industrial, and regulatory domains, such as in atmospheric research studies, industrial emission monitoring, and in enforcement of environmental regulations. As a result of this interdisciplinarity, progress to date has been reported across a broad spread of scientific and non-scientific databases, including scientific journals, press releases, company websites, and field reports. The aim of this paper is to assemble all of these pieces of information into a comprehensive, structured and updated review of the field of chemical sensing using small drones. We exhaustively review current and emerging applications of this technology, as well as sensing platforms and algorithms developed by research groups and companies for tasks such as gas concentration mapping, source localization, and flux estimation. We conclude with a discussion of the most pressing technological and regulatory limitations in current practice, and how these could be addressed by future research

Signal and data processing for machine olfaction and chemical sensing: A review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing

Chemical Sensor Systems and Associated Algorithms for Fire Detection: A Review

Indoor fire detection using gas chemical sensing has been a subject of investigation since the early nineties. This approach leverages the fact that, for certain types of fire, chemical volatiles appear before smoke particles do. Hence, systems based on chemical sensing can provide faster fire alarm responses than conventional smoke-based fire detectors. Moreover, since it is known that most casualties in fires are produced from toxic emissions rather than actual burns, gas-based fire detection could provide an additional level of safety to building occupants. In this line, since the 2000s, electrochemical cells for carbon monoxide sensing have been incorporated into fire detectors. Even systems relying exclusively on gas sensors have been explored as fire detectors. However, gas sensors respond to a large variety of volatiles beyond combustion products. As a result, chemical-based fire detectors require multivariate data processing techniques to ensure high sensitivity to fires and false alarm immunity. In this paper, we the survey toxic emissions produced in fires and defined standards for fire detection systems. We also review the state of the art of chemical sensor systems for fire detection and the associated signal and data processing algorithms. We also examine the experimental protocols used for the validation of the different approaches, as the complexity of the test measurements also impacts on reported sensitivity and specificity measures. All in all, further research and extensive test under different fire and nuisance scenarios are still required before gas-based fire detectors penetrate largely into the market. Nevertheless, the use of dynamic features and multivariate models that exploit sensor correlations seems imperative

Gas identification with tin oxide sensor array and self-organizing maps: adaptive correction of sensor drifts

Low-cost tin oxide gas sensors are inherently nonspecific. In addition, they have several undesirable characteristics such as slow response, nonlinearities, and long-term drifts. This paper shows that the combination of a gas-sensor array together with self-organizing maps (SOM's) permit success in gas classification problems. The system is able to determine the gas present in an atmosphere with error rates lower than 3%. Correction of the sensor's drift with an adaptive SOM has also been investigate

Global Calibration Models for Temperature-Modulated Metal Oxide Gas Sensors: A Strategy to Reduce Calibration Costs

Tolerances in the fabrication of metal oxide (MOX) gas sensors lead to inter-device variability in baseline and sensitivity, even for sensors of the same fabrication batch. This has traditionally forced the use of individual calibration models (ICMs) built specifically for each sensor unit, which requires an expensive and time-consuming calibration process and hinders sensor replacement. We propose Global calibration models (GCMs) built using the responses of multiple sensor units, and then applied to a new sensor unit that is not part of the calibration set. GCM have been already successfully applied to transfer calibration models between sensor arrays (electronic noses) for classification tasks. In this work, we investigate the use of such models for regression purposes in temperature-modulated sensors, aiming at the quantification of low concentrations of carbon monoxide (CO) in the presence of variable humidity levels (20–80% r.h. at 26 ± 1 °C). Using a laboratory dataset containing data from 6 replicas of the FIS SB-500–12 model, we evaluate the performance of global models built with data from 1 to 4 sensors when applied to unseen sensor units. Results show that the performance of global models improves with an increasing number of sensors in the calibration set, approaching the performance of individual calibration models (1.38 ± 0.15 ppm for GCM; 1.05 ± 0.24 ppm for ICM), and surpassing their performance only if few calibration conditions per sensor are available (2.09 ± 0.10 ppm for GCM;; 2.76 ± 0.22 ppm for ICM, if only 5 samples per sensor are used).We would like to acknowledge, the Departament d’Universitats, Recerca i Societat de la Informació de la Generalitat de Catalunya (expedient 2017 SGR 1721); the Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya; and the European Social Fund (ESF). Additional financial support has been provided by the Institut de Bioenginyeria de Catalunya (IBEC). IBEC is a member of the CERCA Programme/Generalitat de Catalunya

Improving Calibration of Chemical Gas Sensors for Fire Detection Using Small Scale Setups

Chemical sensing may be better suited than conventional smoke-based detectors for the detection of certain type of fires, in particular in fires where smoke appears after gas emissions. However, chemical-based systems also respond to non-fire scenarios that also release volatiles. For this reason, discrimination models need to be trained under different fire and non-fire scenarios. This is usually performed in standard fire rooms, the access to which is very costly. In this work, we present a calibration model combining experiments from standard fire room and small-scale setup. Results show that the use of small-scale setup experiments improve the performance of the system

Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the 'bout' detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m²) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments