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  • 1.
    Burgues, Javier
    et al.
    Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Marco, Santiago
    Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain.
    Gas Distribution Mapping and Source Localization Using a 3D Grid of Metal Oxide Semiconductor Sensors2020In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 304, article id 127309Article in journal (Refereed)
    Abstract [en]

    The difficulty to obtain ground truth (i.e. empirical evidence) about how a gas disperses in an environment is one of the major hurdles in the field of mobile robotic olfaction (MRO), impairing our ability to develop efficient gas source localization strategies and to validate gas distribution maps produced by autonomous mobile robots. Previous ground truth measurements of gas dispersion have been mostly based on expensive tracer optical methods or 2D chemical sensor grids deployed only at ground level. With the ever-increasing trend towards gas-sensitive aerial robots, 3D measurements of gas dispersion become necessary to characterize the environment these platforms can explore. This paper presents ten different experiments performed with a 3D grid of 27 metal oxide semiconductor (MOX) sensors to visualize the temporal evolution of gas distribution produced by an evaporating ethanol source placed at different locations in an office room, including variations in height, release rate and air flow. We also studied which features of the MOX sensor signals are optimal for predicting the source location, considering different lengths of the measurement window. We found strongly time-varying and counter-intuitive gas distribution patterns that disprove some assumptions commonly held in the MRO field, such as that heavy gases disperse along ground level. Correspondingly, ground-level gas distributions were rarely useful for localizing the gas source and elevated measurements were much more informative. We make the dataset and the code publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments.

  • 2.
    Fan, Han
    et al.
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments2018In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 259, p. 183-203Article in journal (Refereed)
    Abstract [en]

    Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process.

    Download full text (pdf)
    A Cluster Analysis Approach Based on Exploiting Density Peaks for Gas Discrimination with Electronic Noses in Open Environments
  • 3.
    Gonzàlez Monroy, Javier
    et al.
    University of Málaga, Málaga, Spain.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Blanco, Jose Luis
    University of Almería, Almería, Spain.
    Gonzàlez Jimenez, Javier
    University of Málaga, Málaga, Spain.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Probabilistic gas quantification with MOX sensors in open sampling systems: a gaussian process approach2013In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 188, p. 298-312Article in journal (Refereed)
    Abstract [en]

    Gas quantification based on the response of an array of metal oxide (MOX) gas sensors in an Open Sampling System is a complex problem due to the highly dynamic characteristic of turbulent airflow and the slow dynamics of the MOX sensors. However, many gas related applications require to determine the gas concentration the sensors are being exposed to. Due to the chaotic nature that dominates gas dispersal, in most cases it is desirable to provide, together with an estimate of the mean concentration, an estimate of the uncertainty of the prediction. This work presents a probabilistic approach for gas quantification with an array of MOX gas sensors based on Gaussian Processes, estimating for every measurement of the sensors a posterior distribution of the concentration, from which confidence intervals can be obtained. The proposed approach has been tested with an experimental setup where an array of MOX sensors and a Photo Ionization Detector (PID), used to obtain ground truth concentration, are placed downwind with respect to the gas source. Our approach has been implemented and compared with standard gas quantification methods, demonstrating the advantages when estimating gas concentrations.

  • 4.
    Palomar, Quentin
    et al.
    Division of Solid-State Electronics, Department of Electrical Engineering, The Ångström Laboratory, Uppsala University, Uppsala, Sweden.
    Svärd, Anna
    Örebro University, School of Medical Sciences. Cardiovascular Research Centre (CVRC), School of Medical Sciences, Örebro University, Örebro, Sweden; Laboratory of Molecular Materials, Division of Biophysics and Bioengineering, Department of Physics, Chemistry and Biology (IFM), Linköping University, Linköping, Sweden.
    Zeng, Shuangshuang
    Division of Solid-State Electronics, Department of Electrical Engineering, The Ångström Laboratory, Uppsala University, Uppsala, Sweden.
    Hu, Qitao
    Division of Solid-State Electronics, Department of Electrical Engineering, The Ångström Laboratory, Uppsala University, Uppsala, Sweden.
    Liu, Funing
    Division of Solid-State Electronics, Department of Electrical Engineering, The Ångström Laboratory, Uppsala University, Uppsala, Sweden.
    Aili, Daniel
    Cardiovascular Research Centre (CVRC), School of Medical Sciences, Örebro University, Örebro, Sweden.
    Zhang, Zhen
    Division of Solid-State Electronics, Department of Electrical Engineering, The Ångström Laboratory, Uppsala University, Uppsala, Sweden.
    Detection of gingipain activity using solid state nanopore sensors2022In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 368, article id 132209Article in journal (Refereed)
    Abstract [en]

    Accurate, robust, and rapid diagnostics is the basis for all well-functioning healthcare. There is a large need in point-of-care biosensors to facilitate diagnosis and reduce the need for cumbersome laboratory equipment. Proteases are key virulence factors in periodontitis. Periodontal disease is very common and characterized by inflammation and infection in the tooth-supporting structures and is linked to many systemic diseases such as cardiovascular disease, diabetes, and Alzheimer's disease. Proteases present in periodontal disease, gingipains, are highly responsible for the disease onset and progression and are therefore a promising biomarker. Here we show a novel nanopore-based biosensor strategy for protease activity monitoring. Solid-state nanopores were modified with a proteolytic substrate, restricting the ionic current through the apertures of the nanopores. Protease can digest the proteolytic substrate thus enlarge the aperture and the ionic current. Trypsin was used as an initial model protease to investigate the performance of the sensor. We show that the solid-state nanoporebiosensor can detect trypsin with a limit of detection (LOD) of 0.005 ng/mL (0.2 pM). The detection system developed for the model enzyme was then applied to the detection of gingipains. The LOD for detection of gingipains was 1 ng/mL (0.02 nM), with a 27% recovery of the signal at 0.1 mu g/mL, indicating that the sensitivity and dynamic range are relevant for the clinical diagnosis of periodontitis. The generic detection of protease activity and high sensitivity make this a promising sensor technology for both diagnosis of periodontal disease and monitoring of other disease-related proteases.

  • 5.
    Solis, J. L.
    et al.
    The Ångström Laboratory, Department of Materials Science, Uppsala University, Uppsala, Sweden.
    Kish, L. B.
    The Ångström Laboratory, Department of Materials Science, Uppsala University, Uppsala, Sweden.
    Vajtai, R.
    The Ångström Laboratory, Department of Materials Science, Uppsala University, Uppsala, Sweden.
    Granqvist, C. G.
    The Ångström Laboratory, Department of Materials Science, Uppsala University, Uppsala, Sweden.
    Olsson, J.
    Department of Microbiology, Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Schnürer, Johan
    Department of Microbiology, Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Lantto, V.
    Microelectronics and Materials Physics Laboratories, University of Oulu, Linnanmaa, Oulu, Finland.
    Identifying natural and artificial odours through noise analysis with a sampling-and-hold electronic nose2001In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 77, no 1-2, p. 312-315Article in journal (Refereed)
    Abstract [en]

    A sampling-and-hold type electronic nose was used to investigate "frozen" sensor dynamics. The sensor was heated to the sensing temperature and exposed to a chemical environment for a short time. Then, while keeping the sensor in the chemical environment, the heating was switched off so that the sensor cooled down to room temperature. Chemicals species become trapped in the sensor film, and therefore, the current transport in the film is changed. The trapped chemicals are usually located at grain boundaries, and they influence the charge transport in the grains and between the grains. This gives random fluctuations to the local conductivity. Resistance noise was employed to extract chemical information from the sensor in the cold state.

  • 6.
    Trincavelli, Marco
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Odour classification system for continuous monitoring applications2009In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 58, no 2, p. 265-273Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the classification performance of an electronic nose system, based on tin dioxide gas sensors. In contrast to previous studies, the electronic nose is mounted on a mobile platform and samples are analyzed using only transient information in the signals. The motivation behind this work is to explore the feasibility of using electronic nose devices for odour classification in a number of future application domains which require fast and possibly real-time odour identification. To perform transient based analysis of the signals, a comparative study of different methods for feature extraction was performed. Additionally, the application of a relevance vector machine classifier is explored to further analyze the classification performance based on quality of the obtained samples. The results presented in this study can be used for the development of electronic nose devices particularly suitable for environmental monitoring applications.

  • 7.
    Vergara, Alexander
    et al.
    University of California, San Diego, USA.
    Fonollosa, Jordi
    University of California, San Diego, USA.
    Mahiques, Jonas
    University of California, San Diego, USA.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Rulkov, Nikolai
    University of California, San Diego, USA.
    Huerta, Ramon
    University of California, San Diego, USA.
    On the performance of gas sensor arrays in open sampling systems using inhibitory support vector machines2013In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 185, no August 2013, p. 462-477Article in journal (Refereed)
    Abstract [en]

    Chemo-resistive transduction presents practical advantages for capturing the spatio-temporal and structural organization of chemical compounds dispersed in different human habitats. In an open sampling system, however, where the chemo-sensory elements are directly exposed to the environment being monitored, the identification and monitoring of chemical substances present a more difficult challenge due to the dispersion mechanisms of gaseous chemical analytes, namely diffusion, turbulence, and advection. The success of such actively changeable practice is influenced by the adequate implementation of algorithmically driven formalisms combined with the appropriate design of experimental protocols. On the basis of this functional joint-formulation, in this study we examine an innovative methodology based on the inhibitory processing mechanisms encountered in the structural assembly of the insect's brain, namely Inhibitory Support Vector Machine (ISVM) applied to training a sensor array platform and evaluate its capabilities relevant to odor detection and identification under complex environmental conditions. We generated - and made publicly available - an extensive and unique dataset with a chemical detection platform consisting of 72 conductometric metal-oxide based chemical sensors in a custom-designed wind tunnel test-bed facility to test our methodology. Our findings suggest that the aforementioned methodology can be a valuable tool to guide the decision of choosing the training conditions for a cost-efficient system calibration as well as an important step toward the understanding of the degradation level of the sensory system when the environmental conditions change.

1 - 7 of 7
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