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Sensors Journal, IEEE

Issue 3 • Date June 2002

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Displaying Results 1 - 15 of 15
  • Foreword

    Publication Year: 2002 , Page(s): 131 - 132
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    Freely Available from IEEE
  • Effects of electrode configuration on polymer carbon-black composite chemical vapor sensor performance

    Publication Year: 2002 , Page(s): 160 - 168
    Cited by:  Papers (10)  |  Patents (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (318 KB)  

    The performance of polymer carbon-black composite chemical vapor sensors as a function of underlying electrode size and geometry has been studied. The sensor performance parameters investigated were sensor response magnitude to a toluene analyte (100, 500, and 1000 ppm), fundamental sensor noise in the presence of air, and two concentrations of toluene (100 and 500 ppm), and signal-to-noise ratio (100 and 500 ppm). An array of sensors with 42 different circular electrode configurations were designed, fabricated, and tested where electrode gap was varied from 10 to 500 μm and the diameter of the sensors was varied from 30 to 2000 μm. Each array of electrodes was coated with an approximately 1 μm-thick layer of conducting polymer carbon-black composite with an insulating poly(alkylacrylate) polymer. The response magnitude, fundamental noise, and signal-to-noise ratio of each sensor was measured and compared to electrode geometry, such as electrode gap, aspect ratio, and overall size. No significant dependence of sensor response magnitude and noise to electrode configuration has been observed to be larger than the variation from sensor to sensor. However, the signal-to-noise ratio tended to decrease for sensors with the smallest scales. View full abstract»

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  • Optimization of gas-sensitive polymer arrays using combinations of heterogeneous and homogeneous subarrays

    Publication Year: 2002 , Page(s): 169 - 178
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (252 KB)  

    Results for optimizing an array of conducting polymer gas sensors for sensing one of five analytes in the presence of up to four interferents are presented. The optimized array consists of subarrays of homogeneous (like) sensors contributing to a larger heterogeneous array of up to ten points (unlike sensors) in multidimensional sensor space. The optimization techniques presented here are linear, since the polymer sensors in their useful (low concentration) operating range exhibit linear and additive response characteristics. The optimization of these arrays produces maximum separability between analytes, demonstrating the trade-off between the addition of both information and variability induced by increasing the size of the heterogeneous array. Optimization results for sensing acetone, hexane, THF, toluene, and ethanol in the presence of interferents result in array sizes that are significantly less than the maximum available number of sensors (ten in the heterogeneous partition of the array). This result adds fuel to the argument that fewer sensors are better; the argument for more sensors, however, is also made in the context of the electronic nose systems where significant chemical diversity is required. Homogeneous subarrays of up to four elements each improve the separability of analytes in these optimized heterogeneous arrays by over 10% and also effectively flag broken or unhealthy sensors in a manner that is independent of analyte and concentration. View full abstract»

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  • A portable electronic nose based on embedded PC technology and GNU/Linux: hardware, software and applications

    Publication Year: 2002 , Page(s): 235 - 246
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (402 KB) |  | HTML iconHTML  

    This paper describes a portable electronic nose based on embedded PC technology. The instrument combines a small footprint with the versatility offered by embedded technology in terms of software development and digital communications services. A summary of the proposed hardware and software solutions is provided with an emphasis on data processing. Data evaluation procedures available in the instrument include automatic feature selection by means of SFFS, feature extraction with linear discriminant analysis (LDA) and principal component analysis (PCA), multi-component analysis with partial least squares (PLS) and classification through k-NN and Gaussian mixture models. In terms of instrumentation, the instrument makes use of temperature modulation to improve the selectivity of commercial metal oxide gas sensors. Field applications of the instrument, including experimental results, are also presented. View full abstract»

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  • SnO2 gas sensing array for combustible and explosive gas leakage recognition

    Publication Year: 2002 , Page(s): 140 - 149
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (362 KB) |  | HTML iconHTML  

    A gas-sensing array with ten different SnO2 sensors was fabricated on a substrate for the purpose of recognizing various kinds and quantities of indoor combustible gas leakages, such as methane, propane, butane, LPG, and carbon monoxide, within their respective threshold limit value (TLV) and lower explosion limit (LEL) range. Nano-sized sensing materials with high surface areas were prepared by coprecipitating SnCl4 with Ca and Pt, while the sensing patterns of the SnO2-based sensors were differentiated by utilizing different additives. The sensors in the sensor array were designed to produce a uniform thermal distribution along with a high and differentiated sensitivity and reproducibility for low concentrations below 100 ppm. Using the sensing signals of the array, an electronic nose system was then applied to classify and identify simple/mixed explosive gas leakages. A gas pattern recognizer was implemented using a neuro-fuzzy network and multi-layer neural network, including an error-back-propagation learning algorithm. Simulation and experimental results confirmed that the proposed gas recognition system was effective in identifying explosive and hazardous gas leakages. The electronic nose in conjunction with a neuro-fuzzy network was also implemented using a digital signal processor (DSP). View full abstract»

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  • Learning from data: a tutorial with emphasis on modern pattern recognition methods

    Publication Year: 2002 , Page(s): 203 - 217
    Cited by:  Papers (23)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (350 KB)  

    The purposes of this tutorial are twofold. First, it reviews the classical statistical learning scenario by highlighting its fundamental taxonomies and its key aspects. The second aim of the paper is to introduce some modern (ensembles) methods developed inside the machine learning field. The tutorial starts by putting the topic of supervised learning into the broader context of data analysis and by reviewing the classical pattern recognition methods: those based on class-conditional density estimation and the use of the Bayes theorem and those based on discriminant functions. The fundamental topic of complexity control is treated in some detail. Ensembles techniques have drawn considerable attention in recent years: a set of learning machines increases classification accuracy with respect to a single machine. Here, we introduce boosting, in which classifiers adaptively concentrate on the harder examples located near to the classification boundary and output coding, where a set of independent two-class machines solves a multiclass problem. The first successful applications of these methods to data produced by the Pico-2 electronic nose (EN), developed at the University of Brescia, Brescia, Italy, are also briefly shown. View full abstract»

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  • Air quality monitoring and fire detection with the Karlsruhe electronic micronose KAMINA

    Publication Year: 2002 , Page(s): 179 - 188
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (340 KB) |  | HTML iconHTML  

    An indoor air monitoring device is one of the most prominent consumer applications of an electronic nose (EN). Integral gas analysis similar to biogenic odor perception can be a versatile tool to obtain continuous information about pollutants, odors, and air compositions indicating gaseous precursors of dangers such as fires. However, an EN to be used as a common household device has to combine high sensitivity and excellent gas discrimination power with inexpensiveness, small size, and low power consumption. A special gas sensor microarray of thumbnail size has been developed at the Forschungszentrum Karlsruhe based on metal-oxide technology to meet these requirements. The microarray is produced by simply partitioning a monolithic metal-oxide layer with parallel electrode strips allowing low cost fabrication. A temperature gradient and a membrane thickness gradient (on metal-oxide layer) are responsible for differentiation between the individual sensor segments and thus for the conductivity patterns that are accordingly produced. The two membranes form the basis of gas discrimination power, reliability self checks, and online noise reduction. Model gas exposures usually show detection limits lower than 1 ppm. Successful practical tests are reported on the detection of overheated wire insulation for fire prevention as well as on air quality analysis for air conditioning purposes (e.g., air quality control during a meeting). View full abstract»

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  • Classification of bacteria responsible for ENT and eye infections using the Cyranose system

    Publication Year: 2002 , Page(s): 247 - 253
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (269 KB)  

    The Cyranose 320 (Cyrano Sciences Inc., USA), comprising an array of 32 polymer carbon black composite sensors, has been used to identify species of bacteria commonly associated with medical conditions. Results from two experiments are presented: one on bacteria causing eye infections and one on a new series of tests on bacteria responsible for some ear, nose, and throat (ENT) diseases. For the eye bacteria tests, pure lab cultures were used and the electronic nose (EN) was used to sample the headspace of sterile glass vials containing a fixed volume of bacteria in suspension. For the ENT bacteria, the system was taken a step closer toward medical application, as readings were taken from the headspace of the same blood agar plates used to culture real samples collected from patients. After preprocessing, principal component analysis (PCA) was used as an exploratory technique to investigate the clustering of vectors in multi-sensor space. Artificial neural networks (ANNs) were then used as predictors, and a multilayer perceptron (MLP) trained with back-propagation (BP) and with Levenberg-Marquardt was used to identify the different bacteria. The optimal MLP was found to correctly classify 97.3% of the six eye bacteria of interest and 97.6% of the four ENT bacteria including two sub-species. A radial basis function (RBF) network was able to discriminate between the six eye bacteria species, even in the lowest state of concentration, with 92.8% accuracy. These results show the potential application of the Cyranose together with neural network-based predictors, for rapid screening and early detection of bacteria associated with these medical conditions, and the possible development of this EN system as a near-patient tool in primary medical healthcare. View full abstract»

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  • Identification of odors using a sensor array with kinetic working temperature and Fourier spectrum analysis

    Publication Year: 2002 , Page(s): 230 - 234
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (242 KB) |  | HTML iconHTML  

    A method for the identification of odors using a dynamically driven sensor array has been developed, in which transient responses of the sensor array were used to recognize target odors. Fourier transformation was used to transform the transient response curves of the sensor array into Fourier spectra, and patterns composed of some of the magnitudes in the spectra were used in the pattern recognition to identify the odors. Identification of odors was performed using this method with three kinds of 10% ethanol solution of tea extract and three kinds of Japanese soy sauce. In consequence, the sample odors could be correctly recognized without any pretreatment device for separation of minor components from the main component. View full abstract»

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  • Medical diagnosis with the gradient microarray of the KAMINA

    Publication Year: 2002 , Page(s): 254 - 259
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (266 KB) |  | HTML iconHTML  

    Investigations with human breath and cultures of oral bacteria have been performed to examine the analytical performance of the KAMINA gradient microarray based on a segmented metal oxide layer. Standard microarray chips with 38 segments, one chip equipped with platinum doped SnO2 and the other with WO3, were inspected. The results show that the gradient microarray is able to detect acetone and methyl-mercaptane as two model gases of medical relevance at lower ppm-levels in the presence of human breath. Even after consumption of smelly nutrition, acetone at lowest concentrations of some 10 ppm could be detected. A principal component analysis (PCA) of the signal patterns showed that both types of microarrays were able to discriminate between the model gases, ethanol and clean air. Moreover, the even more delicate distinction of different oral bacteria grown on an agar substrate proved to be feasible by the signal pattern analysis of their gaseous metabolites. The signal patterns obtained for mixed bacteria cultures even seem to allow assignment to and quantification of the main cultures of a mixture. View full abstract»

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  • System identification of electronic nose data from cyanobacteria experiments

    Publication Year: 2002 , Page(s): 218 - 229
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (310 KB)  

    Linear black-box modeling techniques are applied to both steady state and dynamic data gathered from two electronic nose experiments involving cyanobacteria cultures. Analysis of the data from a strain identification experiment shows that very simple low order MISO black box model structures are able to produce very high success rates (up to 100%) when identifying the toxic strain of cyanobacteria. This is comparable with the best success rates for steady state data reported elsewhere using artificial neural networks. Analysis of data from a growth phase identification experiment using MIMO black-box models produces success rates of 82.3% for steady state data and 76.6% for dynamic data. This compares poorly with the best performing nonlinear artificial neural networks, which obtained a 95.1% success rate on the same data. This demonstrates the limitations of these linear techniques when applied to more difficult problems. View full abstract»

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  • Pattern analysis for machine olfaction: a review

    Publication Year: 2002 , Page(s): 189 - 202
    Cited by:  Papers (149)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (423 KB)  

    Pattern analysis constitutes a critical building block in the development of gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds, a technology that has been proposed as an artificial substitute for the human olfactory system. The successful design of a pattern analysis system for machine olfaction requires a careful consideration of the various issues involved in processing multivariate data: signal-preprocessing, feature extraction, feature selection, classification, regression, clustering, and validation. A considerable number of methods from statistical pattern recognition, neural networks, chemometrics, machine learning, and biological cybernetics have been used to process electronic nose data. The objective of this review paper is to provide a summary and guidelines for using the most widely used pattern analysis techniques, as well as to identify research directions that are at the frontier of sensor-based machine olfaction. View full abstract»

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  • Gas sensitivity comparison of polymer coated SAW and STW resonators operating at the same acoustic wave length

    Publication Year: 2002 , Page(s): 150 - 159
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (455 KB) |  | HTML iconHTML  

    Results from systematic gas sensing experiments on polymer coated surface-transverse-wave (STW) and surface-acoustic-wave (SAW) based two-port resonators on rotated Y-cut quartz, operating at the same acoustic wavelength of 7.22 μm, are presented. The acoustic devices are coated with chemosensitive films of different viscoelastic properties and thicknesses, such as solid hexamethyldisiloxane (HMDSO), semisolid styrene (ST), and soft allyl alcohol (AA). The sensor sensitivities to vapors of different chemical analytes are automatically measured in a sensor head, evaluated, and compared. It is shown that thin HMDSO- and ST-coated STW sensors are up to 3.8 times more sensitive than their SAW counterparts, while SAW devices coated with thick soft AA-films are up to 3.6 times more sensitive than the STW ones. This implies that SAWs are more suitable for operation with soft coatings while STWs perform better with solid and semisolid films. A close-to-carrier phase noise evaluation shows that the vapor flow homogeneity, the analyte concentration, its sorption dynamics, and the sensor oscillator design are the major limiting factors for the sensor noise and its resolution. A well designed ST-coated 700 MHz STW sensor provides a 178 kHz sensor signal at a 630 ppm concentration of tetra-chloroethylene and demonstrates short-term stability of 3×10-9/s which results in a sensor resolution of about 7 parts per billion (ppb). View full abstract»

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  • Bioelectronic sniffer device for trimethylamine vapor using flavin containing monooxygenase

    Publication Year: 2002 , Page(s): 133 - 139
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (231 KB) |  | HTML iconHTML  

    A bioelectronic sniffer device for trimethylamine (TMA) in the gas phase "fish-odor substance" was constructed using a flavin-containing monooxygenase 3 (FMO3, one of xeno-biotic metabolizing enzymes) and a reaction unit with both gas and liquid cells separated by a porous poly(tetrafluoroethylene) diaphragm membrane (pore size: 30-60 μm, thickness: 0.20 mm). A substrate regeneration cycle was applied to the FMO3 immobilized device in order to amplify the output signal by coupling the monooxygenase with a reducing reagent system of ascorbic acid (ASA) in phosphate buffer. The sniffer device with 10.0 mmol/l AsA could be used to measure TMA vapor from 0.52 to 105 ppm; this covers the maximum permissible concentration in the work place (5.0 ppm of time weighted average concentration) and the sensing level-5 of smell in humans (3.0 ppm). Since the application of the substrate regeneration cycle was possibly successful, it improved the sensitivity of the FMO3 immobilized device. The sniffer device possessed high selectivity for TMA being attributable to the FMO3 substrate specificity, continuous measurability, and good reproducibility in the repeatedly measurements (coefficient of variation = 2.41%, n=10). View full abstract»

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  • Distributed odor source localization

    Publication Year: 2002 , Page(s): 260 - 271
    Cited by:  Papers (114)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (381 KB) |  | HTML iconHTML  

    This paper presents an investigation of odor localization by groups of autonomous mobile robots. First, we describe a distributed algorithm by which groups of agents can solve the full odor localization task. Next, we establish that conducting polymer-based odor sensors possess the combination of speed and sensitivity necessary to enable real world odor plume tracing and we demonstrate that simple local position, odor, and flow information, tightly coupled with robot behavior, is sufficient to allow a robot to localize the source of an odor plume. Finally, we show that elementary communication among a group of agents can increase the efficiency of the odor localization system performance. View full abstract»

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The IEEE Sensors Journal is a peer-reviewed, monthly online/print  journal devoted to sensors and sensing phenomena

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Editor-in-Chief
Krikor Ozanyan
University of Manchester
Manchester, M13 9PL, U.K.