Temperature Gradient Effect on Gas Discrimination Power of a Metal-Oxide Thin-Film Sensor Microarray

Sysoev, Victor V.; Kiselev, I.; Frietsch, M.; Goschnick, J.

doi:10.3390/s40400037

Cited by 67 publications

(52 citation statements)

References 10 publications

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“…In this case, the sensors of the array have a single signal type while the variation of both the properties and output characteristics is achieved using a differentiation of either sensor internal characteristics or external working conditions [2]. The most widely used arrays of this type are based on semiconductor chemiresistors [9]. Such a chemiresistor is usually formed on an insulating substrate by applying the electrodes over/under a gas-sensitive semiconductor such as a metal oxide.…”

Section: The "Electronic Nose" Device Based On Multisensorarraymentioning

confidence: 99%

“…The chip is housed into a 120pin package (PGA-120, Kyocera Co., Japan) on ceramic columns [11]. To make a differentiation of the sensor segment properties over array the substrate has been inhomogeneously heated [9] so that only one side of the chip substrate has been secured on the columns by ceramic adhesive. Electrical connections to the electrodes, heaters and thermoresistors have been made by Au wires, 38 mkm thick, by ultrasonic welding.…”

Section: The "Electronic Nose" Device Based On Multisensorarraymentioning

confidence: 99%

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Application of genetic algorithm to configure artificial neural network for processing a vector multisensor array signal

Dykin

Musatov

Varezhnikov

et al. 2015

2015 International Siberian Conference on Control and Communications (SIBCON)

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The possibility of applying genetic algorithms to configure a topology of artificial neural network which process a multisensor array vector signal for gas-analytical tasks has been considered. Such a configuration is implemented according to the criteria of increasing the percentage of correct recognition and reducing the computing cost by using a set of predefined possible values of the parameters of the neural network architecture. The optimized characteristics are the number of hidden layers of neurons in each layer and the amount of training sampling. The tests have been performed in the © © © ©Matlab neural network operated under Levenberg-Marquardt back-propagation learning function. The obtained results confirm the efficiency of the genetic algorithm to optimize the topology of designed artificial neural network with advanced characteristics.

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Section: The "Electronic Nose" Device Based On Multisensorarraymentioning

confidence: 99%

Section: The "Electronic Nose" Device Based On Multisensorarraymentioning

confidence: 99%

Application of genetic algorithm to configure artificial neural network for processing a vector multisensor array signal

Dykin

Musatov

Varezhnikov

et al. 2015

2015 International Siberian Conference on Control and Communications (SIBCON)

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“…The multisensor microarray employed in this study was fabricated according to earlier reported protocols [9][10][11]. In brief, the SnO 2 :Pt film was deposited by r.f.…”

Section: The Multisensor Microarray and Data Acquisitionmentioning

confidence: 99%

Could We Apply a NeuroProcessor For Analyzing a Gas Response Of Multisensor Arrays?

Sysoev¹,

Musatov²,

Maschenko³

et al. 2009

AIP Conference Proceedings

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We describe an effort of implementation of hardware neuroprocessor to carry out pattern recognition of signals generated by a multisensor microarray of Electronic Nose type. The multisensor microarray is designed with the SnO 2 thin film segmented by co-planar electrodes according to KAMINA (KArlsruhe Micro NAse) E-nose architecture. The response of this microarray to reducing gases mixtured with a synthetic air is processed by principal component analysis technique realized in PC (Matlab software) and the neural microprocessor NeuroMatrix NM6403. It is shown that the neuroprocessor is able to successfully carry out a gas-recognition algorithm at a real-time scale.

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“…Therefore, they are not applicable for exacting analysis of gas mixtures. This is the reason that other means are necessary like gas sensor arrays of MOGs [1,2] or the operation of MOGs in a thermo-cyclic mode. The simultaneous sampling of the conductance values, gained from this operation mode, leads to so-called "conduction over time profiles" (CTPs) [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Analysis of Sensor Data for Breath Control in Respiratory Air

et al. 2018

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There is a broad field of applications of breath monitoring in human health care, medical applications and alcohol control. In this report, an innovative mobile sensor system for breath control in respiratory air called AGaMon will be introduced. The sensor system is able to recognize a multitude of different gases like ethanol (which is the leading component of alcoholic drinks), H 2 S (which is the leading component for halitosis), H 2 (which is the leading component for dyspepsia and food intolerance), NO (which is the leading component for asthma) or acetone (which is the leading component for diabetes), thus ,covering almost all significant aspects. An innovative calibration and evaluation procedure called SimPlus was developed which is able to evaluate the sensor data simultaneously. That means, SimPlus is able to identify the samples simultaneously; for example, whether the measured sample is ethanol or another substance under consideration. Furthermore, SimPlus is able to determine the concentration of the identified sample. This will be demonstrated in this report for the application of ethanol, H 2 , acetone and the binary mixture ethanol-H 2. It has been shown that SimPlus could identify the investigated gases and volatile organic compounds (VOCs) very well and that the relative analysis errors were smaller than 10% in all considered applications.

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Temperature Gradient Effect on Gas Discrimination Power of a Metal-Oxide Thin-Film Sensor Microarray

Cited by 67 publications

References 10 publications

Application of genetic algorithm to configure artificial neural network for processing a vector multisensor array signal

Application of genetic algorithm to configure artificial neural network for processing a vector multisensor array signal

Could We Apply a NeuroProcessor For Analyzing a Gas Response Of Multisensor Arrays?

Simultaneous Analysis of Sensor Data for Breath Control in Respiratory Air

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