Multifrequency Interrogation of Nanostructured Gas Sensor Arrays: A Tool for Analyzing Response Kinetics

Vergara, Alexander; Calavia, R.; Vázquez, R.M.; Mozalev, Alexander; Abdelghani, Adnane; Huerta, Ramón; Hines, Evor H.; Llobet, Eduard

doi:10.1021/ac301506t

Cited by 34 publications

(19 citation statements)

References 17 publications

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“…They are also present in mammals [51] and we still do not have the sensor technology capable of reaching their reaction times, selectivity and stability [48,49,50]. Each type of olfactory receptor cell in the antenna connects to a specific glomerulus in the AL [52,53,54].…”

Section: Early Codementioning

confidence: 99%

“…Perhaps at this level slow lateral excitation between the KCs may better encode temporal information of the plume [123]. This implies that the discrimination and recognition of the gas may happen quickly, but the gas concentration estimation requires temporal integration over long time scales as shown in gas source localization using artificial sensor arrays [43,48,49,50,44].…”

Section: The Formation Of Memories In the Mushroom Bodiesmentioning

confidence: 99%

“…Recordings using an artificial sensor array [43,48,49,50,44] during carbon monoxide presence for 180 seconds in a wind tunnel under turbulent flow.…”

Section: Introductionmentioning

confidence: 99%

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Learning pattern recognition and decision making in the insect brain

Huerta¹

2013

AIP Conference Proceedings

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Abstract. We revise the current model of learning pattern recognition in the Mushroom Bodies of the insects using current experimental knowledge about the location of learning, olfactory coding and connectivity. We show that it is possible to have an efficient pattern recognition device based on the architecture of the Mushroom Bodies, sparse code, mutual inhibition and Hebbian leaning only in the connections from the Kenyon cells to the output neurons. We also show that despite the conventional wisdom that believes that artificial neural networks are the bioinspired model of the brain, the Mushroom Bodies actually resemble very closely Support Vector Machines (SVMs). The derived SVM learning rules are situated in the Mushroom Bodies, are nearly identical to standard Hebbian rules, and require inhibition in the output. A very particular prediction of the model is that random elimination of the Kenyon cells in the Mushroom Bodies do not impair the ability to recognize odorants previously learned.

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Section: Early Codementioning

confidence: 99%

Section: The Formation Of Memories In the Mushroom Bodiesmentioning

confidence: 99%

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Learning pattern recognition and decision making in the insect brain

Huerta¹

2013

AIP Conference Proceedings

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“…Aging and long term exposure to reactive gases can result in a change of the gas sensor properties [4]. The two most critical challenges for gas identification are the drift and nonselectivity of the sensors [5].…”

Section: Introductionmentioning

confidence: 99%

An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

et al. 2016

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Abstract-Increasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore feature reduction is required to extract the most important information from the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA) based feature reduction algorithms have been analyzed using data obtained from two different types of gas sensors i.e. seven commercial Figaro sensors and in-house fabricated 4x4 tin-oxide gas array sensor . A decision tree (DT) based classifier is used to examine the performance of both PCA and LDA approaches. The software implementation is carried out in MATLAB and the hardware implementation is performed using the Zynq system on chip (SoC) platform. It has been found that with the 4x4 array sensor, two discriminant function (DF) of LDA provides 3.3% better classification than five PCA components, while for the seven Figaro sensors two principal components (PC) and one DF show the same performances. The hardware implementation results on the programmable logic of the Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being 11% faster with a maximum running frequency of 122 MHz.

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“…Variations in gas concentrations generate changes in the concentration of the chemical species within the sensing layer, which result in changes in the resistivity of the sensing layer. Temperature modulations have been applied, thereby improving the selectivity of the sensors, but the time response still remains slow, [1]. Extensive work has been done in the obtention of dynamic models, [2,3].…”

Section: Introductionmentioning

confidence: 99%

Smart control of chemical gas sensors for the reduction of their time response

Domínguez-Pumar

Kowalski

Calavia

et al. 2016

Sensors and Actuators B: Chemical

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The objective of this paper is to show the first results obtained with a gas sensor made of Au-functionalized WO 3 nanoneedles working under a closed-loop control designed to reduce its time response. The average temperature applied to the sensor is modulated to keep constant the average surface potential of the sensing nanostructures. This is done by periodically monitoring the resistivity of the sensing layer and generating temperature waveforms that enforce the condition: constant resistivity of the sensing layer at a reference temperature.Changes induced by the target gases must be compensated by changes in the average temperature being applied to the sensing layer. This signal, the average temperature applied to the sensor, is the new sensor output.

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Multifrequency Interrogation of Nanostructured Gas Sensor Arrays: A Tool for Analyzing Response Kinetics

Cited by 34 publications

References 17 publications

Learning pattern recognition and decision making in the insect brain

Learning pattern recognition and decision making in the insect brain

An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

Smart control of chemical gas sensors for the reduction of their time response

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