Electronic nose for space program applications

Young, Rebecca C.; Buttner, William J.; Linnell, Bruce R.; Ramesham, R.

doi:10.1016/s0925-4005(03)00338-1

Cited by 80 publications

(51 citation statements)

References 8 publications

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“…For these devices, a suitable feature needed to be chosen for extraction and use in the main processing step. Peak magnitude was selected due to its prevalent use within standard e-nose devices [1]- [3]. Both an e-nose ( ) and an advanced form of z-nose™ (an e-nose preceded by a GC column) ( ) were extracted and processed, so both classes of device could be considered as comparison (7) (8) The final feature extraction considered is referred to as crossconvolution.…”

Section: B Feature Extractionmentioning

confidence: 99%

“…Their sensing ability is heavily affected by environmental factors: general drift due to temperature, humidity and background noise, sensor variations and sensor poisoning. These problems, in addition to often wanting to detect very low concentrations (below PPM) of the odor in air [2], [3], make the design of an electronic nose difficult even with expensive autosamplers and the supply of clean air.…”

Section: Introductionmentioning

confidence: 99%

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Novel Convolution-Based Signal Processing Techniques for an Artificial Olfactory Mucosa

Gardner

Taylor

2009

IEEE Sensors J.

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Abstract-As our understanding of the human olfactory system has grown, so has our ability to design artificial devices that mimic its functionality, so called electronic noses (e-noses). This has led to the development of a more sophisticated biomimetic system known as an artificial olfactory mucosa (e-mucosa) that comprises a large distributed sensor array and artificial mucous layer. In order to exploit fully this new architecture, new approaches are required to analyzing the rich data sets that it generates. In this paper, we propose a novel convolution based approach to processing signals from the e-mucosa. Computer simulations are performed to investigate the robustness of this approach when subjected to different real-world problems, such as sensor drift and noise. Our results demonstrate a promising ability to classify odors from poor sensor signals.

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Section: B Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Convolution-Based Signal Processing Techniques for an Artificial Olfactory Mucosa

Gardner

Taylor

2009

IEEE Sensors J.

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“…• Ein Leitendes-Polymer-Sensor [8,43,46] • Ein Schwingquarz-Sensor [7,38,44] • der Luftüberwachung in Raumstationen [114].…”

Section: A Elektronische Nasenunclassified

Eine neue Methode zur Quellenlokalisierung auf der Basis räumlich verteilter, punktweiser Konzentrationsmessungen

Matthes¹

2004

At - Automatisierungstechnik

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“…They widely uses chemical gas sensors which shows drift in time series computations and starts to give improper readings containing certain amounts of drift. There are several commercial applications of EN emerging such as food industry [3], environment monitor and control [4], public safety [5], fire detection [5], space applications [6], etc. In the human olfactory system the main components are mucous membrane (which dissolves the molecule of the breath in air) epithelium (recepting layer of human nose which produces electrical signals on each chemical reaction), bulb (receives the electrical signal sent by the epithelium after reception), cortex (receiving end of the brain and further it redirects the signals to other regions of brain accordingly) and higher brain (which finally recognizes the odour).…”

Section: Introductionmentioning

confidence: 99%

Sensor Drift Compensation in Time Series Prediction through Regularized Ensemble of Classifiers

Asmita¹,

Shukla²

2015

International Journal of Advanced Research in Computer and Comm

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Concept drift is a major problem that the chemical sensor community is facing in their research and development. This arises when the predictive characteristic feature of the target variable in the sensor setup, changes due to some chemical or physical interaction of the environment elements with the surface of the sensor and other factors such as aging and poisoning of the surface. This is not a new problem but is having a wide scope of further developments. Recent research suggests an upcoming solution for this drift compensation as ensemble of classifiers with uniform weightage to all the participating classifiers or some non-uniform weightage according to performance of individual classifier. There arise some ill posed problems with this ensemble of classifiers. This paper introduces a new machine learning approach to solve this problem through regularized weighted ensemble of classifiers for overcoming the time dependent drift occurrence. The weights are chosen by regularized majority voting which are then associated with the individual classifiers to form the ensembles. This regularized drift compensation algorithm is applied to solve the gas discrimination problem for classification of 6 different volatile organic compounds over time series data set consisting of data recorded using an array of 16 metal oxides sensors for 3 years. Our experiment tries various different regularization approaches and finds best improvement in case of double regularization of Single Value Decomposition and L2 regularization. Results clearly indicate improved classification accuracy in presence of drift and better results than recent reporting. To the best of our knowledge, such a machine learning approach has not yet been applied for drift correction in sensor community.

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Electronic nose for space program applications

Cited by 80 publications

References 8 publications

Novel Convolution-Based Signal Processing Techniques for an Artificial Olfactory Mucosa

Novel Convolution-Based Signal Processing Techniques for an Artificial Olfactory Mucosa

Eine neue Methode zur Quellenlokalisierung auf der Basis räumlich verteilter, punktweiser Konzentrationsmessungen

Sensor Drift Compensation in Time Series Prediction through Regularized Ensemble of Classifiers

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