Quantitative gas mixture analysis using temperature-modulated micro-hotplate gas sensors: Selection and validation of the optimal modulating frequencies

2007 ◽  
Vol 123 (2) ◽  
pp. 1002-1016 ◽  
Author(s):  
A. Vergara ◽  
E. Llobet ◽  
J. Brezmes ◽  
P. Ivanov ◽  
C. Cané ◽  
...  
Keyword(s):  
Gas Sensors ◽  
Gas Mixture ◽  
Mixture Analysis

Gas mixture analysis by a micro-hotplates gas sensors array

2000 ◽  
Author(s):  
S. Capone ◽  
P. Siciliano ◽  
L. Vasanelli ◽  
N. Bârsan ◽  
R. Kümer ◽  
...  
Keyword(s):  
Gas Sensors ◽  
Gas Mixture ◽  
Mixture Analysis ◽  
Sensors Array

A logarithmic multi-parameter model using gas sensor main and cross sensitivities to estimate gas concentrations in a gas mixture for SnO2 gas sensors

2007 ◽  
Vol 123 (2) ◽  
pp. 1064-1070 ◽  
Author(s):  
A. Chaiyboun ◽  
R. Traute ◽  
T. Haas ◽  
O. Kiesewetter ◽  
T. Doll
Keyword(s):  
Gas Sensor ◽  
Gas Sensors ◽  
Gas Mixture

Gas mixture analysis using silicon micro-reactor systems

2000 ◽  
Vol 9 (4) ◽  
pp. 478-484 ◽  
Author(s):  
T. Becker ◽  
S. Muhlberger ◽  
C. Bosch-v.Braunmuhl ◽  
G. Muller ◽  
A. Meckes ◽  
...  
Keyword(s):  
Gas Mixture ◽  
Mixture Analysis ◽  
Micro Reactor

A modified multilayer perceptron model for gas mixture analysis

1993 ◽  
Vol 16 (1-3) ◽  
pp. 344-348 ◽  
Author(s):  
S.W. Moore ◽  
J.W. Gardner ◽  
E.L. Hines ◽  
W. Göpel ◽  
U. Weimar
Keyword(s):  
Multilayer Perceptron ◽  
Gas Mixture ◽  
Mixture Analysis

Methods to Improve the Accuracy of Urease Respiratory Diagnosis of Helicobacter Pylori

2015 ◽  
Vol 22 (3) ◽  
pp. 86-92
Author(s):  
Ясовеев ◽  
V. Yasoveev ◽  
Матанцев ◽  
A. Matantsev ◽  
Уразбахтина ◽  
...  
Keyword(s):  
Neural Network ◽  
Gas Sensors ◽  
Gas Mixture ◽  
Network Technology ◽  
Electronic Database ◽  
Cross Sensitivity ◽  
Diagnosis Method ◽  
H Pylori ◽  
Mixed Gases ◽  
Neural Network Technology

This article describes existing methods of H. pylori diseases diagnosis, procedures of interpreting acquired results and the diagnosis method with the use of semi-conductor catalytic gas sensors combined into the system. Gas sensors have cross-sensitivity to various gases in addition for which they are designed. The use of several sensors allows to reduce the influence of mixed gases. This method is especially useful within medical entities, where air inside can contain alcohol or chloramine vapors. Sensors are selected in the way to overlap main sensor´s cross-sensitivity zone to the maximum extent possible. This is how mixed gases´ influence on the main sensor is compensated. The proposed system uses methods of artificial neural network technology, which allows to enhance system´s stability in changing gas mixture. Due to microcontroller driven calculations, the system can automatically provide data processing. The proposed system can reduce the influence of factors that contribute uncertainty to the measurement result. These results can be transmitted to PC, which one can use to create electronic database or to hold case history.

Calibration of mass spectrometers for quantitative gas mixture analysis

1987 ◽  
Vol 5 (1) ◽  
pp. 134-139 ◽  
Author(s):  
Robert E. Ellefson ◽  
David Cain ◽  
Charles N. Lindsay
Keyword(s):  
Gas Mixture ◽  
Mixture Analysis ◽  
Mass Spectrometers

Multicomponent gas mixture analysis using a single tin oxide sensor and dynamic pattern recognition

2001 ◽  
Vol 1 (3) ◽  
pp. 207-213 ◽  
Author(s):  
E. Llobet ◽  
R. Ionescu ◽  
S. Al-Khalifa ◽  
J. Brezmes ◽  
X. Vilanova ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Tin Oxide ◽  
Gas Mixture ◽  
Mixture Analysis ◽  
Dynamic Pattern ◽  
Multicomponent Gas ◽  
Multicomponent Gas Mixture
Sign in / Sign up

Export Citation Format

Share Document