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Sensors 2008, 8(11), 7410-7427;

Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors

División de Estudios de Posgrado e Investigación del Instituto Tecnológico de Chihuahua. Ave. Tecnológico No. 2909, Chihuahua Chih. México
División de Estudios de Posgrado de la Facultad de Ingeniería de la Universidad Autónoma de Querétaro. Cerro de las Campanas S/N. Col. Las Campanas, Santiago de Querétaro Qro. México
Author to whom correspondence should be addressed.
Received: 28 August 2008 / Revised: 21 October 2008 / Accepted: 17 November 2008 / Published: 19 November 2008
(This article belongs to the Special Issue Intelligent Sensors)
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The development of intelligent sensors involves the design of reconfigurable systems capable of working with different input sensors signals. Reconfigurable systems should expend the least possible amount of time readjusting. A self-adjustment algorithm for intelligent sensors should be able to fix major problems such as offset, variation of gain and lack of linearity with good accuracy. This paper shows the performance of a progressive polynomial algorithm utilizing different grades of relative nonlinearity of an output sensor signal. It also presents an improvement to this algorithm which obtains an optimal response with minimum nonlinearity error, based on the number and selection sequence of the readjust points. In order to verify the potential of this proposed criterion, a temperature measurement system was designed. The system is based on a thermistor which presents one of the worst nonlinearity behaviors. The application of the proposed improved method in this system showed that an adequate sequence of the adjustment points yields to the minimum nonlinearity error. In realistic applications, by knowing the grade of relative nonlinearity of a sensor, the number of readjustment points can be determined using the proposed method in order to obtain the desired nonlinearity error. This will impact on readjustment methodologies and their associated factors like time and cost. View Full-Text
Keywords: Self-adjustment; calibration; interpolation; linearization; thermistor; smart sensor Self-adjustment; calibration; interpolation; linearization; thermistor; smart sensor

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Rivera, J.; Herrera, G.; Chacón, M.; Acosta, P.; Carrillo, M. Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors. Sensors 2008, 8, 7410-7427.

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