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

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...

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Detalles Bibliográficos
Autores principales: Rivera, José, Herrera, Gilberto, Chacón, Mario, Acosta, Pedro, Carrillo, Mariano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3787452/
https://www.ncbi.nlm.nih.gov/pubmed/27873936
http://dx.doi.org/10.3390/s8117410
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author Rivera, José
Herrera, Gilberto
Chacón, Mario
Acosta, Pedro
Carrillo, Mariano
author_facet Rivera, José
Herrera, Gilberto
Chacón, Mario
Acosta, Pedro
Carrillo, Mariano
author_sort Rivera, José
collection PubMed
description 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.
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spelling pubmed-37874522013-10-17 Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors Rivera, José Herrera, Gilberto Chacón, Mario Acosta, Pedro Carrillo, Mariano Sensors (Basel) Article 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. Molecular Diversity Preservation International (MDPI) 2008-11-19 /pmc/articles/PMC3787452/ /pubmed/27873936 http://dx.doi.org/10.3390/s8117410 Text en © 2008 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Rivera, José
Herrera, Gilberto
Chacón, Mario
Acosta, Pedro
Carrillo, Mariano
Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors
title Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors
title_full Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors
title_fullStr Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors
title_full_unstemmed Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors
title_short Improved Progressive Polynomial Algorithm for Self-Adjustment and Optimal Response in Intelligent Sensors
title_sort improved progressive polynomial algorithm for self-adjustment and optimal response in intelligent sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3787452/
https://www.ncbi.nlm.nih.gov/pubmed/27873936
http://dx.doi.org/10.3390/s8117410
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