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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2008
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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. |
format | Online Article Text |
id | pubmed-3787452 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
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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