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

José Rivera1,2, Gilberto Herrera3, Mario Chacón4

  • 1División de Estudios de Posgrado e Investigación del Instituto Tecnológico de Chihuahua. Ave. Tecnológico No. 2909, Chihuahua Chih. México. jrivera@itchihuahua.edu.mx.

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Summary

This study introduces an improved self-adjustment algorithm for intelligent sensors, optimizing reconfigurable systems. The enhanced method minimizes nonlinearity error by carefully selecting adjustment points, reducing readjustment time and cost.

Keywords:
Self-adjustmentcalibrationinterpolationlinearizationsmart sensorthermistor

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Area of Science:

  • * Sensor Technology
  • * Signal Processing
  • * Metrology

Background:

  • * Intelligent sensors require reconfigurable systems for diverse input signals.
  • * Efficient readjustment is crucial for minimizing downtime in sensor systems.
  • * Self-adjustment algorithms must accurately correct issues like offset, gain variation, and nonlinearity.

Purpose of the Study:

  • * To evaluate a progressive polynomial algorithm for sensor signal nonlinearity.
  • * To present an improved algorithm for minimizing nonlinearity error through optimal adjustment point selection.
  • * To verify the enhanced algorithm's performance using a highly nonlinear thermistor-based temperature system.

Main Methods:

  • * Application of a progressive polynomial algorithm to sensor output signals with varying nonlinearity.
  • * Development of an improved algorithm focusing on the number and sequence of readjustment points.
  • * Implementation and testing of the improved algorithm within a thermistor-based temperature measurement system.

Main Results:

  • * The improved algorithm effectively minimizes nonlinearity error in sensor signals.
  • * Optimal selection and sequencing of readjustment points are critical for achieving minimal error.
  • * The thermistor system demonstrated significant nonlinearity reduction using the proposed method.

Conclusions:

  • * The proposed method provides a criterion for determining the optimal number of readjustment points based on sensor nonlinearity.
  • * This approach enhances readjustment methodologies, impacting efficiency, time, and cost in intelligent sensor applications.
  • * Accurate nonlinearity correction is achievable through intelligent algorithm design and strategic point selection.