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Implementation of software-based sensor linearization algorithms on low-cost microcontrollers.

¹Başkent üniversitesi, Mühendislik fakültesi-EEM bölümü, Bağlica kampusu, Eskişehir yolu 20 km, Ankara, Turkey. herdem@baskent.edu.tr

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|May 15, 2010

Summary

This study presents six software algorithms to linearize nonlinear sensor data for embedded systems. These algorithms offer a practical solution for accurately processing sensor inputs using low-cost microcontrollers.

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Area of Science:

Embedded Systems Engineering
Sensor Technology
Signal Processing

Background:

Many embedded systems rely on sensors with nonlinear input-output characteristics.
Processing nonlinear sensor data with integer microcontrollers presents significant design challenges.
Accurate data acquisition is crucial for reliable embedded system performance.

Purpose of the Study:

To implement and compare six software-based sensor linearization algorithms.
To evaluate algorithm performance for low-cost microcontrollers.
To provide guidance for selecting appropriate linearization techniques.

Main Methods:

Implementation of six distinct software linearization algorithms.
Utilizing a nonlinear optical distance-measuring sensor for empirical testing.
Performance evaluation based on memory usage, accuracy, and execution time.

Main Results:

Comparative analysis of the six algorithms' efficiency and effectiveness.
Quantification of memory space, linearization accuracy, and execution time for each algorithm.
Identification of algorithm trade-offs based on specific application requirements.

Conclusions:

Software-based linearization is a viable approach for nonlinear sensors in embedded systems.
Algorithm selection should consider the sensor's transfer function, desired accuracy, and microcontroller limitations.
The study provides valuable data for optimizing sensor data processing in resource-constrained environments.