Machine Learning: A Crucial Tool for Sensor Design

Weixiang Zhao,Cristina E Davis,Abhinav Bhushan,Anthony D Santamaria,Melinda G Simon

doi:10.3390/a1020130

Weixiang Zhao, Cristina E Davis + Show 3 more

Open Access

https://doi.org/10.3390/a1020130

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Abstract

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies.

Highlights

Sensors have been applied to a variety of fields such as environmental quality monitoring [1], noninvasive disease diagnosis [2], food quality control [3], and industrial process analysis [4], because of the following advantages: (1) ability to function in a harsh environment, (2) ability to operate continuously and automatically, and (3) high accuracy and sensitivity
Developing a sensor depends on two major components: analytical instruments and data analysis techniques
This paper has provided a review of the methods that are popularly used in the machine learning step for sensor design

Summary

Introduction

Sensors have been applied to a variety of fields such as environmental quality monitoring [1], noninvasive disease diagnosis [2], food quality control [3], and industrial process analysis [4], because of the following advantages: (1) ability to function in a harsh environment, (2) ability to operate continuously and automatically, and (3) high accuracy and sensitivity. Developing a sensor depends on two major components: analytical instruments and data analysis techniques. Algorithms 2008, 1 analytical instruments allow producing a great amount of information (data) and permit the exploration of new fields. These generated sensor data may contain irrelevant information and the principles of the new fields could be very complex and even totally unknown, so reliable sensor systems are becoming increasingly reliant on sophisticated data processing techniques.

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Conclusion