Real-time gas identification on mobile platforms using a nanomechanical membrane-type surface stress sensor

Ricardo Jose S Guerrero,Genki Yoshikawa,Francis Nguyen

doi:10.1140/epjti/s40485-014-0009-z

Abstract

Here we show real-time multiple gas identification on a mobile platform through the use of an array of nanomechanical membrane-type surface stress sensors (MSS). Commercially available hardware is used to integrate the MSS array into a portable unit with wireless capability. This unit transmits data to a consumer mobile tablet where data is displayed and processed in real-time. To achieve real-time processing with the limited computational power of commercial mobile hardware, a machine learning algorithm known as Random Forest is implemented. We demonstrate the real-time identification capability of the device by measuring the vapours of water, ethanol, isopropanol, and ambient air.

Highlights

We show real-time multiple gas identification on a mobile platform through the use of an array of nanomechanical membrane-type surface stress sensors (MSS)
An Arduino Mega 2560 received this data via a Serial Peripheral Interface (SPI) to the analog-to-digital converter
We have demonstrated that the combination of an advanced algorithm (Random Forest) and the optimized nanomechanical sensor (MSS) can achieve real-time gas identification with commerical off-the-shelf hardware

Summary

WiFi Signal

Data processing Random Forests [4] allow short characterization times of arbitrary input; characterization time is tunable through the size of the Forest. Classification of data using a Random Forest involves traversal of many decision trees, which can be multithreaded for fast computation on multicore processors. While this approach is sometimes coupled with Principle Component Analysis (PCA) to determine better candidates for predictors [5], the device is capable of identifying the chosen samples without requiring the full dataset in contrast to PCA. Voltage variations as a result of sample flowing through the device form unique curves when measured over time These curves have several identifying characteristics, which can be extracted quickly by splitting the input into several windows, obtaining the difference of their averages, and using these as predictors for the Random Forest analysis. Though the ADS1258 measured at a rate of 460 samples per second (SPS) per channel, data transmission was Channel 1 Channel 2 Channel 3 Channel 4

Data Points

Conclusion

Findings

Additional files