English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

A method for online decorrelation of chemical sensor signals from the effectsof environmental humidity and temperature variations is proposed. The goal isto improve the accuracy of electronic nose measurements for continuousmonitoring by processing data from simultaneous readings of environmentalhumidity and temperature. The electronic nose setup built for this studyincluded eight metal-oxide sensors, temperature and humidity sensors with awireless communication link to external computer. This wireless electronic nosewas used to monitor air for two years in the residence of one of the authorsand it collected data continuously during 537 days with a sampling rate of 1samples per second. To estimate the effects of variations in air humidity andtemperature on the chemical sensors signals, we used a standard energy bandmodel for an n-type metal-oxide (MOX) gas sensor. The main assumption of themodel is that variations in sensor conductivity can be expressed as a nonlinearfunction of changes in the semiconductor energy bands in the presence ofexternal humidity and temperature variations. Fitting this model to thecollected data, we confirmed that the most statistically significant factorsare humidity changes and correlated changes of temperature and humidity. Thissimple model achieves excellent accuracy with a coefficient of determination$R^2$ close to 1. To show how the humidity-temperature correction model worksfor gas discrimination, we constructed a model for online discrimination amongbanana, wine and baseline response. This shows that pattern recognitionalgorithms improve performance and reliability by including the filtered signalof the chemical sensors.

Online Decorrelation of Humidity and Temperature in Chemical Sensors for Continuous Monitoring