Integrated Optic Micro ring Resonator-based sensors are suitable for lab-on-chip applications due to their smaller footprint. Optical sensors are sensitive to detecting small changes in external parameters. Simultaneous detection of multiple gases present in the atmosphere is crucial for several civilian and military applications. Integrated optic micro ring resonators are promising sensing devices. In this paper, machine learning techniques are used in the classification and detection of gases for a sensor of a Micro Ring Resonator (MRR) array . In this paper machine learning techniques are used to reduce the data to be used for the analysis and improve accuracy of the sensor. Three target gases in the proposed model are Ammonia, Methane and Carbon on each ring simulated in this work. The features and influences on wavelength, transmittance, concentration of gases, and ring radius have also been analyzed. Principal Component Analysis (PCA) and K-Clustering algorithm has been used for the classification and detection of different gases. The Davies Boulden Index is calculated as 0.57 which shows the distance between the clusters. The sensor has a sensitivity of 0.35 nm/ppm.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.