Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
Conferences > CLEO_SI > 2017 > SM1C > SM1C.6

Computational Sensing in Plasmonics: Design of Low-cost and Mobile Plasmonic Readers Using Machine Learning

Zachary S. Ballard, Daniel Shir, Aashish Bhardwaj, Sarah Bazargan, Shyama Sathianathan, and Aydogan Ozcan

Not Accessible

Your library or personal account may give you access

Abstract

We introduce a computational sensing framework to select the optimal set of illumination bands for a given plasmonic sensor design and fabrication method. This framework enables optimized designs for cost-effective, sensitive, and mobile plasmonic readers.

© 2017 Optical Society of America

PDF Article
More Like This
Mobile Microscopy and Machine Learning Provide Accurate and High-throughput Monitoring of Air Quality

Yichen Wu, Ashutosh Shiledar, Yicheng Li, Jeffrey Wong, Steve Feng, Xuan Chen, Christine Chen, Kevin Jin, Saba Janamian, Zhe Yang, Zach Ballard, Zoltán Göröcs, Alborz Feizi, and Aydogan Ozcan
JTh5A.2 CLEO: Applications and Technology (CLEO:A&T) 2017

Automated Detection and Enumeration of Waterborne Pathogens Using Mobile Phone Microscopy and Machine Learning

Hatice Ceylan Koydemir, Steve Feng, Kyle Liang, Rohan Nadkarni, Parul Benien, and Aydogan Ozcan
SM2C.3 CLEO: Science and Innovations (CLEO:S&I) 2017

Potential for Predicting Soil Properties Using Low Cost Near-Infrared Spectroscopy and Machine Learning

Wenbo Wang, Liming Hu, Benjamin K. Wilson, and Matthew D. Keller
AW4I.2 Applied Industrial Spectroscopy (AIS) 2020

View More...
Select as filters
Select Topics Cancel
Top
       
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.
Privacy | Terms of Use