Skip to main content Accessibility help
×
Home
Hostname: page-component-544b6db54f-vq995 Total loading time: 0.17 Render date: 2021-10-24T13:53:25.593Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

Pioneering the Use of Neural Network Architectures and Feature Engineering for Real-Time Augmented Microscopy and Analysis

Published online by Cambridge University Press:  01 August 2018

Matthew L. Gong
Affiliation:
University of Utah, Scientific Computing Imaging Institute, Department of Electrical and Computer Engineering, Salt Lake City, UT Idaho National Laboratory, Nuclear Science and Technology Division, Idaho Falls, ID
Su Jong Yoon
Affiliation:
Idaho National Laboratory, Nuclear Science and Technology Division, Idaho Falls, ID
Raymond R. Unocic
Affiliation:
Oak Ridge National Laboratory, Center for Nanophase Materials Science, Oak Ridge, TN
Hope Ishii
Affiliation:
University of Hawai’i at Manoa, School of Ocean and Earth Science and Technology, Honolulu, HI
John P. Bradley
Affiliation:
University of Hawai’i at Manoa, School of Ocean and Earth Science and Technology, Honolulu, HI
Brandon D. Miller
Affiliation:
Idaho National Laboratory, Nuclear Science and Technology Division, Idaho Falls, ID
Daniel Masiel
Affiliation:
Integrated Dynamic Electron Solutions, Pleasanton, CA
Bryan Reed
Affiliation:
Integrated Dynamic Electron Solutions, Pleasanton, CA
Tolga Tasdizen
Affiliation:
University of Utah, Scientific Computing Imaging Institute, Department of Electrical and Computer Engineering, Salt Lake City, UT
Jeffery A. Aguiar
Affiliation:
University of Utah, Scientific Computing Imaging Institute, Department of Electrical and Computer Engineering, Salt Lake City, UT University of Utah, Department of Materials Science and Engineering, Salt Lake City, UT
Rights & Permissions[Opens in a new window]

Abstract

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Abstract
Copyright
© Microscopy Society of America 2018 

References

[1] Work supported through the INL Laboratory Directed Research& Development (LDRD) Program under DOE Idaho Operations Office Contract DE-AC07-05ID145142. Drs. Brian van Deevner and Ian Harvey are thanked for her many useful discussions and contributions to this work. Authors also acknowledge Sudhajit Misra, Dr. Jing Gu, and Robert Mariani for helpful discussions..Google Scholar
You have Access

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Pioneering the Use of Neural Network Architectures and Feature Engineering for Real-Time Augmented Microscopy and Analysis
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Pioneering the Use of Neural Network Architectures and Feature Engineering for Real-Time Augmented Microscopy and Analysis
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Pioneering the Use of Neural Network Architectures and Feature Engineering for Real-Time Augmented Microscopy and Analysis
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *