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Visit USCOA Universal Convolution Neural Network (U-CNN) for Highly Accurate Defect Detection in Civil Infrastructure Inspection
Computer File Registration
Copyright Title
A Universal Convolution Neural Network (U-CNN) for Highly Accurate Defect Detection in Civil Infrastructure Inspection
Status
Published
on 23 Jul 2019
Year of Creation
2018
Copyright Claimant
University of Nevada Reno
Registration Number
TX0008779913
on 23 Jul 2019Copyright Summary
The U.S. Copyright record (Registration Number: TX0008779913) dated 23 Jul 2019, pertains to an electronic file (eService) titled "A Universal Convolution Neural Network (U-CNN) for Highly Accurate Defect Detection in Civil Infrastructure Inspection" created in 2018. The copyright holder is University of Nevada Reno, known for their creative contributions in computer file registration. For any inquiries concerning this copyrighted material, kindly reach out to University of Nevada Reno.
Application Details
Registration Number
TX0008779913
Registration Date
7/23/2019
Year of Creation
2018
Agency Marc Code
DLC-CO
Record Status
Changed
Corporate Author
University of Nevada Reno
Physical Description
Electronic file (eService)
First Publication Nation
Brazil
Notes
Rights Note: Shannon Sheehan, University of Nevada Reno, 1664 N. Virginia St MS 0321, Reno, NV, 89512, United States, (775) 784-7721, ssheehan@unr.edu
Statements
Application Title Statement: A Universal Convolution Neural Network (U-CNN) for Highly Accurate Defect Detection in Civil Infrastructure Inspection
Author Statement: University of Nevada Reno employer for hire Citizenship: United States Authorship: computer program
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