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Deep Neural Networks for applications in public safety
Deep Neural networks have revolutionized machine learning and in particular computer vision. The revolution was achieved by a combination of big data, graphical processing units and advances in numerical optimization. In this work we propose to extend and develop machine learning techniques, focusing on deep learning methods for public health and safety applications. We will use and extend deep learning methodology to deal with 3D seismic and electromagnetic data for signals that are emitted for public safety
Faculty Supervisor:
Eldad Haber
Student:
Jingrong Lin;Keegan Lensink;Tue Boesen
Partner:
Xtract AI
Discipline:
Geography / Geology / Earth science
Sector:
Professional, scientific and technical services
University:
University of British Columbia
Program:
Accelerate
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