Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Poorendra Ramlall, Senior Maximus Langis, Senior Josephine Parker, Senior Raymond Picquet, Senior Leanna Pollicar, Senior Naomi Rodriguez, Senior Simon Sroka, Senior
Lead Presenter's Name
Poorendra Ramlall
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Mihhail Berezovski
Abstract
In order to understand how changes to a material at the atomic and nano-scales impact the way a material behaves, it is necessary to measure the material at that scale. Scanning transmission Electron Microscopy (STEM) is one such approach. Over the last 5 years, computer vision has been completely revolutionized by the advent of deep neural networks (DNN). One challenge of using DNNs for image processing is that they require often large amounts of labelled data in order to build a usable algorithm (model). Researchers at Pacific Northwest National Laboratory (PNNL) have developed a few-shot algorithm which allows the model to be operated using limited data. Using data from a sample micrograph, a new neural network is implemented with the objective of aptly segmenting the micrograph and obtaining classifications for the microstructures. This model investigates the use of existing image segmentation techniques, particularly region-based techniques. The success of this approach can provide a rapid and reconfigurable tool for identifying these microstructures. This research conducted in collaboration with Pacific Northwest National Laboratory.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Characterization of Material Micro & Nano Structures Using Machine Learning Algorithms
In order to understand how changes to a material at the atomic and nano-scales impact the way a material behaves, it is necessary to measure the material at that scale. Scanning transmission Electron Microscopy (STEM) is one such approach. Over the last 5 years, computer vision has been completely revolutionized by the advent of deep neural networks (DNN). One challenge of using DNNs for image processing is that they require often large amounts of labelled data in order to build a usable algorithm (model). Researchers at Pacific Northwest National Laboratory (PNNL) have developed a few-shot algorithm which allows the model to be operated using limited data. Using data from a sample micrograph, a new neural network is implemented with the objective of aptly segmenting the micrograph and obtaining classifications for the microstructures. This model investigates the use of existing image segmentation techniques, particularly region-based techniques. The success of this approach can provide a rapid and reconfigurable tool for identifying these microstructures. This research conducted in collaboration with Pacific Northwest National Laboratory.