Index Catalog // Arch : Northwestern University Institutional Repository

Description:: In the late 2000’s, scientific studies in cultural heritage saw a great advancement in macro X-ray fluorescence (XRF) imaging of paintings. These images are used to generate elemental distribution maps, which aid in identifying chemical elements and paint pig- ments as well as their locations throughout the layers of the...
Keyword:: machine learning, x-ray fluorescence, and image processing
Subject:: Artificial intelligence, Materials Science, and Computer science
Creator:: Chopp, Henry Hayashi
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 08/23/2023
Date Created:: 2023-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_998414 and http://dissertations.umi.com/northwestern:16642

Description:: The past decade has seen the rapid progress of deep learning, which becomes a game-changing technique in different data-intensive domains, with the availability of large scale data, cost-effective computing hardware and more advanced learning theory and algorithms. Despite of the rapid progress of deep learning methods in daily-life applications, such...
Keyword:: Computational Photography, Representation Learning, Dataset Construction, Data Science, Computer Vision, and Deep Learning
Subject:: Materials Science, Information technology, and Computer science
Creator:: Jiang, Weixin
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 06/22/2022
Date Created:: 2022-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_899812 and http://dissertations.umi.com/northwestern:16024

Description:: At its core, the purpose of microscopy is to make objects and their underlying structures visible under high magnification. With the remarkable progress of electron microscopy, the sub-micron “high” magnification of light microscopy has been completely refashioned to encompass subatomic length scales. Unfortunately, higher-magnification does little to negate existing interpretability...
Keyword:: Image Similarity, Computer Vision, Atomic-Resolution Microscopy, Global Structure Optimization, Self-Annotated Datasets, and Information Extraction
Subject:: Materials Science, Computational physics, and Computer science
Creator:: Schwenker, Eric
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/02/2022
Date Created:: 2021-01-01
Resource Type:: Dissertation
Alternate Identifier:: http://dissertations.umi.com/northwestern:15860 and etdadmin_upload_862806

Description:: Supervised learning model is one of the most fundamental machine learning models. It can provide powerful capability of prediction by learning complex patterns hidden in many, sometimes thousands, predictors. It can also be used as a building block of other machine learning tasks, like unsupervised learning and reinforcement learning. Such...
Keyword:: Stochastic Microstructures, Fisher Score, Deep Learning, Machine Learning, Concept Drift, and Nonstationarity Analysis
Subject:: Materials Science, Statistics, and Computer science
Creator:: Zhang, Kungang
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/01/2021
Date Created:: 2020-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_765218 and http://dissertations.umi.com/northwestern:15297

Description:: Connecting structure and function in nanoscale engineered materials and devices relies on the analysis of the fundamental arrangement of matter, frequently under dynamic conditions. The demand to image structures at fundamental length scales has touched inorganic materials, biology, and frequently hybrid hard/soft materials with unique phenomena driven by heterogeneous components....
Keyword:: Imaging, Materials Imaging, Soft Materials, Crystallography, Machine Learning, and Electron Microscopy
Subject:: Nanoscience, Materials Science, and Computer science
Creator:: Hujsak, Karl
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/12/2020
Date Created:: 2018-01-01
Resource Type:: Dissertation
Alternate Identifier:: http://dissertations.umi.com/northwestern:14515 and etdadmin_upload_638887

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