Simpler Transfer Learning (Using "Bellwethers"). ARXIV link: https://arxiv.org/abs/1703.06218
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Updated
Mar 21, 2017 - Python
Simpler Transfer Learning (Using "Bellwethers"). ARXIV link: https://arxiv.org/abs/1703.06218
Code Quality and Software Agility is an invaluable resource for understanding the software quality trade-offs at both the code and organizational levels. It covers testing and monitoring strategies, requirements management, team agility, and decision making.
Material defect detection with Pulse Thermography
Monte Carlo codes to simulate cation disorder in kesterite semiconductors
Non-negative matrix factorization is applied for classification of defects on steel surface using CNN
InjuredPixels (for Android) can help you check your entire screen for dead pixels, scratches or other defects.
A python package to compute zero-field-splitting tensors for molecules and spin quantum bits in semiconductors.
Application for searching for defects in plates (Ellipse method and Time-reversal)
Topological modeling of twinning in 10M NiMnGa magnetic shape memory alloy
Testing Harness for transactions, messages, defects, validation, and protocol benchmarking
Supplementary material accompanying Frey, N. C.; Akinwande, D.; Jariwala, D.; Shenoy, V. B. Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing, ACS Nano (2020).
How to create a Pareto Chart using Pandas and Matplotib.
Melt pool monitoring in a metal 3D printer. Tracks area of the melt pool, intensity, and radius. The variations of the melt pool are used to detect build defects. Meshflow Limited
This repository contains a fully trained neural network which is used as a binary classifier to distinguish clearwood and defects. Several Jupyter Notebooks are presented in the GitHub page to help you use the wood classifier.
Imaging system for analyzing defects of semiconductor wafers and chips
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