Sequential Adaptive Detection for In-Situ Transmission Electron Microscopy (TEM)
Applications
2017-11-01 v1
Abstract
We develop new efficient online algorithms for detecting transient sparse signals in TEM video sequences, by adopting the recently developed framework for sequential detection jointly with online convex optimization [1]. We cast the problem as detecting an unknown sparse mean shift of Gaussian observations, and develop adaptive CUSUM and adaptive SSRS procedures, which are based on likelihood ratio statistics with post-change mean vector being online maximum likelihood estimators with . We demonstrate the meritorious performance of our algorithms for TEM imaging using real data.
Cite
@article{arxiv.1710.11297,
title = {Sequential Adaptive Detection for In-Situ Transmission Electron Microscopy (TEM)},
author = {Y. Cao and S. Zhu and Y. Xie and J. Key and J. Kacher and R. R. Unocic and C. M. Rouleau},
journal= {arXiv preprint arXiv:1710.11297},
year = {2017}
}