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arXiv · 1710.11297

Sequential Adaptive Detection for In-Situ Transmission Electron Microscopy (TEM)

Y. Cao·S. Zhu·Y. Xie·J. Key·J. Kacher·R. R. Unocic·C. M. Rouleau
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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 $\ell_1$. We demonstrate the meritorious performance of our algorithms for TEM imaging using real data.

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Y. Cao, S. Zhu, Y. Xie, J. Key, J. Kacher, R. R. Unocic, C. M. Rouleau. 2017-10-31. Sequential Adaptive Detection for In-Situ Transmission Electron Microscopy (TEM). https://arxiv.org/abs/1710.11297

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