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SEQUENTIAL ADAPTIVE DETECTION FOR IN-SITU TRANSMISSION ELECTRON MICROSCOPY (TEM)

Citation Author(s):
Yang Cao, Shixiang Zhu, Yao Xie, Jordan Key, Josh Kacher, Raymond Unocic, Christopher Rouleau
Submitted by:
Shixiang Zhu
Last updated:
13 April 2018 - 11:40pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Shixiang Zhu
Paper Code:
3970
 

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 ℓ1. We demonstrate the meritorious performance of our algorithms for TEM imaging using real data.

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