Event detection for undersampled electron microscopy experiments: A control chart case study
Event detection for undersampled electron microscopy experiments: A control chart case study
复制标题
欠采样电子显微镜实验的事件检测：控制图案例研究
作者：
S. Reehl;B. Stanfill;Maggie Johnson;D. Ries;N. Browning;B. Layla Mehdi;L. Bramer
对实时系统生成的数据进行有效和及时的推断是具有挑战性的，因为它们通常由大容量，高速数据流组成。特别是，当用户与实时系统交互以获得洞察力、检测事件并对系统做出决策时，用户需要处理的信息的速度和数量通常是压倒性的。此外，分析处理大量数据的计算成本可能很高，并且在实时情况下，传统的推理方法实际上是无用的。缓解这些挑战的一种方法是减少呈现给用户的信息量和放置在流中的数据量。与其他多变量质量控制技术类似，除了开发和部署在线工具外，我们还将描述一种专门为高通量图像构建的方法，用于子采样图像的实时事件检测器（REDSI），旨在通过表征和检测感兴趣的事件为实时系统提供反馈。我们将在扫描透射电子显微镜（STEM）的背景下讨论REDSI，这是一个强大的实时系统，可以提供纳米尺度结构和过程的高空间和时间分辨率。原位STEM实验产生的数据是一系列图像，将结构、成分和动态间相信息传递给从微生物学、神经科学到材料科学和能量学等领域的科学家。
Abstract Making efficient and timely inferences about data generated by real-time systems is challenging, as they often consist of high-volume, high-velocity data streams. In particular, when a user interacts with a real-time system to gain insights, detect events, and make decisions about the system, the rate and amount of information the user is required to process is generally overwhelming. In addition, analytically processing large volumes of data can be computationally expensive and, in real-time, renders traditional inferential methods effectively useless. One approach to mitigate these challenges is to reduce both the amount of information presented to the user and the volume of data placed in the stream. Similar to other multivariate quality control techniques, we will describe a method constructed specifically for high throughput images in addition to the development and deployment of an online tool, Real time Event Detector for Subsampled Images (REDSI), designed to provide feedback on a real-time system by characterizing and detecting events of interest. We will discuss REDSI in the context of scanning transmission electron microscopy (STEM), which is a powerful real-time system that provides high spatial and temporal resolution on nanoscale structures and processes. The data produced by in situ STEM experiments are a stream of images relaying structural, compositional, and dynamic interphase information to scientists in fields ranging from microbiology and neuroscience to materials science and energetics.