Online Spatiotemporal Filtering and Bayesian Topology for Tracking in Dynamically Designed Sensor Networks

用于动态设计传感器网络中跟踪的在线时空过滤和贝叶斯拓扑

• 批准号: 1821241
• 负责人: Vasileios Maroulas
• 金额: $ 10万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1821241&HistoricalAwards=false
• 关键词: Online; Spatiotemporal; Filtering; Bayesian; Topology

This project focuses on the problem of predicting threats' trajectories in realtime designed sensor networks. Sensor networks facilitate the collection of information in different environments. The quality of this information relies on the design of the network, which in turn is associated with the network's coverage and online predictability. Indeed, sensor networks are prone to threats, which are quite spatiotemporally unpredictable, and if they are not detected and monitored promptly, they may have a devastating socioeconomic impact.This project engages homological techniques with data assimilation to (i) establish a novel method for online designing a sensor network whose coverage is queried in real time, and (ii) predict trajectories of spatiotemporally dynamic objects moving in the network using an innovative sequential empirical Bayes scheme. The coverage of the network is verified within a new probabilistic setting in the space of persistence diagrams by computing their distribution. Assimilating the collected data and estimating the parameters associated with spatiotemporal uncertainty in the network, the real-time design is derived as a function of the underlying posterior filtering distribution, related to the threats' underlying motion.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

本项目主要研究实时设计传感器网络中威胁轨迹的预测问题。传感器网络便于在不同环境中收集信息。这些信息的质量依赖于网络的设计，而设计又与网络的覆盖范围和在线可预测性有关。事实上，传感器网络很容易受到威胁，这些威胁在时空上是不可预测的，如果不及时发现和监测，它们可能会产生毁灭性的社会经济影响。该项目采用数据同化的同源技术来(i)建立一种在线设计传感器网络的新方法，该传感器网络的覆盖范围是实时查询的，（ii）使用创新的顺序经验贝叶斯方案预测网络中移动的时空动态物体的轨迹。通过计算持久图空间的分布，在一个新的概率设置内验证网络的覆盖范围。通过吸收收集到的数据并估计网络中与时空不确定性相关的参数，推导出与威胁潜在运动相关的后验滤波分布函数的实时设计。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams

• 发表时间: 2018-03
• 期刊: J. Mach. Learn. Res.
• 作者: V. Maroulas;Joshua L. Mike;Christopher Oballe

Topological reconstruction of sub-cellular motion with Ensemble Kalman velocimetry

使用集成卡尔曼测速技术对亚细胞运动进行拓扑重建

• DOI: 10.3934/fods.2020007
• 发表时间: 2019
• 期刊: Foundations of Data Science
• 影响因子: 2.3
• 作者: Yin, Le;Sgouralis, Ioannis;Maroulas, Vasileios
• 通讯作者: Maroulas, Vasileios

Materials Fingerprinting Classification

• DOI: 10.1016/j.cpc.2021.108019
• 发表时间: 2021-01
• 期刊: Comput. Phys. Commun.
• 作者: Adam Spannaus;K. Law;P. Luszczek;Farzana Nasrin;Cassie Putman Micucci;P. Liaw;L. Santodonato;D. Keffer;V. Maroulas

A Bayesian Framework for Persistent Homology

• DOI: 10.1137/19m1268719
• 发表时间: 2020-01-01
• 期刊: SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE
• 影响因子: 3.6
• 作者: Maroulas, Vasileios;Nasrin, Farzana;Oballe, Christopher
• 通讯作者: Oballe, Christopher

A stable cardinality distance for topological classification

拓扑分类的稳定基数距离

• DOI: 10.1007/s11634-019-00378-3
• 发表时间: 2019
• 期刊: Advances in Data Analysis and Classification
• 影响因子: 1.6
• 作者: Maroulas, Vasileios;Micucci, Cassie Putman;Spannaus, Adam
• 通讯作者: Spannaus, Adam