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Graph fusion has emerged as a promising research area for addressing challenges associated with noisy, uncertain, multi-source data. While many ad-hoc graph fusion techniques exist in the current literature, an analytical approach for analyzing the fundamentals of the graph fusion problem is lacking. We consider the setting where we are given multiple Erdős-Rényi modeled adjacency matrices containing...
Every disaster is unique, yet all response efforts face common challenges regarding the collection and processing of disparate data sources, and dissemination of accurate and timely information both to and from disaster areas. Recent advances in graph theory and data fusion can be leveraged to address many of these challenges. This paper describes a graph-based approach for fusing and representing...
Network science is often used to understand underlying phenomena that are reflected through data. In real-world applications, this understanding supports decision makers attempting to solve complex problems. Practitioners designing such systems must overcome difficulties due to the practical limitations of the data and the fidelity of a network abstraction. This paper explores the design of a network...
Fine details revealed by synthetic aperture radar (SAR) coherent change detection (CCD), such as foot prints, require SAR imagery with both high resolution and precision. These large data requirements are at odds with the low bandwidths often available for SAR change detection systems such as those that utilize small unmanned aerial vehicles (UAVs). Here we investigate the interplay between SAR data...
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