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Data-Optimized Spatial Field Predictions For Robotic Adaptive Sampling: A Gaussian Process Approach, Zachary Nathan
Data-Optimized Spatial Field Predictions For Robotic Adaptive Sampling: A Gaussian Process Approach, Zachary Nathan
Computer Science Senior Theses
We introduce a framework that combines Gaussian Process models, robotic sensor measurements, and sampling data to predict spatial fields. In this context, a spatial field refers to the distribution of a variable throughout a specific area, such as temperature or pH variations over the surface of a lake. Whereas existing methods tend to analyze only the particular field(s) of interest, our approach optimizes predictions through the effective use of all available data. We validated our framework on several datasets, showing that errors can decline by up to two-thirds through the inclusion of additional colocated measurements. In support of adaptive sampling, …
Data For Cybersecurity Research: Process And ‘Wish List’, Jean Camp, Lorrie Cranor, Nick Feamster, Joan Feigenbaum, Stephanie Forrest, David Kotz, Wenke Lee, Patrick Lincoln, Vern Paxson, Mike Reiter, Ron Rivest, William Sanders, Stefan Savage, Sean Smith, Eugene Spafford, Sal Stolfo
Data For Cybersecurity Research: Process And ‘Wish List’, Jean Camp, Lorrie Cranor, Nick Feamster, Joan Feigenbaum, Stephanie Forrest, David Kotz, Wenke Lee, Patrick Lincoln, Vern Paxson, Mike Reiter, Ron Rivest, William Sanders, Stefan Savage, Sean Smith, Eugene Spafford, Sal Stolfo
Other Faculty Materials
This document identifies data needs of the security research community. This document is in response to a request for a “data wish list”. Because specific data needs will evolve in conjunction with evolving threats and research problems, we augment the wish list with commentary about some of the broader issues for data usage.