The objective of this project is to advance classification capabilities and efficiencies by designing an efficient Cloudbased workflow for processing dynamic electromagnetic induction (EMI) data, leveraging the Cloud-based framework developed during MR-201713, and enhancing the Virtual Site synthetic code set.
The project team plans to systematically advance innovative, new technologies that facilitate technically correct and efficient behaviors across the entire Military Munitions Response Program project delivery team (PDT) by designing cloud based processing schemes for dynamic data and by enhancing the ESTCP-directed Virtual Site code base. The benefits for moving the geophysical management and data analysis tools to the cloud include uniform security polices, transparent collaboration and communication, automated version control, activity logging and auditing, no local information technology requirements, and on-demand processing speed and resources. The foci of MR-201713 consists of the mechanics of Cloud computing and generating an acceptable user experience for processing cued EMI data for the entire PDT. The project team plans to leverage all previous work to design a workflow, user graphics, and associated requirements for processing dynamic data.
The ESTCP-directed Virtual Site code base is a collection of routines that allow users to generate dynamic and static EMI data for performance testing and advancement training. To date, the Virtual Site code base has very limited capabilities for systematically creating data deficiencies that challenge analysts. The project team will advance the usefulness of the Virtual Site code base by building advanced failure modes that allow trainers and testers to more thoroughly evaluate analyst performance and quality-control measures.
The technologies to be used for this effort provide a fundamentally different experience for the users of EMI classification than current technologies. The benefits include:
a) Cloud-based solution for the entire Project Development Team that has
b) The generation of synthetic EMI data sets that replicate realistic and complex sensor failure modes for testing and training purposes.