Distributed Computational Intelligence for Unattended Radiation Detection Networks

BluEyeQ LLC and Los Alamos National Laboratory (LANL) are pleased to propose a persistent, unattended radiation detection network based on distributed computational intelligence. Our basic premise is long duration, "unattended" operation is only achieved by judicious use of power resources at all levels of the system. We take a holistic system design approach for maximizing detection probability while minimizing power consumption at each tier of the solution. Advanced techniques for background radiation suppression and target isotope feature extraction are investigated. By leveraging Distributed Computational Intelligence detection decisions are made efficiently at the source, and are made jointly with auxiliary sensors for reliable threat assessment and advanced modality triggering. Our research results have direct application to diverse border security and law enforcement deployments, and are compatible with application to remote machine health monitoring in the commercial Industrial Internet of Things (IIoT) market.