Systems and Methods for Identifying Anomalous Nuclear Radioactive Sources

Background

Real-time radiation monitoring is critical for public health and emergency response. High-frequency monitoring can generate large amounts of data for dozens of radioactive isotopes though. There is a growing demand for compact radiation detection devices that are also able to quickly and autonomously process these large datasets for anomalies. A UC Santa Cruz researcher has developed machine learning software that synthesizes real-time radiation monitoring data in situ to detect radioactive anomalies.

Technology Description

A UC Santa Cruz researcher has designed software that is used in line with a radiation detector to identify radioactive isotope anomalies. The software uses a field-programmable gate array-based neuromorphic architecture and a spiking neural network to synthesize and display real-time anomalies in radioactive isotope spectra data. This technology is compact, portable, and low-power, and can be used for unmanned and unmanned aerial monitoring.

Applications

• Environmental monitoring
• Public health emergencies
• Radiation Monitoring and detection

Advantages

• Compact, portable, low power
• Autonomous processing
• Fast processing times
• Low detection thresholds and data storage needs

Intellectual Property Information

Additional Patent Pending

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