Systems and Methods for Identifying Anomalous Nuclear Radioactive Sources

Tech ID: 32781 / UC Case 2020-294-0


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.




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


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

Intellectual Property Information

Country Type Number Dated Case
Patent Cooperation Treaty Published Application WO 2022/094625 05/05/2022 2020-294

Additional Patent Pending

Related Materials


Learn About UC TechAlerts - Save Searches and receive new technology matches


  • Abbaszadeh, Shiva

Other Information


Radiation Detection, Machine Learning, Ambient Monitoring, Nuclear contamination, UAV, Drone

Categorized As