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.
Abstract:
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.
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Advantages:
Compact, portable, low power autonomous processing
Fast processing times
Low detection thresholds and data storage needs
Potential Applications:
Environmental monitoring
Public health emergencies
Radiation Monitoring and detection
Contact Information:
Name: University of California, Santa Cruz Industry Alliances & Technology Commercialization
Email: innovation@ucsc.edu
Phone: 831.459.5415