REAL-TIME MONITORING OF RADIATION ANOMALIES

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…

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.

Website:

https://techtransfer.universityofcalifornia.edu/NCD/32781.html?utm_source=AUTMGTP&utm_medium=webpage&utm_term=ncdid_32781&utm_campaign=TechWebsites

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