Efficient Real-Time Isotope Identification on SoC FPGA.

Area of Science:

• Nuclear spectroscopy
• Applied physics
• Embedded systems engineering

Background:

• Real-time isotope identification is crucial for radiation monitoring, nuclear waste management, and medical imaging.
• Existing methods often face challenges in efficiency and computational demands for real-time applications.

Purpose of the Study:

• To develop a novel System-on-Chip (SoC) FPGA system for efficient, real-time isotope classification.
• To integrate hardware-accelerated Principal Component Analysis (PCA) with a software-based Random Forest classifier for enhanced performance.

Main Methods:

• Utilized FPGA parallel processing for hardware-accelerated PCA feature extraction from digitized nuclear signals.
• Implemented a Random Forest classifier on an embedded microprocessor for direct, real-time pulse data classification.
• Applied PCA quantization to optimize power consumption and resource utilization.

Main Results:

• Achieved real-time classification without storing Analog-to-Digital Converter (ADC) samples.
• Demonstrated classification accuracy exceeding 98% for challenging, closely matched isotope pairs.
• Significantly reduced latency compared to conventional software-only implementations.

Conclusions:

• The proposed SoC FPGA system offers a scalable, precise, and energy-efficient solution for real-time isotope identification.
• This approach overcomes limitations of traditional methods by leveraging hardware acceleration for critical computational tasks.
• The system is validated for practical applications in nuclear science and technology.