Speaker
Description
We present an FPGA-based autonomous logging system integrated into the control infrastructure of a proton therapy facility, designed to optimize detector signal
processing while maximizing memory efficiency. The system manages up to four beam
monitors connected via optical communication links, each providing measurement samples at 10 µs intervals.
The FPGA implements intelligent threshold-based data acquisition, automatically
initiating logging when signals exceed a pre-defined on-threshold and terminating when
they fall below an off-threshold. This autonomous operation ensures that only relevant
data segments are captured, eliminating unnecessary storage of baseline and transition
periods. The system provides 218 × 16 byte recording capacity, enabling continuous
logging of up to 2.6 seconds at full sampling rate. Extended observation periods are
achieved through configurable 2n sample compression, where the FPGA calculates realtime mean values for all four channels and variance/standard deviation for two selected
channels.
The control system, running on an IOC board with attached FPGA, processes the
logged data during idle states. Post-processing algorithms automatically trim signal
rise and fall-off periods from each recorded data bundle, isolating the stable plateau
regions across all four detectors. Averaged plateau values enable precise calculation of
detector signal ratios, which are used to calibrate scaling factors that compensate for
beam-line transmission variations, ensuring stable downstream beam currents essential
for reliable treatment delivery.
This integrated approach combining autonomous FPGA-based data acquisition
with efficient post-processing demonstrates significant improvements in memory utilization and signal quality for real-time beam monitoring applications.
| Minioral | Yes |
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| IEEE Member | No |
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