Waveform Compression for Particle Detector Data Transmission
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Solution Overview
Problem
Particle detectors face challenges in transmitting and processing large volumes of sensor data in near-realtime due to bandwidth limitations, leading to difficulties in evaluating events such as particle detection and energy emissions, with existing filtering techniques being inadequate for complex data sets and potentially removing relevant information.
Innovation Solution
Implementing a waveform compression technique to reduce the sensor data set, allowing for efficient transmission and processing by compressing the data using methods like discrete cosine transform, which enables lossless or near-lossless representation and supports continuous, incremental compression suitable for real-time processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the entire uncompressed sensor data set is transmitted to the particle event processor, then complete event information is preserved, but transmission bandwidth requirements exceed available capacity
Solution Approach 1:
The patent extracts and transmits only the most significant waveform characteristics (peak amplitude, peak time, integrated charge) rather than the complete raw waveform data. This extraction approach preserves essential event information while dramatically reducing data volume for transmission across the limited bandwidth interface.
Solution Approach 2:
The patent creates a simplified copy of the waveform data containing only critical parameters (peak amplitude, peak time, integrated charge) instead of transmitting the full high-resolution waveform. This copy contains sufficient information for particle event discrimination while fitting within bandwidth constraints.
2Quantity of substance
If filtering is applied to reduce data volume, then transmission bandwidth is sufficient, but relevant particle event information may be removed
Solution Approach 1:
The patent transforms the waveform data from time-domain raw samples into a different parameter space characterized by peak amplitude, peak time, and integrated charge. This parameter transformation maintains the essential physics information needed for particle identification while reducing data volume, effectively changing how the information is represented rather than filtering it away.
3Loss of time
If near-realtime processing is implemented, then event evaluation timeliness is improved, but complex data sets cannot be processed within available time
Solution Approach 1:
The patent performs preliminary processing of the waveform data at the sensor level, calculating peak amplitude, peak time, and integrated charge before transmission. This preliminary action reduces the computational burden on the remote particle event processor, enabling near-realtime processing of complex particle events despite limited bandwidth and processing power at the remote site.
Data Source
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AI summary
Presented herein are systems (200,500,600), methods (300), and nonvolatile computer-readable storage devices (442) for informing a particle event processor (224) of a particle detector (202) of events arising within a sensor period. The systems, methods, and nonvolatile computer-readable storage devices involve the generation of a sensor data set (214) detected by a particle event sensor (212) and representing the events arising within the particle detector during the sensor period. The systems, methods, and nonvolatile computer-readable storage devices also involve the compression of the sensor data set with a waveform compression technique (228) to generate a compressed sensor data set (230), and the transmission of the compressed sensor data set to the particle event processor.