Signal Processing System Auto-Calibration for Particle Measurement
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Solution Overview
Problem
Existing signal processing systems for measuring spherical objects, such as particles and droplets, face challenges with varying temporal and spatial characteristics, low signal-to-noise ratios, and measurement uncertainties due to changing conditions, leading to inaccurate and unreliable size and velocity measurements.
Innovation Solution
A machine-implemented method and system that automatically adjusts gain, aperture, and sampling frequencies to optimize signal processing, using a signal processor with a photodetector section, analog section, and digital section to receive and process signals, and calibrates the system to minimize measurement uncertainty and ensure accurate data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of measurement parameters is performed frequently to adapt to changing conditions, then measurement accuracy may be maintained, but time consumption increases and productivity decreases
Solution Approach 1:
The system automatically monitors signal-to-noise ratio and measurement uncertainty, then self-adjusts gain, aperture, and sampling frequency without manual intervention. The processor continuously adapts to changing measurement conditions by detecting signal characteristics and modifying parameters autonomously, eliminating the need for frequent manual adjustments while maintaining measurement accuracy.
Solution Approach 2:
The system implements continuous feedback by monitoring signal-to-noise ratio and measurement uncertainty in real-time. Based on this feedback, the processor automatically adjusts measurement parameters (gain, aperture, sampling frequency) to optimize measurement quality. This closed-loop control ensures measurement accuracy is maintained while eliminating manual intervention.
2Ease of operation
If measurement parameters are fixed to simplify operation, then ease of operation improves, but adaptability to changing conditions deteriorates
Solution Approach 1:
The system transforms fixed measurement parameters into dynamic, adaptive parameters. The processor continuously adjusts gain, aperture, and sampling frequency based on real-time signal characteristics and measurement conditions. This dynamic adaptation maintains operational simplicity while enabling the system to respond automatically to changing conditions such as varying particle concentrations, sizes, and velocities.
Solution Approach 2:
The system autonomously adapts to changing measurement conditions by monitoring signal-to-noise ratio and measurement uncertainty, then self-adjusting parameters without user intervention. This self-service capability provides both ease of operation (no manual adjustment needed) and adaptability (automatic response to changing conditions).
3Reliability
If signal processing gain is increased to improve signal-to-noise ratio, then measurement reliability improves, but measurement precision may deteriorate due to saturation and distortion
Solution Approach 1:
The system dynamically adjusts gain based on real-time signal characteristics and measured signal-to-noise ratio. The processor monitors the signals and automatically optimizes gain settings to achieve the best possible signal-to-noise ratio without causing saturation or distortion. This dynamic adjustment ensures both reliability (adequate signal-to-noise ratio) and precision (avoidance of saturation effects).
Solution Approach 2:
The system changes the gain parameter dynamically based on measured signal characteristics. By adjusting gain as a variable parameter rather than using a fixed setting, the system can optimize signal-to-noise ratio for each measurement condition while preventing saturation and distortion that would degrade measurement precision.
4Measurement precision
If aperture is reduced to decrease measurement uncertainty, then measurement precision improves, but signal intensity decreases and signal-to-noise ratio worsens
Solution Approach 1:
The system dynamically adjusts aperture size based on real-time measurement of signal-to-noise ratio and measurement uncertainty. The processor monitors these parameters and automatically optimizes aperture settings to achieve the best compromise between reducing measurement uncertainty and maintaining adequate signal-to-noise ratio. This dynamic adjustment allows the system to adapt aperture to each specific measurement condition.
Solution Approach 2:
The system treats aperture as a variable parameter that can be adjusted dynamically. By changing aperture size based on measured signal characteristics and uncertainty, the system optimizes the trade-off between measurement precision (reduced uncertainty with smaller aperture) and signal-to-noise ratio (maintained with adequate aperture).
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides reliable and accurate measurements of size and velocity with predictable uncertainty, reducing errors and the need for frequent manual adjustments, by automatically setting up and calibrating the signal processing system to adapt to changing conditions.
Implementation Method 1
A signal processing system includes a photodetector section to convert light scattered from a spherical object to electrical signals
Implementation Method 2
The light scattered by the spherical object, as it passes through the sample volume, produces an interference fringe pattern at the plane of the detector
Implementation Method 3
laser Doppler velocimetry ('LDV'), a laser Doppler anemometry ('LDA')
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
Machine-implemented methods and apparatuses to automatically set-up a signal processing system are described. The signal processing system is set to a first bandwidth. A sampling frequency of the signal processing system is set to a first sampling frequency. Next, first samples of first signals are received at the first bandwidth and the first sampling frequency. First parameters of the first signals based on the first samples are determined. Next, a second sampling frequency is determined based on the first parameters to sample second samples. The first parameters of the first signals may be a mean transit time, a minimum transit time, a mean frequency of the signals, and a standard deviation of the frequency of the signals. Next, a mixer frequency is determined based on the first parameters. A low pass filter is set based on the mixer frequency.