Adaptive Light Barrier Signal Processing for Clinical Analyzers
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Solution Overview
Problem
Existing automated analyser systems for clinical diagnostics and life sciences face challenges in differentiating signal from background noise due to their static implementation, leading to potential errors in process control.
Innovation Solution
A method for adaptive signal processing that involves illuminating a container's content with a light source, measuring the optical signal with a sensor, assigning voltage values, preparing a voltage curve, determining background noise and target signal levels, and performing plausibility checks to establish optimal discrimination limits between noise and signal levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static signal processing is used to distinguish between signal and background noise, then the system structure remains simple, but measurement precision deteriorates due to inability to adapt to changes between calibrations
Solution Approach 1:
The patent implements dynamic signal processing by continuously analyzing voltage curve characteristics and adapting discrimination limits in real-time. The system transitions from static threshold-based detection to dynamic adaptation where discrimination limits are continuously optimized based on measured voltage patterns, enabling the system to respond to changes in the measurement system without recalibration.
Solution Approach 2:
The system employs feedback mechanisms by analyzing the voltage curve and using the measured characteristics to adjust discrimination limits. The voltage levels and their derivatives are fed back into the processing algorithm to continuously refine the distinction between signal and background noise, improving measurement precision through adaptive feedback control.
2Reliability
If static process control with discrete step reactions is used, then the system remains simple to operate, but reliability deteriorates due to incorrect evaluations when changes occur between calibrations
Solution Approach 1:
The system performs self-adjustment by automatically analyzing voltage curve patterns and adapting discrimination limits without external intervention. The adaptive signal processing algorithm autonomously detects changes in the measurement system and modifies its parameters accordingly, eliminating the need for manual recalibration and reducing operational complexity while improving reliability.
3Measurement precision
If adaptive signal processing is implemented to dynamically adapt to changes, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent changes the processing parameters dynamically by adjusting discrimination limits based on measured voltage curve characteristics. Instead of using fixed thresholds, the system modifies its decision parameters in real-time based on the observed signal patterns, enabling adaptive detection that improves precision while managing complexity through parameter adaptation rather than structural complexity.
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
This approach enables dynamic adaptation to changes in the measurement system, reducing errors by ensuring optimal signal differentiation and allowing for effective process control in automated analyser systems.
Implementation Method 1
illuminating the content in a container with a light source from one side of the container; measuring the optical signal with a sensor resulting from light shining through the container and its content
Data Source
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Figure 3
AI summary
The present invention relates to a method for adaptive signal processing of each trace originating from the photosensor. The method comprises an automatic detection of the voltage level of the target signal and the background noise. Based on the determined voltages advantageous decision limits are calculated. Such decision limits can be used to detect, for example, the temporal length of the signal. Due to a dynamic calculation of the decision limits, these limits are optimal for each measured voltage curve. The system is also able to detect a measurement with only noise and mark it as incomplete.