Gas Sensor Analyte Estimation Using Weighted Signal Processing
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Solution Overview
Problem
Sensing systems face challenges in maintaining high sensitivity and accuracy due to external environmental factors such as temperature, humidity, and electromagnetic interference, which can lead to calibration inaccuracies and sensor drift, affecting the reliability of analyte concentration estimation.
Innovation Solution
The implementation of robust algorithms and detection mechanisms that process sensing data to generate event signals, determine sensor status, and select optimal metrics for concentration estimation, incorporating derivatives, integrals, and dynamic moments to adapt to varying operating conditions and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high sensitivity sensing is implemented to detect analyte presence, then detection sensitivity improves, but sensitivity to external environmental factors increases causing decreased measurement accuracy
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring sensor responses and using machine learning algorithms to adjust and correct measurements in real-time. The system learns from environmental variations and compensates for their effects, maintaining accurate analyte detection despite sensitivity to external factors.
Solution Approach 2:
The patent introduces machine learning algorithms and data processing intermediaries between the raw sensor signals and final measurements. These intermediaries filter out environmental noise and isolate the analyte-specific signals, resolving the contradiction between sensitivity and accuracy.
2Reliability
If robust compensation mechanisms are added to handle calibration inaccuracies and sensor drift, then measurement reliability improves, but device complexity increases
Solution Approach 1:
The patent implements self-service through automated machine learning algorithms that continuously calibrate and compensate for sensor drift without manual intervention. The system self-adjusts to environmental changes and maintains accuracy autonomously, reducing the need for complex manual calibration mechanisms.
Solution Approach 2:
The patent dynamically adjusts measurement parameters and processing algorithms based on environmental conditions and sensor performance. By changing parameters adaptively rather than using fixed complex compensation circuits, the system maintains reliability while managing complexity.
3Measurement precision
If multiple processing metrics are used to improve concentration estimation accuracy, then measurement precision improves, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by selectively using only the necessary processing metrics based on the specific sensing situation. The machine learning algorithm determines which metrics are needed for accurate estimation, avoiding unnecessary computations and reducing processing time while maintaining precision.
Solution Approach 2:
The patent implements dynamic processing where the selection and application of metrics adapt to real-time conditions. The system adjusts its processing approach based on signal quality, environmental factors, and detection requirements, optimizing the balance between accuracy and processing speed.
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 enhances the sensing system's ability to cope with environmental instability, optimizing concentration estimation and reducing the impact of noise and calibration errors, thereby improving the reliability and accuracy of analyte detection.
Implementation Method 1
detection events may be based on the change in resistance or capacitance of a semiconducting thin-film structure that is influenced by the adsorption of gas molecules
Data Source
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AI summary
A method of gas sensing and corresponding gas sensing system include obtaining a first signal by a gas sensor, generating one or more second signals according to the first signal, and calculating a first weight value corresponding to the first signal. The first signal indicates a response of the gas sensor to a concentration of a target gas at the gas sensor. The method of gas sensing further includes calculating one or more second weight values corresponding to each of the one or more second signals and calculating an estimated concentration of the target gas at the gas sensor according to the first weight value and the one or more second weight values.