AES Detection Noise Reduction Algorithm
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Solution Overview
Problem
Current methods for improving the signal-to-noise ratio in AES detection, such as customizing detectors and enhancing excitation efficiency, are either costly or lack industrial reliability, and existing solutions often degrade other parameters or are not industrially viable.
Innovation Solution
A method involving multi-gradient quadratic processing and normalization of detection data using Sobel operators to reduce noise, which includes steps like forming sets of data elements, sorting, and constructing noise arrays to minimize noise fluctuations and improve detection limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If low noise detectors are customized and developed, then detection performance is improved, but manufacturing cost increases and supply stability is compromised
Solution Approach 1:
The patent uses software algorithm copying (noise reduction algorithm) instead of hardware detector copying/customization. The algorithm processes detection data to reduce noise effects, providing a low-cost, easily manufacturable solution that achieves detection performance improvement without custom hardware development
Solution Approach 2:
The patent replaces the mechanical/hardware approach (custom detector development) with a software/computational approach (noise reduction algorithm). This substitution eliminates manufacturing complexity and cost while achieving the same goal of improving detection performance
2Measurement precision
If light-transmitting aperture is increased to improve response, then detection limit is improved, but other optical parameters are degraded
Solution Approach 1:
The patent converts the harmful effect of noise (which persists even when aperture is increased) into a manageable computational problem. The noise reduction algorithm identifies and removes noise components from the detection data, allowing aperture optimization for sensitivity without suffering from the resulting noise degradation of other optical parameters
3Measurement precision
If solution pretreatment is applied to increase excitation efficiency, then photo response is improved, but reliability and stability across all elements are compromised
Solution Approach 1:
The patent develops a universal noise reduction algorithm that can be applied to all elements and detection conditions without requiring element-specific pretreatment procedures. The algorithm provides consistent noise reduction across different elements, maintaining reliability and stability while improving photo response
Solution Approach 2:
The noise reduction algorithm processes the detection data itself to remove noise, without requiring external pretreatment of the solution. The system serves itself by computationally removing noise from the raw detection data, eliminating the need for element-specific chemical or physical pretreatment steps
Data Source
AI summary
The present disclosure provides a method for reducing noise in AES detection, including steps: obtaining G based on a sub-array {tilde over (Z)} of detection data; for each element in G, forming a set of data using three adjacent elements including the element in a column direction, and sorting the set of data in a descending order to obtain an array {tilde over (D)}; performing normalization processing on the array {tilde over (D)} to obtain an array D; for each element in {tilde over (Z)}, forming a set of data using three adjacent elements including the element in the column direction, and sorting the set of data in a descending order to obtain an array U of m rows by n columns; calculating a noise difference value in the column direction, i.e., an array C of m rows by n−1 columns; formulating a noise array N of m rows by n columns; and constructing a new sub-array PN.