Digital Signal Stabilization for Weak Light Noise Filtering
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Solution Overview
Problem
Ambient light sensors struggle to effectively filter noise from weak light signals, leading users to mistakenly interpret continuous fluctuations in light intensity as noise rather than actual signal variations.
Innovation Solution
A method is introduced that stabilizes digital signals by setting boundary range coefficients, stage correction coefficients, and trend correction coefficients, allowing for real-time comparison and adjustment of digital input data to reduce noise and improve signal stability, which can be manually or automatically set based on input or output value changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional noise filtering methods are used on weak light signals, then noise reduction is achieved, but actual signal variations are also filtered out, leading to loss of information
Solution Approach 1:
The patent applies different filtering strengths to different signal characteristics. Boundary range coefficients provide strong filtering for random noise, while trend correction coefficients provide gentle correction for actual signal variations. This local differentiation allows the system to filter noise effectively while preserving meaningful signal changes.
Solution Approach 2:
The patent uses multiple adjustable parameters (boundary range coefficients, stage correction coefficients, trend correction coefficients) to control the filtering process. By changing these parameters, the system can adapt to different signal conditions and achieve optimal balance between noise reduction and signal preservation.
2Loss of information
If no filtering is applied to light signals, then signal variations are preserved, but noise cannot be effectively removed, leading to inaccurate readings
Solution Approach 1:
The patent segments the filtering process into multiple stages with different functions: boundary range filtering for random noise, stage correction for intermediate adjustments, and trend correction for systematic variations. This segmentation allows each stage to address specific aspects of signal quality without over-filtering.
Solution Approach 2:
The patent implements feedback mechanisms where output values from previous stages are used to adjust subsequent processing. The system continuously monitors signal characteristics and adjusts correction coefficients accordingly, enabling adaptive noise filtering that responds to actual signal conditions.
3Object-affected harmful factors
If complex filtering algorithms are used, then noise reduction effectiveness is improved, but device complexity increases
Solution Approach 1:
The patent implements self-adjusting mechanisms where the system automatically determines appropriate correction coefficients based on input signal characteristics. This self-service capability reduces the need for complex external control logic and manual parameter tuning, simplifying the overall device architecture.
4Stability of the object's composition
If multiple correction coefficients are used, then signal stability is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary calculations to determine boundary range coefficients and trend correction coefficients before the main filtering operation. By pre-computing these parameters based on signal characteristics, the system reduces the computational burden during real-time signal processing while maintaining stability.
Data Source
AI summary
A method of stabilizing data of digital signals is provided. The method includes steps of: (a) determining whether or not next input data is larger than previous output data, if yes, adding a base value to a trend value and then performing step(c), if no, performing step(b); (b) determining whether or not the next input data is smaller than the previous output data, if yes, subtracting the base value from the trend value and performing step(c), if no, performing step(c); (c) determining whether or not the trend value is larger than a positive threshold, if yes, subtracting a trend correction coefficient from the previous output data, if no, performing step(d); and (d) determining whether or not the trend value is smaller than a negative threshold, if yes, adding the trend correction coefficient to the previous output data; if no, outputting the previous output data.