Adaptive Noise Filtering for Sensor Data Sequences
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Solution Overview
Problem
Existing digital data processing techniques for improving information quality in data sequences are often pre-determined and application-specific, failing to adapt effectively to changing noise and signal profiles over time, leading to suboptimal noise attenuation and bandwidth utilization.
Innovation Solution
A computer-implemented method that dynamically calculates a time-constant for a noise attenuation filter based on the relationship between noise and signal profiles for each element in input data sequences, applying adaptive filtering to attenuate noise and compress dynamic range, while also adjusting gain and gamma correction to enhance signal quality and bandwidth utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-determined and application-specific digital data processing techniques are used, then the processing is simple and straightforward, but the information quality improvement is suboptimal because the techniques cannot adapt to changing noise and signal profiles over time
Solution Approach 1:
The patent implements dynamic adaptability by continuously calculating the time-constant based on the relationship between noise profile and signal profile. The filter adapts its parameters over time rather than using fixed pre-determined settings, allowing it to respond to changing conditions in the data sequences while maintaining a relatively simple computational approach.
Solution Approach 2:
The patent changes the parameter (time-constant) of the noise attenuation filter based on the relationship between noise and signal profiles. By dynamically adjusting this parameter according to actual data conditions, the system achieves adaptability without requiring complex reconfiguration of the entire processing pipeline.
2Measurement precision
If a set gamma level is applied to image data, then the processing is computationally efficient, but the information quality is not optimized for different regions or time periods of the data sequence
Solution Approach 1:
The patent applies different time-constant values to different elements in the input data sequences based on their local noise and signal profiles. This allows each region or time period to be processed with optimized parameters specific to its characteristics, improving information quality without requiring excessive computational resources.
Solution Approach 2:
The patent applies noise attenuation selectively based on the calculated relationship between noise and signal profiles. Rather than applying uniform processing to all data, the system adjusts the degree of filtering applied to different elements, optimizing information quality while maintaining processing efficiency.
3Reliability
If fixed filter settings are used, then the device complexity is low and processing is straightforward, but the noise attenuation performance is suboptimal for varying data conditions
Solution Approach 1:
The patent uses feedback from the data sequences themselves by calculating the relationship between noise profile and signal profile to dynamically adjust the time-constant. This feedback mechanism improves noise attenuation effectiveness by adapting to actual conditions while keeping the computational overhead relatively low.
Solution Approach 2:
The noise attenuation filter serves itself by automatically adjusting its own time-constant parameter based on the characteristics of the input data. This self-adjusting capability improves reliability without requiring external control systems or complex configuration mechanisms.
Data Source
AI summary
A computer-implemented data processing method to improve information quality in data sequences by attenuating noise in the data sequences, the method including: receiving input data sequences, having a plurality of elements, from one or more sensors, each of the elements having at least one dimensional component; performing a spectral analysis on the dimensional component of each of the elements, independently, to estimate a signal profile of the input data sequences; estimating a noise profile of the input data sequences using calibration data associated with the sensor; dynamically calculating a time-constant for a noise attenuation filter, and adapting the time-constant over time, for each one of the elements in the input data sequences, based on the relationship between the noise profile and the signal profile; applying the noise attenuation filter for each one of the elements to each one of the elements, respectively, to filter the input data sequences to derive filtered data sequences; and outputting the filtered data sequences.