Spectral Distribution Encoding Using Filter Calibration
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Solution Overview
Problem
Current methods for encoding and decoding the spectral distribution of light are limited by variations in filter transmission functions and light intensity, which affect the accuracy of spectral distribution identification, particularly when using multiple filters and sensors.
Innovation Solution
A method involving a filter set with unique transmission functions for each wavelength, combined with computational processing to normalize intensity values, ensures accurate encoding and decoding of spectral distributions by compensating for filter and light variations using calibration data and intensity references.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple filters are used to encode spectral distribution, then spectral resolution is improved, but measurement precision deteriorates due to variations in filter transmission functions
Solution Approach 1:
The patent applies preliminary calibration by measuring the actual transmission functions of filters before use and storing reference values. This preliminary action allows the system to compensate for manufacturing variations and aging effects during subsequent spectral measurements, thereby maintaining measurement precision despite filter variations.
Solution Approach 2:
The system implements feedback by continuously monitoring filter transmission characteristics and using this information to adjust spectral distribution calculations. The measured transmission functions are fed back into the decoding algorithm to correct for deviations, ensuring reliable spectral identification over time.
2Adaptability or versatility
If light intensity varies, then measurement range is improved, but measurement precision deteriorates due to intensity normalization challenges
Solution Approach 1:
The patent uses feedback by measuring the actual light intensity with sensors and using this information to normalize spectral measurements. The intensity feedback allows the system to adjust for variations in light source output and environmental lighting conditions, maintaining spectral accuracy across a wide intensity range.
Solution Approach 2:
The system dynamically adjusts measurement parameters based on detected light intensity levels. When intensity varies, the system modifies exposure times, gain settings, or normalization factors to optimize the measurement, thereby maintaining precision across different intensity conditions.
3Measurement precision
If filter set complexity increases to improve spectral resolution, then device complexity increases
Solution Approach 1:
The patent performs preliminary characterization of each filter's transmission function and stores this data for use in decoding algorithms. This preliminary action allows the system to use simpler filter sets while achieving high spectral resolution through computational methods rather than requiring complex physical filter arrangements.
Solution Approach 2:
The system replaces complex mechanical filter configurations with computational spectral reconstruction. Instead of using many precisely engineered filters with specific transmission characteristics, the system uses a smaller set of filters combined with advanced algorithms that computationally reconstruct the spectral distribution, thereby reducing device complexity while maintaining resolution.
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 accuracy and reliability of spectral distribution identification, enabling effective encoding and decoding of light spectra despite variations in filter characteristics and light intensity, improving data processing in applications involving physical objects.
Implementation Method 1
filtering the received light by a filter set with N >= 3 filters with each filter of the filter set separately, wherein each filter has a filter-specific transmission function that describes a wavelength-specific transmission of the filter
Implementation Method 2
measuring the intensity of the filtered light to obtain a filter-specific intensity value
Data Source
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AI summary
The particular spectral distribution (310) of light (210) is encoded to a spectral distribution identifier (250/270). The light (210) is separately filtered by a set of filters (120-n), that together comply with uniqueness conditions. The filtered light (220-n) is measured (130) to obtain a provisional intensity vector ({B}, 230-n). To compensate for variations, computing functions (140, 150, 160, 170) use an intensity reference value (L_DATA) to accommodate light variations and use pre-determined calibration data ({CAL}) to accommodate filter variations. The computing functions thereby turn the provisional intensity vector to the spectral distribution identifier (250/270).