Light Spectral Encoding and Decoding with Calibrated Filter Sets
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Solution Overview
Problem
Existing methods for processing light data face challenges in accurately capturing and analyzing spectral distributions due to limitations in human visual inspection, spatial constraints, temporal limitations, and the need for specialized equipment for different wavelength ranges, leading to inefficiencies in data processing and analysis.
Innovation Solution
A method and device that encode the spectral distribution of light using a filter set of at least three filters with unique transmission functions, followed by computational processing to generate a spectral distribution identifier, which is then decoded using neural networks or lookup tables to accurately represent and analyze the light data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If light information is processed in the form of digital data to overcome limitations of visual inspection, then measurement precision and analysis capability are improved, but device complexity increases
Solution Approach 1:
The spectral distribution measurement is segmented into discrete wavelength bins, where the spectral power distribution is integrated over specific wavelength ranges. This segmentation allows complex spectral information to be processed as manageable digital data blocks, improving measurement precision while keeping device complexity acceptable through systematic data organization.
Solution Approach 2:
A spectral calibration curve serves as an intermediary between the raw detector signals and the actual spectral power distribution. This calibration curve, established through reference measurements, mediates the transformation of detector responses into accurate spectral data, enhancing measurement precision without requiring direct complex physical measurements for each wavelength.
2Measurement precision
If spectral resolution is increased to differentiate subtle variations in spectral distribution, then measurement precision is improved, but data volume and processing time increase
Solution Approach 1:
The continuous spectral range is segmented into discrete wavelength bins with specific resolution. This segmentation allows the system to process spectral information at manageable resolution levels, balancing measurement precision with processing efficiency by integrating power over defined wavelength intervals rather than requiring continuous spectral analysis.
Solution Approach 2:
The system allows dynamic adjustment of spectral resolution parameters based on measurement requirements. By changing the wavelength binning parameters and integration ranges, the system can optimize between precision and processing time, using higher resolution only when necessary for distinguishing subtle spectral variations.
3Adaptability or versatility
If the wavelength range is expanded to cover both visible and non-visible radiation, then adaptability is improved, but device complexity and cost increase
Solution Approach 1:
The system employs a universal measurement approach that can handle multiple wavelength ranges (visible, UV, IR) through a single integrated platform. The same basic measurement principles, detector types, and data processing methods are applied across different spectral ranges, allowing the system to achieve broad adaptability without proportionally increasing complexity for each wavelength range.
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 enables efficient and accurate encoding and decoding of spectral distributions, reducing data volume and processing time, allowing for real-time analysis of large and small objects, and overcoming limitations of human visual inspection and equipment constraints.
Implementation Method 1
The light is filtered by a filter set of at least N = 3 filters and with each filter of the filter set separately. Each filter in the filter set has a filter-specific transmission function that describes a wavelength-specific transmission of the filter.
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 (120) of filters (120-n), that together comply with a uniqueness condition and with an efficiency condition. 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).