Planar Filter-Sensor Layers for Calibrated Light-Spectrum Encoding
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Solution Overview
Problem
Existing methods for encoding and decoding the spectral distribution of light face challenges such as varying human perception, spatial limitations, temporal constraints, and the need for precise hardware and data handling, which can be inefficient and limited by unidirectional data chains.
Innovation Solution
A device comprising a filter layer and sensor layer with specific filter arrangements and computational modules to encode and decode spectral distributions using unique filter transmission functions and calibration data, enabling efficient encoding and decoding of light spectra.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectral encoding methods are used, then spectral information can be captured, but the system complexity and data handling requirements increase significantly
Solution Approach 1:
The filter layer is divided into multiple discrete filter locations, each with a specific filter transmission function. This segmentation allows the complex spectral encoding task to be broken down into simpler, manageable filter units that can be independently designed and characterized
Solution Approach 2:
The patent transitions from traditional spatial filtering to spectral filtering by introducing filter transmission functions as a new dimension. Instead of using spatial arrangement alone to encode spectral information, the system uses the transmission properties of filters at different locations to create a multidimensional encoding scheme that reduces overall system complexity
2Measurement precision
If high spectral resolution is achieved, then precise spectral distribution can be encoded, but the amount of data to be handled increases
Solution Approach 1:
The filter transmission functions are predetermined and designed before the actual spectral encoding process. This preliminary design allows the system to pre-establish the relationship between filter locations and spectral bands, reducing the computational burden during real-time operation and minimizing data handling requirements
Solution Approach 2:
The patent changes the parameter space by using filter transmission functions characterized by central wavelengths and bandwidths instead of traditional spectral binning. This parameter transformation allows for more efficient data representation and reduces the amount of information that needs to be processed and stored
3Measurement precision
If multiple filters are used at each location, then spectral encoding accuracy improves, but the device complexity increases
Solution Approach 1:
Each filter location in the array serves multiple functions: it acts as a spatial element for imaging, a spectral filter for wavelength selection, and a data point for spectral encoding. This multi-functionality allows the system to achieve high spectral encoding accuracy without proportionally increasing device complexity
Solution Approach 2:
The patent merges the functions of spatial filtering and spectral filtering into a single filter layer structure. By combining these functions at each pixel location, the system achieves both imaging capability and spectral encoding without requiring separate components, thereby reducing overall device complexity
4Speed
If real-time spectral analysis is implemented, then temporal response improves, but the computational requirements and data processing load increase
Solution Approach 1:
The spectral encoding relationships are predetermined through the filter transmission function design, creating a lookup table or pre-computed mapping between filter responses and spectral distributions. This preliminary action allows real-time spectral analysis to be performed through simple table lookups or basic computations rather than complex iterative algorithms
Solution Approach 2:
The patent replaces complex computational spectral analysis with a simplified mathematical model based on the predetermined filter transmission functions. This substitution transforms the problem from one requiring intensive real-time computation to one that can be solved with basic linear algebra operations, reducing computational complexity while maintaining real-time performance
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
The solution provides accurate and efficient encoding and decoding of light spectra, overcoming spatial and temporal limitations, and reducing data handling requirements, allowing for real-time and precise analysis of light properties.
Implementation Method 1
Each filter location holds a filter with a filter-specific transmission function that describes a wavelength-specific transmission of the filter
Implementation Method 2
The sensor locations are adapted to quantify the intensity of the transmitted light by integrating it during a measurement time interval
Data Source
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AI summary
The particular spectral distribution of light (210) is encoded to a spectral distribution identifier (250/270) by a device with planar filter and sensor layers (825, 835). The light (210) is separately filtered by a set of filters, that together comply with uniqueness conditions. The filtered light (220) is measured to obtain a provisional intensity vector ({B}). Non-filtered light (220) is measured to obtain one or more intensity reference values (LUMINANCE). To compensate for variations, computing functions (840, 850, 860, 870) use the intensity reference values (LUMINANCE) 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).