Spectral Light Encoding With Multi-Filter Decoding Accuracy
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Solution Overview
Problem
Existing methods for analyzing the spectral distribution of light face limitations such as human perception variability, spatial and temporal constraints, and the need for precise data processing to accurately identify object properties, which are often constrained by hardware requirements and data handling capabilities.
Innovation Solution
A method and device for encoding and decoding the spectral distribution of light using a filter set of at least three filters, each with a unique wavelength-specific transmission function, combined to create a unique transmission vector, and computer-implemented functions to generate a spectral distribution identifier, which is decoded using neural networks or libraries to accurately represent the light's spectral distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If human inspectors visually inspect objects to determine object properties, then the inspection can be performed without specialized equipment, but different inspectors perceive light with identical spectral distribution differently and human eyes are not able to inspect light in absolute terms
Solution Approach 1:
The patent replaces the human visual inspection system with a digital imaging system comprising a camera and computer. The camera captures light from the object, and the computer processes the captured light to determine spectral distribution and object properties. This substitution eliminates human perception variability while maintaining ease of operation, as the system requires no complex manual setup or movement.
Solution Approach 2:
The patent introduces a computer as an intermediary between the captured light and the determination of object properties. The computer analyzes the spectral distribution of captured light to identify object properties such as ripeness, color, and composition. This intermediary enables absolute, objective measurements that are not subject to human perception limitations.
2Measurement precision
If developers design technical systems to process light information digitally, then object properties can be investigated with greater precision and automation, but hardware requirements and data handling capabilities become constraints
Solution Approach 1:
The patent applies partial action by processing only the spectral distribution information necessary for identifying specific object properties, rather than analyzing the complete spectral range with maximum detail. The computer determines object properties based on characteristic spectral features, reducing computational complexity and data handling requirements while maintaining sufficient measurement precision for practical applications.
3Productivity
If a filter set with at least three filters is used to encode spectral distribution, then precise and efficient encoding of light spectral data is achieved, but the device complexity increases
Solution Approach 1:
The patent segments the spectral distribution analysis into discrete wavelength ranges using a filter set of at least three filters. Each filter captures a specific portion of the spectrum, and the computer processes these segmented spectral components to determine object properties. This segmentation enables efficient encoding of spectral data by focusing on characteristic wavelength ranges relevant to specific object properties, rather than requiring continuous spectral analysis.
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 precise and efficient encoding and decoding of light spectral data, overcoming human perception limitations and hardware constraints, allowing for accurate identification of object properties in various scenarios, including large and small objects, in real-time or laboratory settings.
Implementation Method 1
The received light is filtered by a filter set of at least N=3 filters with each filter of the filter set separately. The filter set is characterized by: (1) a filter-specific transmission function for each filter in the filter set that describes a wavelength-specific transmission of the filter
Implementation Method 2
a sensor measures an intensity of the filtered light to obtain a provisional intensity value
Data Source
AI summary
The particular spectral distribution of light is encoded to a spectral distribution identifier. The light is separately filtered by a set of filters, that together comply with conditions, such as uniqueness and/or efficiency conditions. The filtered light is measured to obtain a provisional intensity vector. To compensate for variations, computing functions use an intensity reference value to accommodate light variations and use pre-determined calibration data to accommodate filter variations. The computing functions thereby turn the provisional intensity vector to the spectral distribution identifier.