Illumination Spectral Encoding Using ML and Multi-Channel Sensors
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Solution Overview
Problem
Existing advanced lighting systems face challenges in accurately simulating complex illumination scenarios due to the high cost and complexity of spectrometers, and the need for luminaire-specific illumination data formatting, which is time-consuming and cumbersome.
Innovation Solution
A multi-channel spectrally sensitive sensor combined with machine learning systems uses lossy spectral measurements and auxiliary data to identify a higher resolution representation of illumination, enabling efficient programming of spectrally programmable lighting systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a spectrometer is used to measure spectral power distribution, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a camera to capture an image of the illumination source, creating a visual copy that contains spectral information. This image copy is then processed through machine learning models to extract spectral power distribution data, replacing the need for a physical spectrometer while maintaining measurement capability.
Solution Approach 2:
The patent replaces the mechanical/optical measurement system (spectrometer) with a computational approach using a camera and machine learning algorithms. The camera captures visual data, and neural networks process this data to infer spectral characteristics, substituting physical measurement mechanisms with information processing.
2Measurement precision
If a spectrometer is used to measure spectral power distribution, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent employs inexpensive cameras and consumer-grade computational resources instead of expensive spectrometers. The system uses readily available components (camera, processor, machine learning models) that are much cheaper than spectral measurement instruments, making the technology accessible for widespread deployment.
Solution Approach 2:
By creating and processing digital copies (images) of the illumination source rather than using expensive physical measurement instruments, the system achieves spectral analysis capability at minimal cost. The image copy serves as a surrogate for direct spectral measurement.
3Adaptability or versatility
If luminaire-specific illumination data is compiled and distributed, then adaptability is improved, but loss of time increases
Solution Approach 1:
The patent pre-trains machine learning models with comprehensive illumination data from multiple luminaires and spectral power distributions. This preliminary training creates generalized models that can quickly adapt to specific luminaires without requiring time-consuming compilation and distribution of luminaire-specific data. The heavy computational work is done in advance.
Solution Approach 2:
The system enables each luminaire to self-characterize by using the pre-trained machine learning model to process its own operational data. The luminaire independently generates its spectral power distribution profile without requiring external compilation or manual configuration, eliminating the time-consuming data distribution process.
Data Source
AI summary
A method for identifying, recording, encoding, or reproducing electromagnetic radiation employs machine learning models. For example, each of multiple training illuminations may be measured using first and second types of systems. The first type of system may be a low-cost system that includes light sensors and measures an array of readings; the second type of system may be a precision spectrometer that measures the spectral power distribution. A trained machine learning model may predict the spectral power distribution of illumination from the array associated with the first type of system and may predict the representation of illumination understood by a spectrally tunable luminaire. The array of readings associated with the first type of system may be used to identify, record, encode, or reproduce electromagnetic radiation.