Spectral Sensor Neural Network Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Spectral sensors with interference-based filters face non-ideal filter responses and sensor performance issues, such as cross-talk, non-linearities, and manufacturing errors, leading to inaccurate spectral representations of incident light.
Innovation Solution
The integration of artificial neural networks within spectral sensors to correct raw spectral output by using calibration data and training methods, such as matrix multiplication and least squares approaches, to generate a correction matrix, and incorporating temperature data for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If interference-based filters are used in spectral sensors, then controlled light wavelengths can be provided, but non-ideal filter responses and sensor performance issues occur leading to inaccurate spectral representations
Solution Approach 1:
The patent applies preliminary action by collecting calibration data under known spectral conditions before actual measurement, then using this pre-collected data to train neural networks that correct subsequent measurements. This preliminary calibration phase allows the system to account for non-ideal filter responses and sensor characteristics in advance, improving spectral accuracy without changing the physical filters themselves.
Solution Approach 2:
The patent introduces an intermediary computational layer (neural network) that mediates between the raw sensor output and the final spectral representation. This neural network intermediary processes the non-ideal sensor responses and transforms them into accurate spectral data, effectively decoupling the physical filter limitations from the measurement accuracy.
2Measurement precision
If artificial neural networks are integrated to correct spectral output, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces physical/mechanical correction approaches with computational methods. Instead of using additional physical components or complex optical arrangements to correct spectral errors, the system uses neural network algorithms to perform the correction computationally, substituting mechanical complexity with software-based solutions.
Solution Approach 2:
The patent changes the operational parameters of the sensor system by introducing temperature data as an additional input parameter to the neural network. This allows the system to adapt to temperature variations and maintain accuracy across different operating conditions, with the neural network learning the relationship between temperature, filter response, and spectral output during calibration.
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 correction of spectral data, reducing errors and providing accurate spectral representations across various input and illumination conditions, enhancing the reliability of spectral sensors.
Implementation Method 1
Interference-based filters, such as Fabry-Pérot filters, when used in conjunction with spectral sensors have been shown to be capable of providing controlled light wavelengths
Data Source
AI summary
A method for one or more modules of one or more processors of a spectral sensor system begins by receiving a plurality of synthetic spectra, where a synthetic spectrum of the plurality of synthetic spectra includes one or more known deviations from a reference spectrum. The method continues by generating a spectral output for each synthetic spectrum of the plurality of synthetic spectra and then training an artificial intelligence engine, using the combined spectral output to generate a trained neural network. The method then continues by calibrating, based on the trained neural network, a spectral response generated by another spectral sensor.