Optical Spectrum Peak Detection with Zone-Trained Neural Networks
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Solution Overview
Problem
Current peak detection algorithms in optical spectrometry face limitations due to limited resolution, channel overlap, and spectrometer crosstalk, leading to inaccurate peak identification and increased hardware costs.
Innovation Solution
The use of a trained neural network (NN) to detect peaks in predefined zones of the optical spectrum, allowing for adaptable peak detection without increasing the number of detectors or channels, thereby improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional weighted average algorithms are used for peak detection, then the method is simple and easy to implement, but the accuracy deteriorates due to limited resolution and spectrometer crosstalk
Solution Approach 1:
The patent replaces traditional mechanical/mathematical signal processing algorithms (weighted average methods) with a neural network-based system. The neural network learns to identify peaks and distinguish them from crosstalk patterns through training on spectral data, substituting complex mathematical operations with a trained computational model that achieves superior accuracy in peak detection under limited resolution conditions.
Solution Approach 2:
The patent transforms the approach by changing from fixed mathematical parameters (weights in weighted average) to learned parameters through neural network training. The network adapts its internal parameters during training to optimize peak detection performance, allowing it to handle varying spectral conditions and crosstalk patterns that fixed algorithms cannot accommodate.
2Measurement precision
If the number of detectors and channels is increased to improve resolution, then peak detection accuracy improves, but hardware cost increases significantly
Solution Approach 1:
The patent creates a computational model (neural network) that copies and learns from spectral patterns without requiring additional physical detectors. The network is trained on data from existing detectors to recognize peaks and distinguish them from crosstalk, effectively creating a virtual enhancement of the detection capability without adding hardware.
Solution Approach 2:
The patent substitutes the need for additional physical detection hardware with a computational system. Instead of adding detectors to improve resolution, the system uses a neural network to process signals from existing detectors, achieving enhanced resolution through software-based pattern recognition and separation of peak signals from crosstalk.
3Adaptability or versatility
If spectrometer crosstalk is present in densely populated channels, then channel overlap increases, but peak identification accuracy deteriorates
Solution Approach 1:
The neural network incorporates feedback mechanisms during training where it learns from spectral data including crosstalk patterns. The network adjusts its internal representations based on training feedback, enabling it to distinguish between true peaks and crosstalk-induced variations. This feedback-driven learning allows the system to maintain accuracy even in densely populated channels with significant overlap.
Solution Approach 2:
The patent replaces traditional signal processing that struggles with crosstalk with a neural network that learns to ignore or correct crosstalk effects. The network substitutes mathematical deconvolution methods with data-driven learning, where it identifies patterns in the spectral data that correspond to actual peaks versus artifacts from channel overlap.
Data Source
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AI summary
The invention provides, amongst other aspects, a method for detecting peaks in an optical spectrum, the method comprising the steps of obtaining the optical spectrum from at least one optical spectrometer, the optical spectrum comprising a wavelength range; and applying a trained neural network, NN, on the optical spectrum to detect the peaks in the optical spectrum, wherein the detecting of peaks relates to output nodes of the NN having been trained w.r.t zones corresponding to subranges of the wavelength range. Further provided is a device carrying out the method, the device preferably comprising a memory including the trained NN. Further provided is a trained NN having been trained w.r.t zones corresponding to subranges of the wavelength range.