Terahertz Signal Analysis via Normal Distribution Fitting
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Solution Overview
Problem
Current terahertz wave signal analysis techniques face challenges in visualizing the features of samples due to overlapping spectrums and difficulty in distinguishing the characteristic waveforms of terahertz waves, making it hard to determine the properties of samples effectively.
Innovation Solution
The approach involves fitting synthetic waveforms of multiple fitting functions to the frequency spectrum of terahertz wave signals, generating graphs based on parameters such as center frequency, amplitude, and width to visualize the sample characteristics in an easy-to-understand manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If absorption spectroscopy using terahertz wave is performed to measure intermolecular vibration, then chemical property measurement is achieved, but spectrum overlap makes it extremely difficult to identify sample features
Solution Approach 1:
The patent applies segmentation by dividing the complex overlapping terahertz spectrum into multiple component spectra through mathematical decomposition. The fitting function separates the measured spectrum into individual absorption components, each corresponding to specific molecular vibrations or rotations, making it possible to identify sample features despite the original overlap.
Solution Approach 2:
The patent introduces a fitting function as an intermediary mathematical model that mediates between the raw overlapping spectrum and the desired sample features. This fitting function acts as a bridge, transforming the unclear overlapping signal into distinct, interpretable spectral components that reveal sample characteristics.
2Difficulty of detecting and measuring
If conventional spectral analysis is used to measure sample characteristics, then measurement capability is provided, but the complex overlapping spectra make it extremely difficult to find characteristic waveforms
Solution Approach 1:
The patent segments the complex characteristic waveform into multiple simpler component waveforms using the fitting function. By decomposing the overlapping spectrum into individual absorption features, the method makes it possible to detect and analyze characteristic waveforms that would otherwise be hidden in the complex overlapping signal.
Solution Approach 2:
The patent creates a simplified copy or representation of the complex spectrum through the fitting function. This fitted model serves as a copy that preserves the essential features while removing the complexity and overlap, making it easier to identify and analyze characteristic waveforms without the distracting overlapping signals.
3Ease of operation
If polarization sensitive terahertz wave detector is used to measure transmittance difference, then tissue difference visualization is achieved, but the method does not disclose specific visualization methodology
Solution Approach 1:
The patent replaces complex mechanical or procedural visualization methods with a mathematical computation approach. Instead of using complicated optical arrangements or manual analysis procedures, the invention uses computational fitting functions to automatically extract and visualize tissue differences from the spectral data, simplifying the overall process.
4Measurement precision
If multiple regression analysis is performed to obtain standard curve, then component content calculation is achieved, but correlation graph display does not enable determination of sample characteristics
Solution Approach 1:
The patent creates a simplified spectral copy through the fitting function that directly represents sample characteristics. This fitted spectrum serves as a clear visual representation that preserves the essential diagnostic information while removing the complexity and overlap of the original spectrum, enabling easy determination of sample characteristics without requiring interpretation of complex correlation graphs.
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 method allows for the clear visualization of sample features, enabling easier analysis and differentiation of sample characteristics, even those that were previously difficult to sense, by approximating the frequency spectrum with synthetic waveforms and generating radar graphs or other graphical representations.
Implementation Method 1
an electromagnetic wave is caused to pass through a sample which is a spectroscopic measurement target, and a physical property or a chemical property of a sample is measured from a change in the electromagnetic wave caused by interaction between the electromagnetic wave and the sample while passing through the sample
Implementation Method 2
a frequency spectrum of a molecule observed by the spectroscopic measurement has a spectral structure unique to the molecule. In particular, in absorption spectroscopy using a terahertz wave, intermolecular vibration caused by a hydrogen bond or the like is observed
Data Source
AI summary
A terahertz wave signal analysis device includes a fitting processing unit 13 that fits synthetic waveforms of a plurality of normal distribution functions which differ in at least one of a center frequency, an amplitude, and a width to a frequency spectrum obtained from a terahertz wave signal and a graph generating unit 14 that generates a graph using at least one of a center frequency, an amplitude, and a width of a plurality of normal distribution functions used in the fitting as parameters, and it is possible to visualize a feature corresponding to a characteristic of a sample in the form of a graph in an easy-to-understand manner by approximating a frequency spectrum which does not clearly appear because a difference in the characteristic of the sample becomes a feature of a waveform by synthetic waveforms of a plurality of normal distribution functions in a form in which the characteristic of the sample is taken over and generating a graph on the basis of parameters of a plurality of normal distribution functions used in the approximation.