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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
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Aliasing01:18

Aliasing

534
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
534
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.0K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.7K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.7K
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

1.8K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
1.8K
Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Related Experiment Video

Updated: Jan 11, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

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Separation of distorted overlapping spectra in FBG sensor networks using self-supervised contrastive learning.

Yizheng Sun, Weiming Zeng, Hengwei Shen

    Optics Express
    |November 11, 2025
    PubMed
    Summary

    This study introduces a contrastive spectrum separation model (CSSM) to resolve overlapping spectra in Fiber Bragg Grating (FBG) sensor networks. CSSM significantly improves strain measurement accuracy for structural health monitoring.

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    Area of Science:

    • Optical Engineering
    • Materials Science
    • Artificial Intelligence

    Background:

    • Fiber Bragg Grating (FBG) sensor networks face challenges with spectral overlap and distortion, particularly in applications like residual strain monitoring in composite structures.
    • These spectral issues limit the multiplexing capability and sensing accuracy of FBG networks, hindering their effectiveness in complex environments.

    Purpose of the Study:

    • To propose and validate a self-supervised learning framework, the contrastive spectrum separation model (CSSM), for effectively separating distorted and overlapping spectra in FBG sensor networks.
    • To enhance the accuracy and multiplexing capabilities of FBG sensor networks in scenarios with non-uniform physical fields.

    Main Methods:

    • Developed a contrastive spectrum separation model (CSSM) utilizing a dual-encoder architecture with parallel convolutional neural networks.
    • Employed a self-supervised learning approach for direct feature extraction from distorted spectra, minimizing the need for extensive labeled training data.
    • Validated the model through simulations and experimental measurements under varying spectral overlap and noise conditions, including strain gradients.

    Main Results:

    • CSSM demonstrated superior robustness, achieving a 54.5% Signal-to-Noise Ratio (SNR) improvement at 15 dB noise levels.
    • Simulation results showed high accuracy in wavelength detection (1.6388 pm) and spectral similarity (0.9074) for separated spectra.
    • Experimental validation with strain gradients up to -650 µε/mm reduced strain measurement error from ±37.2 µε to approximately 1.35 µε.

    Conclusions:

    • The CSSM framework effectively separates distorted overlapping spectra in FBG sensor networks, overcoming limitations of current methods.
    • The model significantly enhances the accuracy and multiplexing capacity of FBG sensors, proving practical effectiveness in real-world structural health monitoring.
    • This advancement enables more reliable monitoring of composite structures and other complex systems using FBG sensor technology.