相关实验视频

Updated: Jan 11, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

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

Published on: August 19, 2021

3.0K

在FBG传感器网络中分离扭曲的重叠光谱,使用自主监督的对比学习

Yizheng Sun, Weiming Zeng, Hengwei Shen

    Optics express
    |November 11, 2025
    PubMed
    概括

    本研究介绍了一种对比的频谱分离模型 (CSSM),用于解决纤维布拉格格 (FBG) 传感器网络中重叠的光谱. CSSM显著提高了结构健康监测的应变测量准确性.

    更多相关视频

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals07:34

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

    Published on: August 22, 2019

    8.4K
    Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures08:49

    Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures

    Published on: December 1, 2023

    2.0K

    相关概念视频

    ¹H NMR: Interpreting Distorted and Overlapping Signals01:02

    ¹H NMR: Interpreting Distorted and Overlapping Signals

    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...
    1.5K
    Aliasing01:18

    Aliasing

    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

    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

    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

    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

    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...
    1.3K