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相关概念视频

Aliasing01:18

Aliasing

119
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...
119
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

915
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...
915
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

762
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...
762

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相关实验视频

Updated: Jun 6, 2025

A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response
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光纤传感器频谱噪声降低基于一个生成对抗网络.

Yujie Lu1, Qingbin Du1, Ruijia Zhang1

  • 1School of Information Engineering, Huzhou University, Huzhou 313000, China.

Sensors (Basel, Switzerland)
|November 27, 2024
PubMed
概括

使用Cycle-GAN的新型深度学习方法有效地消除光纤传感器光谱. 这种方法显著提高了信号噪声比 (SNR) 和精度,增强了研究和工业中的实际应用.

关键词:
纤维光学传感传感器生成性的对抗性网络.降低噪音 减少噪音信号处理 信号处理 信号处理

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相关实验视频

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科学领域:

  • 光电学和光子学的光电子学和光子学.
  • 信号处理 信号处理
  • 人工智能在感知中的作用

背景情况:

  • 减少光纤传感器光谱中的噪声对于准确的测量至关重要.
  • 像波形变换 (WT) 和经验模式分解 (EMD) 这样的现有消极化方法有局限性.

研究的目的:

  • 开发和评估基于深度学习的光纤传感器光谱的除方法.
  • 为了提高信号噪声比 (SNR) 和光纤传感数据的准确性.

主要方法:

  • 预处理传感器光谱成二维图像.
  • 训练一个循环一致的生成对抗网络 (Cycle-GAN) 模型.
  • 评估来自FPI,FBG,的FBG和FBG对传感器的模拟光谱的性能.

主要成果:

  • 与传统方法相比,实现了高达13.71dB的SNR改进.
  • 减少RMSE高达三倍,并保持R2≥99.70%与原始信号.
  • 在温度响应中显示出优异的线性 (R2为99.95%) 降低多模式噪声.

结论:

  • 拟议的Cycle-GAN无声化方法有效地减少光纤传感中的各种噪音类型.
  • 这种方法提高了专业应用的光纤传感器的实用性和可靠性.
  • 这种方法比传统的除尘技术具有显著的优势.