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相关概念视频

Atomic Emission Spectroscopy: Interference01:30

Atomic Emission Spectroscopy: Interference

162
In atomic emission spectroscopy (AES), high-temperature atomizers excite a broad range of elements and molecules that generate complex emissions from sources such as oxides, hydroxides, and flame combustion products in the flame or plasma. Several strategies can be employed to minimize spectral interferences caused by overlapping emission lines or bands. These include increasing instrument resolution, choosing alternative emission lines, optimally placing the detector in low-background regions,...
162
Atomic Emission Spectroscopy: Lab01:29

Atomic Emission Spectroscopy: Lab

143
AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
143
Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

183
Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used....
183
Atomic Emission Spectroscopy: Overview01:20

Atomic Emission Spectroscopy: Overview

1.4K
Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
1.4K
Atomic Emission Spectroscopy: Instrumentation01:22

Atomic Emission Spectroscopy: Instrumentation

329
The instrumentation of atomic emission spectrometry (AES) involves various components, including atomization devices that convert samples into gas-phase atoms and ions. There are two main types of atomization devices: continuous and discrete atomizers.  Continuous atomizers, like plasmas and flames, introduce samples in a constant stream, while discrete atomizers inject individual samples using syringes or autosamplers. The most common discrete atomizer is the electrothermal atomizer.
329
Aliasing01:18

Aliasing

115
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...
115

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相关实验视频

Updated: May 28, 2025

Extraction of the EPP Component from the Surface EMG
07:16

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Published on: December 16, 2009

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通过时间波形频谱一致性对有限样本的特定发射体识别方法.

Chunyang Tang1,2, Jing Lian1, Li Zheng1

  • 1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了使用TFC-CNN的特定发射者识别 (SEI) 的新方法,在有限的数据中提高了准确性. 该技术提高了无线电发射器的识别性能,即使训练样本稀少.

关键词:
比较学习学习是一种比较学习.连续波形变换连续波形变换.数据增强数据增强深度学习是一种深度学习.特定的排放者识别标识.

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相关实验视频

Last Updated: May 28, 2025

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科学领域:

  • 信号处理 信号处理
  • 机器学习 机器学习
  • 无线电频率工程 无线电频率工程

背景情况:

  • 特定发射器识别 (SEI) 使用无线电信号特征来识别发射器.
  • 深度学习已经改善了SEI,但与短时间或低频发射器的有限训练数据作斗争.
  • 当深度学习模型在稀缺样本上接受训练时,不足降低了准确性.

研究的目的:

  • 为了应对SEI中有限的样本和数据稀缺性的发射器分类的挑战.
  • 提出一种新的TFC-CNN方法,以在数据有限的条件下提高SEI性能.

主要方法:

  • 利用连续波段变换 (CWT) 来增强数据,创建时间波段频谱对.
  • 使用复杂值神经网络 (CVNN) 和深卷积神经网络 (DCNN) 来进行特征提取.
  • 训练模型使用正常化的温度尺度交叉 (NT-Xent) 和交叉 (CE) 损失与共弦值损失来实现特征一致性.

主要成果:

  • 与WiFi和ADS-B数据集上现有的最先进的方法相比,TFC-CNN方法显示出更高的性能.
  • 在ADS-B测试数据集上,只用5%的培训样本,实现了84.10%的识别准确度.
  • 在仅5%的培训样本中,在WiFi测试数据集上实现了96.99%的识别准确度.

结论:

  • 提议的TFC-CNN方法有效地处理SEI任务,只有少数样本,性能优于传统方法.
  • 该技术在识别非法发送者和在数据有限的身份验证系统中显示出巨大的潜力.
  • 通过CWT和先进的神经网络架构进行数据增强是实现低数据SEI场景中高精度的关键.