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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

940
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...
940
Bandpass Sampling01:17

Bandpass Sampling

206
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
206
IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

1.0K
In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
1.0K
NMR Spectrometers: Overview01:20

NMR Spectrometers: Overview

1.1K
NMR spectrometers consist of a strong magnet, a radiofrequency transmitter, and a detector attached to a computer console for recording spectra of samples containing NMR-active nuclei. In first-generation NMR instruments called continuous-wave spectrometers, the resonance frequencies of the nuclei are determined by frequency-sweep or field-sweep methods. The magnetic field strength is fixed and the rf signal is swept in the former, while the radiofrequency signal is fixed and the magnetic field...
1.1K
NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

840
A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
840
Aliasing01:18

Aliasing

162
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...
162

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相关实验视频

Updated: Jul 20, 2025

Wideband Optical Detector of Ultrasound for Medical Imaging Applications
08:21

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

11.3K

一个无线电频率兴趣区域卷积神经网络用于宽带频谱传感.

Adam Olesiński1, Zbigniew Piotrowski1

  • 1Communications Systems, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, Poland.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
概括

本研究介绍了一种深度学习方法,用于在宽带频谱传感中检测射频 (RF) 信号. 这种新的方法提高了检测准确性,特别是对于低信号噪声比 (SNR) 信号,其性能优于传统技术.

科学领域:

  • 无线通信无线通信
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 宽带频谱传感对于无线通信至关重要.
  • 传统的能量检测方法在低信号噪声比 (SNR) 检测方面遇到了困难.

研究的目的:

  • 开发一种新的深度学习方法,用于在宽带频谱中增强射频信号检测.
  • 准确估计和从无线电谱图中减去噪声分布,以改善检测.

主要方法:

  • 利用卷积神经网络 (CNN) 来分析射电谱图.
  • 开发了一种RFROI-CNN方法,用于精确估计噪声分布.

主要成果:

  • 该RFROI-CNN方法显著优于传统的能量检测和值.
  • 在检测性能方面实现了高达6dB的改进.
  • 证明了宽带频谱传感系统的增强功能.

结论:

  • 拟议的深度学习方法为射频信号检测提供了一个有希望的解决方案.
  • 精确的噪声估计和考虑邻近的信号功率提高了检测性能.
关键词:
认知无线电是一种认知无线电.深度学习是一种深度学习.使用CNN进行对象检测.无线电频率机器学习 (RFML)信号检测 信号检测

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