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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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...
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Bandpass Sampling01:17

Bandpass Sampling

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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....
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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...
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NMR Spectrometers: Overview01:20

NMR Spectrometers: Overview

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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...
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NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

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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.
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Aliasing01:18

Aliasing

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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...
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A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing.

Adam Olesiński1, Zbigniew Piotrowski1

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

Sensors (Basel, Switzerland)
|July 29, 2023
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This study introduces a deep learning method for radio frequency (RF) signal detection in wideband spectrum sensing. The novel approach enhances detection accuracy, especially for low signal-to-noise ratio (SNR) signals, outperforming traditional techniques.

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cognitive radiodeep learningobject detection using CNNradio frequency machine learning (RFML)signals detection

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Machine Learning

Background:

  • Wideband spectrum sensing is vital for wireless communications.
  • Traditional energy detection methods struggle with low signal-to-noise ratio (SNR) detection.

Purpose of the Study:

  • To develop a novel deep learning approach for enhanced RF signal detection in wideband spectrum.
  • To accurately estimate and subtract noise distribution from radio spectrograms for improved detection.

Main Methods:

  • Utilized convolutional neural networks (CNNs) for analyzing radio spectrograms.
  • Developed a RFROI-CNN approach for precise noise distribution estimation.

Main Results:

  • The RFROI-CNN method significantly outperforms traditional energy detection with thresholding.
  • Achieved up to 6 dB improvement in detection performance.
  • Demonstrated enhanced capabilities for wideband spectrum sensing systems.

Conclusions:

  • The proposed deep learning approach offers a promising solution for RF signal detection.
  • Accurate noise estimation and consideration of neighboring signal power enhance detection performance.