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Related Concept Videos

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....
246
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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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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A Low Complexity Sensing Algorithm for Non-Sparse Wideband Spectrum.

Shiyu Ren1, Wantong Chen1, Hailong Wu1

  • 1School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China.

Sensors (Basel, Switzerland)
|August 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces AdNoR, an advanced wideband spectrum sensing (WSS) algorithm designed for non-sparse spectrum environments. It offers low computational complexity and effective sensing performance without spectrum reconstruction.

Keywords:
folded time-frequency spectrumnon-sparse spectrumtime-frequency subband classificationwideband spectrum sensing

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Wireless Communications

Background:

  • Existing wideband spectrum sensing (WSS) methods often assume sparse spectrum occupancy.
  • Future wideband spectrum environments are predicted to become non-sparse.
  • This necessitates new WSS algorithms capable of handling denser spectrum usage.

Purpose of the Study:

  • To propose a novel sub-Nyquist sampling WSS algorithm adaptable to non-sparse spectrum scenarios.
  • To maintain low computational complexity by building upon the "no reconstruction (NoR)" concept.
  • To introduce an advanced version of the NoR algorithm, termed AdNoR.

Main Methods:

  • Developed the AdNoR algorithm, an advancement of the NoR WSS technique.
  • Established a folded time-frequency (TF) spectrum model with a specific structure.
  • Employed a comprehensive sampling technique including multicoset sampling, digital fractional delay, and TF transform.

Main Results:

  • AdNoR demonstrates effective performance in non-sparse spectrum scenarios.
  • The algorithm maintains low computational complexity.
  • Simulations confirm the algorithm's viability and efficiency.

Conclusions:

  • The AdNoR algorithm provides a viable solution for wideband spectrum sensing in anticipated non-sparse environments.
  • It achieves efficient spectrum sensing with reduced computational load.
  • AdNoR represents a significant advancement for future wireless communication systems.