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Related Concept Videos

Bandpass Sampling01:17

Bandpass Sampling

224
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....
224
Upsampling01:22

Upsampling

277
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...
277
Downsampling01:20

Downsampling

214
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
214
Aliasing01:18

Aliasing

181
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...
181
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

281
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
281
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

1.1K
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.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
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Wideband Optical Detector of Ultrasound for Medical Imaging Applications
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Wideband Spectrum Sensing Using Modulated Wideband Converter and Data Reduction Invariant Algorithms.

Gilles Burel1, Emanuel Radoi1, Roland Gautier1

  • 1Univ Brest, CNRS, Lab-STICC, CS 93837, 6 Avenue Le Gorgeu, CEDEX 3, 29238 Brest, France.

Sensors (Basel, Switzerland)
|February 28, 2023
PubMed
Summary
This summary is machine-generated.

Compressed sensing with a modulated wideband converter enables efficient wideband spectrum sensing. New algorithms ensure accurate spectrum reconstruction despite data reduction, improving cognitive radio applications.

Keywords:
LASSO algorithmOMP algorithmXsamplingcompressed sensingdata reductionmodulated wideband converterwideband spectrum sensing

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Wireless Communications

Background:

  • Wideband spectrum sensing faces challenges due to high sampling rates required by traditional methods.
  • Cognitive radio and spectrum surveillance demand efficient wideband sensing techniques.

Purpose of the Study:

  • To address high sampling rate challenges in wideband spectrum sensing using compressed sensing.
  • To analyze the impact of data reduction from modulated wideband converters on sparse reconstruction algorithms.
  • To develop data reduction invariant reconstruction algorithms for improved spectrum sensing.

Main Methods:

  • Utilized a compressed sensing approach with a sub-Nyquist sampling scheme (modulated wideband converter).
  • Analyzed the performance of sparse reconstruction algorithms (Orthogonal Matching Pursuit, LASSO) after data reduction.
  • Developed a new mathematical framework to prove algorithm invariance to data reduction.
  • Introduced a data reduction invariant version of the LASSO algorithm.

Main Results:

  • Demonstrated that greedy reconstruction algorithms are invariant to the proposed data reduction.
  • Introduced a data reduction invariant LASSO algorithm.
  • Achieved good wideband spectrum sensing reconstruction results using both synthetic and measured data.

Conclusions:

  • Compressed sensing with modulated wideband converters offers an effective solution for wideband spectrum sensing.
  • The proposed invariant reconstruction algorithms maintain accuracy despite data reduction, enhancing efficiency.
  • The approach is validated for practical wideband spectrum sensing scenarios.