Ultrasonic signal compressive detection using improved random equivalent sampling

Zhao, Yu; Zhuang, Xinhua; Wang, H.-J.; Dai, Zhijian

doi:10.1049/iet-smt.2011.0168

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…CS is an emerging data sampling paradigm that has received much attention [9–11]. For a class of signals that exhibit a ‘sparseness’ property, CS theory promises perfect recovery of the signal using very few measurements.…”

Section: Review Of Analog‐to‐information Conversionmentioning

confidence: 99%

Frequency domain sensing system using random modulation pre‐integrator

Zhao

Wang

Zhuang

et al. 2013

IET Science, Measurement & Technology

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A major challenge in wideband analog signal processing is the requirement for very high sampling rate. The emerging compressed sensing (CS) theory makes processing wideband signal at its information rate possible if the signal has a sparse representation in a certain space. This study introduces a frequency domain sensing system based on CS. First, the proposed system employs a random demodulator to sensing the signal in frequency domain and an integrator to compress the signal. Second, the incoherence between the proposed measurement matrix and the sparse representation basis is proved mathematically. Different from the conventional analog-to-information conversion, the integrator of the system does not need to be reset after each acquisition. Experimental results indicate that, for spectrally sparse signal, the system is able to recover analog waveform at a high equivalent sampling rate from a small number of low-speed samples.

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Section: Review Of Analog‐to‐information Conversionmentioning

confidence: 99%

Frequency domain sensing system using random modulation pre‐integrator

Zhao

Wang

Zhuang

et al. 2013

IET Science, Measurement & Technology

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“…Recently, compressive sensing (CS) theory [1][2][3][4][5] was proposed, in which sparse signals can be sampled at an extremely low frequency and recovered accurately. Researchers have applied CS theory to actual analogue signal acquisition [6][7][8][9][10][11] such as in the random demodulator (RD) [6,9], multi-coset sampler [8,12], random modulation preintegration [13], and modulated wideband converter (MWC) [14][15][16][17]. Mishali and Eldar developed the MWC [14][15][16][17], which is based on the CS technique, and which can be used to sample analogue multi-band signals over a wide spectral range.…”

Section: Introductionmentioning

confidence: 99%

Successive‐phase correction calibration method for modulated wideband converter system

Jiang

Deng

et al. 2019

IET signal process.

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The modulated wideband converter (MWC) is a sub-Nyquist sampling system recently proposed for sampling sparse multi-band signals. However, due to non-ideal analogue components, transfer-matrix calibration is required for the successful reconstruction. In practice, it is difficult to control the phases of the consecutive sinusoidal inputs precisely which are used to calibrate the transfer-matrix. Here, the authors propose a method of transfer-matrix calibration for the MWC with digital expanded channels when the phases of the sinusoidal inputs are unknown. As the oblique elements of the MWC transfer-matrix are equal, the authors can estimate the unknown phases first. The authors then obtain the actual transfer-matrix by phase correction. Finally, the validity of this method is verified by simulation and hardware experiment. The system performance is measured by computing the signal-to-noise ratio and the mean-square error between the original and reconstructed signals.

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“…Compressed sensing is an efficient approach for reconstruction of sparse signal from random samples. Numerous works have demonstrated its effectiveness in various applications, including ultrasonics [5][6][7]. This paper will illustrate the effectiveness of compressed sensing in the reconstruction of corrupted ultrasonic signals.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Signal Reconstruction Using Compressed Sensing

Liou

2016

AMM

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Ultrasonic signal reconstruction for Structural Health Monitoring is a topic that has been discussed extensively. In this paper, we will apply the techniques of compressed sensing to reconstruct ultrasonic signals that are seriously damaged. To reconstruct the data, the application of conventional interpolation techniques is restricted under the criteria of Nyquist sampling theorem. The newly developed technique - compressed sensing breaks the limitations of Nyquist rate and provides effective results based upon sparse signal reconstruction. Sparse representation is constructed using Fourier transform basis. An l1-norm optimization is then applied for reconstruction. Signals with temperature characteristics were synthetically created. We seriously corrupted these signals and tested the efficacy of our approach under two different scenarios. Firstly, the signal is randomly sampled at very low rates. Secondly, selected intervals were completely blank out. Simulation results show that the signals are effectively reconstructed. It outperforms conventional Spline interpolation in signal-to-noise ratio (SNR) with low variation, especially under very low data rates. This research demonstrates very promising results of using compressed sensing for ultrasonic signal reconstruction.

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Ultrasonic signal compressive detection using improved random equivalent sampling

Cited by 8 publications

References 17 publications

Frequency domain sensing system using random modulation pre‐integrator

Frequency domain sensing system using random modulation pre‐integrator

Successive‐phase correction calibration method for modulated wideband converter system

Ultrasonic Signal Reconstruction Using Compressed Sensing

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