Compressed Sensing Technology Research Articles

Research on Atmospheric Turbulence Distortion Measurement Based on Compressed Sensing Technology

The Hartmann wavefront sensor can measure the phase change of atmospheric turbulence in real time and realize the measurement of the distorted wavefront signal of atmospheric turbulence. However, existing Hartmann wavefront sensor microlens arrays are generally small, which limits their spatial resolution capabilities. By introducing compressed sensing technology into wavefront detection, this paper breaks through the limitations of the traditional Nyquist sampling theorem and achieves higher resolution spatial wavefront detection with a smaller microlens array.

A High-Frequency Measurement Method of Downhole Vibration Signal Based on Compressed Sensing Technology and Its Application in Drilling Tool Failure Analysis

A High-Frequency Measurement Method of Downhole Vibration Signal Based on Compressed Sensing Technology and Its Application in Drilling Tool Failure Analysis

IGBT Gate Waveform Acquisition Based on Compressed Sensing Technology

As the core device of power electronic equipment, IGBT (Insulated Gate Bipolar Transistor) is related to the reliability of the system, so its online health monitoring is particularly important. Due to the large amount of data and high sampling rate for IGBT online health monitoring, data transmission has become a major problem, the method of effectively reconstructing the original signal has become one of the current research hotspots. Based on the theory of compressed sensing, the acquisition of IGBT gate waveforms is studied in this paper. First, the theoretical principle of compressed sensing is expounded and analyzed, and the evaluation criteria for signal reconstruction performance are given. Then, from the theory and simulation, the sparseness of IGBT gate drive signals under different sparse bases and different measurement matrices are discussed and analyzed. Next, aiming at the shortcomings of the stagewise weak orthogonal matching pursuit algorithm, a SWOMP algorithm based on the backtracking strategy of different Sigmoid functions is proposed. Simulation and experimental results show that the improved SWOMP4 algorithm has the best reconstruction effect. Finally, the SWOMP4 algorithm is applied to the process of IGBT gate drive signal reconstruction.This paper mainly collects and reconstructs the IGBT gate drive signal through the improved SWOMP4 algorithm. The designed algorithm has high reconstruction accuracy and can be used in the IGBT module online health status system.

Data Reconstruction Method for CD Basis Weight Analysis of Paper by using Compressed Sensing Technology

The control of the cross-directional (CD) basis weight of paper is very challenging. The actuator of the system needs to control the properties of the entire sheet according to a set of restricted data measured by a scanning sensor. The scanning sensor measures the sheet in the cross direction of the paper. For the moving sheet, CD variations were those along an axis perpendicular to the motion of the sheet, while MD (machine direction) variations were those along the axis of motion. One of the problems that make the CD control difficult is data sampling, which is obtained by continuously measuring the CD basis weight of paper through multiple scanning sensors. As the paper moves vertically, the scanning sensor can only measure the “Z” shape area on the paper. When the scanning frequency was not at least twice the maximum frequency of the paper variation, it was difficult to estimate the sheet profile. According to the sparse characteristics of paper signal, a novel approach to accurately reconstruct the sheet properties by a random sampling protocol was proposed by using compressed sensing technology. Compared with the simple bandwidth based uniform sampling theory, it could accurately reconstruct the real process variations from less measurement data. Based on the reconstructed data and actual industrial process data, the CD response model was identified and the reconstruction effect was verified. The CD and MD basis weight data were separated by the predictive separation algorithm, which ensured the basis for CD control. The approach of the compressed sensing technology was found to be effective.

Rapid T2-weighted turbo spin echo MultiVane brain MRI using compressed SENSE: a qualitative analysis

•Non-cartesian MRI sequences can be combined with Compressed Sensing technology. •A novel Compressed Sensing PROPELLER MRI sequence implementation is feasible. •A Compressed Sensing PROPELLER MRI sequence implementation shortens scan times.

Fault Diagnosis of Marine Wind Turbine Gearbox Based on New Technology of Compressed Sensing

Xing, T.T.; Shen, J.L., and Meng, Z., 2020. Fault diagnosis of marine wind turbine gearbox based on new technology of compressed sensing. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 406–409. Coconut Creek (Florida), ISSN 0749-0208.In order to solve the problems of high failure rate and inaccurate fault location of wind turbine gearbox, a new fault diagnosis method of offshore wind turbine gearbox based on compressed sensing technology is proposed. By analyzing the main failure modes of transmission mode and gear of wind turbine, a new compression sensing technology is proposed. Combined with the method of multi-mode state recombination, the dynamic compression and reconstruction results of fault data are adaptively distributed. The fuzzy clustering distribution model of the data was established, and the least square programming algorithm was used to obtain the optimal planning function of gear fault diagnosis, so as to realize the gearbox fault diagnosis of wind turbine. Experimental proof: The results of circuit status of wind turbine are accurate, which provides a reliable basis for the diagnosis of subsequent gear faults. The new compression sensing technology can ensure a high gear fault reconstruction rate.

Fast Brillouin optical time-domain analysis using frequency-agile and compressed sensing.

A fast Brillouin optical time-domain analysis (BOTDA) sensor has been proposed and experimentally demonstrated based on the frequency-agile and compressed-sensing technique. The proposed scheme employs a data-adaptive sparse base obtained by the principle component analysis algorithm, enabling the sparse representation of Brillouin spectrum. Then, it can be reconstructed successfully with random frequency sampling and orthogonal matching-pursuit algorithms. In the experiment, the Brillouin gain spectrum (BGS) is mapped by the conventional fast BOTDA, where the frequency step and span are 4 MHz and 500 MHz, respectively. By using compressed-sensing technology, the BGS is successfully recovered with 37 random frequency samples, the number of which is only 30% of the full data. With fewer sampling frequencies, the compressed-sensing technology is able to improve the sensing performance of the conventional fast BOTDA, including a 3.3-time increase in sampling rate and 70% reduction in data storage.

Voltage Sag Data Sampling Method for Papermaking Process Based on Compressed Sensing Technology

Important production equipment and sensitive load in the papermaking process are susceptible to the voltage sag event, and production may be interrupted in severe cases. Sampling and recording this event are conducive to judge and analyze the cause of production interruption. To effectively reduce the number of samples and storage costs of the voltage sag data, this paper studies and discusses a data sampling method based on compressed sensing technology, designs an experimental device for verifying the method, proposes relevant indicators and test steps for measuring the performance of the method. The results show that the voltage sag data per unit sampling time is much lower than the minimum value specified by the traditional data sampling method and the original signal can be accurately recovered. Moreover, combined with the actual situation of papermaking, the method is featured by good anti-noise robustness and strong scalability.

An Attitude Jitter Correction Method for Multispectral Parallax Imagery Based on Compressive Sensing

Attitude jitter is a common problem for high-resolution earth-observation satellites and can diminish the geo-positioning and mapping performance of observed images. It is especially necessary to address this problem when high-performance attitude measurements are unavailable. Therefore, an attitude jitter correction method for multispectral parallax imagery that utilizes the compressive-sensing technology is proposed in this letter. In the proposed method, the attitude jitter is estimated from the parallax disparities of different band images, and then the image displacement caused by attitude jitter can be corrected. Using the normalized cross correlation method and compressive-sensing technology, the proposed method can deal with the condition of texture-feature deficiency in the partial image. The multispectral images of the Terra and ZY-3 satellites are used as experimental data to evaluate the proposed method. The registration errors of different bands are greatly reduced in both the cross- and along-track directions, and the experiment results indicate that the proposed method is effective for correcting the attitude jitter of both satellites.

Noisy Signal Processing Research based on Compressed Sensing Technology

<span lang="EN-US">Compressed sensing (CS) is a kind of sampling method based on signal sparse property, it can effectively extract the signal which was contained in the message. In this study, a new noise speech enhancement method was proposed based on CS process. </span><span lang="EN-US">V</span><span lang="EN-US">oice sparsity </span><span lang="EN-US">is </span><span lang="EN-US">use</span><span lang="EN-US">d to this a</span><span lang="EN-US">lgorithm in the discrete fast Fourier transform (Fast Fourier transform, FFT),</span><span lang="EN-US">and observation matrix is designed</span><span lang="EN-US"> in </span><span lang="EN-US">complex domain, and the noisy speech compression measurement and de-noising are made by soft threshold, and the speech signal is sparsely reconstructed</span><span lang="EN-US"> and </span><span lang="EN-US">restore</span><span lang="EN-US">d</span><span lang="EN-US"> by separable approximation (Sparse Reconstruction by Separable Approximation, SpaRSA) algorithm, speech enhancement</span><span lang="EN-US">is improved. Experimental results show that the denoising compression reconstruction is </span><span lang="EN-US">made for </span><span lang="EN-US">the noisy signal in the algorithm, SNR margin is improved greatly, and the background noise can be more effectively suppressed .</span>

Compressed Sensing Technology Applied to Fault Diagnosis of Train Rolling Bearing

Compressed sensing technology is a new approach in the field of signal processing. It can achieve data acquisition and compression at the same time, and express the original signal in a simple way. Network monitoring technology used in detecting the fault of the train’s rolling bearing always produce large amounts of datum. Too much of the parse inspection data cause a lot of unnecessary cost of energy and network bandwidth. In this article we propose a compressed sensing method to deal with the inspection date of train’s rolling bearing which will be transferred in the sensor network to reduce the data quantity. Experimental result show that, after compression and reconstruction, the reconstructed signal still contains most of the information and energy of the original signal, we can still detect the fault of train’s rolling bearing from the reconstructed signal accurately.

Implementation of Compressed Sensing Technology with ROMP Reconstruction for Sparse Channel Estimation in OFDM Systems

Implementation of Compressed Sensing Technology with ROMP Reconstruction for Sparse Channel Estimation in OFDM Systems