Adaptive Matching Pursuit Algorithm Research Articles

Single-photon lidar depth imaging based on sparsity adaptive matching pursuit method

Photon counting lidar has the ability to detect weak echo photons. However, in imaging applications, a small number of echo signals obtained in a very short acquisition time cannot get an accurate image estimation through the photon arrival time distribution histogram. Therefore, high-precision and fast imaging of an object using a small amount of weak echo photon signals is urgent to be solved. In this work, a sparse adaptive matching pursuit (SAMP) algorithm is proposed to process the echo photons without prior information to obtain the effective photon interval, which improves the reconstruction quality and imaging speed. By constructing a non-coaxial scanning imaging system to detect the face model, it has been experimentally proven that the proposed algorithm can achieve depth and reflectance image estimation under the condition of low power and low acquisition time of 4µs per pixel on average, and the mean absolute error (MAE) value is reduced by about 10 times compared to conventional reconstruction algorithm.

Detection and recovery of anomalous vibration signal of rotating machinery based on LOF-MSAMP

The collected vibration signals of rotating machinery contain pulses, missing, and other low-quality anomalous data due to environmental noise interference, unstable data transmission, and data acquisition instrument failure. These low-quality data obstruct the analysis of the healthy operation condition of rotating machinery. This paper proposes a method for anomalous vibration signal detection and recovery based on the local outlier factor algorithm and the modified sparsity adaptive matching pursuit algorithm. The method combines the local outlier factor algorithm and compressive sensing theory to realize anomalous vibration signal detection and recovery. This paper evaluates the recovery performance both qualitatively and quantitatively and discusses how the proposed method’s hyperparameter selection affects the recovery results. A set of simulated signal and measured hob base signal are used to verify the proposed method. The results indicate that, when compared to the other seven reconstruction algorithms, the proposed method’s recovered signal has the lower error level and the higher waveform similarity which reaches more than 98% to the original signal, effectively improving data quality.

Hybrid approach for virtual machine allocation in cloud computing

In this manuscript, a Combined Approach of Generalized Backtracking Regularized Adaptive Matching Pursuit Algorithm and Adaptive β-Hill Climbing Algorithm for Virtual Machine Allocation in Cloud Computing (BA-VMA-CC) is proposed. Generalized Backtracking Regularized Adaptive Matching Pursuit Algorithm (GBRAMP) is used for Virtual Machine (VM) Migration process and Adaptive β-Hill Climbing Algorithm is used to Virtual Machine Placement. These two tasks are essential elements of VM allocation. GBRAMP is used to minimize cost and energy for both cloud service providers and users with help of migration process and to save time and energy. Adaptive β-Hill Climbing Algorithm (AβHCA) is employed for maximizing efficiency, minimizing power consumption and resource wastage. By Combining both GBRAMPA-AβHCA VM is optimally allocated in PM with high efficiency by minimizing cost and energy consumptions. The proposed BA-VMA-CC is implemented in MATLAB platform. The performance of proposed method attains 23.84 %, 28.94 %, 33.94 % lower energy consumption, 28.94 %, 34.95 %, 25.36 % lower CPU utilization is analyzed with existing methods, such as sine cosine with ant lion optimization for VM allocation in Cloud Computing (SCA-ALO-VMA-CC), hybrid distinct multiple object whale optimization and multi-verse optimization for VM allocation in Cloud Computing (DMOWOA-MVO-VMA-CC) and Cuckoo search optimization algorithm and particle swarm optimization algorithm (CSO-PSO-VMA-CC) respectively.

Target Detection Method Based on Adaptive Step-Size SAMP Combining Off-Grid Correction for Coherent Frequency-Agile Radar

Coherent frequency-agile radar (FAR) has a low probability of intercept (LPI) and excellent performance of electronic counter-countermeasures (ECCM) and electromagnetic compatibility, which can improve radar cooperation and survivability in complex electromagnetic environments. However, due to the nonlinearity of radar carrier frequency and the limitation of the Doppler tolerance of high-resolution range cells, the undesirable blind-speed sidelobes are generated in the two-dimensional (2D) range–velocity plane after coherent integration (CI) using the traditional methods based on a matching filter, which may degrade the target detection performance. To solve this problem, an adaptive step-size sparsity adaptive matching pursuit (SAMP) algorithm combining off-grid correction (ASSAMP-OC) is proposed in this paper, which seeks to achieve a better trade-off between recovery efficiency and detection performance. Firstly, an adaptive iteration step size based on the Spearman correlation coefficients (SCCS) is devised, which solves the problem of the traditional SAMP algorithm being insensitive to the change in iteration step size when the residuals vary slightly, and improves the recovery speed. Secondly, the off-grid correction method by combining a regularized stagewise backtracking idea and gradient descent optimization (GDO) is adopted to improve the recovery accuracy and suppress the blind-speed sidelobe energy (BSSE), which helps to reduce CI gain loss and improve the target detection performance without the prior information of the sparsity lever. Finally, simulation and experimental results demonstrate the effectiveness and efficiency of the proposed method in terms of target detection probability, target signal energy ratio after recovery, and computational cost, compared to several existing methods.

Fast Dynamic Phasor Estimation Algorithm Considering DC Offset for PMU Applications

Because of the volatility and intermittence of the renewable energy units in the novel power system, the electrical variables usually exhibit dynamic characteristics. Under fault conditions, the electrical variables usually contain a decaying DC (DDC) component. Currently, when considering the DDC component and the dynamic characteristics of electrical variables, it is difficult to obtain highly precise estimation results in most references. In this paper, a fast dynamic phasor estimation algorithm considering the DC offset is proposed to obviate errors caused by dynamic characteristics and the DDC component. The algorithm consists of two parts. The first part establishes the mathematical model of DDC using exponential functions and obtains the exact DDC component by iteration. The second part establishes the compressive sensing Taylor Fourier Multi-Frequency (CSTFM) model of the dynamic phasor. The improved sparsity adaptive matching pursuit (ISAMP) algorithm is used to quickly and accurately estimate the dynamic phasor, frequency and rate of change of frequency (ROCOF). Simulation and field data test results show that the proposed algorithm can accurately estimate the fault current phasor, frequency, and ROCOF compared with existing phasor estimation algorithms.

Recovery performance improvement of image compressive sensing using complex‐valued Vandermonde matrix

AbstractHere, a novel image‐based quantized compressive sensing (QCS) framework based on complex‐valued Vandermonde (Vander) matrix is proposed. In the proposed QCS framework, a discrete wavelet transform (DWT) serves as a sparse basis and a partial complex‐valued Vander matrix serves as a measurement matrix. The theoretical analysis based on mutual coherence metric of compressive sensing (CS) theory shows that the proposed Vander measurement matrix has the best reconstruction performance among other conventional measurement matrices. The simulation results also show that the recovery quality using the proposed measurement matrix can be greatly improved compared with the other existing real‐valued measurement matrices. In particular, the experiment results also show that under the same measurement matrix, the reconstruction performance of Smoothed l0 norm (SL0) algorithm is better than that of Orthogonal Matching Pursuit (OMP) algorithm, sparsity adaptive matching pursuit (SAMP) algorithm and approximate message passing (AMP) algorithm. In addition, a sparse measurement matrix scheme is further proposed to achieve a trade‐off between recovery performance and computational complexity. The theoretical analysis and simulation results both show the proposed image‐based QCS is efficient.

Data evaluation algorithm for compensation litigation for ecotope pollution damage in the Yellow River valley caused by industrial wastewater from the perspective of green development.

Since the reform and opening up, China's economy has achieved amazing growth. At the same time of industrialization transformation, the traditional high-energy consumption and external growth model has brought serious environmental problems, and the pollution control situation is facing many difficulties. The Yellow River Valley gives birth to the Chinese civilization. With the formulation of the national strategy for ecological protection and high-quality development in the Yellow River Basin and the demand for green development, the control of industrial pollution has become the main task of environmental protection in the Yellow River Basin. However, the research on the ecotope pollution in the Yellow River Valley mainly stays in the theory and strategy. In response to the above problems, this paper would carry out scientific data analysis and high-dimensional research on the ecotope pollution in the Yellow River Valley from the perspective of green development and data analysis algorithms. The research results showed that the reconstruction probability of the Threshold-based Sparsity Adaptive Matching Pursuit (TSAMP) algorithm remained 100% when K was less than 50.

Effective Sparse Recovery Framework for Ultrawideband Robust Plane Wave Generator

This letter proposes a novel ultrawideband plane wave generator (PWG) design methodology suitable for a low-cost small anechoic chamber. Several novel strategies are proposed to handle various crucial concerns in practice to make the proposed methodology reliable and robust. Specifically, the subarray-based amplitude-only excitation method is capable of reducing the required number of control channels without the need for phase shifters, which can significantly simplify the feeding network complexity. One major concern of the PWG application is the nonnegligible multipath reflections in small anechoic chambers. Thus, a novel regression approach, imposing the constraint of maximum energy outside the quiet zone (QZ), is implemented to suppress the reflection level. For the design of the PWG layout, a compressed sensing-based adaptive backtracking orthogonal matching pursuit (ABOMP) algorithm is proposed. In the simulation, a spherical test zone supporting the ultrawideband frequency over 2.4–5 GHz is synthesized with the proposed methodology, and the effectiveness and robustness are comprehensively investigated.

Improved sparsity adaptive matching pursuit algorithm based on compressed sensing

Traditional greedy algorithms need to know the sparsity of the signal in advance, while the sparsity adaptive matching pursuit algorithm avoids this problem at the expense of computational time. To overcome these problems, this paper proposes a variable step size sparsity adaptive matching pursuit (SAMPVSS). In terms of how to select atoms, this algorithm constructs a set of candidate atoms by calculating the correlation between the measurement matrix and the residual and selects the atom most related to the residual. In determining the number of atoms to be selected each time, the algorithm introduces an exponential function. At the beginning of the iteration, a larger step is used to estimate the sparsity of the signal. In the latter part of the iteration, the step size is set to one to improve the accuracy of reconstruction. The simulation results show that the proposed algorithm has good reconstruction effects on both one-dimensional and two-dimensional signals.

Cluster Adaptive Matching Pursuit for Multipolarization Through-Wall Radar Imaging

Among various types of sensors, through-wall radar is a promising sensor that can penetrate obstacles and sense the targets behind them. In this article, by exploiting the common sparsity property of the multipolarization images, we propose a cluster adaptive matching pursuit (CAMP) algorithm for multipolarization through-wall radar imaging (TWRI). Our proposed CAMP algorithm is developed from compressive sensing (CS) theory. In the proposed CAMP algorithm, the common sparsity among the multipolarization images is utilized through a multiple-measurement vector (MMV) model. The target area is obtained through a support set. We model the support set as a combination of several clusters, thus the number of clusters and the pattern of each cluster are two important factors that can describe the support set. To obtain the pattern of a cluster that corresponds to a certain target in the imaging scene, we propose an adaptive clustering strategy based on the backtracking scheme. On this basis, the number of clusters is adaptively selected through a two-layer matching pursuit method. Different from conventional matching pursuit algorithms that often use atom methods, the support set is acquired cluster by cluster in the two-layer matching pursuit method, which accelerates the speed of acquiring the support set. Experimental results show that our proposed algorithm can locate targets accurately in TWRI with a high target-to-clutter ratio (TCR) performance.

An Improved Structural Health Monitoring Method Utilizing Sparse Representation for Acoustic Emission Signals in Rails

The structural health of rails significantly impacts the safety of railway transport, which requires long-term and accurate monitoring. An improved structural health monitoring (SHM) method is proposed to enhance the damage information and construct an adaptive weighted index for evaluating the structural health states of rails. In this method, modified singular value decomposition (SVD) with a double-layer Hankel matrix compensation algorithm enhances the damage information and ensures real-time detection. Based on the inflection point of the inner product gradient, the step size of the sparsity adaptive matching pursuit (SAMP) algorithm is adaptively adjusted to reconstruct damage signals. Subsequently, an innovative index is constructed with adaptive weighting for SHM of rails. Tensile tests are performed to verify the effectiveness of the proposed method. The results show that the proposed method avoids the defects of traditional nondestructive testing (NDT) techniques that rely on a priori information about the damage signal (such as damage frequency acoustic emission (AE) hits rate and waveform characteristics). Furthermore, using the variation in signal energy, the proposed method has the capability to accurately delineate the three structural health stages under complex waveform conditions, which is not possible with conventional detection methods. This method provides guidance for the damage analysis and monitoring techniques of rails.

A Correlation Coefficient Sparsity Adaptive Matching Pursuit Algorithm

This paper presents a correlation coefficient sparsity adaptive matching pursuit (CCSAMP) algorithm for practical compressed sensing (CS). The sparsity adaptive matching pursuit (SAMP) has been enhanced using the CCSAMP algorithm. The CCSAMP's capacity to accurately reconstruct the signal with fewer repetitions is its most novel characteristic when compared to other state-of-the-art SAMP enhancement methods. This makes it a candidate for many practical applications that need fast reconstruction. The proposed algorithm constructs two correlation vectors, which represent the input signals recovered from the support set and candidate set. The step size is transformed by their Pearson correlation coefficients (PCCS). Compared to the residual energy, the correlation coefficient is more sensitive. The CCSAMP reduces the number of iterations while maintaining the SAMP's capability of signal reconstruction without prior knowledge of the sparsity. Simulation shows that the CCSAMP can significantly reduce the number of iterations compared to the SAMP algorithm. The CCSAMP can be used for radar detection, radar 3D imaging, and other fields where fast and accurate reconstruction of signals is required.

Adaptive Approach to Time-Frequency Analysis of AE Signals of Rocks

The paper describes a new adaptive approach to the investigation of acoustic emission of rocks, the anomalies of which may serve as short-term precursors of strong earthquakes. The basis of the approach is complex methods for monitoring acoustic emission and for analysis of its time-frequency content. Piezoceramic hydrophones and vector receivers, installed at the bottom of natural and artificial water bodies, as well as in boreholes with water, are used as acoustic emission sensors. To perform a time-frequency analysis of geoacoustic signals, we use a sparse approximation based on the developed Adaptive Matching Pursuit algorithm. The application of this algorithm in the analysis makes it possible to adapt to the concrete characteristics of each geoacoustic pulse. Results of the application of the developed approach for the investigation of acoustic emission anomalies, occurring before earthquakes, are presented. We analyzed the earthquakes, that occurred from 2011 to 2016 in the seismically active region of the Kamchatka peninsula, which is a part of the circum-Pacific orogenic belt also known as the "Ring of Fire". It was discovered that geoacoustic pulse frequency content changes before a seismic event and returns to the initial state after an earthquake. That allows us to make a conclusion on the transformation of acoustic emission source scales before earthquakes. The obtained results may be useful for the development of the systems for environmental monitoring and detection of earthquake occurrences.

A Novel Sparsity Adaptive Algorithm for Underwater Acoustic Signal Reconstruction.

In view of the fact that most of the traditional algorithms for reconstructing underwater acoustic signals from low-dimensional compressed data are based on known sparsity, a sparsity adaptive and variable step-size matching pursuit (SAVSMP) algorithm is proposed. Firstly, the algorithm uses Restricted Isometry Property (RIP) criterion to estimate the initial value of sparsity, and then employs curve fitting method to adjust the initial value of sparsity to avoid underestimation or overestimation, before finally realizing the close approach of the sparsity level with the adaptive step size. The algorithm selects the atoms by matching test, and uses the Least Squares Method to filter out the unsuitable atoms, so as to realize the precise reconstruction of underwater acoustic signal received by the sonar system. The experimental comparison reveals that the proposed algorithm overcomes the drawbacks of existing algorithms, in terms of high computation time and low reconstruction quality.

Bearing Fault Feature Extraction Based on Adaptive OMP and Improved K-SVD

The condition of the bearing is closely related to the condition and remaining life of the rotating machine. Targeting the problem of the large number of harmonic signals and noise signals during the operation of rolling bearings, and given that it is difficult to identify the fault in time, an adaptive orthogonal matching pursuit algorithm (OMP) and an improved K-singular value decomposition (K-SVD) for bearing fault feature extraction are proposed. An adaptive OMP algorithm is applied, which uses the Fourier dictionary to improve the solution method of the OMP algorithm so that it can separate the harmonic components in the signal faster and more accurately. At the same time, the stopping criterion of the adaptive sparsity is improved in dictionary learning. There is no need to manually set the sparsity in the algorithm initialization process, which avoids the problem of algorithm performance degradation due to improper sparsity settings, and improves the efficiency of the K-SVD algorithm. As shown by theoretical verification, algorithm comparison, and experimental comparisons, the algorithm has certain advantages in fault feature extraction during rolling bearing operation, and the algorithm still has considerable practical value in long-duration and strong noise environments.

An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks

Aiming at the problems of low data reconstruction accuracy in wireless sensor networks and users unable to receive accurate original signals, improvements are made on the basis of the stagewise orthogonal matching pursuit algorithm, combined with sparseness adaptation and the pre-selection strategy, which proposes a sparsity adaptive pre-selected stagewise orthogonal matching pursuit algorithm. In the framework of the stagewise orthogonal matching pursuit algorithm, the algorithm in this article uses a combination of a fixed-value strategy and a threshold strategy to screen the candidate atom sets in two rounds to improve the accuracy of atom selection, and then according to the sparsity adaptive principle, the sparse approximation and accurate signal reconstruction are realized by the variable step size method. The simulation results show that the algorithm proposed in this article is compared with the orthogonal matching pursuit algorithm, regularized orthogonal matching pursuit algorithm, and stagewise orthogonal matching pursuit algorithm. When the sparsity is 35 < K < 45, regardless of the size of the perception matrix and the length of the signal, M = 128, N = 256 or M = 128, N = 512 are improved, and the reconstruction time is when the sparsity is 10, the fastest time between 25 and 25, that is, less than 4.5 s. It can be seen that the sparsity adaptive pre-selected stagewise orthogonal matching pursuit algorithm has better adaptive characteristics to the sparsity of the signal, which is beneficial for users to receive more accurate original signals.

Artificial Intelligence Algorithm-Based Computed Tomography Image of Both Kidneys in Diagnosis of Renal Dysplasia.

The objective of this study was to explore the accuracy of low-dosage computed tomography (CT) images based on the expectation maximization algorithm denoising algorithm (EM algorithm) in the detection and diagnosis of renal dysplasia, so as to provide reasonable research basis for accuracy improvement of clinical diagnosis of renal dysplasia. 120 patients with renal dysplasia in hospital were randomly selected as the research objects, and they were divided into two groups by random number method, with 60 patients in each group. The low-dosage CT images of patients in the control group were not processed (nonalgorithm group), and the low-dosage CT images of patients in the observation group were denoised using the EM algorithm (algorithm group). In addition, it was compared with the results of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients and the consistency with the results of the pathological diagnosis. The results were compared with those of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients. The results showed that the peak signal-to-noise ratio (PSNR) (15.9 dB) of the EM algorithm was higher than the regularized adaptive matching pursuit (RAMP) algorithm (1.69 dB) and the mean filter (4.3 dB) (P < 0.05). The time consumption of EM algorithm (21 s) was shorter than that of PWLS algorithm (34 s) and MS-PWLS algorithm (39 s) (P < 0.05). The diagnosis accuracy of dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney was obviously higher in the algorithm group than the control group (P < 0.05), which were 66.67% vs. 90%, 60% vs. 88.89%, 71.42% vs. 100%, and 60% vs. 88.89%, respectively. The incidence of hypertension in patients with autosomal dominant polycystic kidney disease (ADPKD) (56.77%) was much higher than that of the other diseases (P < 0.05). After denoising by the EM algorithm, low-dosage CT image could improve the diagnostic accuracy of several types of renal dysplasia except ADPKD, showing certain clinical application value. In addition, ADPKD was easy to cause hypertension.

SAR Imaging From Azimuth Missing Raw Data via Sparsity Adaptive StOMP

Synthetic aperture radar (SAR) raw data missing occurs when the radar is interrupted for various reasons during the work. Different solutions have been proposed to this problem. In recent years, with the continuous deepening of the research on compressed sensing (CS), it has also been fully utilized in solving the problem of missing data. When using traditional greedy algorithms to recover missing data, we need to know the sparsity, but it is often unknowable in practice. This letter proposes to apply the sparsity adaptive segmented orthogonal matching pursuit (SAStOMP) algorithm to the recovery of SAR missing data. Simulation results show that the proposed method can recover missing SAR data under the condition of unknown sparsity and can adapt to a wider range of threshold parameters. It has good recovery performance for periodic and nonperiodic missing SAR raw data, thus improving SAR imaging results.

Optimized Compressive Sensing Based ECG Signal Compression and Reconstruction

In wireless body sensor network (WBSN), the set of electrocardiograms (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. However, due to the size of the ECG data, the performance of the signal compression and reconstruction is degraded. For efficient wireless transmission of ECG data, compressive sensing (CS) frame work plays significant role recently in WBSN. So, this work focuses to present CS for ECG signal compression and reconstruction. Although CS minimizes mean square error (MSE), compression rate and reconstruction probability of the CS is further to be improved. In this paper, we provide an efficient compressive sensing framework which strives to improve the reconstruction process, by adjusting the sensing matrix during the compression phase using the rain optimization algorithm (ROA). With the optimal sensing matrix, the compressed signal is reconstructed using Step Size optimized Sparsity Adaptive Matching Pursuit algorithm (SAMP). The results of this work demonstrate that the optimised CS framework achieves a higher compression rate and probability of reconstruction than the standard CS framework.

An Improved Sparsity Adaptive Matching Pursuit Algorithm and Its Application in Shock Wave Testing

In the compressed sensing (CS) reconstruction algorithms, the problems of overestimation and large redundancy of candidate atoms will affect the reconstruction accuracy and probability of the algorithm when using Sparsity Adaptive Matching Pursuit (SAMP) algorithm. In this paper, we propose an improved SAMP algorithm based on a double threshold, candidate set reduction, and adaptive backtracking methods. The algorithm uses the double threshold variable step-size method to improve the accuracy of sparsity judgment and reduces the undetermined atomic candidate set in the small step stage to enhance the stability. At the same time, the sparsity estimation accuracy can be improved by combining with the backtracking method. We use a Gaussian sparse signal and a measured shock wave signal of the 15psi range sensor to verify the algorithm performance. The experimental results show that, compared with other iterative greedy algorithms, the overall stability of the DBCSAMP algorithm is the strongest. Compared with the SAMP algorithm, the estimated sparsity of the DBCSAMP algorithm is more accurate, and the reconstruction accuracy and operational efficiency of the DBCSAMP algorithm are greatly improved.