Research on Sparse Representation Method of Acoustic Microimaging Signals

Acoustic microimaging (AMI), a technology for high-resolution imaging of materials using a scanning acoustic microscope, has been widely used for non-destructive testing and evaluation of electronic packages. Recently, the internal features and defects of electronic packages have reached the resolution limits of conventional time domain or frequency domain AMI methods with the miniaturization of electronic packages. Various time-frequency domain AMI methods have been developed to achieve super-resolution. In this paper, the sparse representation of AMI signals is studied, and a constraint dictionary-based sparse representation (CD-SR) method is proposed. First, the time-frequency parameters of the atom dictionary are constrained according to the AMI signal to constitute a constraint dictionary. Then, the AMI signal is sparsely decomposed using the matching pursuit algorithm, and echoes selection and echoes reconstruction are performed. The performance of CD-SR was quantitatively evaluated by simulated and experimental ultrasonic A-scan signals. The results demonstrated that CD-SR has superior longitudinal resolution and robustness.

Forward-Reverse Orthogonal Matching Pursuit-Union-Subspace Pursuit-Based Multiuser Detector for Uplink Grant-Free NOMA Networks

Multiuser Detection (MUD) is quite challenging in uplink grant-free non-orthogonal multiple access wireless communication networks in which users sporadically transmit data. The reason for this is that the base station (BS) must perform detection of both multiuser activity and user signals concurrently, because knowledge of user activity status is not available at the BS. In this paper, a new multiuser detector, named the Forward-Reverse Orthogonal Matching Pursuit–Union–Subspace pursuit (FROMPUS)-based MUD, is proposed. The detector takes advantage of the concept of an initial support set. This serves as initial knowledge that is then employed in the reconstruction of active users’ signals. In addition, the detector uses the “serial-include” technique of incorporating a likely support set element candidates and a reliability testing procedure in which the most prominent elements of the support set are selected. To assess the performance of the proposed detector, computer simulations are performed. The results obtained for various parameter settings show that the FROMPUS performs better than any of the other five detectors considered in this paper. However, this excellent performance comes with a slightly higher computational complexity cost. Nonetheless, the cost is inconsequential, since the detector operates at the BS where complexity is of low priority in comparison to performance.

Sparse Water Fracture Channel Detection from Subsurface Sensors Via a Smart Orthogonal Matching Pursuit

Water front movement in fractured carbonate reservoirs occurs in micro-fractures, corridors and interconnected fracture channels (above 5 mm in size) that penetrate the carbonate reservoir structure. Determining the fracture channels and the water front movements within the flow corridors is critical to optimize sweep efficiency and increase hydrocarbon recovery. In this work, we present a new smart orthogonal matching pursuit (OMP) algorithm for water front movement detection in carbonate fracture channels. The method utilizes a combined artificial intelligence) AI-OMP approach to first analyze and extract the potential fracture channels and then subsequently deploys a deep learning approach for estimating the water saturation patterns in the fracture channels. The OMP utilizes the sparse fracture to sensor correlation to determine the fracture channels impacting each individual sensor. The deep learning method then utilizes the fracture channel estimates to assess the water front movements. We tested the AI-OMP framework on a synthetic fracture carbonate reservoir box model exhibiting a complex fracture system. Fracture Robots (FracBots, about 5mm in size) technology will be used to sense key reservoir parameters (e.g., temperature, pressure, pH and other chemical parameters) and represent an important step towards enhancing reservoir surveillance (Al Shehri, et al. 2021). The technology is comprised of a wireless micro-sensor network for mapping and monitoring fracture channels in conventional and unconventional reservoirs. The system establishes wireless network connectivity via magnetic induction (MI)-based communication, since it exhibits highly reliable and constant channel conditions with sufficiently communication range inside an oil reservoir environment. The system architecture of the FracBots network has two layers: FracBot nodes layer and a base station layer. A number of subsurface FracBot sensors are injected in the formation fracture channels to record data affected by changes in water saturation. The sensor placement can be adapted in the reservoir formation in order to improve sensor measurement data quality, as well as better track the penetrating water fronts. They will move with the injected fluids and distribute themselves in the fracture channels where they start sensing the surrounding environment’s conditions; they communicate the data, including their location coordinates, among each other to finally transmit the information in multi-hop fashion to the base station installed inside the wellbore. The base station layer consists of a large antenna connected to an aboveground gateway. The data collected from the FracBots network are transmitted to the control room via aboveground gateway for further processing. The results exhibited strong estimation performance in both accurately determining the fracture channels and the saturation pattern in the subsurface reservoir. The results indicate that the framework performs well; especially for fracture channels that are rather shallow (about 20 m from the wellbore) with significant changes in the saturation levels. This makes the in-situ reservoir sensing a viable permanent reservoir monitoring system for the tracking of fluid fronts, and determination of fracture channels. The novel framework presents a vital component in the data analysis and interpretation of subsurface reservoir monitoring system of fracture channels flow in carbonate reservoirs. The results outline the capability of in-situ reservoir sensors to deliver accurate tracking water-fronts and fracture channels in order to optimize recovery.

A Novel Downward-Looking Linear Array SAR Imaging Method Based on Multiple Measurement Vector Model with L 2 , 1 -Norm

Downward-looking linear array synthetic aperture radar (DLLA SAR) is a kind of three-dimensional (3-D) radar imaging system. To obtain the superresolution along the crosstrack direction of DLLA SAR, the sparse regularization models with single measurement vector (SMV) have been widely applied. However, the robustness of the sparse regularization models with SMV is unsatisfactory, especially in the low signal-to-noise rate (SNR) environment. To solve this problem, we proposed a novel imaging method for DLLA SAR based on the multiple measurement vector (MMV) model with L 2 , 1 -norm. At first, we exchange the processing order between the along-track (AT) domain and the crosstrack (CT) domain to keep the same sparse structure of the signal in the crosstrack domain so that we can establish the imaging problem as a sparse regularization model based on the MMV model. Moreover, the mixed L 2 , 1 -norm is introduced into the regularization term of the MMV model. Finally, the modified orthogonal matching pursuit (OMP) algorithm is designed for the MMV model with the L 2 , 1 -norm. The simulations verify that the proposed method has better performance in the lower SNR environment and requires lower computation compared with the conventional methods.

Ultrasound Defect Localization in Shell Structures with Lamb Waves Using Spare Sensor Array and Orthogonal Matching Pursuit Decomposition

Lamb waves have multimodal and dispersion effects, which reduces their performance in damage localization with respect to resolution. To detect damage with fewest sensors and high resolution, a method, using only two piezoelectric transducers and based on orthogonal matching pursuit (OMP) decomposition, was proposed. First, an OMP-based decomposition and dispersion removal algorithm is introduced, which is capable of separating wave packets of different propagation paths and removing the dispersion part successively. Then, two simulation signals, with nonoverlapped and overlapped wave packets, are employed to verify the proposed method. Thereafter, with the proposed algorithm, the wave packets reflected from the defect and edge are all separated. Finally, a sparse sensor array with only two transducers succeeds in localizing the defect. The experimental results show that the OMP-based algorithm is beneficial for resolution improvement and transducer usage reduction.

Intelligent Diagnosis of Rotating Machinery Based on Optimized Adaptive Learning Dictionary and 1DCNN

In the intelligent fault diagnosis of rotating machinery, it is difficult to extract early weak fault impact features of rotating machinery under the interference of strong background noise, which makes the accuracy of fault identification low. In order to effectively identify the early faults of rotating machinery, an intelligent fault diagnosis method of rotating machinery based on an optimized adaptive learning dictionary and one-dimensional convolution neural network (1DCNN) is proposed in this paper. First of all, based on the original signal, a redundant dictionary with impact components is constructed by K-singular value decomposition (K-SVD), and the sparse coefficients are solved by an optimized orthogonal matching pursuit (OMP) algorithm. The sparse representation of fault impact features is realized, and the reconstructed signal with a concise fault impact feature structure is obtained. Secondly, the reconstructed signal is normalized, and the experimental dataset is divided into samples. Finally, the training set is input into the 1DCNN model for model training, and the test set is input into the trained model for classification and detection to complete the intelligent fault classification diagnosis of rotating machinery. This method is applied to the fault diagnosis of bearing data of Case Western Reserve University and worm gear reducer data of Shanghai University of Technology. Compared with other methods and models, the results show that the diagnosis method proposed in this paper can achieve higher diagnosis accuracy and better generalization ability than other diagnosis models under different datasets.