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The method of compressive sensing is applied to moving source data created by simulation to estimate the mode wavenumbers, mode depth functions and mode amplitudes without any environmental acoustic information, such as sound speed profile or bottom properties. The method needs in principle only data covering a short range span and is thus applicable to a range-dependent environment to estimate the...
In this paper, we present a new approach of time of flight (TOF) estimation using wide band signals, based on compressive sensing (CS) reconstruction in warped domain. In real conditions, constraints related to signal's transmitting/receiving (such as the transducers configuration or the experimental setup) can affect the informational content of the signal. Thus, the received signals need content...
In recent years, accurate location and characterization of damage has motivated the engineering community to develop several damage identification techniques. Many of the nondestructive evaluations and structural health monitoring techniques are based on the analysis of huge amount of data collected from acousto-ultrasonic sensors. Such analysis is typically a very time-consuming process. Therefore,...
In ultrasonic NDE applications, the received signal carries valuable physical information along the wave propagation path such as locations, orientation and sizes of discontinuities. Various signal processing algorithms have been utilized to interrogate ultrasonic echoes, highly overlapped and sometimes noise-contaminated. As of assessing and monitoring the in-situ outsized structures, it becomes...
In this investigation, compressed sensing (CS) is utilized to examine the sparsity of ultrasonic NDE signal, which consequently improves the efficacy of data sampling with significant lower rate than the Nyquist rate. The time-of-arrivals (TOAs) and amplitudes of dominant echoes are estimated through annihilating filter in the CS via matrix operation. The matrix size is proportional to the number...
In this investigation, a compressed sensing (CS) sampling scheme is closely incorporated into ultrasound signal decomposition. The CS is used to exploit the sparsity of ultrasound echo signals and thereby significantly reduce the sampling rate with 20–30 times lower than the Nyquist rate. Furthermore, the time-of-arrivals (TOAs) of dominant echoes are estimated with the sparse sampling. The estimated...
The recently developed compressed sensing (CS) framework has been applied to various data intensive imaging applications. It provides significant reductions in sampling rate with minimal loss in image quality. In this investigation, a CS-based sampling scheme is introduced for ultrasonic nondestructive evaluation (NDE) signals. The feasibility of parameter estimation based on the new sampling method...
Compressive Sensing (CS) has emerged as a potentially viable technique for the efficient acquisition of high-resolution signals that have a sparse representation in a fixed basis. In this work, we have developed a general approach for low rate sampling and efficient CS impulse response recovery algorithms that exploits convolution signal models of dispersive ultrasonic guided waves with a sparse representation...
Stationarity of the sparse coefficients as well as the sparseness of their support, along with incoherence assumptions related to restricted isometry, are fundamental to compressive sensing and sparse optimization. However, scientific study of many sparse processes encountered in nature as well as engineering applications necessitates solving ill-conditioned optimization metrics and tracking rapidly...
We study the use of distributed average consensus and compressed sensing to perform decentralized estimation of a field measured by networked sensors. We examine field reconstruction of multiple acoustic sources from isotropic magnitude measurements. Compressed projections of global network observations are spread throughout the network using consensus, after which all nodes may invert the source...
We consider the estimation of multiple room impulse responses from the simultaneous recording of several known sources. Existing techniques are restricted to the case where the number of sources is at most equal to the number of sensors. We relax this assumption in the case where the sources are known. To this aim, we propose statistical models of the filters associated with convex log-likelihoods,...
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