Manipulation of speckle fringes for non-destructive testing of defects in composites

Paoletti, Domenica; Spagnolo, Giuseppe Schirripa; Zanetta, P.; Facchini, M.; Albrecht, Daniel

doi:10.1016/0030-3992(94)90082-5

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…To produce two different states of deformation of the structure it is possible to use depressurization [20,21], photothermal stimulation [21,22] or vibrations. In the late case, to define two states, it is necessary to produce a stroboscopic illumination with an acousto-optic modulator in the laser beam [23] as seen in Figure 8a.…”

Section: Principle Of Shearographymentioning

confidence: 99%

Nondestructive Evaluation of Cooperative Structures (NDECS)

Balageas

2008

Encyclopedia of Structural Health Monitoring

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SHM is born from the conjunction of several techniques and has common basis with NDE. In fact, several NDE techniques can be converted into SHM techniques by integrating the sensors and the actuators inside the monitored structure. NDE is mature, since several decades of research and development have been made. Contrariwise, SHM just begins to come out from laboratories, and applications at a wide scale remain a long-term objective. In these conditions, we may wander if an intermediate way, less performing but feasible at a shorter term, would not be an attractive and relevant position.This third way consists in only embedding at well-chosen locations into the structure the stimulation (actuators or emitters) and leaving the detection (receivers or sensors) outside. To optimize such a system, Lamb waves could be used for the emission and full-field real time imaging techniques used for the reception. So, using the more recent and promising techniques of both domains -SHM and NDE -, the proposed technique could be applied at a short term, with existing technologies and would permit time and money saving for maintenance operations. A well-suited appellation for this type of technique could be: Non-Destructive Evaluation of Cooperative Structures (NDECS).The principle and first validation experiments of two possible NDECS systems, stroboscopic shearography and lock-in ultrasonic vibrothermography, imaging Lamb waves generated by embedded piezoelectric patches, are presented.

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Section: Principle Of Shearographymentioning

confidence: 99%

Nondestructive Evaluation of Cooperative Structures (NDECS)

Balageas

2008

Encyclopedia of Structural Health Monitoring

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“…Another important application is phase unwrapping where the general idea is to infer the structure of an object by transmitting waveforms, and capturing the phase coherence (measured modulo 2π radians) between the transmitted and scattered waveforms. This arises for instance in InSAR (synthetic radar aperture interferometry) for estimating the depth map of a terrain (e.g., [16,40]); MRI, for estimating the position of veins in tissues (e.g., [17,23]), and non destructive testing of components (e.g., [29,19]), to name a few applications. A main difference between phase unwrapping and moludo samples coming from a self-reset ADC is that folding is deliberately injected in the latter case, while in phase unwrapping problems the data is assumed to be available in the form of modulo 2π information.…”

Section: Introductionmentioning

confidence: 99%

Denoising modulo samples: k-NN regression and tightness of SDP relaxation

Fanuel

Tyagi

2021

Information and Inference: A Journal of the IMA

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Many modern applications involve the acquisition of noisy modulo samples of a function $f$, with the goal being to recover estimates of the original samples of $f$. For a Lipschitz function $f:[0,1]^d \to {{\mathbb{R}}}$, suppose we are given the samples $y_i = (f(x_i) + \eta _i)\bmod 1; \quad i=1,\dots ,n$, where $\eta _i$ denotes noise. Assuming $\eta _i$ are zero-mean i.i.d Gaussian’s, and $x_i$’s form a uniform grid, we derive a two-stage algorithm that recovers estimates of the samples $f(x_i)$ with a uniform error rate $O\big(\big(\frac{\log n}{n}\big)^{\frac{1}{d+2}}\big)$ holding with high probability. The first stage involves embedding the points on the unit complex circle, and obtaining denoised estimates of $f(x_i)\bmod 1$ via a $k$NN (nearest neighbor) estimator. The second stage involves a sequential unwrapping procedure which unwraps the denoised mod $1$ estimates from the first stage. The estimates of the samples $f(x_i)$ can be subsequently utilized to construct an estimate of the function $f$, with the aforementioned uniform error rate. Recently, Cucuringu and Tyagi proposed an alternative way of denoising modulo $1$ data, which works with their representation on the unit complex circle. They formulated a smoothness regularized least squares problem on the product manifold of unit circles, where the smoothness is measured with respect to the Laplacian of a proximity graph $G$ involving the $x_i$’s. This is a nonconvex quadratically constrained quadratic program (QCQP) hence they proposed solving its semidefinite program (SDP) based relaxation. We derive sufficient conditions under which the SDP is a tight relaxation of the QCQP. Hence under these conditions, the global solution of QCQP can be obtained in polynomial time.

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“…This arises for instance in InSAR (synthetic radar aperture interferometry) for estimating the depth map of a terrain (e.g., [9,32]); MRI, for estimating the position of veins in tissues (e.g., [10,16]), and non destructive testing of components (e.g., [21,12]), to name a few applications. Given the measurement model in (1.1), where we assume from now that ζ = 1, we are interested in unwrapping (y i ) i to recover the original samples f (x i ) for i = 1, .…”

Section: Introductionmentioning

confidence: 99%

Denoising modulo samples: k-NN regression and tightness of SDP relaxation

Fanuel¹,

Tyagi²

2020

Preprint

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Many modern applications involve the acquisition of noisy modulo samples of a function f , with the goal being to recover estimates of the original samples of f . For a Lipschitz function f : [0, 1] d → R, suppose we are given the samples y i = (f (x i ) + η i ) mod 1; i = 1, . . . , n where η i denotes noise. Assuming η i are zero-mean i.i.d Gaussian's, and x i 's form a uniform grid, we derive a two-stage algorithm that recovers estimates of the samples f (x i ) with a uniform error rate O(() holding with high probability. The first stage involves embedding the points on the unit complex circle, and obtaining denoised estimates of f (x i ) mod 1 via a kNN (nearest neighbor) estimator. The second stage involves a sequential unwrapping procedure which unwraps the denoised mod 1 estimates from the first stage.Recently, Cucuringu and Tyagi [7] proposed an alternative way of denoising modulo 1 data which works with their representation on the unit complex circle. They formulated a smoothness regularized least squares problem on the product manifold of unit circles, where the smoothness is measured with respect to the Laplacian of a proximity graph G involving the x i 's. This is a nonconvex quadratically constrained quadratic program (QCQP) hence they proposed solving its semidefinite program (SDP) based relaxation. We derive sufficient conditions under which the SDP is a tight relaxation of the QCQP. Hence under these conditions, the global solution of QCQP can be obtained in polynomial time.

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Manipulation of speckle fringes for non-destructive testing of defects in composites

Cited by 11 publications

References 10 publications

Nondestructive Evaluation of Cooperative Structures (NDECS)

Nondestructive Evaluation of Cooperative Structures (NDECS)

Denoising modulo samples: k-NN regression and tightness of SDP relaxation

Denoising modulo samples: k-NN regression and tightness of SDP relaxation

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