Daniel R. Pipa

A Sparse Reconstruction Algorithm for Ultrasonic Images in Nondestructive Testing

Ultrasound imaging systems (UIS) are essential tools in nondestructive testing (NDT). In general, the quality of images depends on two factors: system hardware features and image reconstruction algorithms. This paper presents a new image reconstruction algorithm for ultrasonic NDT. The algorithm reconstructs images from A-scan signals acquired by an ultrasonic imaging system with a monostatic transducer in pulse-echo configuration. It is based on regularized least squares using a l1 regularization norm. The method is tested to reconstruct an image of a point-like reflector, using both simulated and real data. The resolution of reconstructed image is compared with four traditional ultrasonic imaging reconstruction algorithms: B-scan, SAFT, ω-k SAFT and regularized least squares (RLS). The method demonstrates significant resolution improvement when compared with B-scan—about 91% using real data. The proposed scheme also outperforms traditional algorithms in terms of signal-to-noise ratio (SNR).

Thermal Imaging of Hydroelectric Generator Stator Using a DTS System

This paper presents a new method for thermal imaging of hydroelectric generators stators. The method is based on distributed temperature optical fiber sensing using Raman scattering. The thermal image is generated by combining the information of temperature and the spatial position of the sensor with the 3-D model of the structure. Regarding the use of conventional sensors, such as resistance temperature detector or PT100, the main advantage is the possibility to identify temperature variations over the entire stator surface. The results were obtained over a 22-h test with a 200-MW hydroelectric generator. The produced thermal images showed a great potential for monitoring the temperature distribution in the stator according to the generator load. The new method can contribute for identification of fault in the structure insulation, which when early identified can reduce damage caused by short circuit in the stator windings.

Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions

Recently, specially crafted unidimensional optimization has been successfully used as line search to accelerate the overrelaxed and monotone fast iterative shrinkage-threshold algorithm (OMFISTA) for computed tomography. In this paper, we extend the use of fast line search to the monotone fast iterative shrinkage-threshold algorithm (MFISTA) and some of its variants. Line search can accelerate the FISTA family considering typical synthesis priors, such as the

Improving Spatial Resolution of Raman DTS Using Total Variation Deconvolution

\ell _{1}

Fast and exact unidimensional L2-L1 optimization as an accelerator for iterative reconstruction algorithms

-norm of wavelet coefficients, as well as analysis priors, such as anisotropic total variation. This paper describes these new MFISTA and OMFISTA with line search, and also shows through numerical results that line search improves their performance for tomographic high-resolution image reconstruction.

Capacitive Multielectrode Direct-Imaging Sensor for the Visualization of Two-Phase Flows

Traditional Raman distributed optical sensor (DTS) exhibits a low-pass characteristic that causes sharp temperature changes to be over smoothed. This behavior can be modeled as the convolution of the real temperature profile with the DTS impulse response. This paper presents a deconvolution algorithm developed to improve the spatial resolution of a Raman DTS system. The algorithm is based on a linear DTS model and total variation regularization. The main advantage is the ability to correctly reconstruct hot regions with dimensions down to 15 cm, which represents a resolution gain of up to six times when compared with the DTS spatial resolution of 1 m. We present simulations and experimental results demonstrating the efficacy of the proposed method.

Bubble shape estimation in gas-liquid slug flow using wire-mesh sensor and advanced data processing

This paper studies the use of fast and exact unidimensional L2-L1 minimization as a line search for accelerating iterative reconstruction algorithms. In L2-L1 minimization reconstruction problems, the squared Euclidean, or L2 norm, measures signal-data discrepancy and the L1 norm stands for a sparsity preserving regularization term. Functionals as these arise in important applications such as compressed sensing and deconvolution. Optimal unidimensional L2-L1 minimization has only recently been studied by Li and Osher for denoising problems and by Wen et al. for line search. A fast L2-L1 optimization procedure can be adapted for line search and used in iterative algorithms, improving convergence speed with little increase in computational cost. This paper proposes a new method for exact L2-L1 line search and compares it with the Li and Oshers, Wen et al.s, as well as with a standard line search algorithm, the method of false position. The use of the proposed line search improves convergence speed of different iterative algorithms for L2-L1 reconstruction such as iterative shrinkage, iteratively reweighted least squares, and nonlinear conjugate gradient. This assertion is validated experimentally in applications to signal reconstruction in compressed sensing and sparse signal deblurring.

An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging

In this paper, a novel capacitive array–sensor to visualize two-phase flow by measuring liquid holdup in pipe cross-section is introduced. The measuring principle is based on the difference between electrical permittivity of the phases. The sensor consists of two copper rings being an emitter and one receiver ring. The receiver ring is divided into segments, which are individually interrogated by the measuring electronics in a fast manner. In this way, flow images are directly generated from acquired signals of electrodes in a way that it visually represents the investigated flow, avoiding the use of image reconstruction algorithms as usual in tomographic techniques. The sensor is fabricated in a flexible printed-circuit board, which is flush-mounted in the inner pipe wall. A measuring electronics is responsible to detect the capacitance changes in the electrodes. The introduced sensor along with the associated electronics has been tested in static and dynamic two-phase flow, both horizontally and vertically. Direct images were generated in these different situations, showing satisfactory results when compared with a reference wire-mesh sensor.

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring

Wire-mesh sensors produce three-dimensional data of void fraction distribution at high resolution thus being an appropriate tool to investigate two-phase gas-liquid flows. Slug flow is typically found in petroleum production lines. This type of flow is characterized by the intermittent occurrence of gas bubbles and liquid slugs along the pipe. An important issue of these flows is the existence of a variety of regimes, depending on the flow rates of gas and liquid. The quantitative and qualitative information about shapes of the bubble nose and tail allows to study and to model the flow characteristics in order to increase safety and profit margins in operation of pipelines. In this paper we investigate two methods to estimate typical bubble shape of gas-liquid slug flow, which are based on ensemble mean and median approaches, for a set of identified bubbles in a given experiment. Results show that both approaches produce similar estimations, however since median is a type of robust estimator, contours of bubbles are better defined. Three-dimensional images of typical bubbles, for five different operational conditions, are generated and reveal some details about bubble shape.

Temperature Sensing of High Power Generator Stator using DTS

Ultrasonic image reconstruction using inverse problems has recently appeared as an alternative to enhance ultrasound imaging over beamforming methods. This approach depends on the accuracy of the acquisition model used to represent transducers, reflectivity, and medium physics. Iterative methods, well known in general sparse signal reconstruction, are also suited for imaging. In this paper, a discrete acquisition model is assessed by solving a linear system of equations by an ℓ1-regularized least-squares minimization, where the solution sparsity may be adjusted as desired. The paper surveys 11 variants of four well-known algorithms for sparse reconstruction, and assesses their optimization parameters with the goal of finding the best approach for iterative ultrasound imaging. The strategy for the model evaluation consists of using two distinct datasets. We first generate data from a synthetic phantom that mimics real targets inside a professional ultrasound phantom device. This dataset is contaminated with Gaussian noise with an estimated SNR, and all methods are assessed by their resulting images and performances. The model and methods are then assessed with real data collected by a research ultrasound platform when scanning the same phantom device, and results are compared with beamforming. A distinct real dataset is finally used to further validate the proposed modeling. Although high computational effort is required by iterative methods, results show that the discrete model may lead to images closer to ground-truth than traditional beamforming. However, computing capabilities of current platforms need to evolve before frame rates currently delivered by ultrasound equipments are achievable.