Chirplet Transform in Ultrasonic Non-Destructive Testing and Structural Health Monitoring: A Review

Ultrasonic non-destructive testing signal can be decomposed into a set of chirplet signals, which makes the chirplet transform a fitting ultrasonic signal analysis and processing method. Moreover, compared to wavelet transform, short-time Fourier transform and Gabor transform, chirplet transform is a comprehensive signal approximation method, nevertheless, the former methods gained more popularity in the ultrasonic signal processing research. In this paper, the principles of the chirplet transform are explained with a simplified presentation and the studies that used the transform in ultrasonic non-destructive testing and in structural health monitoring are reviewed to expose the existing applications and motivate the research in the potential ones

Non Invasive Foetal Monitoring with a Combined ECG - PCG System

Although modern ultrasound provides remarkable images and biophysical measures, the technology is expensive and the observations are only available over a short time. Longer term monitoring is achieved in a clinical setting using ultrasonic Doppler cardiotocography (CTG) but this has a number of limitations. Some pathologies and some anomalies of cardiac functioning are not detectable with CTG. Moreover, although frequent and/or long-term foetal heart rate (FHR) monitoring is recommended, mainly in high risk pregnancies, there is a lack of established evidence for safe ultrasound irradiation exposure to the foetus for extended periods (Ang et al., 2006). Finally, high quality ultrasound devices are too expensive and not approved for home care use. In fact, there is a remarkable mismatch between ability to examine a foetus in a clinical setting, and the almost complete absence of technology that permits longer term monitoring of a foetus at home. Therefore, in the last years, many efforts (Hany et al., 1989; Jimenez et al., 1999; Kovacs et al., 2000; Mittra et al., 2008; Moghavvemi et al., 2003; Nagal, 1986; Ruffo et al., 2010; Talbert et al., 1986; Varady et al., 2003) have been attempted by the scientific community to find a suitable alternative

Clutter Mitigation in Echocardiography Using Sparse Signal Separation

In ultrasound imaging, clutter artifacts degrade images and may cause inaccurate diagnosis. In this paper, we apply a method called Morphological Component Analysis (MCA) for sparse signal separation with the objective of reducing such clutter artifacts. The MCA approach assumes that the two signals in the additive mix have each a sparse representation under some dictionary of atoms (a matrix), and separation is achieved by finding these sparse representations. In our work, an adaptive approach is used for learning the dictionary from the echo data. MCA is compared to Singular Value Filtering (SVF), a Principal Component Analysis- (PCA-) based filtering technique, and to a high-pass Finite Impulse Response (FIR) filter. Each filter is applied to a simulated hypoechoic lesion sequence, as well as experimental cardiac ultrasound data. MCA is demonstrated in both cases to outperform the FIR filter and obtain results comparable to the SVF method in terms of contrast-to-noise ratio (CNR). Furthermore, MCA shows a lower impact on tissue sections while removing the clutter artifacts. In experimental heart data, MCA obtains in our experiments clutter mitigation with an average CNR improvement of 1.33 dB

Application and Challenges of Signal Processing Techniques for Lamb Waves Structural Integrity Evaluation: Part A-Lamb Waves Signals Emitting and Optimization Techniques

Lamb waves have been widely studied in structural integrity evaluation during the past decades with their low-attenuation and multi-defects sensitive nature. The performance of the evaluation has close relationship with the vibration property and the frequency of Lamb waves signals. Influenced by the nature of Lamb waves and the environment, the received signals may be difficult to interpret that limits the performance of the detection. So pure Lamb waves mode emitting and high-resolution signals acquisition play important roles in Lamb waves structural integrity evaluation. In this chapter, the basic theory of Lamb waves nature and some environment factors that should be considered in structural integrity evaluation are introduced. Three kinds of typical transduces used for specific Lamb waves mode emitting and sensing are briefly introduced. Then the development of techniques to improve the interpretability of signals are discussed, including the waveform modulation techniques, multi-scale analysis techniques and the temperature effect compensation techniques are summarized

Reconstruction of enhanced ultrasound images from compressed measurements

L'intérêt de l'échantillonnage compressé dans l'imagerie ultrasonore a été récemment évalué largement par plusieurs équipes de recherche. Suite aux différentes configurations d'application, il a été démontré que les données RF peuvent être reconstituées à partir d'un faible nombre de mesures et / ou en utilisant un nombre réduit d'émission d'impulsions ultrasonores. Selon le modèle de l'échantillonnage compressé, la résolution des images ultrasonores reconstruites à partir des mesures compressées dépend principalement de trois aspects: la configuration d'acquisition, c.à.d. l'incohérence de la matrice d'échantillonnage, la régularisation de l'image, c.à.d. l'a priori de parcimonie et la technique d'optimisation. Nous nous sommes concentrés principalement sur les deux derniers aspects dans cette thèse. Néanmoins, la résolution spatiale d'image RF, le contraste et le rapport signal sur bruit dépendent de la bande passante limitée du transducteur d'imagerie et du phénomène physique lié à la propagation des ondes ultrasonores. Pour surmonter ces limitations, plusieurs techniques de traitement d'image en fonction de déconvolution ont été proposées pour améliorer les images ultrasonores. Dans cette thèse, nous proposons d'abord un nouveau cadre de travail pour l'imagerie ultrasonore, nommé déconvolution compressée, pour combiner l'échantillonnage compressé et la déconvolution. Exploitant une formulation unifiée du modèle d'acquisition directe, combinant des projections aléatoires et une convolution 2D avec une réponse impulsionnelle spatialement invariante, l'avantage de ce cadre de travail est la réduction du volume de données et l'amélioration de la qualité de l'image. Une méthode d'optimisation basée sur l'algorithme des directions alternées est ensuite proposée pour inverser le modèle linéaire, en incluant deux termes de régularisation exprimant la parcimonie des images RF dans une base donnée et l'hypothèse statistique gaussienne généralisée sur les fonctions de réflectivité des tissus. Nous améliorons les résultats ensuite par la méthode basée sur l'algorithme des directions simultanées. Les deux algorithmes sont évalués sur des données simulées et des données in vivo. Avec les techniques de régularisation, une nouvelle approche basée sur la minimisation alternée est finalement développée pour estimer conjointement les fonctions de réflectivité des tissus et la réponse impulsionnelle. Une investigation préliminaire est effectuée sur des données simulées.The interest of compressive sampling in ultrasound imaging has been recently extensively evaluated by several research teams. Following the different application setups, it has been shown that the RF data may be reconstructed from a small number of measurements and/or using a reduced number of ultrasound pulse emissions. According to the model of compressive sampling, the resolution of reconstructed ultrasound images from compressed measurements mainly depends on three aspects: the acquisition setup, i.e. the incoherence of the sampling matrix, the image regularization, i.e. the sparsity prior, and the optimization technique. We mainly focused on the last two aspects in this thesis. Nevertheless, RF image spatial resolution, contrast and signal to noise ratio are affected by the limited bandwidth of the imaging transducer and the physical phenomenon related to Ultrasound wave propagation. To overcome these limitations, several deconvolution-based image processing techniques have been proposed to enhance the ultrasound images. In this thesis, we first propose a novel framework for Ultrasound imaging, named compressive deconvolution, to combine the compressive sampling and deconvolution. Exploiting an unified formulation of the direct acquisition model, combining random projections and 2D convolution with a spatially invariant point spread function, the benefit of this framework is the joint data volume reduction and image quality improvement. An optimization method based on the Alternating Direction Method of Multipliers is then proposed to invert the linear model, including two regularization terms expressing the sparsity of the RF images in a given basis and the generalized Gaussian statistical assumption on tissue reflectivity functions. It is improved afterwards by the method based on the Simultaneous Direction Method of Multipliers. Both algorithms are evaluated on simulated and in vivo data. With regularization techniques, a novel approach based on Alternating Minimization is finally developed to jointly estimate the tissue reflectivity function and the point spread function. A preliminary investigation is made on simulated data

Review of photoacoustic imaging plus X

Photoacoustic imaging (PAI) is a novel modality in biomedical imaging technology that combines the rich optical contrast with the deep penetration of ultrasound. To date, PAI technology has found applications in various biomedical fields. In this review, we present an overview of the emerging research frontiers on PAI plus other advanced technologies, named as PAI plus X, which includes but not limited to PAI plus treatment, PAI plus new circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. We will discuss each technology's current state, technical advantages, and prospects for application, reported mostly in recent three years. Lastly, we discuss and summarize the challenges and potential future work in PAI plus X area

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings : a review

A rolling bearing is an essential component of a rotating mechanical transmission system. Its performance and quality directly affects the life and reliability of machinery. Bearings’ performance and reliability need high requirements because of a more complex and poor working conditions of bearings. A bearing with high reliability reduces equipment operation accidents and equipment maintenance costs and achieves condition-based maintenance. First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, and finally, the advantages, disadvantages, and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized. This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings.The Natural Science Foundation of China (NSFC) (grant numbers: 51675403, 51275381 and 51505475), National Research Foundation, South Africa (grant numbers: IFR160118156967 and RDYR160404161474), and UOW Vice-Chancellor’s Postdoctoral Research Fellowship.International Journal of Advanced Manufacturing Technology2019-04-01hj2018Electrical, Electronic and Computer Engineerin