Ultrasonic flaw detection using threshold modified S-transform

Interference noising originating from the ultrasonic testing defect signal seriously influences the accuracy of the signal extraction and defect location. Time–frequency analysis methods are mainly used to improve the defects detection resolution. In fact, the S-transform, a hybrid of the Short time Fourier transform (STFT) and wavelet transform (WT), has a time frequency resolution which is far from ideal. In this paper, a new modified S-transform based on thresholding technique, which offers a better time frequency resolution compared to the original S-transform is proposed. The improvement is achieved by the introduction of a new scaling rule for the Gaussian window used in S-transform. Simulation results are presented and show correct time frequency information of multiple Gaussian echoes under low signal-to-noise ratio (SNR) environment. In addition, experimental results demonstrate better and reliable detection of close echoes drowned in the noise.     

Ultrasonic flaw detection in model railway wheels

Rayleigh and Lamb waves have been excited in $\text{1}\text{4}$ scale model railway wheels by an ultrasonic source located on the wheel tread. Simulated flaws in treads and plates have been located.     

Ultrasonic flaw detection in NDE of highly scattering materials using wavelet and Wigner-Ville transform processing

In ultrasonic non-destructive evaluation of highly scattering materials the backscattering noise may attain peak values greater than the searched flaw pulse and the mean value of noise spectrum is very similar to the searched echo spectrum. Several specific methods have been proposed for the reduction of this type of noise, but the comparison of the performance of different methods is still an open problem. In this paper, we make a comparison among some methods based on simultaneous representations in time and frequency/scale domains of the ultrasonic traces. Synthetic and experimental traces are de-noised using a discrete wavelet processor with decomposition level-dependent threshold selection and a method that combines Wigner-Ville transform and filtering in the time-frequency domain. The results are comparatively evaluated in terms of signal to noise ratio and probability of detection.     

Ultrasonic elastography using sector scan imaging and a radial compression

Elastography is an imaging technique based on strain estimation in soft tissues under quasi-static compression. The stress is usually created by a compression plate, and the target is imaged by an ultrasonic linear array. This configuration is used for breast elastography, and has been investigated both theoretically and experimentally. Phenomena such as strain decay with tissue depth and strain concentrations have been reported. However in some in vivo situations, like prostate or blood vessels imaging, this set-up cannot be used. We propose a device to acquire in vivo elastograms of the prostate. The compression is applied by inflating a balloon that covers a transrectal sector probe. The 1D algorithm used to calculate the radial strain fails if the center of the imaging probe does not correspond to the center of the compressor. Therefore, experimental elastograms are calculated with a 2D algorithm that accounts for tangential displacements of the tissue. In this article, in order to gain a better understanding of the image formation process, the use of ultrasonic sector scans to image the radial compression of a target is investigated. Elastograms of homogeneous phantoms are presented, and compared with simulated images. Both show a strain decay with tissue depth. Then experimental and simulated elastograms of a phantom that contains a hard inclusion are presented, showing that strain concentrations occur as well. A method to compensate for strain decay and therefore to increase the contrast of the strain elastograms is proposed. It is expected that such information will help to interpret and possibly improve the elastograms obtained via radial compression.     

Diffuseness effect and radial basis function network for optimizing a decay calculations

A radial basis function network(RBFN)approach is adopted for the first time to optimize the calcula-tion of a decay half-life in the generalized liquid drop mod...     

Efficient mesh motion using radial basis functions with data reduction algorithms

Mesh motion using radial basis functions has been demonstrated previously by the authors to produce high quality meshes suitable for use within unsteady and aeroelastic CFD codes. In the aeroelastic case the structural mesh may be used as the set of control points governing the deformation, which is efficient since the structural mesh is usually small. However, as a stand alone mesh motion tool, where the surface mesh points control the motion, radial basis functions may be restricted by the size of the surface mesh, as an update of a single volume point depends on all surface points. In this paper a method is presented that allows an arbitrary deformation to be represented to within a desired tolerance by using a significantly reduced set of surface points intelligently identified in a fashion that minimises the error in the interpolated surface. This method may be used on much larger cases and is successfully demonstrated here for a 10⁶ cell mesh, where the initial solve phase cost reduces by a factor of eight with the new scheme and the mesh update by a factor of 55. It has also been shown that the number of surface points required to represent the surface is only geometry dependent (i.e. grid size independent), and so this reduction factor actually increases for larger meshes.     

Ultrasonic characterization of ultra-thin elastic layer using retrieve function

lIntfoductionTherearevarioussituationsoftechnologicalimportanceinwhichonewishestocarryoutanon-destructiveevaluation(NDE)oftheacousticproperties(thickness,wave-speed,attenua-tionanddensity)ofthinelasticlayers,suchascharacterizingthequalityofweldingseam,thestrengthofadhesivebondinglayers,layersincompositestructures,andthinfilmsonsubstrate.overthepastthirtyyears,ultrasQnicdeterminationoftheacousticalpropertieshasreceivedconsiderableattentionandanumberoftechniqueshavebeendeveloped.Amongtheseareti…     

Accurate radial basis function based neural network approach for analysis of photonic crystal fibers

In this paper, a new and an accurate artificial neural network approach (ANN) is presented for the analysis and design of photonic crystal fibers (PCFs). The new ANN approach is based on the radial basis functions which offer a very quick convergence and high efficiency during the ANN learning. The accuracy of the suggested approach is demonstrated via the excellent agreement between the results obtained using the presented approach and the results of the full vectorial finite difference method (FVFDM). In addition, a new design of highly birefringence PCF with low losses for the two polarized modes is presented using the proposed approach.     

Reduced surface point selection options for efficient mesh deformation using radial basis functions

Previous work by the authors has developed an efficient method for using radial basis functions (RBFs) to achieve high quality mesh deformation for large meshes. For volume mesh deformation driven by surface motion, the RBF system can become impractical for large meshes due to the large number of surface (control) points, and so a particularly effective data reduction scheme has been developed to vastly reduce the number of surface points used. The method uses a chosen error function on the surface mesh to select a reduced subset of the surface points; this subset contains a sufficiently small number of points so as to make the volume deformation fast, and a correction function is used to correct those surface points not included. Hence, the scheme is split such that both parts are working on appropriate problems. RBFs are an excellent way of finding smooth orthogonality preserving global deformations, but are less suitable for enforcing an exact geometry for a large number of points, while a simpler approach is ideal for diffusing small changes evenly but has quality (and possibly expense) drawbacks if used for the entire volume. However, alternatives exist for the error function used to select the reduced data set, so here a comparison is made between three different options: the surface error function, the unit function and the power function. Tests run on structured and unstructured meshes show that the surface error function gives the lowest errors, but this also requires a deformed surface shape to be known in advance of the simulation. The unit and power functions both avoid the need for a deformed surface, and the unit function is shown to be superior.     

Hash function construction using weighted complex dynamical networks

A novel scheme to construct a hash function based on a weighted complex dynamical network (WCDN) generated from an original message is proposed in this paper. First, the original message is divided into blocks. Then, each block is divided into components, and the nodes and weighted edges are well defined from these components and their relations. Namely, the WCDN closely related to the original message is established. Furthermore, the node dynamics of the WCDN are chosen as a chaotic map. After chaotic iterations, quantization and exclusive-or operations, the fixed-length hash value is obtained. This scheme has the property that any tiny change in message can be diffused rapidly through the WCDN, leading to very different hash values. Analysis and simulation show that the scheme possesses good statistical properties, excellent confusion and diffusion, strong collision resistance and high efficiency.     

Overlapping community detection using a generative model for networks

Detecting overlapping communities is a challenging task in analyzing networks, where nodes may belong to more than one community. Many present methods optimize quality functions to extract the communities from a network. In this paper, we present a probabilistic method for detecting overlapping communities using a generative model. The model describes the probability of generating a network with the model parameters, which reflect the communities in the network. The community memberships of each node are determined based on a probabilistic approach using those model parameters, whose values can be obtained by fitting the model to the network. This method has the advantage that the node participation degrees in each community are also computed. The proposed method is compared with some other community detection methods on both synthetic networks and real-world networks. The experiments show that this method is efficient at detecting overlapping communities and can provide better performance on the networks where a majority of nodes belong to more than one community.     

Identification of tartrazine and sunset yellow by fluorescence spectroscopy combined with radial basis function neural network

The fluorescence spectra of synthetic food dyes of sunset yellow and tartrazine are analyzed. The fluorescence peak wavelengths of sunset yellow and tartrazine are 576 and 569 nm, respectively, while the fluorescence spectra widths are 480-750 and 500 750 nm induced by ultraviolet light between 310-400 nm. The fluorescence spectra of sunset yellow overlap heavily with those of tartrazine, so it is difficult to distinguish them. Based on the principle of radial basis function neural network, a neural network is obtained from the training of the 14 groups of experimental data. The results show that the species of sunset yellow and tartrazine could be recognized accurately. This method has potential applications in other synthetic food dyes detection and food safety inspection.     

Multi-species detection using multi-mode absorption spectroscopy (MUMAS)

The detection of multiple species using a single laser and single detector employing multi-mode absorption spectroscopy (MUMAS) is reported. An in-house constructed, diode-pumped, Er:Yb:glass micro-laser operating at 1,565 nm with 10 modes separated by 18 GHz was used to record MUMAS signals in a gas mixture containing C₂H₂, N₂O and CO. The components of the mixture were detected simultaneously by identifying multiple transitions in each of the species. By using temperature- and pressure-dependent modelled spectral fits to the data, partial pressures of each species in the mixture were determined with an uncertainty of ±2 %.     

Cancer detection on a cell-by-cell basis using a fractal dimension analysis

We show that the application of imaging and pattern recognition techniques developed for basic heavy ion research has useful applications in medical imaging. In particular, we utilize the fractal dimension of the perimeter surface of cell sections as a new observable to characterize cells of different types. We propose that it is possible to distinguish cancerous from healthy cells with the aid of this new approach. As a first application we show in an exploratory study that it is possible to perform this distinction between patients with hairy-cell lymphocytic leukemia and those with normal blood lymphocytes.     

Community detection in networks by using multiobjective evolutionary algorithm with decomposition

Community structure is an important property of complex networks. Most optimization-based community detection algorithms employ single optimization criteria. In this study, the community detection is solved as a multiobjective optimization problem by using the multiobjective evolutionary algorithm based on decomposition. The proposed algorithm maximizes the density of internal degrees, and minimizes the density of external degrees simultaneously. It can produce a set of solutions which can represent various divisions to the networks at different hierarchical levels. The number of communities is automatically determined by the non-dominated individuals resulting from our algorithm. Experiments on both synthetic and real-world network datasets verify that our algorithm is highly efficient at discovering quality community structure.