Guang-Ming Zhang

Bio: Guang-Ming Zhang is an academic researcher from Liverpool John Moores University. The author has contributed to research in topics: Ultrasonic sensor & Wavelet transform. The author has an hindex of 11, co-authored 54 publications receiving 509 citations. Previous affiliations of Guang-Ming Zhang include South China University of Technology & Uppsala University.

Microelectronic package characterisation using scanning acoustic microscopy

Abstract: In a highly competitive market, reliable techniques for manufacturing quality control of electronic devices are demanded. Characterisation of modern microelectronic package integrity becomes more difficult due to the continued miniaturisation of electronic device and the complexity of advanced micro-assembling technologies such as chip-scale packages and 3D IC stacks. In this paper, sparse representations of acoustic signals are sought to improve the scanning acoustic microscopy (SAM), a common non-destructive tool for failure analysis of microelectronic packages. Sparse representation of an ultrasonic signal is obtained by decomposing it in an overcomplete dictionary. Detection and location of ultrasonic echoes are then performed on the basis of the resulting redundant representation. The method offers a solution to the deconvolution problem for restoration of the ultrasonic reflectivity function. It can restore closely space overlapping echoes beyond the resolution of the conventional SAM system. It also produces high resolution and accurate estimates for ultrasonic echo parameters, i.e., time-of-flight, amplitude, centre frequency, and bandwidth. These merits of the proposed method are explored in various potential applications for microelectronic package characterisation.

Contemporary ultrasonic signal processing approaches for nondestructive evaluation of multilayered structures

Abstract: Various signal processing techniques have been used for the enhancement of defect detection and defect characterisation. Cross-correlation, filtering, autoregressive analysis, deconvolution, neural network, wavelet transform and sparse signal representations have all been applied in attempts to analyse ultrasonic signals. In ultrasonic nondestructive evaluation (NDE) applications, a large number of materials have multilayered structures. NDE of multilayered structures leads to some specific problems, such as penetration, echo overlap, high attenuation and low signal-to-noise ratio. The signals recorded from a multilayered structure are a class of very special signals comprised of limited echoes. Such signals can be assumed to have a sparse representation in a proper signal dictionary. Recently, a number of digital signal processing techniques have been developed by exploiting the sparse constraint. This paper presents a review of research to date, showing the up-to-date developments of signal processing t...

A support vector machine approach for classification of welding defects from ultrasonic signals

Abstract: Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly...

Two-sample test of high dimensional means under dependence

Abstract: Summary. The paper considers in the high dimensional setting a canonical testing problem in multivariate analysis, namely testing the equality of two mean vectors. We introduce a new test statistic that is based on a linear transformation of the data by the precision matrix which incorporates the correlations between the variables. The limiting null distribution of the test statistic and the power of the test are analysed. It is shown that the test is particularly powerful against sparse alternatives and enjoys certain optimality. A simulation study is carried out to examine the numerical performance of the test and to compare it with other tests given in the literature. The results show that the test proposed significantly outperforms those tests in a range of settings.

Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism

Abstract: Testing for the significance of a subset of regression coefficients in a linear model, a staple of statistical analysis, goes back at least to the work of Fisher who introduced the analysis of variance (ANOVA). We study this problem under the assumption that the coefficient vector is sparse, a common situation in modern high-dimensional settings. Suppose we have p covariates and that under the alternative, the response only depends upon the order of p^(1−α) of those, 0 ≤ α ≤ 1. Under moderate sparsity levels, that is, 0 ≤ α ≤ 1/2, we show that ANOVA is essentially optimal under some conditions on the design. This is no longer the case under strong sparsity constraints, that is, α > 1/2. In such settings, a multiple comparison procedure is often preferred and we establish its optimality when α ≥ 3/4. However, these two very popular methods are suboptimal, and sometimes powerless, under moderately strong sparsity where 1/2 1/2. This optimality property is true for a variety of designs, including the classical (balanced) multi-way designs and more modern “p > n” designs arising in genetics and signal processing. In addition to the standard fixed effects model, we establish similar results for a random effects model where the nonzero coefficients of the regression vector are normally distributed.

Guided-wave signal processing using chirplet matching pursuits and mode correlation for structural health monitoring

Abstract: Signal processing algorithms for guided wave pulse echo-based structural health monitoring (SHM) must be capable of isolating individual reflections from defects in the structure, if any, which could be overlapping and multimodal. In addition, they should be able to estimate the time–frequency centers, the modes and individual energies of the reflections, which would be used to locate and characterize defects. Finally, they should be computationally efficient and amenable to automated processing. This work addresses these issues with a new algorithm employing chirplet matching pursuits followed by a mode correlation check for single point sensors. Its theoretical advantages over conventional time–frequency representations for SHM are elaborated. Results from numerical simulations and experiments in isotropic plate structures are presented, which show the capability of the proposed algorithm. Finally, the issue of in-plane triangulation is discussed and experimental work done to explore this issue is presented.