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991.
992.
C.-H. Yeh 《The International Journal of Advanced Manufacturing Technology》2003,21(3):223-233
This paper presents a novel approach for localising open and short boundary defect candidates on ball grid array (BGA) substrate
conducting paths by using a 2D wavelet transform (2D WT). Once the potential defects are identified, traditional printed circuit
board (PCB) inspection algorithms can focus on these candidates for further analysis to identify true open and short defects.
The defect-detecting scope and the inspection effort are thereby significantly reduced. The binary BGA substrate image is
processed. It shows only the boundaries of BGA substrate conducting paths, which are further decomposed directly by 2D WT.
Since most of the wavelet energy is clustered at the edges of image objects, the wavelet transform modulus sum (WTMS) of each
edge pixel on BGA substrate conducting path boundaries is initially collected. By comparing the WTMS of an edge pixel on decomposition
level j with its WTMS on an adjacent decomposition level j + 1, an across-level ratio can be estimated to verify the irregularity of an edge pixel. That is, an edge pixel is classified
as strongly irregular (e.g. potential open or short defects) if its across-level ratio reaches a predefined threshold. The
proposed approach is template-free and easy to implement, so it is suitable for small-batch production. Real BGA substrates
with synthetic boundary defects are used as test samples to evaluate the performance of the proposed approach. Experimental
results show that the proposed method is capable of capturing all the open and short defects on BGA substrate conducting paths
without missing any errors by using a selected across-level ratio threshold and appropriate decomposition level. 相似文献
993.
994.
Suparerk JANJARASJITT 《浙江大学学报:C卷英文版》2014,(12):1147-1153
Self-similarity or scale-invariance is a fascinating characteristic found in various signals including electroencephalogram (EEG) signals. A common measure used for characterizing self-similarity or scale-invariance is the spectral exponent. In this study, a computational method for estimating the spectral exponent based on wavelet transform was examined. A series of Daubeehies wavelet bases with various numbers of vanishing moments were applied to analyze tile self-similar characteristics of intracranial EEG data corresponding to different pathological states of the brain, i.e., ictal and interictal states, in patients with epilepsy. The computational results show that the spectral exponents of intracranial EEG signals obtained during epileptic seizure activity tend to be higher than those obtained during non-seizure periods. This suggests that the intracranial EEG signals obtained during epileptic seizure activity tend to be more self-similar than those obtained during non-seizure periods. The computational results obtained using the wavelet-based approach were validated by comparison with results obtained using the power spectrum method. 相似文献
995.
The application of continuous wavelet transformation in the phase field crystal model has yielded excellent results for the crystal lattice orientation and grain boundaries with different misorientation angles [H.M. Singer, I. Singer, Phys. Rev. E 74 (2006) 031103]. However, we show here that the orientation map from this simple method cannot distinguish symmetric orientations using a single convolution template. By introducing additional rotational templates, the grain orientation can be uniquely visualized in two and three dimensions. 相似文献
996.
997.
In a bio-imaging context, the main issues which obstruct the CS (Compressed sensing) application are image reconstruction time and computational cost. This paper presents an effective compressed sensing-based MRI reconstruction through a hybrid optimization algorithm. Initially, the preprocessing stage is performed using Cross guided bilateral filter. Then the K-space is generated by the Fourier transform. The hybrid Walsh Hadamard Transform and Discrete Wavelet Transform (HWHDWT) is utilized for the compressive sensing of the images. Finally, the Hybrid Galactic Swarm Optimization and Grey Wolf Optimization (HGSGWO) algorithm are developed for MRI reconstruction. The dataset collected from a hospital which contains MRI images both in JPEG and DICOM format. The performance of SSIM (Structural Similarity Index), PSNR (Peak Signal to Noise Ratio), MSE (mean square error) and reconstruction time are evaluated for images and it is compared with the existing methods. 相似文献
998.
Time–frequency representations (TFRs) of signals, such as the windowed Fourier transform (WFT), wavelet transform (WT) and their synchrosqueezed versions (SWFT, SWT), provide powerful analysis tools. Here we present a thorough review of these TFRs, summarizing all practically relevant aspects of their use, reconsidering some conventions and introducing new concepts and procedures to advance their applicability and value. Furthermore, a detailed numerical and theoretical study of three specific questions is provided, relevant to the application of these methods, namely: the effects of the window/wavelet parameters on the resultant TFR; the relative performance of different approaches for estimating parameters of the components present in the signal from its TFR; and the advantages/drawbacks of synchrosqueezing. In particular, we show that the higher concentration of the synchrosqueezed transforms does not seem to imply better resolution properties, so that the SWFT and SWT do not appear to provide any significant advantages over the original WFT and WT apart from a more visually appealing pictures. The algorithms and Matlab codes used in this work, e.g. those for calculating (S)WFT and (S)WT, are freely available for download. 相似文献
999.
Fault detection plays an important role in both conventional AC and upcoming DC power systems. This paper aims to study the application of discrete wavelet transform (WT) for detecting the DC fault in the high voltage DC (HVDC) system. The methods of choosing the mother wavelet suited for DC fault is presented, based on degree of correlation to the fault pattern and the time delay. The wavelet analysis is performed on a multi-terminal HVDC system, built in PSCAD/EMTDC software. Its performance is judged for critical parameter like the fault location, resistance and distance. The analysis is further extended to validation using results from experiment, which is obtained from a lab-scale DC hardware setup. Load change, one of the transient disturbances in power system, is carried out to understand the effectiveness of the wavelet transform to differentiate it from the DC fault. The noise in the experimental result gives rise to non-zero wavelet coefficient during the steady-state. This can be improved by removing the unwanted noise using right filter while still retaining the fault-induced transient. The wavelet transform is compared with short-time Fourier transform to highlight the issue with window size and noise. 相似文献
1000.
Signals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features are first generated by advanced signal processing techniques. Then feature selection takes place, often requiring human expertise. This approach, besides time-consuming, is highly dependent on the machinery configuration as in general the results obtained for a mechanical system cannot be reused by other systems. Moreover, the information about time events is often lost along the process, preventing the discovery of faulty state patterns in machines operating under time-varying conditions. In this paper a novel method for automatic feature extraction and estimation of fault severity is proposed to overcome the drawbacks of classical techniques. The proposed method employs a Deep Convolutional Neural Network pre-trained by a Stacked Convolutional Autoencoder. The robustness and accuracy of this new method are validated using a dataset with different severity conditions on failure mode in a helical gearbox, working in both constant and variable speed of operation. The results show that the proposed unsupervised feature extraction method is effective for the estimation of fault severity in helical gearbox, and it has a consistently better performance in comparison with other reported feature extraction methods. 相似文献