A Sparse Analysis Window for Discrete Gabor Transform

Zhou, Jian; Fang, Xianyong; Tao, Liang

doi:10.1007/s00034-017-0510-0

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…It's usually used in signal processing. To describe the local frequency information of the image signal, the Gabor kernel adds a window function to the signal in the frequency domain [61]. Gabor filter kernel is similar to the receptive field of vertebrate visual cortex [62], which provides a satisfactory result for texture representation and discrimination [63].…”

Section: Gabor Filtermentioning

confidence: 99%

PCB Component Detection Using Computer Vision for Hardware Assurance

Zhao

Gurudu

Taheri

et al. 2022

BDCC

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Printed circuit board (PCB) assurance in the optical domain is a crucial field of study. Though there are many existing PCB assurance methods using image processing, computer vision (CV), and machine learning (ML), the PCB field is complex and increasingly evolving, so new techniques are required to overcome the emerging problems. Existing ML-based methods outperform traditional CV methods; however, they often require more data, have low explainability, and can be difficult to adapt when a new technology arises. To overcome these challenges, CV methods can be used in tandem with ML methods. In particular, human-interpretable CV algorithms such as those that extract color, shape, and texture features increase PCB assurance explainability. This allows for incorporation of prior knowledge, which effectively reduces the number of trainable ML parameters and, thus, the amount of data needed to achieve high accuracy when training or retraining an ML model. Hence, this study explores the benefits and limitations of a variety of common computer vision-based features for the task of PCB component detection. The study results indicate that color features demonstrate promising performance for PCB component detection. The purpose of this paper is to facilitate collaboration between the hardware assurance, computer vision, and machine learning communities.

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Section: Gabor Filtermentioning

confidence: 99%

PCB Component Detection Using Computer Vision for Hardware Assurance

Zhao

Gurudu

Taheri

et al. 2022

BDCC

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“…Due to the complex structure and changeable working conditions of rotating machinery, the vibration signals often take on the characteristics of multi-component and non-stationary. The time-frequency analysis method is widely applied to process the vibration signals of rotating machinery in order to extract fault features by using signal decomposition and filtering [7][8][9][10][11][12][13][14][15][16]. There exists a lot of time-frequency analysis methods, such as the Gabor transform, wavelet transform, Hilbert-Huang transform, empirical mode decomposition (EMD), local mean decomposition (LMD), empirical wavelet transform (EWT), and so on [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Research on an Adaptive Variational Mode Decomposition with Double Thresholds for Feature Extraction

et al. 2018

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A motor bearing system is a nonlinear dynamics system with nonlinear support stiffness. It is an asymmetry system, which plays an extremely important role in rotating machinery. In this paper, a center frequency method of double thresholds is proposed to improve the variational mode decomposition (VMD) method, then an adaptive VMD (called DTCFVMD) method is obtained to extract the fault feature. In the DTCFVMD method, a center frequency method of double thresholds is a symmetry method, which is used to determine the decomposed mode number of VMD according to the power spectrum of the signal. The proposed DTCFVMD method is used to decompose the nonlinear and non-stationary vibration signals of motor bearing in order to obtain a series of intrinsic mode functions (IMFs) under different scales. Then, the Hilbert transform is used to analyze the envelope of each mode component and calculate the power spectrum of each mode component. Finally, the power spectrum is used to extract the fault feature frequency for determining the fault type of the motor bearing. To test and verify the effectiveness of the DTCFVMD method, the actual fault vibration signal of the motor bearing is selected in here. The experimental results show that the center frequency method of double thresholds can effectively determine the mode number of the VMD method, and the proposed DTCFVMD method can accurately extract the clear time frequency characteristics of each mode component, and obtain the fault characteristics of characteristics; frequency, rotating frequency, and frequency doubling and so on.

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“…To bridge these gaps, the long-periodic DGT [9,10,19] of complex-valued kernel and real-valued kernel has been presented to utilize the short window to analyze and process the long-periodic (or infinite) signal sequences in practical applications. Because existing canonical algorithms [11,[20][21][22][23][24][25][26][27] are mainly used in periodic/finite DGT and derived from Gabor frame theory, 2 Complexity in this paper, a fast and effective algorithm, based on the orthogonal analysis approach and FFT algorithm, is proposed to obtain the analysis window for the long-periodic/infinite DGT in both the critical sampling case and the oversampling case.…”

Section: Introductionmentioning

confidence: 99%

Effective Approach to Calculate Analysis Window in Infinite Discrete Gabor Transform

Huang

Liu

2018

Complexity

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The long-periodic/infinite discrete Gabor transform (DGT) is more effective than the periodic/finite one in many applications. In this paper, a fast and effective approach is presented to efficiently compute the Gabor analysis window for arbitrary given synthesis window in DGT of long-periodic/infinite sequences, in which the new orthogonality constraint between analysis window and synthesis window in DGT for long-periodic/infinite sequences is derived and proved to be equivalent to the completeness condition of the long-periodic/infinite DGT. By using the property of delta function, the original orthogonality can be expressed as a certain number of linear equation sets in both the critical sampling case and the oversampling case, which can be fast and efficiently calculated by fast discrete Fourier transform (FFT). The computational complexity of the proposed approach is analyzed and compared with that of the existing canonical algorithms. The numerical results indicate that the proposed approach is efficient and fast for computing Gabor analysis window in both the critical sampling case and the oversampling case in comparison to existing algorithms.

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A Sparse Analysis Window for Discrete Gabor Transform

Cited by 3 publications

References 34 publications

PCB Component Detection Using Computer Vision for Hardware Assurance

PCB Component Detection Using Computer Vision for Hardware Assurance

Research on an Adaptive Variational Mode Decomposition with Double Thresholds for Feature Extraction

Effective Approach to Calculate Analysis Window in Infinite Discrete Gabor Transform

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