Adaptive step-size fast iterative shrinkage-thresholding algorithm and sparse-spike deconvolution

Pan, Shulin; Yan, Ke; Lan, Haiqiang; Badal, José; Qin, Ziyu

doi:10.1016/j.cageo.2019.104343

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…When terahertz waves propagate in different media, their transmission and reflection occur due to a change in the refractive index at the media interface [ 16 ]. The transmission of terahertz waves in a ceramic–glue structure is shown in Figure 1 .…”

Section: Theoretical Modelmentioning

confidence: 99%

“…A thorough theoretical analysis in [ 16 ] proves the convergence of this iterative shrinkage algorithm, guaranteeing that the solution is the global minimizer for convex f . Obviously, the time resolution of the obtained impulse response function f by sparse deconvolution depends upon the time resolution of the reference signal h , which is itself determined by the discretization precision corresponding to the data sampling period T s .…”

Section: Theoretical Modelmentioning

confidence: 99%

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Accurate Characterization of the Adhesive Layer Thickness of Ceramic Bonding Structures Using Terahertz Time-Domain Spectroscopy

Yang

Zhang

et al. 2022

Materials

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Ceramic adhesive structures have been increasingly used in aerospace applications. However, the peaks of the signal on the upper and lower surface of the adhesive layer are difficult to measure directly due to the thin thickness of the adhesive layer and the effect of the attenuation dispersion of the ceramic layer. Thus, the existing non-destructive testing techniques have been ineffective in detecting adhesive quality. In this paper, the thickness of the adhesive layer is measured using terahertz time-domain spectroscopy. A sparse deconvolution method is proposed for the terahertz time-domain spectral signal of ceramic adhesive structures with different adhesive layer thicknesses. The results show that the methods proposed in this paper can realize the separation of reflection signals for glue layers with a thickness of 0.20 mm. By comparing with a wavelet denoising method and a modified covariance method (AR/MCM), the effectiveness of the sparse deconvolution method in estimating the thickness of the glue layer is demonstrated. This work will provide the theoretical and experimental basis for using terahertz time-domain spectroscopy to detect the homogeneity of ceramic adhesive structures.

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Section: Theoretical Modelmentioning

confidence: 99%

Section: Theoretical Modelmentioning

confidence: 99%

Accurate Characterization of the Adhesive Layer Thickness of Ceramic Bonding Structures Using Terahertz Time-Domain Spectroscopy

Yang

Zhang

et al. 2022

Materials

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“…Then SSD [13][14][15] which is widely used in earthquake impact detection can be used here for fault extraction. Shulin Pan [16][17][18] has contributed a lot to the field of SSD and made a great progress on it in these years. His research proves that this algorithm is sensitive to impact based on sparsity assumption.…”

Section: Introductionmentioning

confidence: 99%

Weak fault detection based on amplification-compression function and sparse spike deconvolution

Zhang,

Hou,

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

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Weak fault detection is still a hotspot in these years. The key of this work is to enhance the ratio of signal to noise (SNR). Then a new extraction method of weak fault impact based on amplification algorithm is proposed. Since the fault impact is related to bigger derivative, the amplification function combined with the derivative of the vibration signal is used to amplify the amplitude of the impact. Then Sparse Spike Deconvolution (SSD) which has been widely used in earthquake for impact detection is able to extract small impact from the signal processed by amplification function. Then, in order to enhance the periodicity of fault impacts, the compression function is used to compress the amplitude of bigger impacts according to different scales. At last, the experiment result shows that the proposed method is more effective than the common envelope analysis.

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“…Currently, the most commonly used devices for the high-resolution imaging of biological or biomedical targets include confocal microscopes [1], stimulated emission depletion (STED) microscopes [2], and structured light illumination microscopes (SIM) [3] etc. Furthermore, many algorithms have been developed to improve the spatial resolution and signal-to-noise ratio (SNR) of biological images, including degenerate-model-based algorithms (e.g., deconvolution [4][5][6][7][8]), mathematical transformation-based algorithms (e.g., spectrum analysis [9,10], DWT analysis [11][12][13][14][15][16]), and machine-learning-based algorithms (e.g., deep learning [17][18][19]). However, most of these algorithms focus on a single task, e.g., inhibiting noise, identifying structure contours, or improving resolution.…”

Section: Introduction 1research Backgroundmentioning

confidence: 99%

Feature Extraction of 3T3 Fibroblast Microtubule Based on Discrete Wavelet Transform and Lucy–Richardson Deconvolution Methods

et al. 2022

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Accompanied by the increasing requirements of the probing micro/nanoscopic structures of biological samples, various image-processing algorithms have been developed for visualization or to facilitate data analysis. However, it remains challenging to enhance both the signal-to-noise ratio and image resolution using a single algorithm. In this investigation, we propose a composite image processing method by combining discrete wavelet transform (DWT) and the Lucy–Richardson (LR) deconvolution method, termed the DWDC method. Our results demonstrate that the signal-to-noise ratio and resolution of live cells’ microtubule networks are considerably improved, allowing the recognition of features as small as 120 nm. The method shows robustness in processing the high-noise images of filament-like biological structures, e.g., the cytoskeleton networks captured by fluorescent microscopes.

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Adaptive step-size fast iterative shrinkage-thresholding algorithm and sparse-spike deconvolution

Cited by 14 publications

References 12 publications

Accurate Characterization of the Adhesive Layer Thickness of Ceramic Bonding Structures Using Terahertz Time-Domain Spectroscopy

Accurate Characterization of the Adhesive Layer Thickness of Ceramic Bonding Structures Using Terahertz Time-Domain Spectroscopy

Weak fault detection based on amplification-compression function and sparse spike deconvolution

Feature Extraction of 3T3 Fibroblast Microtubule Based on Discrete Wavelet Transform and Lucy–Richardson Deconvolution Methods

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