Radial wavelet and radon transform on the Heisenberg group

Yin, Mingkai; He, Jianxun

doi:10.1080/00036811.2012.750295

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…• Computed Tomography [7] [6], where the angle-dependent wavelet weighted ramp filter can be made free from angle. As a result, direct and simplified reconstruction algorithms can be designed for different scanning geometries, and interior tomography.…”

Section: Introductionmentioning

confidence: 99%

On compactly supported discrete radial wavelets in $L^2(\mathbb{R}^2)$ and application in Tomography

Najiya¹,

Ravichandran²,

Sastry³

2020

Preprint

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Radially symmetric wavelets possessing multiresolution framework are found to be useful in different fields like Pattern recognition, Computed Tomography (CT) etc. The compactly supported wavelets are known to be useful for localized operations in applications such as reconstruction, enhancement etc. In this work we introduce a novel way of designing compactly supported radial wavelets in L 2 pR 2 q from a 1D Daubechies wavelets and obtain a reconstruction formula possessing multiresolution framework. Further, we demonstrate the usefulness of our radial wavelets in Tomography.

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Section: Introductionmentioning

confidence: 99%

On compactly supported discrete radial wavelets in $L^2(\mathbb{R}^2)$ and application in Tomography

Najiya¹,

Ravichandran²,

Sastry³

2020

Preprint

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Controllability and Optimal Control for a Class of Time-Delayed Fractional Stochastic Integro-Differential Systems

2020

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An effective health indicator for bearing using corrected conditional entropy through diversity-driven multi-parent evolutionary algorithm

Chauhan

Singh

Aggarwal

2020

Structural Health Monitoring

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The bearing vibration signal possesses nonlinear and non-stationary characteristics; hence; it is difficult to diagnosis the faults in the bearing under different working conditions. In this article, a new scheme has been proposed based on complete ensemble empirical mode decomposition with adaptive noise and corrected conditional entropy to recognize the different class of faults in bearing. The mode with minimum corrected conditional entropy is treated as a prominent mode from which sensitive features are extracted. A filter-based feature selection scheme is used for the same and for ranking the features based on variance to reduce the redundancy of data set. This data set is made input to support vector machine. The performance of the support vector machine classifier is improved by optimizing its parameters to obtain maximum classification accuracy. To address this issue, an evolutionary algorithm (diversity-driven multi-parent evolutionary algorithm) is used. With optimized support vector machine parameters, the support vector machine is trained to build a classification model with 10-fold cross-validation. After training, the built model is tested against test data set for fitness evaluation. The support vector machine classifier gives 100% accuracy at regularization and kernel parameter’s value of 1.3343 and of 782.6329, respectively, with 27.93 s of training time for a single iteration.

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Radial wavelet and radon transform on the Heisenberg group

Cited by 3 publications

References 16 publications

On compactly supported discrete radial wavelets in $L^2(\mathbb{R}^2)$ and application in Tomography

On compactly supported discrete radial wavelets in $L^2(\mathbb{R}^2)$ and application in Tomography

Controllability and Optimal Control for a Class of Time-Delayed Fractional Stochastic Integro-Differential Systems

An effective health indicator for bearing using corrected conditional entropy through diversity-driven multi-parent evolutionary algorithm

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