Two-Dimensional Compact Variational Mode Decomposition

Zosso, Dominique; Dragomiretskiy, Konstantin; Bertozzi, Andrea L.; Weiss, Paul S.

doi:10.1007/s10851-017-0710-z

Cited by 56 publications

(30 citation statements)

References 76 publications

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“…Compact variational mode decomposition (CVMD) is a spectrally sparse and spatially compact signal (one dimensional or two dimensional) decomposition method [18]. It is a fully adaptive, non-recursive decomposition technique.…”

Section: Compact Variational Mode Decompositionmentioning

confidence: 99%

“…It allows decomposition of a signal (one dimensional)/image (two dimensional) into modes that may have smooth or sharp boundaries. CVMD is well explained in [18]. The algorithm is explained as follow:…”

Section: Compact Variational Mode Decompositionmentioning

confidence: 99%

“…Define dual ascent data fidelity until convergence. The complete algorithm is described as [18]: Input: signal is ( ), K is number of modes, and parameters are: T, τ, , , , , , , Output: modes ( ), support functions ( ) and center frequencies…”

Section: Compact Variational Mode Decompositionmentioning

confidence: 99%

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Current Research on Glaucoma Detection using Compact Variational Mode Decomposition from Fundus Images

Kirar¹,

Agrawal²

2019

IJIES

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Glaucoma is one of the foremost causes of blindness over the world. It develops slowly and damages optic nerve head. Existing methods of glaucoma detection are expensive and sluggish. Hence quick and low-priced methods are required. In this paper, a novel fully variational and adaptive computer based glaucoma detection using compact variational mode decomposition (CVMD) from fundus images is proposed. Efficient sub band images having narrow fourier bandwidth, clear and sharp boundaries are obtained using CVMD is the key idea of the proposed method. This gives robust estimate of features. Texture feature capture subtle variation and give more information therefore these features are extracted from efficient sub band images and helps to increase the glaucoma detection accuracy. Extracted features are normalized and classified using support vector machine classifier. The obtained classification accuracies are 86.67 %, 86.67 %, 85.42% and 89.18 % for three, five, eight and tenfold cross validation respectively with kernel parameter 2. The obtained accuracy, sensitivity, specificity, precision and Fmeasure are 89.18 %, 90 %, 85 %, 93.34 % and 89.34 % respectively for tenfold cross validation. Proposed method is found to be better compared to the existing.

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Section: Compact Variational Mode Decompositionmentioning

confidence: 99%

Section: Compact Variational Mode Decompositionmentioning

confidence: 99%

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Current Research on Glaucoma Detection using Compact Variational Mode Decomposition from Fundus Images

Kirar¹,

Agrawal²

2019

IJIES

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show abstract

“…However, the above research is still based on single-sensor information collection. Dragomiretskiy extended VMD to two-dimensional areas and proposed two-dimensional variational mode decomposition [47] in 2014, which makes simultaneous analysis and noise reduction of multivariate data possible.…”

Section: Introductionmentioning

confidence: 99%

A New Method of Two-stage Planetary Gearbox Fault Detection Based on Multi-Sensor Information Fusion

Zhang

Cheng

et al. 2019

Applied Sciences

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Due to their high transmission ratio, high load carrying capacity and small size, planetary gears are widely used in the transmission systems of wind turbines. The planetary gearbox is the core of the transmission system of a wind turbine, but because of its special structure and complex internal and external excitation, the vibration signal spectrum shows strong nonlinearity, asymmetry and time variation, which brings great trouble to planetary gear fault diagnosis. The traditional time-frequency analysis technology is insufficient in the condition monitoring and fault diagnosis of wind turbines. For this reason, we propose a new method of planetary gearbox fault diagnosis based on Compressive sensing, Two-dimensional variational mode decomposition (2D-VMD) and full-vector spectrum technology. Firstly, the nonlinear reconstruction and noise reduction of the signal is carried out by using compressed sensing, and then the signal with multiple degrees of freedom is adaptively decomposed into multiple sets of characteristic scale components by using 2D-VMD. Then, Rényi entropy is used as the optimization index of 2D-VMD analysis performance to extract the effective target intrinsic mode function (IMF) component, reconstruct the dynamics signal in the planetary gearbox, and improve the signal-to-noise ratio. Then, using the full-vector spectrum technique, the homologous information collected by numerous sensors is data layer fused in the spatial domain and the time domain to increase the comprehensiveness and certainty of the fault information. Finally, the Teager–Kaiser energy operator is used to demodulate the potential low-frequency dynamics frequency characteristics from the high-frequency domain and detect the fault characteristic frequency. Furthermore, the correctness and validity of the method are verified by the fault test signal of the planetary gearbox.

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“…Motivated by some of the current developments in material sciences [33,62] and crystalline material image analysis [7,8,12,30,40,70] , we focus on two dimensional lattice, which plays major roles in crystallography [23,60], sampling theory [50], ecology [66] and many others. For example, crystal structures of halite (NaCl) and gold (Au) have distinct scales (NaCl constant: 5.640Å [26]; Au constant: 4.065Å [19]), which explains their proprietary differences.…”

Section: Introductionmentioning

confidence: 99%

Lattice Identification and Separation: Theory and Algorithm

He¹,

Kang²

2019

SIAM J. Imaging Sci.

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Motivated by lattice mixture identification and grain boundary detection, we present a framework for lattice pattern representation and comparison, and propose an efficient algorithm for lattice separation. We define new scale and shape descriptors, which helps to reduce the size of equivalence classes of lattice bases considerably. These finitely many equivalence relations are fully characterized by modular group theory. We construct the lattice space L based on the equivalent descriptors and define a metric d L to accurately quantify the visual similarities and differences between lattices. Furthermore, we introduce the Lattice Identification and Separation Algorithm (LISA), which identifies each lattice patterns from superposed lattices. LISA finds lattice candidates from the high responses in the image spectrum, then sequentially extracts different layers of lattice patterns one by one. Analyzing the frequency components, we reveal the intricate dependency of LISA's performances on particle radius, lattice density, and relative translations. Various numerical experiments are designed to show LISA's robustness against a large number of lattice layers, moiré patterns and missing particles.

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Two-Dimensional Compact Variational Mode Decomposition

Cited by 56 publications

References 76 publications

Current Research on Glaucoma Detection using Compact Variational Mode Decomposition from Fundus Images

Current Research on Glaucoma Detection using Compact Variational Mode Decomposition from Fundus Images

A New Method of Two-stage Planetary Gearbox Fault Detection Based on Multi-Sensor Information Fusion

Lattice Identification and Separation: Theory and Algorithm

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