Complex empirical mode decomposition, Hilbert-Huang transform, and fourier transform applied to moving objects

Wallis, Kristen; Akers, Geoffrey A.; Collins, P.J.; Davis, Richard M.; Frazier, Alan; Oxley, Mark E.; Terzuoli, A.J.

doi:10.1109/igarss.2012.6350399

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…It has been employed in applications in radar, geophysics, biomedicine, etc [7,8,9]. The technique adaptively decomposes the signal into an a priori unknown number of intrinsic modes and a residue.…”

Section: Empirical Mode Decomposition and Rtimentioning

confidence: 99%

Target recognition using scattering features extracted with EMD

Jouny

2014

2014 IEEE Radar Conference

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Scattering features extracted via complex Empirical Mode Decomposition (EMD) are used for radar target recognition. EMD, which is associated with the Hilbert-Huang transform, can be used to extract inherent oscillations known as Implicit Mode Functions (IMFs) from a radar backscatter. These IMFs can be associated with the locations of the target scattering centers, and consequently with the target geometry or down range profile as seen by the interrogating radar. Features extracted using EMD are then presented to a distance-based target recognition system. The proposed target classification scheme is tested using synthetic and real radar data recorded in a compact range.

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Section: Empirical Mode Decomposition and Rtimentioning

confidence: 99%

Target recognition using scattering features extracted with EMD

Jouny

2014

2014 IEEE Radar Conference

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“…There are several feature extraction methods [11], the more famous ones are envelope analysis (EA) [12], wavelet transforms (WT) [13], Hilbert-Huang transform (HHT) [14], variational mode decomposition (VMD) [15], and fast Fourier transform (FFT) methods [16]. HHT was proposed by Norden E. Huang et al, at Academia Sinica, Taiwan [17], to decompose the analysis data into intrinsic mode functions (IMF) [18], called empirical mode decomposition (EMD) [19]. The IMF is transformed into a Hilbert transform to obtain the instantaneous frequency of the processed data [20].…”

Section: Introductionmentioning

confidence: 99%

A Feature Selection Approach Based on Memory Space Computation Genetic Algorithm Applied in Bearing Fault Diagnosis Model

Lee

Hung

2023

IEEE Access

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The main objective of this study is to propose a motor fault diagnosis model based on machine learning. Compared with the traditional motor fault diagnosis model, the proposed model can reduce the computation time. This model can be divided into three steps: feature extraction, feature selection, and classification. In the feature extraction step, the original signal is extracted by Hilbert-Huang transform (HHT), envelope analysis (EA), and variational mode decomposition (VMD) methods. A feature selection method based on memory space computation genetic algorithm (MSCGA) is proposed and applied in the feature selection step. The advantage of MSCGA is that it eliminates the need to compute data fitness values, saving unnecessary computation time repeatedly. The classifiers use k-nearest neighbor (KNN) and support vector machines (SVM). In order to verify the stability and efficiency of the model, the university of California Irvine (UCI) benchmark dataset, the current signal of motor fault datasets, and case western reserve university (CWRU) were used. The UCI dataset is used to test the efficiency and computation time of the feature selection method. Other datasets are used to compare with traditional motor fault diagnosis models. The simulation results of the proposed model have demonstrated the effectiveness in reducing the computation time without affecting the computation results compared to the traditional motor fault diagnosis model. Furthermore, the performance of MSCGA is proven to be better than that of the other algorithm.INDEX TERMS Rotating machine, bearing fault diagnosis model, feature selection method, genetic algorithm.

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CEMD Solar Noise Suppression for OFDM based Visible Light Vehicular Communication

Deepa

2023

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)

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Complex empirical mode decomposition, Hilbert-Huang transform, and fourier transform applied to moving objects

Cited by 4 publications

References 5 publications

Target recognition using scattering features extracted with EMD

Target recognition using scattering features extracted with EMD

A Feature Selection Approach Based on Memory Space Computation Genetic Algorithm Applied in Bearing Fault Diagnosis Model

CEMD Solar Noise Suppression for OFDM based Visible Light Vehicular Communication

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