Signal Feature Extraction From Microbarograph Observations Using the Hilbert–Huang Transform

Roy, Arnab; Wen, C.-H.; Doherty, J.F.; Mathews, J. D.

doi:10.1109/tgrs.2007.909916

Cited by 16 publications

(7 citation statements)

References 4 publications

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“…HHT makes the instantaneous frequency meaningful by EMD method and presents the result of time-frequency analysis in a Hilbert spectrum plot [7,8] . Considering the validity and rationality of HHT method used for analyzing non-stationary signal [11][12][13][14][15] , this paper presents an identification technique based on HHT method to identify distortion model and distortion frequency, but discuss little about HHT method itself.…”

Section: Identification Technique Based On Hht Methodsmentioning

confidence: 99%

Distortion identification technique based on Hilbert-Huang transform in video stabilization

Liu

Mou-Yan

Wang

2011

Trans. Tianjin Univ.

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Abstract：A distortion identification technique is presented based on Hilbert-Huang transform to identify distortion model and distortion frequency of distorted real-world image sequences. The distortion model is identified simply based on Hilbert marginal spectral analysis after empirical mode decomposing. And distortion frequency is identified by analyzing the occurrence frequency of instantaneous frequency components of every intrinsic mode functions. Rational digital frequency filter with suitable cutoff frequency is designed to remove undesired fluctuations based on identification results. Experimental results show that this technique can identify distortion model and distortion frequency of displacement sequence accurately and efficiently. Based on identification results, distorted image sequence can be stabilized effectively.Video shot of a digital camera usually suffers from various distortions due to unstable random camera motion. Video stabilization technique removes undesired fluctuations, estimates intentional global motions of the camera and compensates them so that we can remove or reduce those jitter fluctuations' influence. Dynamic displacement field model is a parameterized model and provides a gradually upgraded and evolutionary process, from simple to complex, to handle image sequence distortion problems [1] . Since jitter distortion is a kind of high-frequency variation of image displacements, conventional frequency domain filter can be used to remove undesired fluctuations [1,2] . According to the relationship between distortion frequency and motion model, video stabilization becomes a facile parameter estimation process. And it is crucial to identify distortion model and distortion frequency of the acquired displacement sequence before filtering. Based on identification results, motion filtering and motion compensation can be used to suppress jitter [1][2][3][4][5][6] . However, displacement sequence is usually non-stationary because of unstable random camera motion. Conventional Fourier analysis is suitable for linear stationary signal, while it is not suitable for nonlinear non-stationary signal.This paper proposes a distortion identification technique based on Hilbert-Huang transform (HHT) [7,8] to identify distortion model and distortion frequency of realworld image sequence. Firstly, displacement sequence is decomposed into a set of intrinsic mode functions by empirical mode decomposition method. Secondly, distortion model and distortion frequency are identified based on Hilbert spectral analysis. Finally, a rational digital filter with suitable cutoff frequency is designed based on identification results. Based on these identification results, undesired fluctuation can be removed with the stabilization methods presented in Refs.[1] and [2].

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Section: Identification Technique Based On Hht Methodsmentioning

confidence: 99%

Distortion identification technique based on Hilbert-Huang transform in video stabilization

Liu

Mou-Yan

Wang

2011

Trans. Tianjin Univ.

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“…The HHT method proposed by Huang et al [12,14] contains two steps: Empirical Mode Decomposition and Hilbert Transform. The procedure is briefly summarized below.…”

Section: A Hilbert-huang Transformmentioning

confidence: 99%

Large Eddy Simulation and Hilbert Huang Transform for fluctuation pressure of high speed train

Wang

Chunju

2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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Fluctuation pressure acting on the surface of high speed trains is the main source of aerodynamic noise. Therefore, it becomes very important to research components of the fluctuation pressure for controlling the aerodynamic noise. In this paper, Large Eddy Simulation (LES) method is applied to calculate the fluctuation pressure acting on a certain type of CRH high speed train and the distribution rule of the fluctuation pressure is studied by Hilbert Huang Transform (HHT) method. Both the fluctuating part and the average part from the surface pressure are extracted by Empirical Mode Decomposition (EMD). A comparison of the distribution rules for fluctuation pressure at two different speed levels is made.

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“…In this Special Issue, Roy et al [49] present an application of how such features can be extracted and merged for the analysis of microbarographs. In this case, a transformed representation of the data is used, namely, the Huang-Hilbert transform, decomposing nonstationary signals in order to highlight moving trends and break it into locally orthogonal components.…”

Section: Feature Extractionmentioning

confidence: 99%

Foreword to the Special Issue on Data Fusion

Gamba

Chanussot

2008

IEEE Trans. Geosci. Remote Sensing

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Signal Feature Extraction From Microbarograph Observations Using the Hilbert–Huang Transform

Cited by 16 publications

References 4 publications

Distortion identification technique based on Hilbert-Huang transform in video stabilization

Distortion identification technique based on Hilbert-Huang transform in video stabilization

Large Eddy Simulation and Hilbert Huang Transform for fluctuation pressure of high speed train

Foreword to the Special Issue on Data Fusion

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