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Nonlinear unsteady-state complex signal self-adapting decomposition method based on GPGPU

A complex signal and unsteady state technology, applied in the field of signal analysis, can solve problems such as time-consuming and limited real-time applications

Active Publication Date: 2016-01-27
WUHAN UNIV
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Problems solved by technology

Therefore, the calculation of EEMD is intensive, and a large number of time-consuming calculations make it limited to real-time applications. Therefore, it is urgent to invent a fast and effective signal decomposition method

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  • Nonlinear unsteady-state complex signal self-adapting decomposition method based on GPGPU
  • Nonlinear unsteady-state complex signal self-adapting decomposition method based on GPGPU
  • Nonlinear unsteady-state complex signal self-adapting decomposition method based on GPGPU

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Embodiment Construction

[0057] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the following in conjunction with Attached picture The present invention will be further described in detail with reference to the examples and examples. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0058] This embodiment adopts CUDA4.2 (Unified Computing Device Architecture), and the traditional wavelet coherence method is implemented based on C language. The hardware environment of the experiment is NVIDIA GeForce GTX295 graphics card, and the processor is Intel(R) Core(TM) i7-2600. The experimental data were obtained from the EEG signals of epileptic patients, using 6-channel (F3, F4, C3, C4, O1 and O2) scalp electrodes to collect, and the electrodes were placed according to the 10-20 system. Raw EEG sequence Such ...

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Abstract

The invention discloses a nonlinear unsteady-state complex signal self-adapting decomposition method based on GPGPU (General Purpose Graphics Processing Units), and belongs to the field of signal analysis. During large-scale signal data processing by a conventional EEMD (Ensemble Empirieal Mode Decomposition) algorithm, the processing is limited by the denseness computation of the algorithm per se, so that the conventional algorithm cannot meet the real-time performance demand in practical application. A plurality of high parallel computation steps included by the EEMD algorithm are analyzed; a GPGPU method based on a CUDA (Compute Unified Device Architecture) is used for performing parallel design on the EEMD algorithm, so that the algorithm reaches an optimal state in the aspects of data precision and time consumption; Hilbert-huang transform is combined; Hilbert transform and Shannon entropy concepts are used for obtaining Hilbert-huang spectral entropy to further studying decomposition signals. Experiments prove that the method has high efficiency and availability in the practical signal decomposition analysis.

Description

technical field [0001] The invention belongs to the technical field of signal analysis, and relates to a method for decomposing complex signals, in particular to a method for adaptively decomposing nonlinear and unsteady complex signals based on GPGPU. Background technique [0002] The human brain is a complex nonlinear system, and the study of EEG signals is one of the important frontier fields of life science today. EEG signal processing is crucial to the detection, diagnosis and treatment of brain-related diseases. However, the study of EEG signals involves the collection and calculation of a large amount of EEG signal data. The storage, management and utilization of a large amount of neural data is a huge challenge. challenge. The practical application of signal analysis is that the brain is a highly complex nonlinear and non-stationary system. EEG signals are generated by the activities of a large number of neurons, and also have the characteristics of nonlinear and no...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 陈丹李小俚蔡畅胡阳阳吕东川李段王帅廷
Owner WUHAN UNIV
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