Qinkai Deng

Bio: Qinkai Deng is an academic researcher. The author has contributed to research in topics: Signal & Wavelet transform. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

De-noisng of ECG based on EMD improved-thresholding and mathematical morphology operation

Abstract: Suppressing the noise of ECG effectively is a prerequisite for the intelligent analysis of ECG. This paper proposed a new ECG denoising method which combined empirical mode decomposition (EMD) with mathematical morphology operation (MMO). First of all, apply the EMD to decompose the noisy ECG into a series of intrinsic mode functions (IMF); then, estimate the intensity of noise for each IMF by MMO, and do improved-thresholding on each IMF based on 3s criterion; finally, reconstruct the signal with the processed IMF to get the de-noised ECG. The experimental results show that the proposed method has better performance than that of single method.

Feature Extraction of Pulse Signal Based on Hilbert-Huang Transformation and Singular Value Decomposition

Abstract: A pulse diagnosis approach based on Hilbert-Huang transformation method and Singular Value Decomposition (SVD) technique is proposed. The Empirical Mode Decomposition (EMD) method is used to decompose the signal into a number of IMF components, then applying the Hilbert transformation to creating analytic signal and obtaining instantaneous frequency and instantaneous amplitude, from which the initial feature vector matrices are formed. By applying the singular value decomposition technique to the initial feature vector matrices, the singular values are obtained, which are regarded as the state feature vectors of the human pulse signals. Finally the first 20 singular values of SVD are showed in a parallel coordinate's graphic form. Practical examples show that the proposed approach can be applied to pulse diagnosis effectively. A method of mining pulse signal is presented for extracting the time-frequency distribution feature of the data based on the technique of the singular value decomposition. By the time-frequency analysis, the important pulse characteristic information is extracted, the research provide the basis for further classification. This provides one new method for the pulse diagnosis thorough research. It will be helpful to make the objectification of pulse study.

Signal-to-noise ratio improvement of Brillouin optical time domain analysis system based on empirical mode decomposition and finite impulse response.

Abstract: We propose a denoising algorithm based on empirical mode decomposition (EMD) and finite impulse response (FIR) to improve the signal-to-noise ratio (SNR) of Brillouin optical time domain analysis. Denoising results indicate EMD-FIR can effectively reduce noise, and the maximum SNR improvement is 11.69 dB, which is 4.98 dB and 4.26 dB larger than the maximum SNR improvement of wavelet and Butterworth. The temperature uncertainty along the heated section is reduced to 0.62°C by EMD-FIR. The improvement of SNR opens opportunities to apply high measurement accuracy to Brillouin optical time domain analysis and other distributed sensing fields.

Segment empirical mode decomposition filtering method on distance correction signal for aerosol lidar

Abstract: Abstract. Aerosol lidar has been widely used in environmental monitoring. It is necessary to correct the distance before calculating the backscatter coefficient. However, the problem in this process is that the noise is greatly amplified, especially for long-distance signals. Based on previous theoretical research and signal characteristics, a segment empirical mode decomposition (EMD) filtering method that can denoise the distance correction signal is developed. Taking measured data of aerosol lidar as an example, the distance correction signal is divided into four segments, and each segment is decomposed by EMD. For low-height signals with low noise, the signal is reconstructed as comprehensively as possible. For high-height signals with high noise, most high-frequency signals are removed. This method is feasible because the aerosol concentration decreases with the increase in height. After segment EMD filtering, four segments of signals are recombined. Compared with the classical EMD, the segmented EMD filtering method not only retains the details of the low-height signal but also removes the high-height noise as much as possible. The segmented EMD filtering method is proven to be effective for aerosol lidar that conducts profile monitoring on the ground.