In present work we discuss the usage of empirical mode decomposition algorithm for bioradar data processing. The algorithm of data processing is considered and the value of the threshold criteria is chosen according t...
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(纸本)9781479974733
In present work we discuss the usage of empirical mode decomposition algorithm for bioradar data processing. The algorithm of data processing is considered and the value of the threshold criteria is chosen according to the results of the experimental data processing. It is shown that preprocessing of raw bioradar data, which compensate the difference in amplitude of breathing and heartbeat signals, increases the effectiveness of empirical mode decomposition algorithm in bioradar data processing.
this study was to discuss the application value of the embedded intelligent fetal heart rate monitoring system (EI system) based on empiricalmodedecomposition (EMD) algorithm in the monitoring of pregnant women with...
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this study was to discuss the application value of the embedded intelligent fetal heart rate monitoring system (EI system) based on empiricalmodedecomposition (EMD) algorithm in the monitoring of pregnant women with pregnancy bacterial infection complications, aiming to provide a reference basis for timely nursing of pregnancy bacterial infection complications. In this study, the blind source separation method was introduced to denoise the screened intrinsic modal function (IMF) components based on the EI system and the EMD algorithm. The signal to noise ratio (SNR) based on the EMD optimization algorithm was compared with that of other algorithms. Two hundred and sixty-eight pregnant women in the obstetric clinic were selected as the research objects and divided into an observation group (abnormal fetal heart rate) and a control group (normal fetal heart rate). The pregnant women in both groups accepted the conventional nursing methods, and the EI system was adopted to monitor the fetal heart rate. The probability of abnormal fetal heart monitoring of pregnant women in observation group and control group was analysed. At the same time, the probability of bacterial infection complications was analysed. It was found that the EI system based on EMD algorithm showed obviously higher SNR in contrast to other algorithms. The waveforms of fetal heart monitoring in the observation group and the control group were significantly different (chi(2) = 11.215, p < 0.05);microscopic examination of pregnant women secretion in the bacterial infection group revealed the presence of a large number of pathogenic bacteria;it was found that the infection rate of vaginosis was significantly different between the control group and the observation group (p < 0.01);there was a significant difference between the observation group and the control group in the complication of premature rupture of fetal membrane caused by bacteria (chi(2) = 11.2, p < 0.01). In conclusion, the EI system based
A CO sensor based on photoacoustic spectroscopy (PAS) with empiricalmodedecomposition (EMD) algorithm is investigated and demonstrated in this paper. In the PAS system, the complicated photo-thermal-acoustic convers...
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A CO sensor based on photoacoustic spectroscopy (PAS) with empiricalmodedecomposition (EMD) algorithm is investigated and demonstrated in this paper. In the PAS system, the complicated photo-thermal-acoustic conversion is a nonlinear and non-stationary process and contains various noise. In order to compensate the low signal-to-noise ratio (SNR), the EMD is introduced in the PAS system to deal with the photoacoustic signal. The experimental results show that a gain factor of similar to 3.0 on the SNR is achieved and the sensor has an excellent linear response to the gas concentration. The minimum detection level (MDL) for CO detection is reduced to 1.14 ppm with a 300 ms integrated time at room temperature and atmospheric pressure.
In this study, the authors have proposed both field-programmable gate array (FPGA) and application specific integrated circuit (ASIC) based realisation of the empiricalmodedecomposition (EMD) algorithm for the real-...
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In this study, the authors have proposed both field-programmable gate array (FPGA) and application specific integrated circuit (ASIC) based realisation of the empiricalmodedecomposition (EMD) algorithm for the real-time signal processing. Here, a single module is used for the calculation of maxima and minima, and another single module is used for the calculation of upper and lower envelopes instead of using separate modules for each calculation. In this work, the traditional cubic spline interpolation has been replaced with sawtooth transform followed by a smoothing module called moving average. In this study firstly, Verilog-HDL code for the EMD is written using Xilinx Vivado and tested in the simulation phase, later dumped into Digilentinc Basys 3 FPGA board to do the hardware verification. For ASIC, the code is synthesised using Cadence Genus tool with the semi-conductor laboratory 180 nm cell library and the layout is made in the Cadence Innovus tool. The proposed EMD can work with a clock/sampling rate up to 25 MHz and has a layout area of 3.9 mm(2). For the reduction of power consumption of the overall system, clock gating has been used which helps to reduce the dynamic power of the modules, when they are not in use.
Electrocardiogram (ECG) beat behaves as a non-linear and non-stationary signal. Since most of the existing data processing tools are poor alternatives for processing such signals, Hilbert-Huang transform (HHT) proves ...
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Electrocardiogram (ECG) beat behaves as a non-linear and non-stationary signal. Since most of the existing data processing tools are poor alternatives for processing such signals, Hilbert-Huang transform (HHT) proves to be an efficient method as it deals with a time-varying frequency spectrum. In this study, a new and efficient methodology is proposed using HHT for feature selection which includes a set of essential features such as weighted mean frequency, Kolmogorov complexity and other statistical features (median, standard deviation, kurtosis, skewness and central moment) computed from the intrinsic mode functions extracted using the empiricalmodedecomposition (EMD) algorithm. Further, one-against-one multi-class support vector machine is employed for the classification of six generic ECG beats, namely: normal, left bundle branch block, right bundle branch block, premature ventricular contraction, paced beat and atrial premature beat. The classification process in this study yields better results than existing methodologies in terms of classification accuracy equal to 99.51% along with sensitivity, specificity and positive predictivity of 98.64, 99.77 and 98.17%, respectively.
A critical component of dealing with heart disease is real-time identifi-cation,which triggers rapid *** main challenge of real-time identification is illustrated here by the rare occurrence of cardiac *** contribu-ti...
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A critical component of dealing with heart disease is real-time identifi-cation,which triggers rapid *** main challenge of real-time identification is illustrated here by the rare occurrence of cardiac *** contribu-tions to cardiac arrhythmia prediction using supervised learning approaches gen-erally involve the use of demographic features(electronic health records),signal features(electrocardiogram features as signals),and temporal *** the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats,it is possible to detect some of the irregularities in the early stages of *** paper describes the training of supervised learning using features obtained from electrocardiogram(ECG)image to correct the limitations of arrhythmia prediction by using demographic and electrocardio-graphic signal *** experimental study demonstrates the usefulness of the proposed Arrhythmia Prediction by Supervised Learning(APSL)method,whose features are obtained from the image formats of the electrocardiograms used as input.
Multiscale synchrony behaviors and nonlinear dynamics of paired financial time series are investigated, in an attempt to study the cross correlation relationships between two stock markets. A random stock price model ...
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Multiscale synchrony behaviors and nonlinear dynamics of paired financial time series are investigated, in an attempt to study the cross correlation relationships between two stock markets. A random stock price model is developed by a new system called three-dimensional (3D) multi-continuum percolation system, which is utilized to imitate the formation mechanism of price dynamics and explain the nonlinear behaviors found in financial time series. We assume that the price fluctuations are caused by the spread of investment information. The cluster of 3D multi-continuum percolation represents the cluster of investors who share the same investment attitude. In this paper, we focus on the paired return series, the paired volatility series, and the paired intrinsic mode functions which are decomposed by empiricalmodedecomposition. A new cross recurrence quantification analysis is put forward, combining with multiscale cross-sample entropy, to investigate the multiscale synchrony of these paired series from the proposed model. The corresponding research is also carried out for two China stock markets as comparison. (C) 2017 Elsevier B.V. All rights reserved.
A simple software correction algorithm based on the empiricalmodedecomposition (EMD) algorithm to correct the nonlinearity of a VCO is proposed. The proposed algorithm divides a dechirp signal into mono-component si...
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A simple software correction algorithm based on the empiricalmodedecomposition (EMD) algorithm to correct the nonlinearity of a VCO is proposed. The proposed algorithm divides a dechirp signal into mono-component signals using EMD so that the nonlinearity is extracted from the decomposed components in the form of phase distortion. The phase distortion due to nonlinearity is compensated by using the resampling method. As a result, the proposed algorithm corrects the phase distortion without assistance from any additional hardware, thus maintaining simplicity and cost effectiveness. The proposed algorithm is verified by a simulation and an experiment, and it yields well corrected range profiles.
Shipboard power systems are the core of maritime electrical automation, playing a crucial role in ensuring the normal operation of vessels. Efficient fault diagnosis facilitates rapid localization and judgment of faul...
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