Application of the Wavelet Transform to the Pulse Signal Analysis of Druggers

According to the randomness of human pulse signals,the multiresolution analysis of the wavelet transform is used to analyze such signals.Its purpose is to extract the abnormal information from the pulse signals of heroin druggers and to obtain the primary judgment criterion which can be used to identify druggers from healthy persons.The scale spectrum based on the wavelet transform of pulse signals carries the important characteristic information of the health situation of human body.The pulse signals of 15 heroin druggers and 15 healthy persons are analyzed and the scale spectrum and the total signal energy of every signal are extracted.It is found that the ratio between the sum(i.e.,scale-wavelet energy) of the scale spectrum in a specific scale-time region and the total signal energy for heroin druggers is generally higher than that of healthy persons.Using the percentage of the ratio between the scale-wavelet energy in the specific scale-time region and the total signal energy as characteristic parameter,a critical parameter is determined that is used to classify heroin druggers and healthy persons.Thus,all of the 15 healthy persons are identified correctly from 30 subjects.Only two heroin persons are misjudged.The experiment results of classification show that the method presented is feasible and effective for detecting the pulse abnormalities of heroin druggers.     

Pulse-Abnormality Analysis for Heroin Addicts

Extracting features from biomedical signals to provide some decision support for medical diagnoses is an important aspect in the development of biomedical engineering.Analyzing power spectra of pulse signals for 15 heroin addicts and 15 healthy persons and calculating the power distributions on specified frequency bands for every power spectrum based on spectral estimation in this paper,we find that the significant difference of power distributions exists between the heroin addicts and the healthy persons.A primary criterion is also obtained,upon which the 13 cases of 15 heroin addicts are identified.The research result of this paper shows that the frequency domain analysis for pulse signals of heroin addicts is really an effective method.     

Parametric Bispectrum Estimation and Slice Analysis for Pulse Signals

Higher-order statistics method is an overhead subject in the fields of signal processing in the world in recent years. The pulse signals are analyzed by applying parametric bispectrum estimation. Non-Gaussian AR model is for calculating the bispectrum of pulse signals for 15 heroin addicts and 15 healthy persons. Characteristic parameters of magnitude bispectrum of the pulse signals are obtained by using horizontal slices of magnitude bispectrum. Characteristic parameters of bispectrum phase of the pulse signals are obtained by using horizontal slices of bispeetrum phase. Moreover,two primary criterions are also obtained by using the characteristic parameters. Exactness ratios of two pnmary criterions reach 93.3% and 90%, respectively. The research result shows that parametric bispectmm estimation for analyzing pulse signals of heroin addicts is really an effective method.     

Application of Bispectrum Estimation to Analyzing Pulse Signals

According to non-Gaussianity and randomness of the Pulse Signals, the bispectrum estimation is used to analyze the signals for the purpose of extracting the unusual information of the signals of-the drug abusers and educing the judgment of how to distinguish drug abusers from healthy persons. The phase of bispectrum carries the important feature information in signal processing. The feature vector of the average phase information based on the indirect nonparametric bispectrum estimation is extracted and used to analyze the Pulse Signals of 15 heroin addicts and 15 healthy persons. It is found that the average phase Ph of heroin addicts on a specified frequency region is generally lower than that of healthy persons. Using the average phase Ph as characteristic parameter, a critical parameter is determined that is used to classify heroin addicts and healthy persons. Thus, all of the 15 healthy addicts are identified. Only one heroin person is misjudged. Experiment results of classification show that the method is feasible and effective.     

A Modern Spectrum Analysis Method for Pulse Abnormalities of Drug Abusers

The pulse sense of PVDF is used to collection pulse signals of subjects into computer and store them in data file style. The author select a series of pulse signals randomly which contains 40 consecutive cardiac cycles of subject and using a modern spectrum analysis method to estimate the power spectrum of data in every cardiac cycle.An average power spectrum is obtained through averaging such 40 power spectra. It is found that the significant difference of power distribution exists between the heroin addicts and the healthy person. The frequency axis of average power spectrum. Calculating the power distributions on specified frequency bands of the average power spectrum for every subject is carved up.A primary criterion is obtained. The above criterion is applied to 30 subjects including 15 heroin addicts and 15 healthy persons. The correctness of classifying heroin addicts and healthy persons arrives 93.3%.The result shows that the modern spectrum analysis method is really an effective measure to analyze pulse abnormalities of heroin addicts.     

Computation of the Wavelet and Scaling Functions Using Coefficients of Wavelet Filter

To associate the discrete wavelet transform with the continuous wavelet transform, an iterative convolution algorithm is given by analyzing the and scaling function using coefficients of wavelet filter. usually used methods of the computation The way of judging the convergence of improved algorithm on of the wavelet function iterative convolution is given. The advantages of improved algorithm is analyzed. The experimental result shows that the modified algorithm is effective.     

PD Pattern Recognition Using Complex Wavelet Transform Coefficient As The Feature

Complex wavelet transform can characterize the partial feature of the PD signal in time-domain and frequency-domain,and provides the unique phasic information.In this paper,the PD pulse waveforms which are created by 4 typical insulated defects are transformed by complex wavelet,and then the complex wavelet coefficient's real part,imaginary part and compound coefficient are clustered by the Fuzzy c-means,the energy of the cluster is the feature of pattern recognition.Discharge samples are got through large number of experiments,and BPNN can identify the PD created by 4 typical insulated defects effectively.The results show that the feature extracted from compound coefficient is better than the feature extracted form the real part and imaginary part of complex wavelet coefficient or wavelet coefficient.     

Multi scale and multi orientation features for image de noising

An adaptive algorithm for image de noising is proposed based on the multi scale and multi orientation features. The coefficients in different scales and different directions are obtained by image decomposition using the nonsubsampled contourlet transform. Then thresholds functions are adaptively set with these coefficients. The texture of the image information is introduced by using the mean of decomposition scale and the energy of regional. The greater the energy, the more information of the texture while the same decomposition scales, the smaller the threshold is set. On the contrary, the greater the threshold is set. After the de noising and then reconstruction of these coefficients, image de noising is implemented. Compare to the wavelet transform threshold and contourlet transform threshold, the nonsubsampled contourlet transform pick up the image detail better and improve the quality of the image.     

Based on Wavelet Transform to Extract and Analyze Rabbit Somatosensory Evoked Potential

To extract rabbit somatosensory evoked potential(SEP),the authors locate waveform of rabbit SEP and analyze it.The rabbit was narcotized and stimulated by 0.5 Hz electric pulse.Potential of scalp was sampled at 3 764 Hz.Rabbit somatosensory evoked potential was extracted by one-dimension multi-resolution analysis,and continuous wavelet transform(CWT) was employed to locate and analyze the wave of SEP.The results show that Single-trail SEP can be extracted by Daubechies wavelet,when compared wavelet transform result of single-trail with result of averaged SEP.Wave component of SEP can be located precisely through the method of continuous wavelet transform.Frequency feature of SEP can also be analyzed by CWT.The technique of continuous wavelet transform,which can project a one-dimension signal into a two-dimension time-frequency space,will become a useful method to process medical electronic signal.     

Application of higher density wavelet transform to composite fault diagnosis of rolling bearing

Aiming at the difficulties in accurate reorganization of several weak faults currently, a composite fault diagnosis method based on higher density discrete wavelet transform and envelope spectrum is proposed. Firstly, the higher density discrete wavelet transform is used to decompose acquired vibration signals of rolling bearings. Then, the single-subband reconstruction is performed on the wavelet coefficients and scaling coefficients at each scale in order to solve frequency aliasing. Finally, the envelope spectra of all subband signals are calculated, and all faults can be recognized according to the characteristic frequencies of the typical faults. The proposed method is applied to the diagnosis of the rolling bearings with composite faults, and is compared with other common fault diagnosis method. The results show that the proposed method can be effectively used for the early composite fault diagnosis of rolling bearings.     

语谱特征的身份认证向量识别方法

针对采用梅尔频率倒谱系数（mel-frequency cepstrum coefficient，MFCC）作为身份认证向量（identity vector，i-vector）进行说话人识别存在语音信息不全的问题，提出一种基于语谱特征的身份认证向量识别说话人的方法。语音信号经过预加重、分帧加窗预处理之后，通过短时傅立叶变换转换成语谱图，语谱图被提交到高斯通用背景模型，在高维均值超向量空间中选择合适的低维线性子空间流型结构以构造符合正态分布的向量作为身份认证向量。这些获取的身份认证向量经过线性判别性分析实现降维并存储。最后采用对数似然比（log-likelihood ratio，LLR）方法对训练和测试阶段的i-vector进行评分，完成说话人识别。以TIMIT数据库为标准的数值实验结果表明，相比采用MFCC作为特征的识别方法，研究的等错误率（equal error rate，EER）更低。     

Texture image retrieval using double density dual tree complex wavelet RH model

For using smooth probability density function to retrieve wavelet coefficient histogram and coefficient module histogram, parameter estimation is complicated, which results in hard to retrieve the texture features effectively. A texture image retrieval method using double density dual tree complex wavelet Refined Histogram(RH) model is proposed. By analyzing the principle of double density dual tree complex wavelet transform (DD-DT CWT) and the inherent relationship between the nonuniform quantizer and RH model, the RH model is extended to retrieve the DD-DT CWT coefficient and the coefficient histogram feature. The RH is used to model the magnitude of the DD-DT CWT. The RH parameters for all magnitude of complex coefficients forms the signature of an image. Image similarity measurement is accomplished by using the Kullback-Leibler divergences . The proposed method combines the advantages of the RH model and the shift-invariant DD-DT CWT. The experiment results show that the proposed methods yields higher retrieval rate than using the General Gaussian Density(GGD) model to fit with the real part or imaginary part of coefficients, and is better than using the Gamma PDF to fit with the magnitude of coefficients.     

Multiresolution Wavelet Transform for Detection of EEG Abnormal Rhythms

The signal of brain activity is a non-stationary random signal including lots of physiology and disease information, which is of important action for doctors to judge pathological changes in brain. So the analysis and process of the EEG signals are always attended. In this paper, the authors take account of the time-frequency localization of wavelet transform and use multiresolution wavelet transform to detect EEG abnormal rhythms. The signals of different scales after EEG signals are transformed by multiresolution wavelet transform not only reflect the frequency information of the signals, namely the more great scale is the lower of the frequency of the signals,but also reflect the time information of the signals, namely EEG state at that time. The test results indicate that the abnormal rhythms of the EEG signals can be detected effectively if right wavelet basis is selected.     

Application of Wavelet Transform to De-noising Method

The wavelet transform is a new subject developed quickly in the past ten years. Compared with the Fourier transform, the wavelet transform is a part of time-frequency transform. The most important character is that it can be used to transform a signal into basic units at different scales and location, each unit represents a component of original signal difference from others. The wavelet transform has been proven to be a powerful and efficient tool for processing signal due to this character. This paper introduces the de-noising principles of the wavelet transform. It is proved to be an effective method by the simulating analysis.     

Systems Analysis Based on Continuous Wavelet Transform

The linear time invariant vibration system is analyzed by Continuous Wavelet Transform(CWT),the relationship of wavelet transform of output signal with the pulse response of system and input signal is put forward.As an exapmle,the wavelet transform of the output signal of system with single degree of freedom is calculated and compared with the direct wavelet transform result of the actual output signal,it shows that the two results are in complete agreement.     

VBR Coding Algorithm Based on Wavelet Transform

The introduction of the asynchronous transfer mode(ATM) concept has significantly influenced the coding theory.According to the characteristics of the ATM network,using the advantages based on the wavelet transform,how to apply the wavelet transform to plane separation and how to apply VBR coding to the layered data are discussed.The results of experiment are described respectively.     

Wavelet Packet Decomposition Based on Entropy Threshold for White-noise Rejection in PD Signal

This paper analyzes the different characteristics of white-noise interference in the signals of partial discharge (PD) after wavelet transform. There is high value in lots of scales for PD and white-noise interference is to zero with increasing scale. The threshold is set for wavelet coefficient in all scales. If the coefficient of signal is higher than the threshold, it is PD signal. Otherwise it is noise interference. A threshold-based wavelet packet transform (WPT) algorithm is put forward to suppress white noise interference in PD signals. The results testifies that it has a favorable adaptability to extract PD signals using WPT.     

Wavelet Analysis Methods to the Signals with Infinite Energy

We study the spectrum analysis to the signals with infinite energy using continuous wavelet transform,the concept of time-frequency power spectrum is presented,and the relations between the time-frequency power spectrum and classical power and average power are studied,the wavelet transform of self-correlation function is also presented, and the relations between it and time-frequency power spectrum are also studied.     

Wavelet-Based Multiresolution Analysis to Noise Elimination

The wavelet transform is a new subject developed quickly in the past ten years Compared with Fourier transform and Gabor transform, the wavelet transform is a part of time-frequency transform, so the message can be obtained from the signals effectively. By means of the fractionized multiresolution analysis to the signals, many problems unalbe to be solved by Fourier tranform have been solved in this way.Based on the fact that the maxima of the noise wavelet transform reduces dramatically with the increase of the scale, we obtain the result that this way is more advanced than the Fourier transform multiresolution analysis to the noise elimination.     

FTIR结合小波变换分析鉴别8种根茎类作物

为了区分鉴别8种根茎类作物,通过采用傅里叶变换红外光谱(FTIR)结合小波变换(WI)、主成分分析(PCA)和聚类分析(HCA)的方法,测试研究了8种根茎类作物40个样品的红外光谱。结果表明：8种样品红外图谱相似,但在1800~700 cm-1范围内,红外光谱的峰位、峰形及吸收强度差异明显。对此范围内的原始红外光谱进行连续小波和离散小波变换。提取连续小波变换的第15层系数和离散小波变换的第5尺度细节系数数据,进行主成分分析和聚类分析。连续小波和离散小波的前3个主成分的累计贡献率分别为93.12%、89.78%,主成分分析和聚类分析正确率为100%。最终结果显示：傅里叶变换红外光谱技术结合小波变换的方法可以区分鉴别不同种的根茎类作物。