Choosing Method of Wavelet Neuron Based on ECG Data Compression

The paper discusses a wavelet network for the ECG data compression and proposes the method for choosing its wavelet neuron.According to the spectrum range of the ECG data,we decide the time-frequency field of ECG.And the time-frequency field of wavelet is also determined by the spectrum range of it.The wavelet neuron is fixed preliminarily by the first two steps.Then the preliminary wavelet neuron is screened by using OLS algorithm.We choose Morlet as the mother wavelet,and use the ECG signal to validate by the method.The result demonstrates that the number of Morlet whose spectrums locate at the ECG's is up to 152.But after screening by the OLS algorithm,it reduces sharply.This method can make the size of the wavelet network driving to optimum and also reduce the training time of the wavelet network sharply.     

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.     

EEG Signal Denoising Based on Wavelet Transform

The article focuses on the method of noise cancellation for EEG signal. The method of notch filter is discussed. According to the frequency of noise and the principle of notch filter, the design result of the notch filter and the denoised signal are presented. Then, the analysis of EEG signal are proposed based on wavelet transform (WT) and noise cancellation using WT. Wavelet transform is a multi-resolution time-frequency analysis method. It can decompose mixed signal into signals at different frequency bands. The EEG signal is analyzed and denoised using WT, then the results are presented respectively. Comparing the experiment results shows that WT can detect and process noise in the EEG signal effectively.     

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.     

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.     

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 Time-frequency Representations in Study on Heart Sound

Heart sound is non-stationary signal, some of its important characteristics can be obtained by time - frequency analysis method. The criteria of different time-frequency representation concentricity of spectrogram, coverage, MSE and regularity of time envelope axe proposed, the performances of Short-time Fourier transform, Wigner - Ville distribution, continuous wavelet transform, and S transform in study on heart sound are compared based on these criteria , and concrete example of application is given.     

浙江省日照时数时空分布特征

为了了解浙江省日照的时空分布特征,更好地为农业生产服务,笔者根据1971-2009年浙江省65个台站逐月日照资料,利用Morlet连续小波变化、功率谱检验及Mann-Kendall检验,研究了浙江省日照时数的时间变化趋势、周期演变规律和突变转折点;并通过等值线分布和EOF分解等方法,分析了浙江省近39年的日照时数的空间分布特征。结果表明：浙江省日照时数存在不同尺度的周期分量,6~9年的周期振荡较强烈,其中8.7年周期通过了功率谱95%的置信度检验;Mann-Kendall检验表明浙江省日照在全时域表现为一致的减少趋势,并且在70年代中后期存在突变;浙江省日照时数空间分布上呈现自东北向西南减少的趋势,EOF分析得出全省日照距平表现为整体一致的减少趋势。     

The Filtering Character of Hilbert-Huang Transform and Its Application

Hilbert-Huang transform(HHT) is a new two-step time-frequency analytic method to analyze the nonlinear and non-stationary signal. The key step of this method is empirical mode decomposition(EMD) method with which any complicated data set can be decompose into a finite and often small number of intrinsic mode functions(IMF). Using Hilbert transform to those IMF components can yield instantaneous frequency, the final presentation of this results is a energy frequency-time distribution, designated as the Hilbert spectrum. Examples from the numerical results of signal de-noising are given to demonstrate the power of this new method, those results can clarity the advance and efficient of this method.     

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.     

Signal Processing of Electroencephalogram and It''s Application

Electroencephalogram (EEG) signals are the electrical activities in the cortex or on the surface of scalp caused by the physiological activities of the brain which play a key role in the diagnosis of brain and the functional determination of brain. The authors discuss some methods for processing EEG signals from the view of the frequency domain and time domain, especially introduce some time-frequency analysis methods such as Wigner distribution, wavelet transform and matching pursuit etc. , ANN and non-linear analysis for EEG signals processing.     

A Virtual Wavelet Transform Analyzer for the Signal Analysis Based on the Direct Algorithm

A virtual wavelet transform analyzer for the signal analysis based on the direct algorithm is introduced so that the discrete wavelet transform and continuous wavelet transform is maken to signal in the direct algorithm. The authors first introduce the direct algorithm of the WT, which is numerical algorithm obtained from the original formula of the wavelet transform by directly numericalizing. Then some conclusions are drawn on the direct algorithm. The examples are the sampling principle and technology for the wavelets, the limitation of the scale range of the wavelets and the measures to solve the edge phenomenal in the direct algorithm of the discrete wavelet transform, and some conclusions in the direct algorithm of the continuous wavelet transform. The virtual wavelet transform analyzer for the signal analysis based on the direct algorithm explored based on these studies and combined with virtual instrument technique can make the discrete wavelet transform and continuous wavelet transform to signal with any basic wavelet. It can be applied in studying the property of any basic wavelet and learning the theory on the wavelet transform, and also in making some engineering signal analysis. In the end, the authors give some typical examples for the application of the virtual analyzer. These examples show that the analyzer can be applied in many situations.     

Optimizing Delay Time Interval by the Means of Time - Energy Analysis Based on Wavelet Transform

In millisecond blast engineering,the key to achieving millisecond blast is to ascertain reasonable delay time interval.Firstly,the authors make use of time-energy density analysis based on wavelet transform,and get the real delay time by identifying the blast moment of short-delay detonators.Secondly,every sub-signal is separated from measured millisecond blast vibration signal by time-frequency domain transform techniques.Lastly,the Optimized delay time interval is obtained by comparing the superposition effect of sub-signal in different delay time interval.The effectiveness of the proposed method is verified with time-energy density analysis of millisecond blasting vibration signals in an underground project.It shows a high theoretical and practical value,and establishes a theoretical and technical foundation for the study of controlling and forecasting blast vibration damage.     

A New Method of Diesel Engine Fuel System Fault Diagnosis

A method of diesel engine fuel system fault diagnosis based on wavelet transform and fuzzy C-means clustering is presented. Five characteristic parameters of reflecting fault state are distilled with wavelet transform of pressure wave of high-pressure oil pipe of diesel. The theory and generic approach of fuzzy C-means clustering algorithm (FCM) is given, and the validity of evaluating fuzzy clustering making use of partition coefficient, partition entropy and parting coefficient is pointed out. Identification of fault mode can be completed utilizing standard fault character modes established by FCM algorithm, and calculating and comparing the similarity degree between this standard mode and sample. The arithmetic is applied to all kinds of typical faults diagnose in the diesel engine fuel system. Measuring results indicate that the precision of fault diagnosis is increased with the analysis of wavelet and FCM.     

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.     

Two Channel Filter Banks and Wavelet Transform of Discrete Sequences

Multirate filter banks and wavelet transform are closely related theoretically and practically. This paper researches the wavelet transform of discrete-time sequences basing on dendriform filer banks in the signal processing field, analyses the theory and design method of two channel conjugate quadrature mirror filter banks and fast implement of discrete orthogonal wavelet transform and generation of orthogonal wavelets, and points out the internal relationship between them.     

Filtering Construct of Adaptive Filter Grou Pwith Multi-scale Decomposition

The principle and method of the adaptive filter and the filtering with wavelet transform were analyzed, and the model and method of adaptive filtering with wavelet transforms for the transient signal was established. The separated noise of signal by the multi-scale decomposition of wavelet transforms, was the input signal of adaptive filter, and accordingly the optimal filtering method of signal-noise decomposition was realized. By the adaptive filter grou Pbased on the wavelet transform, the optimal filtering to the multi-noise of signal is achieved at the same time, and the method presented in this paper has the excellent filtering capability. Examples of application demonstrate that this method presented is excellent to realize the optimal estimate to the valuable signal and noise of the transient signal in the same frequency segment.     

Suppression of Periodic Narrowband Interferences in Cable Partial Discharge Detections

There are background noises and interferences in the signal acquired,due to partial discharge(PD) detection system covers a broad frequency band.To suppress periodic narrowband signal which is a quite serious interference in PD measurement,the existing suppression method is introduced,the new method of wavelet packet transform is mainly studied to de-noise the periodic narrowband in XLPE cable PD detection system,which is based on the db4 basic wavelet using soft threshold.The results of the experiment show that wavelet packet transform is effective in restraining the periodic narrowband interferences to extract the PD pulses in XLPE cable PD monitoring system.     

Application of Wavelet Transform and HMM Model in Recognition of Speech Signals

Some parameters based on wavelet transform and ARMA Model are presented. Wavelet transform provides a high frequency resolution, and the ARMA model is more powerful due to its including of the zeros pole in the model. The experimental result of alphabet of A to N from National Institute of Standard Technology (NIST) database is given. The error rate has been improved, especially C.     

Wavelet Neural Network Equalizer Using Error Feedback Structure and Algorithm

The inter-symbol interference in the nonlinear time-varying channel is a serious problem in the wireless communication. In order to overcome it, wavelet neural network equalizer using error feedback is employed to cut the auto-correlation of the error signal. Exploiting the decent time-frequency localization of the wavelet analysis, as well as the self-training feature of the neural network, a quicker convergent nalysis and computer simulation confirm the effectiveness of the equalizer and a lower BER are attained. Theoretical aalgorithm. It at The wavelet neural network equalizer based on error feedback advances the communication in the nonlinear time-varying channel.