Parameter optimized Morlet wavelet de noising method based on cross validation method

A new de noising method based on parameter optimized Morlet wavelet is put forward. The Morlet wavelet is chosen as the mother wavelet because its shape is similar to the mechanical shock signals. The mother Morlet wavelet is improved by adding two parameters which decide the shape of the mother wavelet in time domain. The added parameters and the appropriate scale parameter for the wavelet transformation are designed by the cross validation method. Finally, the useful components of the signal can be obtained by the improved Morlet wavelet de noising method. The gear fault diagnosis experimental result shows that the proposed method has a good de nosing performance and it is effective in fault feature extraction.     

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.     

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。     

Modal parameters identification for short data sequences based on stratified sampling and optimism complex Morlet wavelet

A novel modal parameter identification method based on stratified sampling and optimism complex Morlet wavelet is proposed for short data sequences. Stratified sampling is applied to divide the structure response signal into different layers which called sub samples with different thresholds, and then free decrement response signal of each layer is extracted by random decrement technique. The optimism complex Morlet wavelet transform is applied to identify modal parameter of each layer, and the weight of the layer is also determined based on the sample standard deviation. The modal parameter of the structure can be obtained by weighted calculation.The engineering application shows that the proposed method has the ability to identify modal parameter accurately, decouple low frequency intensive modal composition and restrain high frequency fake modal 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.     

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.     

QRS complexes detection using quadratic b-spline wavelet transform algorithm

Aiming at the low accuracy and low adaptability of wave detection, a QRS complexes detection algorithm is proposed based on quadratic b-spline wavelet, while combined with binary search algorithm and arc approximating curve algorithm. The signal is decomposed with quadratic b-spline wavelet through Mallat algorithm and the R wave is detected by adjusting the threshold with binary search and modulus maximumizing. The T wave and P wave are detected by using arc approximating curve algorithm based on the least square. This algorithm is certified with the ECG signals from MIT-BIH database and is demonstrated that the algorithm enhanced the adaptability of R wave detection and improved the accuracy of T wave and P wave detection. The simulation experiment shows that the improved algorithm can effectively improve the automatic detection capabilities of ECG signals.     

Detection Method for R-Wave of ECG Signal With Wavelet Transform

The R-wave of ECG signal represents the electrical activation of the ventricles, which initiates ventricle contraction, and the typical peak value singular signal, so the R-wave of ECG signal is localized precisely and analyzed accurately using the wavelet transform. The principium of the precise detection method for R-wave in ECG signal is researched. The special properties of Mexican hat wavelet in time-domain are analyzed, too. This wavelet has every order continuity, symmetry, exponential attenuation and one vanishing moment. For this reason, the mexican hat wavelet basis has the excellent localization and analyzing precision. Using the MIT/BIH (Massachusetts Institute of Technology / Boston's Beth Israel Hospital) Arrhythmia Database and the applications in clinic, the precise detection method can detect accurately and localize precisely to the R-wave in ECG signal in the serious noise signal. This method has the quite high locating precision (its error is not more than one sampling point and the points of the R-wave in ECG signal about 80 percent are localized precisely) and analyzing accuracy (no accumulative error). The real-time of the method is excellent, and the real-time detection to the R-wave of ECG signal can achieve using this method.     

Time-frequency Analysis of Heart Sound

The structural defects of the heart are often reflected in the sounds that heart produces. Because of the non-stationary of the PCG signal, it is important to maintain its time-frequency character. We discuss the Short-Time Fourier Transform (STFT) method and the wavelet method. Some normal and abnormal heart sounds were analyzed by these methods. We can see the advantage and disadvantage of them obviously from the examples.     

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.     

Fetal electrocardiogram extraction based on radial basis function neural networks

A novel method for extracting fetal electrocardiogram (FECG) from the abdominal composite signal of a pregnant woman is proposed. The maternal component in the abdominal electrocardiogram (ECG) signal is a nonlinearly transformed version of the mother's ECG (MECG). This nonlinear relationship was identified using radial basis function (RBF) neural networks. The FECG is extracted by subtracting the nonlinearly transformed version of the MECG from the abdominal ECG signal. The baseline shift and noise in the FECG are suppressed by wavelet packet denoising technique. Experimental results obtained from the actual ECG signals demonstrate the effectiveness of the proposed method in extracting FECG even when it is totally embedded within the maternal(QRS) complex.     

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.     

豫东农区冬小麦生育期气候变化的多时间尺度分析

为了研究豫东农区冬小麦生育期内的气候变化特征,预测气候变化趋势,选取豫东地区5个气象观测站1951—2011年的气候观测资料,集成Mann-Kendall、一元线性回归、5年滑动平均、Morlet小波及变异系数等方法,分析了豫东农区冬小麦生育期10月至翌年5月气候的突变事实、变化趋势、变化周期及年际变率特征。结果表明,豫东农区冬小麦生育期内气温序列存在突变年份,主要表现为增温突变;降水序列不存在突变年份,其变化趋势不明显。近60年来气温和降水变化趋势的震荡周期特征复杂,存在多重时间尺度上的嵌套结构。气温序列变异系数普遍较小,降水序列变异系数普遍较大。豫东农区近60年气温升高趋势明显,主要由近20年增温贡献,未来一段时期内气温可能会出现升降温交替现象,但变化幅度较小,总体上呈持续增温趋势,气候变暖对农业生产的影响既有利也有弊,冬春季增温显著一方面有利于提高冬小麦产量,另一方面会加剧病虫害及霜冻干旱灾害的发生。降水变化趋势在60年时间序列上不明显,周期预测显示未来一段时期内降水有增加趋势,年际变率较大,给降水准确预测带来难度,加剧豫东农区旱涝灾情。     

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.     

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.     

Realizing Continuous Wavelet Transform with linear Analog filter

A method of realizing wavelet transform with linear analog filter is proposed. It shows another new way of obtaining fast wavelet transform. It has also given out a procedure of constructing such a filter network and verified by the Mexican hat Wavelet.     

2007年夏季淮河流域多时间尺度降水分布和传播特征

针对2007年夏季淮河流域致洪暴雨,笔者采用美国NCEP/NCAR全球日平均分析场资料,对此次过程进行了详细地分析。Morlet小波分析结果表明：降水存在准35天、准18天和准8天周期变化现象。准35天周期低频降水从西南传播至淮河流域南部,另一方面从西北方向传播至淮河流域中南部;南海为低频降水传播源地也很清楚。降水的周期愈短,传播特征愈不明显。200 hPa与850 hPa水平散度差与降水有很好的对应关系,水平散度差＞0（＜0）对应降水区域（无降水区域）,降水的传播与水平散度差基本一致。200 hPa与850 hPa水平散度差是不同时间尺度地面降水的预报因子。     

Denoise Based On Translation Invariance Wavelet TransformAnd Its Applications

De_noising algorithm based on traditional wavelet transform may produce artifacts on discontinuities of the signal. The reason is that the de_noising algorithm lacks of wavelet translation invariant. This paper proposes a de_noising method based on translation invariant. The method performs the cycle_spinning for the signal to be analyzed. And then, the soft (hard) thresholding is used to shrink the wavelet coefficient of the signal and reconstruct the signal. Consequently, the shift dependence of wavelet basis is eliminated. This method can suppress the artifacts effectively so that de_noised signal is more smooth and has better approximation to original signal.     

1958—2018年广西早稻高温热害时空变化规律

为了掌握广西水稻高温热害的变化规律特征,为水稻生产制定防灾减灾对策提供决策依据,本研究利用广西89个气象观测站1958—2018年观测资料及地理信息数据,采用GIS技术、M-K突变分析和Morlet小波周期分析等方法,探究广西早稻高温热害发生的时间变化规律和空间分布等特征。结果表明：（1）1958—2018年广西早稻高温热害呈增长趋势,20世纪90年代为发生低谷期,21世纪10年代为高发期;（2）高温热害发生次数在2009年发生增多突变,除重度高温热害外,轻、中度高温热害次数变化均有突变发生;（3）高温热害发生频次存在18、32年左右的显著周期变化,预计2018年之后广西早稻高温热害仍处于高发期;（4）高温热害高发区位于左、右江河谷,桂中盆地和西江—浔江—郁江等流域区域,沿海地区发生频次少于内陆。以上研究结论对实现水稻丰产稳产优产具有重要的现实意义。     

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.