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.     

The Signals Analysis System Based on Fast Wavelet Transform

The fast wavelet transform (FWT) algorithm in wavelet analysis was introduced in the paper. With quadrature mirror filters (QMF) associated with popular wavelet bases, the fast wavelet decomposition and reconstruction for signals were implemented. Combined with virtual instrument technique, the FWT analysis system for signals was successfully developed. The system can break up signal not only into approximations, which are the high-scale and low-frequency components of the signal, but also into details which are the low-scale and high-frequency components. Especially it can identify singularity signal, which contain some important message of equipment condition and fault, and refine signal from noisy signal, which is corrupted by noise.     

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.     

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.     

A Simplified Denoising Algorithm with Wavelet Transform

This article explains the mathematical characterization of singularities with Lipschitz exponents and how to compute Lipschitz exponents.The principle and advantage of denoising with wavelet are reviewed.At last,a new simplified practical denoising algorithm is given.     

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.     

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 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.     

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.     

Speedy Recognition of Noise in Real Time Signal Based on Wavelet Transform

It's necessary to process position and gesture of rocket on real time within the flight of rocket. This work should base exact data. But much noise has been found in the data and the noise recognize is regarded as a focus. A speedy online arithmetic recognizing noise is proposed based on wavelet transform. The computing complexity measured by time of this arithmetic is a constant which is greatly reduces the works of calculation of wavelet transform. It can recognize the noise fast when the signal is gathered. The applications in these problems show that the effective arithmetic satisfies the needs of real time and can handle the real time data measured in other yields.     

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。

     

A Vector Quantification Image Coding Scheme Based on Wavelet Transform

The paper proposes an image compression and coding scheme based on discrete wavelet transform (DWT), which makes the best of the relativities among original image and coefficients of wavelet, the relativities among pixels of subimages, the relativities among directions of subimages. Human visual specialities were considered, so the compression scheme leads up to make full use of the statistical redundancy and visual redundancy of image. To get high compression ratio and good quality of image, the coefficients of image working on the quality of image greatly were reserved accurately, but the coefficients of image working on the quality of image slightly were quantified roughly. Also, the regrouping and coding of the coefficients could be used universally. The experimental results show that the compression method is provided with simply calculating, little time for coding and decoding and satisfying quality companied with high compression ratio.     

Study on the Development of Wavelet Theory by Analysing References of Wavelet

By CD ROM and Internet Service System,lots of references on wavelet are searched.These references are analysed in the paper,a new viewpoint is put forward by the authors.Recent studies by the authors on wavelets are also introduced.     

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。     

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.     

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.