Frequency domain analysis and the application for extracting electroencephalogram signal characteristic waves

A new quantitative analysis method to describe the dynamic variation of electroencephalogram (EEG) signals was proposed. Based on the Fourier transformation, the method is called Fourier multi resolution analysis (FMRA). FMRA decomposes the frequency domain with a binary system and can resolve EEG signals into the basic rhythms of the four waves to study the dynamic characteristics of EEG signal rhythms. FMRA has clear physical meaning, and can obtain more information than wavelet multi resolution analysis does. FMRA can extract perfectly the rhythmic characteristics of EEG signals in the time and frequency domains.     

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.     

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.     

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.     

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.     

Analysis of Vehicle-Bridge Coupled Vibrations for Simply Supported Girder Bridge Considering Bridge Surface Roughness

Bridge surface roughness is usually considered to be the main Influencing factors of vehicle-bridge coupled vibrations.The way that obtaine Bridge surface roughness by the transformation of power spectral density is effective and rapid.The Bridge surface roughness is obtained by Fourier inverse transform method and trigonometric series method respectively. It can be concluded from the comparision that the precision of Fourier inverse transform method is higher than trigonometric series method. The vibration functions for 4-dofs Vehicle and bridge were derived with the employment of d'Alembert principle and computational procedures for vehicle-bridge coupled vibration were compiled. The dynamic response of a simply supported beam and vehicle was analyzed considering bridge surface roughness which was obtained by Fourier inverse transform method.The research shows that the influence due to the bridge surface roughness is significiant and vehicle speed also effects the bridge vibrations by changing the frequency of the force acting on the bridge due to vehicle and the force acting on the vehicle due to bridge deformation.     

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.     

Binary phase only filter based watermarking combined with discrete wavelet transform

The binary phase only filter (BPOF) based digital image watermarking combined with a discrete wavelet transform (DWT) was proposed. Firstly, the DWT transform was applied to an image. Then, the discrete Fourier transform (DFT) was applied to the low frequency subband of the DWT transform. Next, the phase information of the DFT was binarized to obtain the BPOF, which would be taken as the watermark and embedded into the corresponding magnitude. Compared to applying the DFT transform to the whole image and embedding the BPOF watermark in the entire frequency range, or in the low frequency range, the watermark robustness in JPEG compression is improved significantly while maintaining the watermark imperceptibility and detection efficiency. This method may be used to demonstrate the authenticity and integrity of an image. The simulation experiments demonstrate the efficiency of this method.     

Wavelet Analysis of Electrically Evoked Surface Electromyography Signals During Muscle Fatigue

By analysis of electricallyevoked surface electromyogram(SEMG) to suralmuscle, study the characteristics of SEMG signal during muscle fatigue. The time stretchingof two semi-ave of M-wave are analyzed respectively by wavelet method choosing wavelet function matching to M-wave. Indexes of muscle fatigue are educed accordingto the scale of wavelet transform, are the quantitative analysis is realized during muscle fatigue. This method overcomes the limitation of Fourier Transform that it has no resolution in time domain, find indicators of SEMG which are correct, reliable and easy to interpret, and try to providetheoretic referencefor the applicationsof these indicators.     

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.     

Application limitation of the Hilbert Huang transform to earthquake ground motion analysis

We presented herein detailed comparison between emulational and real earthquake signals. Intrinsic Mode Functions (IMF) were regarded as products of the empirical mode decomposition (EMD) during Hilbert Huang transform (HHT) processing. This could be ascribed primarily to the failure of separating the correct frequency from mixed and similar frequency components. It was difficult to remove IMF signal from the earthquake record and to obtain correct earthquake energy distribution in frequency. Furthermore, The Hilbert transform sometimes induced erroneous instantaneous frequencies. Thus, care must be taken in the application of HHT in earthquake engineering. The combination of the Hilbert transform with wavelet transform could produce better analysis of earthquake record.     

Chemical structural characteristics of microbial flocculant

Active bacteria from activated sludge in Tangjiaqiao sewage treatment plant were enriched, cultivated, separated and purified, and their degree of activity tested. Structural characteristics of the microbial flocculant produced by the activated bacteria were analyzed using coloration reaction and Fourier transform infrared spectrometry. The results show that the most active bacterium is saccharomycetes with a flocculation ratio reaching as high as 95%. Yellow crystal microbial flocculent was obtained through fermentation cultivation, separation and purification from these saccharomycetes. Results of the coloration reaction and infrared absorption spectrometry indicate this microbial flocculent is a type of sugar in which there are numerous functional groups.     

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.     

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.     

A Method to Eliminate the Boundary Distortion in Subband Analysis / Synthesis

This paper analyses the principle of analysis/synthesis in subband coding and the cause of the boundary distortion,and presents a method using the Hadamard transform as a analysis/synthesis system to eliminate the boundary distortion completely.     

Some Discussion On the Calculation of the Discrete Fourier Transform(DFT)

In the field of signal analyies for time frequency, the DFT is a familiar term. Especially,when its efficient algorithm FFT given, the other operations in signal analyses are also usually translated to DFT as soon as possible so as to enhance the calculating speed. But in our exploning the virtual instrument for time frequency analyzing, two sorts of formulas to calculate DFT and its inverse transform (IDFT) are found. These results are in different value of operation from different signal analysts or from different instruments for time frequency analyzing, their physical meaning are also not clear enough.The period extent theory some previous document based on when defined DFT is firstly expatiated. Then, separately with future defining coefficient method and analogy evolvement method, the authors argue that, based on the period extent theory, it is more rational to calculate DFT with the formula given. A shortage is pointed out and corrected is appeared in some previous documents when having given the formula of DFT. The improper view is also clarified that the reason why there are two sorts of formulas is make by custom.The rationality of the authors view with three examples is visually verified in the end.     

Analyzing the StiffnesS of Foundation Plate By BEM

This.paper derives the basic solution of elastic half-space foundation plate byHankel transform,Then its boundary integral equation of distortion is estabished,The paper discuss-es bou ndary integral equation on plate of fixxing and/or simple support boundary.Then a systemof algebraic equations for the boundary values are established.A numerical example is given , its re-sult shows that this Method is correct.     

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.     

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.     

Application on Extracting Feature in State Monitoring and Fault Diagnosis

With the development and application of information and internet and virtual instrument technology ,the virtual globular company based on internet arises .To study remote state monitoring, remote fault detection and diagnosis about large scale, complicated and integrative equipment become very important. In the whole fault diagnosis system , the detecting ,data acquisition is original ,processing; transform and extracting features with the signal detected is a key factor. The theories and methods used in mechanical fault diagnosis is stated. The application of signal process and its feature extracting methods is introduced which are time domain, frequency domain and time frequency domain analysis, in state monitoring and fault diagnosis with its signal analysis. The processing method of random time variant special signal is given also.