Application of fast wavelet transformation in signal processing of MEMS gyroscope

Decomposition and reconstruction of Mallat fast wavelet transformation （WT） is described. A fast algorithm, which can greatly decrease the processing burden and can be very easy for hardware implementation in real-time, is analyzed. The algorithm will no longer have the processing of decimation and interpolation of usual WT. The formulae of the decomposition and the reconstruction are given. Simulation results of the MEMS （micro-electro mechanical systems） gyroscope drift signal show that the algorithm spends much less processing time to finish the de-noising process than the usual WT. And the de-noising effect is the same. The fast algorithm has been implemented in a TMS320C6713 digital signal processor. The standard variance of the gyroscope static drift signal decreases from 78. 435 5 （°）/h to 36. 763 5 （°）/h. It takes 0. 014 ms to process all input data and can meet the real-time analysis of signal.

Wavelet analysis and its application to signal processing

The construction of basic wavelet was discussed and many basic analyzing wavelets was compared. Acomplex analyzing wavelet which is continuous, smoothing, orthogonal and exponential decreasing was presented, andit was used to decompose two blasting seismic signals with the continuous wavelet transforms (CWT). The resultshows that wavelet analysis is the better method to help us determine the essential factors which create damage effectsthan Fourier analysis.

Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.

Implementation of VLSI on Signal Processing-Based Digital Architecture Using AES Algorithm

Continuous improvements in very-large-scale integration(VLSI)technology and design software have significantly broadened the scope of digital signal processing(DSP)applications.The use of application-specific integrated circuits(ASICs)and programmable digital signal processors for many DSP applications have changed,even though new system implementations based on reconfigurable computing are becoming more complex.Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation(DWT)and sophisticated computerized design techniques,which are much needed in today’s modern world.New research and commercial efforts to sustain power optimization,cost savings,and improved runtime effectiveness have been initiated as initial reconfigurable technologies have emerged.Hence,in this paper,it is proposed that theDWTmethod can be implemented on a fieldprogrammable gate array in a digital architecture(FPGA-DA).We examined the effects of quantization on DWTperformance in classification problems to demonstrate its reliability concerning fixed-point math implementations.The Advanced Encryption Standard(AES)algorithm for DWT learning used in this architecture is less responsive to resampling errors than the previously proposed solution in the literature using the artificial neural networks(ANN)method.By reducing hardware area by 57%,the proposed system has a higher throughput rate of 88.72%,reliability analysis of 95.5%compared to the other standard methods.

Realization of Wavelet Transform Using SAW Devices

Based on the characteristics of surface acoustic wave(SAW) devices, the theory for realizing wavelet transform (WT) by SAW is deduced. Simulated experiment shows that the method of implementing WT using SAW devices has virtues of high speed and utility and is compatible with digital technique. It is important to implement wavelet transform.

Fourier Self-deconvolution Using Approximation Obtained from Frequency Domain Wavelet Transform as a Linear Function

A new method of resolving overlapped peak, Fourier self-deconvolution （FSD） using approximation CN obtained from frequency domain wavelet transform of F（ω） yielded by Fourier transform of overlapped peak signals f（t） as the linear function, was presented in this paper. Compared with classical FSD, the new method exhibits excellent resolution for different overlapped peak signals such as HPLC signals, and have some characteristics such as an extensive applicability for any overlapped peak shape signals and a simple operation because of no selection procedure of the linear function. Its excellent resolution for those different overlapped peak signals is mainly because F（ω） obtained from Fourier transform of f（t） and CN obtained from wavelet transform of F（ω） have the similar linearity and peak width. The effect of those fake peaks can be eliminated by the algorithm proposed by authors. This method has good potential in the process of different overlapped peak signals.

Novel Adaptive Beamforming Algorithm Based on Wavelet Packet Transform

An analysis of the received signal of array antennas shows that the received signal has multi-resolution characteristics, and hence the wavelet packet theory can be used to detect the signal. By emplying wavelet packet theory to adaptive beamforming, a wavelet packet transform-based adaptive beamforming algorithm (WP-ABF) is proposed . This WP-ABF algorithm uses wavelet packet transform as the preprocessing, and the wavelet packet transformed signal uses least mean square algorithm to implement the ~adaptive beamforming. White noise can be wiped off under wavelet packet transform according to the different characteristics of signal and white under the wavelet packet transform. Theoretical analysis and simulations demonstrate that the proposed WP-ABF algorithm converges faster than the conventional adaptive beamforming algorithm and the wavelet transform-based beamforming algorithm. Simulation results also reveal that the convergence of the algorithm relates closely to the wavelet base and series; that is, the algorithm convergence gets better with the increasing of series, and for the same series of wavelet base the convergence gets better with the increasing of regularity.

Phase-unwrapping algorithm combined with wavelet transform and Hilbert transform in self-mixing interference for individual microscale particle detection

The self-mixing interferometry(SMI)technique is an emerging sensing technology in microscale particle classification.However,due to the nature of the SMI effect raised by a microscattering particle,the signal analysis suffers from many problems compared with a macro target,such as lower signal-to-noise ratio(SNR),short transit time,and time-varying modulation strength.Therefore,the particle sizing measurement resolution is much lower than the one in typical displacement measurements.To solve these problems,in this paper,first,a theoretical model of the phase variation of a singleparticle SMI signal burst is demonstrated in detail.The relationship between the phase variation and the particle size is investigated,which predicts that phase observation could be another alternative for particle detection.Second,combined with continuous wavelet transform and Hilbert transform,a novel phase-unwrapping algorithm is proposed.This algorithm can implement not only efficient individual burst extraction from the noisy raw signal,but also precise phase calculation for particle sizing.The measurement shows good accuracy over a range from 100 nm to 6μm with our algorithm,proving that our algorithm enables a simple and reliable quantitative particle characteristics retrieval and analysis methodology for microscale particle detection in biomedical or laser manufacturing fields.

Characteristics analysis of muzzle impulse noise based on wavelet energy spectrum

Because muzzle impulse noise could cause damage to or have an intluence on the operator, tiae ettecnve protecnve measures should be taken. Therefore, correct analysis of impulse noise characteristics is very significant. Considering the shortcomings of fast Fourier transform method （FFT） in analysis of muzzle impulse noise frequency characteristics, wavelet energy spectrum method is put forward. Based on specific experiment data, the frequency characteristics and spectral energy dis tribution can be obtained. The experiment results show that wavelet energy spectrum method is applicable in muzzle impulse noise characteristic analysis.

De-noising of Raman spectrum signal based on stationary wavelet transform

In this paper, the Raman spectrum signal de-noising based on stationary wavelet transform is discussed. Haar wavelet is selected to decompose the Raman spectrum signal for several levels based on stationary wavelet transform. The noise mean square j is estimated by the wavelet details at every level, and the wavelet details toward 0 by a threshold , where n is length of the detail, then recovery signal is reconstructed. Experimental results show this method not only suppresses noise effectively, but also preserves as many target characteristics of original signal as possible. This de-noising method offers a very attractive alternative to Raman spectrum signal noise suppress.

Modal identification based on Hilbert-Huang Transform of structural response with S VD preprocessing

In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-Huang Transform to extract modal parameters for closely spaced modes and low-energy components. The proposed method is applied to a simulated airplane model built in Automatic Dynamic Analysis of Mechanical Systems software. The results demonstrate that the identified modal parameters are in good agreement with the baseline model.

Application of spline wavelet transform in differential of electroanalytical signal

Investigating characteristics of spline wavelet, we found that if the two-order spline function, the derivative function of the three-order B spline function, is used as the wavelet base function, the spline wavelet transform has both the property of denoising and that of differential. As a result, the relation between the spline wavelet transform and the differential was studied in theory. Experimental results show that the spline wavelet transform can well be applied to the differential of the electroanalytical signal. Compared with other kinds of wavelet transform, the spline wavelet transform has a characteristic of differential. Compared with the digital differentia] and simulative differential with electronic circuit, the spline wavelet transform not only can carry out denoising and differential for a signal, but also has the advantages of simple operation and small quantity of calculation, because step length, RC constant and other kinds of parameters need not be selected. Compared with

A New OFDM System Based on Lifting Wavelet Transform for Wireless Channel

It is well known that the Orthogonal Frequency Division Multiplexing （ OFDM ） system has a high ability to overcome the effect of multipath and can obtain high spectral efficiency in the wireless communication channel. However, avoid Interchannel Interference （ICI） and Intersymbol Interference （ISI） in wireless channel, a guard interval longer than channel delay is used in conventional OFDM system, which cause the efficiency of bandwidth usage reduced. Due to the superior spectral containment of wavelets, this paper proposed a new OFDM system based on Lifting Wavelet Transform （LWT-OFDM）, which adopts lifting wavelet transform to replace the conventional Fourier transform. This new OFDM system doesn＇t need the Cyclic Prefix （CP） so its structure is more simply than FFT-OFDM and its algorithm is as simply as FFT-OFDM. The new LWT-OFDM system can mitigates some disadvantages of FFT-OFDM system, such as a relatively large peak-to-average power ratio, more sensitive to carrier frequency offset and phase noise. Simulations show that the LWT-OFDM system is more effective and attractive than conversional FFT-OFDM in wireless channel.

Practical implementation of Hilbert-Huang Transform algorithm

Hilbert-Huang Transform (HHT) is a newly developed powerful method for nonlinear and non-stationary time series analysis. The empirical mode decomposition is the key part of HHT, while its algorithm was protected by NASA as a US patent, which limits the wide application among the scientific community. Two approaches, mirror periodic and extrema extending methods, have been developed for handling the end effects of empirical mode decomposition. The implementation of the HHT is realized in detail to widen the application. The detailed comparison of the results from two methods with that from Huang et al. (1998, 1999), and the comparison between two methods are presented. Generally, both methods reproduce faithful results as those of Huang et al. For mirror periodic method (MPM), the data are extended once forever. Ideally, it is a way for handling the end effects of the HHT, especially for the signal that has symmetric waveform. The extrema extending method (EEM) behaves as good as MPM, and it is better than MPM for the signal that has strong asymmetric waveform. However, it has to perform extrema envelope extending in every shifting process.

Improvement of signal processing in Coriolis mass flowmeters for gas-liquid two-phase flow

As an increasingly popular flow metering technology,Coriolis mass flowmeter exhibits high measurement accuracy under single-phase flow condition and is widely used in the industry.However,under complex flow conditions,such as two-phase flow,the measurement accuracy is greatly decreased due to various factors including improper signal processing methods.In this study,three digital signal processing methods—the quadrature demodulation(QD)method,Hilbert method,and sliding discrete time Fourier transform method—are analyzed for their applications in processing sensor signals and providing measurement results under gas-liquid two-phase flow condition.Based on the analysis,specific improvements are applied to each method to deal with the signals under two-phase flow condition.For simulation,sensor signals under single-and two-phase flow conditions are established using a random walk model.The phase difference tracking performances of these three methods are evaluated in the simulation.Based on the digital signal processor,a converter program is implemented on its evaluation board.The converter program is tested under single-and two-phase flow conditions.The improved signal processing methods are evaluated in terms of the measurement accuracy and complexity.The QD algorithm has the best performance under the single-phase flow condition.Under the two-phase flow condition,the QD algorithm performs a little better in terms of the indication error and repeatability than the improved Hilbert algorithm at 160,250,and 420 kg/h flow points,whereas the Hilbert algorithm outperforms the QD algorithm at the 600 kg/h flow point.

Improved Wavelet-based Spatial Filter of Damage Imaging Method on Composite Structures

Piezoelectric sensor array-based spatial filter technology is a new promising method presented in research area of structural health monitoring （SHM） in the recent years. To apply this method to composite structures and give the actual position of damage, this paper proposes a spatial filter-based damage imaging method improved by complex Shannon wavelet transform. The basic principle of spatial filter is analyzed first. Then, this paper proposes a method of using complex Shannon wavelet transform to construct analytic signals of time domain signals of PZT sensors array. The analytic signals are synthesized depending on the principle of the spatial filter to give a damage imaging in the form of angle-time. A method of converting the damage imaging to the form of angle-distance is discussed. Finally, an aircraft composite oil tank is adopted to validate the damage imaging method. The validating results show that this method can recognize angle and distance of damage successfully.

Classification of Imagined Speech EEG Signals with DWT and SVM

With the development of human-computer interaction technology,brain-computer interface(BCI)has been widely used in medical,entertainment,military,and other fields.Imagined speech is the latest paradigm of BCI and represents the mental process of imagining a word without making a sound or making clear facial movements.Imagined speech allows patients with physical disabilities to communicate with the outside world and use smart devices through imagination.Imagined speech can meet the needs of more complex manipulative tasks considering its more intuitive features.This study proposes a classification method of imagined speech Electroencephalogram(EEG)signals with discrete wavelet transform(DWT)and support vector machine(SVM).An open dataset that consists of 15 subjects imagining speaking six different words,namely,up,down,left,right,backward,and forward,is used.The objective is to improve the classification accuracy of imagined speech BCI system.The features of EEG signals are first extracted by DWT,and the imagined words are clas-sified by SVM with the above features.Experimental results show that the proposed method achieves an average accuracy of 61.69%,which is better than those of existing methods for classifying imagined speech tasks.

Hilbert-Huang变换及其在电力系统中的应用

通过综述国内外Hilbert-Huang变换及其相关端点效应、模态混叠、筛分停止准则等问题的研究现状,讨论了该变换的特点。详细分析了Hilbert-Huang变换在电力系统设备故障检测与故障诊断、电力系统振荡分析、电能质量检测和负荷预测等方面的应用现状。最后,对其在电力系统中的应用进行了展望,认为Hilbert-Huang变换可以联合其他方法克服本身缺点,并根据电力系统信号的特点,更有效地应用于电力系统。

利用Hilbert变换提取信号瞬时特征参数的问题研究

解析Hilbert变换利用其实部与虚部的关系,定义出任意时刻的瞬时频率、瞬时相位及瞬时幅度,因而工程中人们设法将其应用对实际信号的瞬时参数提取中。然而,在利用Hilbert变换提取瞬时特征参数的工程实现中存在一些问题,如何解决这些问题,特别是如何高精度提取瞬时频率特征参数问题是文中的研究重点。

Hilbert-小波变换的齿轮箱故障诊断

采用希尔伯特—小波变换对振动加速度传感器获取的齿轮箱振动响应信号进行特性分析。利用小波变换分解获得振动响应信号的各层高频信号小波系数和低频信号小波系数,对小波系数进行重构获得具有不同特征时间尺度的各高频信号和低频信号;再对分解的信号进行希尔伯特变换获得时频信息谱以提取系统的统计特征信息,实现监测齿轮运转工作状态,及时发现齿轮的早期故障,提高机械运行的安全性。仿真研究结果表明,小波变换分解和希尔伯特边际谱方法在故障信息诊断方面是可行和有效的,提高了故障检测的可靠性。