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.

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.

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.

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.

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.

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.

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.

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.

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.

基于面积加权GWT-GFT的水声目标识别

由于海洋环境的复杂性,水声目标的识别具有很大的挑战性。为解决这类复杂环境下特征提取的问题,提出了一种基于面积加权的图小波变换-图傅里叶变换(GWT-GFT)的分析方法。在完成数据预处理后,为了能够凸显顶点之间的关系,提出了一种新的基于顶点三角形面积的加权方法来构建图信号;构建好的图信号通过GWT分解为多尺度图分量;然后,利用GFT将这些分量从图域变换到特征值谱域进行分析;在此基础上,提取各分量特征值谱的特征;最后,利用基于高斯核函数的支持向量机(SVM)对获取的特征向量进行分类。基于水声信号ShipsEar数据库,采用5折交叉验证方法进行验证。与现有的其它方法相比,所提的模型以36个特征在376656个样本上取得了97.22%的准确率,证明了该分析方法的有效性和鲁棒性。

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.

两步式自适应阈值法滤除心电信号中运动伪迹

心电信号广泛应用于心脏疾病的医学检测中,可穿戴动态心电监测设备可以实现对心律失常的风险识别并预警.相比于静息心电信号,动态心电信号在采集过程中会受到更大运动伪迹的干扰,这些干扰会覆盖心电信号的关键信息,限制其临床应用.本文兼顾心电信号局部和全局特征,利用其周期性,研究了一种将心电信号低频PT波和高频QRS波群分开处理的两步式自适应阈值滤波算法,适用于单通道心电信号中的运动伪迹滤除.第一步先通过多分辨率阈值初步抑制心电信号低频部分中的运动伪迹;第二步,对受运动伪迹影响而不平衡的QRS波进行自适应阈值修复,通过对QRS波形调节,减少心电信号中高频部分运动伪迹,同时设置自适应阈值对心电信号P波、T波对应的小波系数进行处理,超出自适应阈值范围的小波系数通过波形缩放进行调整,进一步抑制低频运动伪迹.研究通过不同心电数据库评估算法的性能.在输入信噪比从-10~10 dB时,心电信号信噪比提升了10.9122 dB和4.3912 dB,滤波后心电信号与纯净心电信号的相关系数分别为0.6876和0.9783,提取的运动伪迹与原运动伪迹相关系数分别为0.9530和0.8529.实验结果表明,算法在不同噪声水平下,利用自适应阈值的优点,能有效复原受运动伪迹污染的心电信号波形特征,最大限度保留心电信号的临床信息,可作为可穿戴心电设备滤除运动伪迹的有效工具.

Processing of array sonic logging data with nulti-scale STC technique

It is desirable to develop new signal processing techniques for effectively extracting reflected waves under the strong interferences of borehole guided waves. We presented a multi-scale semblance method for the separation and velocity （slowness） analysis of the reflected waves and guided waves in borehole acoustic logging. It was specially designed for the newly developed tools with ultra-long source- receiver spacing for acoustic reflection survey. This new method was a combination of the dual tree com- plex wavelets transform （DT-CWT） and the slowness travel time coherence （STC） method. Applications to the 3D finite difference （FD） modeling simulated data and to the field array sonic waveform signals have demonstrated the ability of this method to appropriately extract the reflected waves under severe interference from the guided waves and to suppress noise in the time-frequency domain.

多参数水质检测智能传感器信号处理及建模

针对多参数水质检测中智能传感器易受到环境、噪声、光源等干扰因素的影响,提出一种结合递推平均滤波算法和小波变换算法的传感器信号处理方法.该方法利用递推平均滤波算法对传感器输入信号的周期性干扰因素进行预处理,并利用小波变换算法进一步减少传感器信号的噪声.小波变换算法能够通过伸缩和平移来细化采集信号,以此实现对时频的局部化处理,最终达到对高频部分进行时间细化、对低频部分进行频率细化的目的.实验结果显示,在同样的阈值条件下,降低噪声的数量越多,对噪声的抑制作用越显著.由此验证了所提方法的有效性.

一种基于Wavelet-Radon变换的宽带双曲调频信号检测方法

动物回声定位所用超声波具有双曲调频的形式,充分利用仿生信号的特性可以提高水下信号处理系统的性能。阐述了宽带仿生信号的时间-尺度特性,针对直线形式的回波小波峰脊,将宽带目标回波检测问题转化为小波变换2D平面上的直线检测问题,提出了一种基于Wavelet-Radon变换的宽带仿生信号检测方法,并进行了仿真验证。结果表明,该检测方法充分利用了宽带仿生信号的特性,同时也验证了选择宽带双曲调频信号作为发射信号的合理性。

光伏板清扫机器人行走机构静力学分析

光伏板清扫机器人行走机构两侧结构对称,由行走同步带、同步带传动机构、辅助支撑轮机构和行走机构支撑架等组成,行走机构支撑架是行走机构的重要组成部分.运用SolidWorks对行走机构支撑架进行建模和简化,通过有限元分析软件ANSYS Workbench对其进行静力学分析,计算出最大变形为0.04 mm、最大应力为31.12 MPa,能够满足使用设计要求,可为后续优化设计提供参考.

A New Feature Space for Partial Discharge Signal Separation Based on DWT Coefficient Variance

In this paper, a new feature space for PD （partial discharge） signal separation is presented. Three typical PD defects were experimentally reproduced in a laboratory for obtaining independent PD sources. Signals were acquired with a digital storage oscilloscope and then post-processed with DWT （discrete Wavelet transform） for de-noising. The new feature space for PD source separation was constructed with the variance of each Wavelet coefficient vector and was compared with an established feature space for PD source separation; based on the energy of DWT coefficient vectors. After a space reduction by mean of PCA （principal components analysis）, the separation capability among them was measured by comparing the final classification error after training a neural network Results showed that with this new feature space it is possible to separate different sources of PD signals. Later, the feature space proposed was used to separate two PD sources from a real equipment tested. Further analysis on the reduced feature space has shown the band location of PD signals information for separating purpose.

基于谱图小波的神经精神疾病患者脑功能网络多尺度分析

基于图信号处理,针对神经精神病患者的脑功能网络展开研究,采用谱图小波变换对脑功能网络进行多尺度分析,根据实验数据构建图拉普拉斯矩阵,从中选取最大特征值对谱图小波变换滤波器进行设计。之后结合血氧水平依赖对比度信号计算出受试者各脑区的谱图小波系数,对其进行多尺度组间差异分析,发现脑功能网络在不同频段下的能量分布存有异常,并对对应的异常脑区予以明确。所采用的谱图小波变换是目前针对不规则数据域中信号展开多尺度分析的最有效方法之一,对其他疾病患者脑功能网络多尺度分析研究具有重要的现实意义。