Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

The Time-frequency Characteristic of a Large Volume Airgun Source Wavelet and Its Influencing Factors

Through analyzing the near-field hydrophone records of the airgun experiment in the Jiemian reservoir,Fujian,we study the time-frequency characteristic of airgun source wavelet and the influence of gun depth and firing pressure,and explain the process of bubble oscillation based on the Johnson( 1994) bubble model. The data analysis shows that:( 1) Airgun wavelet is composed of primary pulse and bubble pulse. The primary pulse,which is of large amplitude,short duration and wide frequency band,is usually used in shallow exploration. The bubble pulse,which is concentrated in the low-frequency range,is usually used in deep exploration with deep vertical penetration and far horizontal propagation.( 2) The variation of primary pulse amplitude with gun depth is very small,bubble pulse amplitude and the dominant frequency increase,and peak-bubble ratio and bubble period decrease. When the gun depth is 10 m,primary pulse amplitude and peakbubble ratio are maximum,which is suitable for shallow exploration; when gun depth is25 m,bubble pulse amplitude is large, and peak-bubble ratio is minimum, which is suitable for deep exploration.( 3) The primary pulse amplitude,bubble pulse amplitude,peak-bubble ratio,and bubble period increase and the dominant frequency decreases with increased firing pressure.

Application of Wavelet Packet De-noising in Time-Frequency Analysis of the Local Wave Method

The local wave method is a very good time-frequency method for nonstationaryvibration signal analysis. But the interfering noise has a big influence on the accuracy oftime-frequency analysis. The wavelet packet de-noising method can eliminate the interference ofnoise and improve the signal-noise-ratio. This paper uses the local wave method to decompose thede-noising signal and perform a time-frequency analysis. We can get better characteristics. Finally,an example of wavelet packet de-noising and a local wave time-frequency spectrum application ofdiesel engine surface vibration signal is put forward.

Phase estimation in bispectral domain based on conformal mapping and applications in seismic wavelet estimation

地震小浪评价是地震数据处理和解释的重要部分，谁的正确直接与 deconvolution 和倒置的结果有关。小浪评价基于高顺序的系列是一个重要新方法。然而，高顺序的系列经常有阶段包装纸问题，它导致小浪阶段光谱偏差并且从而影响混合阶段小浪评价。解决这个问题，我们建议一个新阶段光谱方法基于在 bispectral 领域的保角的印射。方法避免由缩小 Fourier 阶段光谱的范围消除在原来的阶段包影响的 bispectral 阶段包问题的阶段光谱评价。方法基于保角的印射组成最少平方的小浪阶段光谱评价它与最少平方的小浪振幅光谱评价被用于混合阶段小浪评价。理论模型和实际地震数据验证这个方法的有效性。我们也扩大到 trispectral 小浪阶段光谱评价的在 bispectral 小浪阶段光谱评价的保角的印射的想法。

A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images

In this paper,we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform(2D-SMCWT).The fusion of the detail 2D-SMCWT cofficients is performed via a Bayesian Maximum a Posteriori(MAP)approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coefficients.For the approx imation coefficients,a new fusion rule based on the Principal Component Analysis(PCA)is applied.We conduct several experiments using three different groups of multimodal medical images to evaluate the performance of the proposed method.The obt ained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics.Robustness of the proposed method is further tested against different types of noise.The plots of fusion met rics establish the accuracy of the proposed fusion method.

Novel Time-frequency Analysis and Representation of EEG

A novel method of EEG time-frequency analysis and representation based on a wavelet network is presented. The wavelet network model can represent the EEG data effectively. Based on the wavelet network model, a novel time-frequency energy distribution function is obtained, which has the same time-frequency resolution as Wigner-Ville distribution and is free of cross-term interference. There is a great potential for the use of the novel time-frequency representation of nonstationary biosignal based on a wavelet network in the field of the electrophysiological signal processing and time-frequency analysis.

基于改进ResNet50的表面肌电信号手势识别

为了提高手势动作在类别众多且相似度高的情况下的识别精度,提出了一种基于连续小波变换和残差神经网络Res-Net50的表面肌电信号手势识别方法。首先对Ninapro DB2和DB3的原始表面肌电信号进行预处理和连续小波变换,得到Multi-sEMG Wavelet Map数据集,然后送入改进的ResNet50模型进行识别分类。实验结果表明,改进后的ResNet50网络模型在Multi-sEMG Wavelet Map DB2和DB3中17种手势动作的平均准确率分别达到了96.40%和94.11%,相比ResNet50网络模型方法提升了4.87%和5.83%。实现了手势动作在类别繁多、相似度较高的情况下的精准识别。为基于非侵入式传感器和机器学习控制的假肢手提供了新方案。

基于特征图像组合与改进ResNet-18的电能质量扰动识别方法

针对传统电能质量扰动(power quality disturbance,PQD)识别体系中单一图像特征信息受限与算法识别能力不足等问题,依据特征融合的思想,提出一种基于特征图像组合与改进ResNet-18的PQD识别方法。首先,对PQD信号进行变分模态分解(variational mode decomposition,VMD)得到一系列固有模态函数(intrinsic mode functions,IMFs)与残差分量;其次,将IMFs、残差分量、原始扰动信号与Subtract分量纵向拼接成分量矩阵,利用信号-图像转化方法生成特征分量彩色图;再次,对原始扰动信号进行连续小波变换(continuous wavelet transform,CWT)生成小波时-频图;最后,将特征分量彩色图与小波时-频图组合输入改进的六通道ResNet-18中训练学习并完成扰动识别。通过仿真对PQD识别方法进行分析并将其与目前常用识别体系进行比较。结果表明,所提方法具有较好的抗噪性能并且能够更好地提取PQD特征信息,达到更高的识别准确率。

基于MDS和改进SSA-SVM的高速铁路道岔故障诊断方法研究

针对高速铁路道岔设备故障频繁,现场维修工作量大等问题,提出基于多维尺度缩放法(MDS)和改进麻雀搜索算法(SSA)优化支持向量机(SVM)的高速铁路道岔故障诊断模型。首先以ZDJ9道岔转换功率曲线为研究对象,总结现场典型道岔故障类型及故障原因,分别提取道岔功率曲线的时域、频域特征指标以及小波包能量熵,组成特征指标向量;其次采用MDS方法进行多维特征指标的降维优化,建立道岔故障特征指标样本数据库;最后利用改进Circle混沌映射初始化种群,并通过自适应t分布增强麻雀种群的多样性,再以改进SSA算法优化SVM模型中的惩罚因子和核函数方差2个关键参数,构建改进SSA-SVM的道岔故障诊断模型。故障诊断结果表明,本模型的故障诊断正确率高达96.25%,诊断效果优于其他方法,可以为道岔设备的故障维修提供理论依据。

双Haar小波变换系数的MAP估计及在图像去噪中的应用

小波变换作为一种新的工具,在信号去噪中得到了重要的应用。本文对双Haar小波变换系数,提出了MAP的估计方法,并对其在图像去噪中的应用进行了讨论。实验表明所提出的小波收缩算法与软门限方法相比较,用于图像去噪时可以给出更好的结果。

基于小波变换和CNN的船用机械故障诊断

针对船用机械故障特征自适应提取与智能化诊断问题,采用连续小波变换与卷积神经网络的船舶机械故障诊断方法。以船用风机为例,首先模拟船用机械不同故障并采集振动信号,通过连续小波变换将一维振动信号转化为特征图谱,其包含大量的时频信息。然后通过多次训练后,确定网络结构参数,建立卷积神经网络结构,将时频图作为卷积神经网络输入,挖掘更深层次的高度抽象的故障特征信息。最后在卷积神经网络的输出层接入softmax分类器,实现船用机械的故障诊断。实验结果表明:所提方法能准确识别故障类型,且具有较强的鲁棒性和泛化能力,诊断准确率可达99.3%。与集成经验模态分解、极限学习机故障诊断方法相比,该方法有更高的诊断精度。

基于小波时频图和ResNet18的焊接状态监测方法研究

针对焊接过程中因外部干扰因素或焊接参数选择不当而导致的气孔和未熔合缺陷的问题,提出一种小波时频图和深度残差网络(ResNet18)相结合的焊接质量检测方法。首先,搭建管道全位置自动焊接试验平台,利用拾音器记录熔合良好、未熔合和气孔焊接状态下的声音信号,将采集到的声音信号进行小波阈值降噪处理并且计算信号的信噪比,从而得到合适的信号降噪方法。其次,使用连续小波变换得到小波时频图,对时频图进行压缩和预处理,将时频图的大小设置为224×224,并剔除时频图上的标题、坐标和能量等。最后,将处理好的小波时频图作为输入,以未熔合、熔合良好和气孔三种状态作为输出,利用ResNet18网络进行训练。结果表明,该模型对三种焊接状态下的声音信号具有良好的监测效果,其准确率为90.78%。

基于MAP估计双树复小波的电能质量扰动信号去噪方法

针对电能质量信号的去噪,提出了一种基于MAP估计的双树复小波电能质量扰动信号的去噪方法。首先对带噪信号进行相关性预处理,然后通过MAP方法对双树复小波分解不同层次的细节系数估计噪声方差和信号方差,并计算各层阀值从而得到去噪方案,针对带噪的电压跌落等扰动信号进行仿真,并与传统实小波去噪进行了信噪比和突变点信息保留能力的比较。仿真结果表明,所提算法速度快,去噪效果理想,且易于实现,实用性强,有良好的发展前景。

基于多混沌系统的多图像加密算法

针对多幅图像在传输中的安全性问题,本文提出了一种基于多混沌系统的多图像加密算法.首先,利用离散小波变换对多幅图像进行预处理,得到一幅拼接的大图像;接着,利用logistic-sine-cosine (LSC)映射生成混沌序列,进而生成用于置乱的矩阵O对像素位置进行置乱;最后,采用超混沌Lorenz系统生成四维混沌序列,利用其对置乱后的图像进行双向扩散和行列置乱,获得最终密文图像.所提算法加解密过程简单,执行效率高.实验结果经多方面分析后得出该算法的密钥空间大,可以抵御多种攻击手段,具有较好的加密效果和安全性.

基于小波时频图和多尺度卷积神经网络的发动机工况识别研究

针对传统工况识别方法对非平稳的汽车发动机音频信号难以准确识别的问题,提出一种基于小波时频图和多尺度卷积神经网络的发动机工况识别方法。首先,将原始信号通过连续小波转化为小波时频图,其次,对小波时频图进行统一的预处理,最后将处理好的图片输入到卷积神经网络中提取多尺度特征并分类识别。该方法有效结合了具有处理非线性平稳信号优势的小波时频分析和卷积神经网络的图像分析能力。在测试集数据转速不同的情况下,识别准确率和鲁棒性更好。

基于ISSA-LSTM的黄鳝池溶氧量多参数预测

为提高溶氧量的多参数预测精度,文中基于改进的麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)与长短期记忆神经网络(Long and Short-Term Memory Neural Networks,LSTM)建立ISSA-LSTM溶氧量预测模型,并将该模型用于上海市农业科学院黄鳝养殖池溶氧量预测。利用混沌映射、透镜成像反向学习、自适应调节和柯西变异对麻雀搜索算法进行优化,通过小波变换进行数据预处理,并利用主成分分析法确定模型训练的输入参数。训练结果表明,相关系数、均方根误差、均方误差和平均绝对误差分别为0.911、1.392 mg·L^(-1)、1.938 mg·L^(-1)和0.992 mg·L^(-1),均优于对照模型。选择模型输入参数对模型预测结果也会产生影响,使用与溶氧量中等相关和强相关的参数同时作为输入参数的模型预测效果最优。训练结果为溶氧量多参数预测模型的发展提供了新视角。

基于小波变换的机载雷达测绘遥感影像信息数据融合方法

在融合机载雷达测绘遥感影像信息数据的过程中,由于原始机载雷达测绘遥感影像反馈信号在频域分布上具有多元化的属性,导致融合后影像的质量相对较低。为此,文章提出基于小波变换的机载雷达测绘遥感影像信息数据融合方法。该方法通过在小波变换设置了以频率为基准的窗口函数,并结合遥感影像反馈信号所处频域与频率函数状态之间的关系,建立了待分析遥感影像信号与频率窗内的高频频域分布参数之间的乘积关系,从而得到所需的信号频谱特性,在进行机载雷达测绘遥感影像信息数据融合阶段,结合遥感影像反馈信号与有限区域范围之间的关系,对其进行适应性融合。测试结果表明,融合后影像在完整度、清晰度以及综合质量方面均处于较高水平。

基于Wavelet leader和优化的等距映射算法的回转支承自适应特征提取

为了解决回转支承振动信号微弱,特征信息不易提取的问题,提出基于Wavelet leader方法和经混合灰狼算法优化的等距映射算法(HGWO-ISOMAP)的多分形自适应特征提取方法.利用Wavelet leader计算多分形特征,挖掘振动数据的几何结构信息,构造高维特征矩阵;通过HGWO优化后的ISOMAP算法对高维特征矩阵进行自适应特征筛选;将筛选后的特征矩阵输入到经遗传算法(GA)优化的最小二乘支持向量机(LSSVM)中进行故障状态识别.为了验证所提方法的优越性,采用课题组自主研发的回转支承综合性能试验台对某型号回转支承进行全寿命实验.结果表明,相比一般时域、时频域、频域特征提取方法,所提方法能提高识别精度,缩短计算时间,为回转支承特征提取提供新的有效途径.

Output only modal identification and structural damage detection using time frequency & wavelet techniques

The primary objective of this paper is to develop output only modal identification and structural damage detection. Identification of multi-degree of freedom （MDOF） linear time invariant （LTI） and linear time variant （LTV--due to damage） systems based on Time-frequency （TF） techniques--such as short-time Fourier transform （STFT）, empirical mode decomposition （EMD）, and wavelets--is proposed. STFT, EMD, and wavelet methods developed to date are reviewed in detail. In addition a Hilbert transform （HT） approach to determine frequency and damping is also presented. In this paper, STFT, EMD, HT and wavelet techniques are developed for decomposition of free vibration response of MDOF systems into their modal components. Once the modal components are obtained, each one is processed using Hilbert transform to obtain the modal frequency and damping ratios. In addition, the ratio of modal components at different degrees of freedom facilitate determination of mode shape. In cases with output only modal identification using ambient/random response, the random decrement technique is used to obtain free vibration response. The advantage of TF techniques is that they arc signal based; hence, can be used for output only modal identification. A three degree of freedom 1：10 scale model test structure is used to validate the proposed output only modal identification techniques based on STFT, EMD, HT, wavelets. Both measured free vibration and forced vibration （white noise） response are considered. The secondary objective of this paper is to show the relative ease with which the TF techniques can be used for modal identification and their potential for real world applications where output only identification is essential. Recorded ambient vibration data processed using techniques such as the random decrement technique can be used to obtain the free vibration response, so that further processing using TF based modal identification can be performed.

Demodulation Based on Harmonic Wavelet and Its Application into Rotary Machinery Fault Diagnosis

The harmonic wavelet transform（HWT） and its fast realization based on fast Fourier transform（FFT） are introduced. Its ability to maintain the same amplitude-frequency feature is revealed. A new method to construct the time-frequency（TF） spectrum of HWT is proposed, which makes the HWT TF spectrum able to correctly reflect the time-frequency-amplitude distribution of the signal. A new way to calculate the HWT coefficients is proposed. By zero padding the data taken out, the non-decimated coefficients of HWT are obtained. Theoretical analysis shows that the modulus of the coefficients obtained by the new calculation way and living at a certain scale are the envelope of the component in the corresponding frequency band. By taking the cross section of the new TF spectrum, the demodulation for the component at a certain frequency band can be realized. A comparison with the Hilbert demodulation combined with band-pass filtering is done, which indicates for multi-components, the method proposed here is more suitable since it realizes ideal band-pass filtering and avoids pass band selecting. In the end, it is applied to bearing and gearbox fault diagnosis, and the results reflect that it can effectively extract the fault features in the signal.