Detection of Bearing Faults Using a Novel Adaptive Morphological Update Lifting Wavelet

The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- sents a novel signal processing scheme, adaptive morpho- logical update lifting wavelet （AMULW）, for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration sig- nals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective roiling element bearings.

Multiple description scalable video coding based on 3D lifted wavelet transform

In this work, a new method to deal with the unconnected pixels in motion compensated temporal filtering (MCTF) is presented, which is designed to improve the performance of 3D lifted wavelet coding. Furthermore, multiple description scalable coding (MDSC) is investigated, and novel MDSC schemes based on 3D wavelet coding are proposed, using the lifting imple- mentation of temporal filtering. The proposed MDSC schemes can avoid the mismatch problem in multiple description video coding, and have high scalability and robustness of video transmission. Experimental results showed that the proposed schemes are feasible and adequately effective.

Lift fin stabilizers based on data fusion with wavelet denoising technology

Fin stabilizers with fin-lift feedback control can shield the mapping error of calculation between the fin angle and fin lift force,which is in the fin stabilizer with fin-angle feedback control.In practice,there are some technical difficulties in lift fin stabilizers,such as lift force detection and lift force sensor installation,so it cannot achieve the good antirolling performance.Therefore,a fin stabilizer system with fin-lift/fin-angle integrated control is brought forward.Data fusion based on wavelet denoising technology is employed in the system,which combines lift with fin angle local information from two sensors with different frequency ranges in order to eliminate redundant and contradictory information,and using complementary information to obtain the relative integrity of the lift force signal.The system model is established in this paper,and the fusion signal and the antirolling performance of this model are simulated respectively.The result shows that the control system can meet the antirolling need in different sea situations.

The Lifting Scheme Based on the Second Generation Wavelets

The lifting scheme is a custom design construclion of Biorthogonal wavelets, a fast and efficient method to realize wavelet transform,which provides a wider range of application and efficiently reduces the computing time with its particular frame. This paper aims at introducing the second generation wavelets, begins with traditional Mallat algorithms, illustrates the lifting scheme and brings out the detail steps in the construction of Biorthogonal wavelets. Because of isolating the degrees of freedom remaining the biorthogonality relations, we can fully control over the lifting operators to design the wavelet for a particular application, such as increasing the number of the vanishing moments.

Vegetation field spectrum denoising via lifting wavelet transform

Field spectrum pretreatment experiments were carried out, and denoising numerical experiment via lifting wavelet transform （LWT） was designed, and several famous test signals including blocks, bumps, heavy sine and doppler were processed via Lw＇r in these experiment. And the field spectrum was processed via Lw＇r. Experiments proved that SNRG-tO-SNRN curves have similar feature and they all have a peak. And SNRG of almost all employed wavelets have higher value with SNRN between 0 and 20 dB. When signal is at high SNR, the SNRG is very little, and the MSED of denoised signal became little by little. LWT is more suite to denoise the low SNR or heavy noise contaminated signals. Bior4.4 have wider SNRN interval for denoising comparing with other five wavelets, includ- ing haar, db6, sym6, bior2.2 and bior3.3. Original field spectrum is processed by 3 stage liftings based on bior4.4 to denoise the trivial noise-contaminated regions. On processing the water band signal, logarithm transform is firstly taken. And then the spectrum is denoised via LWT based on bior4.4. The results show that an excellent denoised spectrum can be get, especially between 350 nm and 1 800 nm, and between 1 960 nm to 2 500 nm. While there is still a bump around 1 900 nm, this maybe due to the spectrum machine＇s limited precision.

基于LWT的可见光OFDM-IM系统研究

针对索引调制正交频分复用(OFDM-IM)技术峰均比过高,对相位噪声和载波频偏敏感,系统复杂度高等问题,提出基于小波提升变换(LWT)的可见光OFDM-IM系统。首先选择正交小波基作为子载波,通过对信号的分裂、预测和更新,将信号分为高频信号和低频信号,再结合可见光信道构成LWT-OFDM-IM系统,最后通过理论分析和蒙特卡罗方法对系统的可靠性、峰均比特性和最佳小波分解层数进行了仿真验证,结果表明:当子载波N=256,子块中子载波数量L=4,激活子载波数量k=2,系统误码率为10~(-4)时,LWT-OFDM-IM比FFT-OFDM-IM改善约8 dB,相较于DWT-OFDM-IM提高约4 dB。当系统互补累计分布函数(CCDF)为10^(-1)数量级时,LWT-OFDM-IM的峰均比较FFT-OFDM-IM改善约2.3 dB。随着小波层数的递增,系统的误码性能越好,当小波层数为3层时,大约在10 dB附近,误码率可到10^(-5)量级。

基于多通道的彩色图像多重水印算法

针对多重水印技术如何确定最优的嵌入区域,以实现多个水印之间的权衡问题,提出一种基于多通道嵌入的彩色图像多重水印算法。当嵌入水印时,选择在彩色图像R、G、B 3个通道嵌入不同的水印图像,目的是嵌入更多的水印信息。通过提升小波变换(LWT)和非下采样剪切波变换(NSST)提取宿主图像每个通道中的方向性特征,确定水印的嵌入位置,以提高对常规攻击和几何攻击的鲁棒性。通过将加密的水印有效信息嵌入到具有良好稳定性的奇异值矩阵中,保证水印的提取效果。实验结果表明,嵌入水印后的图像峰值信噪比(PSNR)在43 dB以上,所提算法具备良好的不可见性,对嵌入水印后的图像进行高强度的旋转、滤波、剪切等攻击,提取水印的归一化系数(NC)值在0.95以上,其中,滤波攻击对应的NC值可以达到0.99以上,水印信息提取完整、清晰可辨,表现出较强的鲁棒性。同时,该算法可以1次嵌入3个水印图像,具有更高的嵌入容量。因此,与现有的多重水印算法相比,该算法的不可见性、鲁棒性和嵌入容量均有较大的提升。

小波分频自注意力Transformer图像去雨网络

针对视觉Transformer对高频信息捕捉能力弱以及目前许多图像去雨方法易丢失细节的问题,提出小波分频自注意力Transformer图像去雨网络(WFDST-Net)。小波分频自注意力Transformer(WFDST)作为WFDST-Net的主要模块,其利用不可分提升小波变换获取特征图的低频分量和高频分量,分别在低频和高频中进行自注意力交互,使模块从低频中学习恢复全局结构的能力,在高频中强化捕捉雨纹等线条细节的能力,增强对不同频域特征的建模能力。WFDST-Net采用U形架构并通过不可分提升小波变换获取多尺度特征,可在捕获不同形状高频雨纹的同时保证信息的完整性。相比其他图像去雨相关的Transformer,WFDST-Net具有更低的参数量。此外,提出VOCRain250数据集用于联合图像去雨和语义分割任务,该数据集比目前广泛使用的BDD150更具优势。实验表明,所提方法增强了视觉Transformer对不同频域信息的捕获能力,并在合成和真实数据集以及VOCRain250中的表现优于目前先进的去雨方法,能有效去除复杂雨纹并保留更多细节特征。

用lifting方法构造具有线性相位的双正交小波

Lifting作为第2代小波的构造方法,同样能够构造出第1代小波。与传统的Daubechies方法相比,该方法简单易懂,并且容易用格状结构(Lattice structrue)构造出满足特定要求的小波。该文首先根据Lawton矩阵的性质证明了几乎所有由此方法构造出的小波满足双正交条件,然后考虑如何构造具有线性相位性质的双正交小波,最后列举几个例子说明lifting方法。

一种生成双正交紧支复小波的Lifting方法

提出了一种构造双正交紧支复小波的方法 :以现有任何一种正交或双正交实小波为基础 ,可构造出消失矩阶数可任意选定的无穷多个相频特性 (包括线性相位特性 )不同的双正交紧支复小波。其特点为 :由于是双正交紧支型 ,可采用Mallat算法等快速算法 ,因此使用时实时性好 ;因为是复小波 ,所以能提取电力系统至关重要的相位信息 ;具有多种相频特性 ,便于与相频特性多变的电力系统信号匹配等 ,因此这类小波在电力系统有广泛的应用前景。

基于提升小波的数字图像混合噪声抑制算法

针对混合噪声特点不一致,抑制难度较大的问题,为提升噪声抑制效果,提高图像清晰度,提出一种基于提升小波的数字图像混合噪声抑制算法。通过概率神经网络将数字图像噪声划分为脉冲噪声和高斯噪声,采用中值滤波方法去除数字图像中的脉冲噪声,运用提升小波方法去除数字图像中的高斯噪声,实现混合噪声的抑制。实验结果表明,所提算法获得的图像清晰度和信噪比更高,且去噪后数字图像的ENOB(Effective Number Of Bits)值明显提升,说明该算法的混合噪声抑制效果更佳。

Lifting Scheme及其在小波图象编码中的应用

L ifting Schem e是构造第二代小波的关键技术 .相对于第一代小波而言 ,L ifting Scheme是一种比 Mallat算法更快、更简单和更容易操作的算法 ,也是 JPEG2 0 0 0推荐的算法 .为了将其应用到小波图象编码中 ,提出了一种对 L ifting Scheme作适当改进以用于小波图象编码的方法 .该方法就是先用 L ifting Scheme来实现 D9/ 7双正交小波变换 ,然后再用这种技术实现的 D9/ 7双正交小波变换来进行图象压缩编码 .在将 L ifting Schem e算法用于小波图象编码的过程中 ,对该算法做了必要的简化 ,以便保证每个提升 (lifting)环节都是 FIR滤波 .同时 ,根据能量守恒的原则 ,重新调整了尺度因子 .实验结果表明 ,这种经过改进的 L ifting Schem e取得了比

基于提升小波-SVD差分谱的煤机设备故障诊断

针对煤机设备微弱故障特征识别困难的问题,提出了提升小波和奇异值分解(SVD)差分谱融合特征提取策略。对煤机设备的振动信号做提升小波分解得到若干个细节信号,利用这些细节信号分别构建Hankel矩阵,并根据实践经验确定Hankel矩阵的行数和列数。利用SVD对每一个Hankel矩阵做正交化分解得到奇异值序列,使用SVD差分谱选择奇异值进行SVD重构,再利用Hilbert解调方法对重构后的信号进行解调,实现煤机设备故障特征的提取。对煤矿现场的电机振动信号进行分析,结果表明,该方法对煤机设备微弱故障特征提取具有良好的效果。

Lifting Scheme的Haar小波在医学图像压缩中的应用研究

针对医学图像进行实时传输和大量存储时需要进行压缩处理的需求,论述了Lifting Scheme的Haar小波在医学图像压缩中的应用方法。首先,分析了Haar小波的变换原理,然后阐述了基于提升Haar小波的医学图像压缩算法。实验表明,在压缩比和峰值信噪比等评价参数方面,基于Lifting Scheme的Haar小波算法在医学图像压缩中取得了较好效果。

面向多浓度非均匀云雾的双域遥感图像去雾算法

光学遥感卫星图像容易受到多浓度非均匀云雾的干扰,造成图像质量严重退化。现有的去雾算法难以有效处理多浓度非均匀云雾,于是提出了一种自注意力强化机制,即在自注意力变换中引入一种简单的无参注意力(SimAM)增强自注意力变换的建模能力,提高对非均匀云雾的感知;为进一步提高网络的纹理细节表征能力,设计了新颖的差分卷积细节增强块,利用差分卷积算子引入梯度级信息,提高去雾网络对纹理细节的恢复能力;为实现RGB域和自适应小波域联合去雾,引入深度自适应提升小波变换实现自适应小波空间,从而实现双域协同去雾。实验结果表明,提出的方法在主流的遥感图像去雾数据集上相较于骨干模型,获得了0.52 dB的PSNR总增益。

A Novel Digital Audio Watermarking Scheme in the Wavelet Domain

We present a novel quantization-based digital audio walermarking scheme inwavelet domain. By quantizing a host audio's wavelet coefficients (Integer Lifting WaveletTransform) and utilizing the characteristics of human auditory system (HAS), the grayimage isembedded using our watermarking method. Experimental results show that the proposed watermarkingscheme is inaudible and robust against various signal processing such as noising adding, lossycompression, low pass filtering, re-sampling, and re-quantifying.

Lifting transform via Savitsky-Golay filter predictor and application of denoising

The Savitsky-Golay filter isa smoothing filter based on polynomial regression.Itemploys the regression fitting capacity to improve the smoothing results.But Savit-sky-Golay filter uses a fix sized window.It has the same shortage of Window FourierTransform.Wavelet mutiresolution analysis may deal with this problem.In this paper,tak-ing advantage of Savitsky-Golay filter's fitting ability and the wavelet transform's multiscaleanalysis ability,we developed a new lifting transform via Savitsky-Golay smoothing filteras the lifting predictor,and then processed the signals comparing with the ordinary Savit-sky-Golay Smoothing method.We useed the new lifting in noisy heavy sine denoising.Thenew transform obviously has better denoise ability than ordinary Savitsky-Golay smooth-ing method.At the same time singular points are perfectly retained in the denoised signal.Singularity analysis,multiscale interpolation,estimation,chemical data smoothing andother potential signal processing utility of this new lifting transform are in prospect.

基于Lifting Scheme的自适应小波滤波器组的研究

具有高阶消失矩的滤波器在分析平稳信号时,要比使用低阶消失矩的滤波器效果好;而在信 号的边缘区和突变区,用低阶消失矩的滤波器会达到更好的效果,所以在这一理论基础的指导下,提 出了一种有效的双正交两通道小波滤波器组的构造方法,它是基于Lifting Scheme的一种自适应结构． 它的突出优点是可以保证信号的完全重建．实验证明,这种自适应的小波滤波器组在很多应用中显然 要优于有固定消失矩的小波滤波器组．

Seismic data compression based on integer wavelet transform

Due to the particularity of the seismic data, they must be treated by lossless compression algorithm in some cases. In the paper, based on the integer wavelet transform, the lossless compression algorithm is studied. Comparing with the traditional algorithm, it can better improve the compression rate. CDF (2, n) biorthogonal wavelet family can lead to better compression ratio than other CDF family, SWE and CRF, which is owe to its capability in can- celing data redundancies and focusing data characteristics. CDF (2, n) family is suitable as the wavelet function of the lossless compression seismic data.

Nonlinear Adaptive Wavelet Transform for Lossless Image Compression

The paper presents a class of nonlinear adaptive wavelet transforms for lossless image compression. In update step of the lifting the different operators are chosen by the local gradient of original image. A nonlinear morphological predictor follows the update adaptive lifting to result in fewer large wavelet coefficients near edges for reducing coding. The nonlinear adaptive wavelet transforms can also allow perfect reconstruction without any overhead cost. Experiment results are given to show lower entropy of the adaptive transformed images than those of the non-adaptive case and great applicable potentiality in lossless image compresslon.