Application of multi-resolution analysis in sonar image denoising

Sonar images have complex background, low contrast, and deteriorative edges; these characteristics make it difficult for researchers to dispose the sonar objects. The multi-resolution analysis represents the signals in different scales efficiently, which is widely used in image processing. Wavelets are successful in disposing point discontinuities in one dimension, but not in two dimensions. The finite Ridgelet transform （FRIT） deals efficiently with the singularity in high dimension. It presents three improved denoising approaches, which are based on FRIT and used in the sonar image disposal technique. By experiment and comparison with traditional methods, these approaches not only suppress the artifacts, but also obtain good effect in edge keeping and SNR of the sonar image denoising.

Multi-Resolution Fourier Analysis Part I: Fundamentals

In the first paper of this series, we propose a multi-resolution theory of Fourier spectral estimates of finite duration signals. It is shown that multi-resolution capability, achieved without further observation, is obtained by constructing multi-resolution signals from the only observed finite duration signal. Achieved resolutions meet bounds of the uncertainty principle (Heisenberg inequality). In the forthcoming parts of this series, multi-resolution Fourier performances are observed, applied to short signals and extended to time-frequency analysis.

Multi-Resolution Fourier Analysis Part II: Missing Signal Recovery and Observation Results

In this paper, we report application procedures and observed results of multi-resolution Fourier analysis proposed in the first part of this series. Missing signal recovery derived from multi-resolution theory is developed. It is shown that multi-resolution Fourier analysis enhances dramatically performances of Fourier spectra suffering limitations traced to implicit time windowing. Observed frequency resolutions, improvement of frequency estimations, contraction of spectral leakage and recovery of missing parts of finite duration signals are in accordance with theoretical predictions.

Application of Fuzzy Decision-Making Theory and Conflict-Resolution Method to Failure Mode and Effect Analysis of Industrial Valve

It is not objective to rate the decision-making factors in the traditional failure mode and effect analysis,so fuzzy semantic theory is used in this paper.Six fuzzy semantic scales and their corresponding semantics are summarized,and a defuzzification method is studied to obtain the fuzzy value table of the six fuzzy semantic scales.For the conflicts between experts in the traditional failure mode and effects analysis,a conflict-resolution algorithm is studied to obtain the failure risk order.Finally,a certain type of industrial valve is used as an example to prove the validity of the theory proposed in this paper.

Multi-resolution EMD and classscatter matrix extracting of tank sound

The acoustic vibration signal of tank is disassembled into the sum of intrinsic mode function （IMF） by multi-resolution empirical mode decomposition （EMD） method. The instantaneous frequency is obtained, and feature transformation matrix is figured out by class scatter matrix. Multi- dimensional scale energy vector is mapped into low-dimensional eigenvector, and classification extraction is realized. This method sufficiently separates of different sound target features. The test result indicates that it is effective.

Wavelet Transform for Image Compression Using Multi-Resolution Analytics: Application to Wireless Sensors Data

The aggregation of data in recent years has been expanding at an exponential rate. There are various data generating sources that are responsible for such a tremendous data growth rate. Some of the data origins include data from the various social media, footages from video cameras, wireless and wired sensor network measurements, data from the stock markets and other financial transaction data, supermarket transaction data and so on. The aforementioned data may be high dimensional and big in Volume, Value, Velocity, Variety, and Veracity. Hence one of the crucial challenges is the storage, processing and extraction of relevant information from the data. In the special case of image data, the technique of image compressions may be employed in reducing the dimension and volume of the data to ensure it is convenient for processing and analysis. In this work, we examine a proof-of-concept multiresolution analytics that uses wavelet transforms, that is one popular mathematical and analytical framework employed in signal processing and representations, and we study its applications to the area of compressing image data in wireless sensor networks. The proposed approach consists of the applications of wavelet transforms, threshold detections, quantization data encoding and ultimately apply the inverse transforms. The work specifically focuses on multi-resolution analysis with wavelet transforms by comparing 3 wavelets at the 5 decomposition levels. Simulation results are provided to demonstrate the effectiveness of the methodology.

Wavelet analysis of pressure fluctuation signals in a gas-solid fluidized bed

It has been shown that much dynamic information is hidden in the pressure fluctuation signals of a gas-solid fluidized bed. Unfortunately, due to the random and capricious nature of this signal, it is hard to realize reliable analysis using traditional signal processing methods such as statistical analysis or spectral analysis, which is done in Fourier domain. Information in different frequency band can be extracted by using wavelet analysis. On the evidence of the composition of the pressure fluctuation signals, energy of low frequency (ELF) is proposed to show the transition of fluidized regimes from bubbling fluidization to turbulent fluidization. Plots are presented to describe the fluidized bed's evolution to help identify the state of different flow regimes and provide a characteristic curve to identify the fluidized status effectively and reliably.

h-ADAPTIVE ANALYSIS BASED ON MESHLESS LOCAL PETROV-G ALERKIN METHOD WITH B SPLINE WAVELET FOR PLATES AND SHELLS

Using the two-scale decomposition technique, the h-adaptive meshless local Petrov- Galerkin method for solving Mindlin plate and shell problems is presented. The scaling functions of B spline wavelet are employed as the basis of the moving least square method to construct the meshless interpolation function. Multi-resolution analysis is used to decompose the field variables into high and low scales and the high scale component can commonly represent the gradient of the solution according to inherent characteristics of wavelets. The high scale component in the present method can directly detect high gradient regions of the field variables. The developed adaptive refinement scheme has been applied to simulate actual examples, and the effectiveness of the present adaptive refinement scheme has been verified.

Online Test and Fault Diagnosis of Yarn Quality Using Wavelet Analysis and FFT

A new online system of monitoring yarn quality and fault diagnosis is presented. This system integrates the technologies of sensor, signal process, communication, network, computer, control, instrument structure and mass knowledge of experts. Comparing with conventional off-line yarn test, the new system can find the quality defects of yarn online in time and compensate for the lack of expert knowledge in manual analysis. It can save a lot of yarn wasted in off-line test and improve product quality. By using laser sensor to sample the diameter signal of yarn and doing wavelet analysis and FFT to extract fault characteristics, a set of reasoning mechanism is established to analyze yarn quality and locate the fault origination. The experimental results show that new system can do well in monitoring yarn quality online comparing with conventional off-line yarn test. It can test the quality of yarn in real-time with high efficiency and analyze the fault reason accurately. It is very useful to apply this new system to upgrade yarn quality in cotton textile industry at present.

A RECTANGULAR SHELL ELEMENT FORMULATION WITH A NEW MULTI-RESOLUTION ANALYSIS

A multi-resolution rectangular shell element with membrane-bending based on the Kirchhoff-Love theory is proposed. The multi-resolution analysis （MRA） framework is formulated out of a mutually nesting displacement subspace sequence, whose basis functions are constructed of scaling and shifting on the element domain of basic node shape functions. The basic node shape functions are constructed from shifting to other three quadrants around a specific node of a basic element in one quadrant and joining the corresponding node shape functions of four elements at the specific node. The MRA endows the proposed element with the resolution level （RL） to adjust the element node number, thus modulating structural analysis accuracy accordingly. The node shape functions of Kronecker delta property make the treatment of element boundary condition quite convenient and enable the stiffness matrix and the loading column vectors of the proposed element to be automatically acquired through quadraturing around nodes in RL adjusting. As a result, the traditional 4-node rectangular shell element is a mono-resolution one and also a special case of the proposed element. The accuracy of a structural analysis is actually determined by the RL, not by the mesh. The simplicity and clarity of node shape function formulation with the Kronecker delta property, and the rational MRA enable the proposed element method to be implemented more rationally, easily and efficiently than the conventional mono-resolution rectangular shell element method or other corresponding MRA methods.

A Quotient Space Approximation Model of Multiresolution Signal Analysis

In this paper, we present a quotient space approximation model of multiresolution signal analysis and discuss the properties and characteristics of the model. Then the comparison between wavelet transform and the quotient space approximation is made. First, when wavelet transform is viewed from the new quotient space approximation perspective, it may help us to gain an insight into the essence of multiresolution signal analysis. Second, from the similarity between wavelet and quotient space approximations, it is possible to transfer the rich wavelet techniques into the latter so that a new way for multiresolution analysis may be found.

A hexahedron element formulation with a new multi-resolution analysis

A multiresolution hexahedron element is presented with a new multiresolution analysis(MRA)framework.The MRA framework is formulated out of a mutually nesting displacement subspace sequence,whose basis functions are constructed of scaling and shifting on element domain of a basic node shape function.The basic node shape function is constructed from shifting to other seven quadrants around a specific node of a basic isoparametric element in one quadrant and joining the corresponding node shape functions of eight elements at the specific node.The MRA endows the proposed element with the resolution level(RL)to adjust structural analysis accuracy.As a result,the traditional 8-node hexahedron element is a monoresolution one and also a special case of the proposed element.The meshing for the monoresolution finite element model is based on the empiricism while the RL adjusting for the multiresolution is laid on the solid mathematical basis.The simplicity and clarity of shape function construction with the Kronecker delta property and the rational MRA enable the proposed element method to be more rational,easier and efficient in its implementation than the conventional mono-resolution solid element method or other MRA methods.The multiresolution hexahedron element method is more adapted to dealing with the accurate computation of structural problems.

Multi-resolution Analysis of Density Fluctuation of Coherent Structures About Supersonic Flow over VG

The density field around a vortex generator （VG） in supersonic flow is studied with a nanoparticle-based planar laser scattering （NPLS） method. Based on the calibration, i.e., the density distribution of the supersonic flow around a wedge, the density field of a supersonic VG is measured. According to movement characteristics of coherent structure in VG’s flow fields and the basic concepts of wavelet, the density fluctuating signals and multi-resolution characteristics of density field images are studied. The multi-resolution characteristics of density fluctuation can be analyzed with wavelet transformation of NPLS images. The wavelet approximate coefficients of density fluctuating signals exhibit their characteristics at different scales, and the corresponding detail coefficients show the difference of diverse layer smooth approximation in some way. Based on 2D wavelet decomposition and reconstruction of density field images, the approximate and detail signals at different scales are studied, and the coherent structures at different scales are extracted and analyzed.

MRA-PCA-PSO组合优化BP神经网络模拟电路故障诊断研究

为解决模拟电路故障诊断中的特征提取困难、输入维数过高和故障信号无法进行有效分类等问题,提出一种组合优化反向传播神经网络故障诊断方法。首先采用多分辨率分析提取故障信号特征,用其能量谱构造特征向量,通过主成分分析进行降维,以减少计算规模,然后利用粒子群算法优化神经网络,克服其易陷入局部极小值的缺陷,从而达到准确识别故障类型的目的。最后以四运放高通滤波电路为测试对象,通过仿真验证该方法在模拟电路故障诊断的有效性和可靠性,结果表明能提高约10%的诊断准确率。

改进WMRA在滚动轴承故障诊断研究中的应用

针对滚动轴承初期故障诊断时故障特征信号微弱,且传统的包络谱分析方法需要预先依靠经验确定出分析频段的问题,提出了基于经验模态分解(EMD)和改进的小波多分辨率分析(WMRA)的诊断方法。首先通过对滚动轴承故障振动信号进行EMD分解,利用峭度系数和振动固有频率特征参数对分解后的本征模态函数(IMF)分量进行了分类,筛选出了最佳IMF分量,然后通过希尔伯特变换(HT)计算得到了所选IMF分量的包络信号,最后利用改进后的WMRA对包络信号进行了重构,所得到的包络谱明显地突出了故障特征频率。实验结果表明:相比单独的EMD或传统的WMRA,该方法有效地提高了信号分析的准确性。

基于MRA信息补偿的图像识别算法

目前的图像识别方法对各类提取等量的特征信息,不可避免的存在过识别与不可分类问题。利用小波分解的信号自然降维特性,及多分辨率分析(MRA)空间的正交互补性,本文提出了一种通过构造信息补偿树(ICT)进行识别的新方法,实现了对各类自适应得提取不同量的特征信息,有效解决了上述问题。针对3类脸部图像进行实验,结果表明该算法是有效的且复杂性低。

Multi-Focus Image Fusion Based on Wavelet Transformation

In the fusion of image,how to measure the local character and clarity is called activity measurement. According to the problem,the traditional measurement is decided only by the high-frequency detail coefficients, which will make the energy expression insufficient to reflect the local clarity. Therefore,in this paper,a novel construction method for activity measurement is proposed. Firstly,it uses the wavelet decomposition for the fusion resource image, and then utilizes the high and low frequency wavelet coefficients synthetically. Meantime,it takes the normalized variance as the weight of high-frequency energy. Secondly,it calculates the measurement by the weighted energy,which can be used to measure the local character. Finally,the fusion coefficients can be got. In order to illustrate the superiority of this new method,three kinds of assessing indicators are provided. The experiment results show that,comparing with the traditional methods,this new method weakens the fuzzy and promotes the indicator value. Therefore,it has much more advantages for practical application.

HCS结合MRA的高分辨率遥感影像融合框架

随着遥感技术的快速发展,各种场景的应用对图像融合质量提出了更高的要求。针对CS方法与MRA方法存在的光谱失真与空间畸变等问题,提出了一种HCS结合MRA的遥感图像融合框架。首先,对MS进行HCT正变换获得强度分量和角度分量,并对PAN进行直方图匹配;然后,对强度分量和匹配后的PAN进行某种MRA变换获得各自的高频系数和低频系数;再使用特定的融合规则将高频系数与低频系数分别融合,并进行相应的MRA逆变换获得新的强度分量;最后对新的强度分量与角度分量进行HCT逆变换获得融合结果。为验证框架的融合效果,对WV-2影像进行了融合实验,并对结果进行了主观评价和客观评价指标的计算。结果表明,HCS结合MRA的方法在融合效果上明显优于CS方法和MRA方法。因此,融合框架能够获得光谱保持和空间质量均较好的融合结果,对遥感技术的应用提供了有力支持。

重大突发事件下中国金融风险跨市场多周期溢出效应研究

在重大突发事件频发的背景下,从多周期角度考察中国金融风险的跨市场溢出效应,对于防范化解重大金融风险具有重要意义。采用小波多分辨分析、DY溢出指数与风险溢出网络模型,从静态和动态角度测度了重大突发事件的短期、中期、长期内我国金融市场间风险溢出的强度和方向,识别不同事件不同周期下的风险中心及演变规律。研究发现:从静态角度来看,中国金融市场平均风险溢出水平呈现随周期增加而增加的趋势,“欧债危机”时期的风险总溢出最小,市场在各个时期不同周期下的风险净溢出情况不同,净风险溢出、溢入市场不断变化;从动态角度来看,重大突发事件冲击下金融市场总体风险传染水平呈现先上升后逐渐平稳并回落的趋势,短期总溢出指数最低,中期最高,但在某些时期,短期总溢出水平可能高于中长期;不同重大突发事件、不同周期下风险的承担中心、风险在各金融市场间的传染路径均会发生变化。因此,在强化金融市场风险监管时,需要建立全流程风险防控体系以实现风险的有效处置,针对不同事件、不同周期的反应差异,实现金融风险的精准识别与重点管理。

基于小波多尺度分析大气边界层高度提取方法研究

大气边界层物理演变特征及复杂边界层结构对大气污染过程的影响机制研究中,迫切需要边界层精细化结构的探测手段。通常使用梯度法和小波协方差变换法的方式进行边界层高度提取,但由于该方式容易受到噪声和气溶胶层结构的影响,实验误差较大。提出使用小波多尺度分析方法细化边界层特征结构,从而筛选出有效的细节信息,提高对探索边界层高度的准确性。此外,基于GBQ L-01激光雷达设备实测合肥地区全天气溶胶分布情况,使用小波多尺度分析方法分析对应的平行分量距离平方矫正信号、垂直分量距离平方矫正信号和退偏振比全天分布情况。实验结果表明,该方法准确性高,与梯度法、小波协方差法相比具有更高的稳定性与连续性。对特殊时间点进行数据分析,明确主要影响边界层高度的主要因素,并确定各个时间段的大气边界层高度值。