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.

Analysis on electric energy measuring method based on multi-resolution analysis

Along with the massive applications of the non-linear loads and the impact loads, many non-stationary stochastic signals such as harmonics, inter-harmonics, impulse signals and so on are introduced into the electric network, and these non-stationary stochastic signals have had effects on the accuracy of the measurement of electric energy. The traditional method like Fourier Analysis can he applied efficiently on the stationary stochastic signals, hut it has little effect on non-stationary stochastic signals. In light of this, the form of the signals of the electric network in wavelet domain will he discussed in this paper. A measurement method of active power based on multi-resolution analysis in the stochastic process is presented. This method has a wider application scope compared with the traditional method Fourier analysis, and it is of good referential value and practical value in terms of raising the level of the existing electric energy measurement.

Fault Feature Extraction of Rotating Machinery Based on Wavelet Transformation and Multi-resolution Analysis

This paper expounded in detail the principle of energy spectrum analysis based on discrete wavelet transformation and multiresolution analysis. In the aspect of feature extraction method study, with investigating the feature of impact factor in vibration signals and considering the non-placidity and non-linear of vibration diagnosis signals, the authors import wavelet analysis and fractal theory as the tools of faulty signal feature description. Experimental results proved the validity of this method. To some extent, this method provides a good approach of resolving the wholesome problem of fault feature symptom description.

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.

MULTI-RESOLUTION MOTION ESTIMATION AND COMPENSATION BASED ON ADJACENT PREDICTION OF FRAME DIFFERENCE IN WAVELET DOMAIN

Aiming at the higher bit-rate occupation of motion vector encoding and more time load of full-searching strategies, a multi-resolution motion estimation and compensation algorithm based on adjacent prediction of frame difference was proposed.Differential motion detection was employed to image sequences and proper threshold was adopted to identify the connected region.Then the motion region was extracted to carry out motion estimation and motion compensation on it.The experiment results show that the encoding efficiency of motion vector is promoted, the complexity of motion estimation is reduced and the quality of the reconstruction image at the same bit-rate as Multi-Resolution Motion Estimation(MRME) is improved.

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.

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.

MULTI-RESOLUTION LEAST SQUARES SUPPORT VECTOR MACHINES

The Least Squares Support Vector Machines (LS-SVM) is an improvement to the SVM. Combined the LS-SVM with the Multi-Resolution Analysis (MRA),this letter proposes the Multi-resolution LS-SVM (MLS-SVM).The proposed algorithm has the same theoretical framework as MRA but with better approximation ability.At a fixed scale MLS-SVM is a classical LS-SVM,but MLS-SVM can gradually approximate the target function at different scales.In experiments,the MLS-SVM is used for nonlinear system identification,and achieves better identification accuracy.

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.

Conformal Wavelet Finite-difference Time-domain Algorithm for Analysis of Milimeter Wave Attenuation on Coplanar Waveguide

Addressed is the calculation of millimeter wave attenuation on coplanar waveguide(CPW). A novel conformal wavelet finite-difference time-domain(CWFDTD) algorithm is proposed with emphasis on its application in calculation of millimeter wave attenuation on CPW, which is the combination of conformal algorithm dealing with the deformed cell with Wavelet-FDTD using multi-resolution analysis(MRA). Derived is the difference formulation for multi-resolution time domain(MRTD) based on Daubechies wavelets, and also given is the stability conditions for wavelet-FDTD algorithm. To validate its accuracy and efficiency, this novel method is applied to calculate the millimeter wave attenuation on lithium niobate CPW. Numerical results demonstrate that this new CWFDTD algorithm has the same accuracy with the conformal finite-difference time-domain(CFDTD) and conformal finite-difference time-domain based on alternating-direction implicit method(ADI-CFDTD), but saves computational time and computer memory.

Model identification of hydraulic flight simulator based on improved particle swarm optimization and wavelet analysis

A new model identification method of hydraulic flight simulator adopting improved panicle swarm optimization （PSO） and wavelet analysis is proposed for achieving higher identification precision. Input-output data of hydraulic flight simulator were decomposed by wavelet muhiresolution to get the information of different frequency bands. The reconstructed input-output data were used to build the model of hydraulic flight simulator with improved particle swarm optimization with mutation （IPSOM） to avoid the premature convergence of traditional optimization techniques effectively. Simulation results show that the proposed method is more precise than traditional system identification methods in operating frequency bands because of the consideration of design index of control system for identification.

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.

基于MRAS的河草坑铀矿田靶区圈定

在分析与研究江西河草坑铀矿田多源信息数据的基础上,借助MRAS技术平台,采用特征分析方法,对该矿田的铀矿资源定位预测与评价,圈定出7片靶区。

MRA-RBF神经网络组合算法的煤矿材料成本预测

为解决煤矿材料成本预测存在的问题.将多元回归模型和RBF神经网络相结合,建立了煤矿材料成本预测的MRA-RBF耦合模型.从自然因素、技术因素、管理因素等方面选取8个变量建立煤矿成本预测指标体系.对实际煤矿材料成本进行预测分析.结果表明:MRA-RBF耦合模型预测最大误差为10.795 145 2%,平均误差为5.459 71%,最小误差仅为0.344 581 7%,预测效果较好,预测精度与单一MRA模型及RBF神经网络相比有了较大提高.验证了所提出模型的科学性、准确性,说明将线性拟合算法(MRA)和非线性拟合算法(RBF)结合起来用于煤矿材料成本预测是一种较为优越的算法,为煤矿材料成本预测及控制提供一种新的方法.

3D-TOF-MRA与3D-FIESTA联合3D-TOF-MRA对面肌痉挛术前评估价值比较的Meta分析

目的以微血管减压术(MVD)为金标准,利用Meta分析方法评价并间接比较3D-TOF-MRA与3D-FIESTA+3D-TOF-MRA对单侧血管压迫性面肌痉挛的术前评估价值。方法计算机检索维普网、万方、知网、PubMed、Cochrane Library、Web of Science、Embase等数据库中的相关文献,采用Review Manager 5.4、Meta-disc 1.4和Stata 12.0软件进行Meta分析,利用相对比值间接比较二者的评估价值。结果经筛选本研究共纳入文献11篇,共计600例患者。Meta分析结果显示,对于单侧血管压迫性面肌痉挛术前评估价值,3D-FIESTA+3D-TOF-MRA与3D-TOF-MRA的Sen合并、Spe合并、+LR、-LR、DOR分别为0.97(95%CI:0.95~0.99),0.85(95%CI:0.81~0.88),6.15(95%CI:4.26~8.87),0.04(95%CI:0.02~0.08),168.23(95%CI:73.19~386.72)与0.89(95%CI:0.85~0.93),0.72(95%CI:0.66~0.78),3.66(95%CI:12.24~5.99),0.13(95%CI:0.06~0.3),29.21(95%CI:10.38~82.17)。3D-FIESTA+3D-TOF-MRA与3D-TOF-MRA间接比较结果RDOR为5.76(95%CI:4.71~7.05)。结论在单侧血管压迫性面肌痉挛术前评估方面,3D-FIESTA+3D-TOF-MRA相较于3D-TOF-MRA具有更高的诊断准确性。

2-Adic MRA的浮点数编码遗传算法

通过2-Adic多分辨率分析,构造正交小波基;证明所构造正交小波用于浮点数编码消噪的正确性;提出用正交小波在浮点数编码遗传算法中进行消噪变异操作,以消除浮点数编码在遗传环境中所产生的噪音对算法性能的影响;构建基于2-Adic多分辨率分析的遗传算法,并进行了实验。仿真实验表明,提出的算法可明显提高浮点数编码遗传算法的收敛速度和精度,具有较高的可靠性。

基于EEMD-MRA方法的LIBS信号处理研究

LIBS技术优点众多,但由于光谱噪声干扰和基体效应等因素,影响了分析的准确度[1];EEMD方法能清晰的将LIBS信号中的不同特征成分自适应的分解开来。MRA方法能够补偿元素信号之间的互干扰,可进一步提高LIBS信号的准确性。通过自行搭建的测试系统获得了标准样品的原始信号,使用EEMD-MRA方法进行处理后,元素浓度曲线的决定系数R^(2)得到了极大的提高,大大的提高数据的准确性,为LIBS信号的处理提供了一种全新的方式。

基于MRA与回归分析法的短期电力负荷预测

为提高电力系统短期负荷预测准确度,首先利用多分辨分析的小波变换对短期电力负荷序列进行分解处理,根据其在小波各尺度上子序列的特性分别进行回归预测,再将预测结果进行小波重构,得到了满意的预测结果。

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.