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.

Numerical simulation of vortex evolution based on adaptive wavelet method

The application of the wavelet method to vortex motion prediction is investigated. First, the wavelet method is used to solve two initial boundary problems so as to verify its abilities of controlling numerical errors and capturing local structures. Then, the adaptive wavelet method is used to simulate the vortex emerging process. The results show that the wavelet method can control numerical errors easily, can capture local structures adaptively, and can predict the vortex fluctuation evolution. Therefore, the application of the wavelet method to turbulence is suggested.

Wavelet domain adaptive filtering algorithm for removing the seamless pipe noise contained in the magnetic flux leakage data

With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.

Incipient Gear Fault Detection Using Adaptive Impulsive Wavelet Filter Based on Spectral Negentropy

Adaptive wavelet filtering is a very important fault feature extraction method in the domain of condition monitoring;however, owing to the time-consuming computation and difficulty of choosing criteria used to represent incipient faults, the engineering applications are limited to some extent. To detect incipient gear faults at a fast speed, a new criterion is proposed to optimize the parameters of the modified impulsive wavelet for constructing an optimal wavelet filter to detect impulsive gear faults. First, a new criterion based on spectral negentropy is proposed. Then, a novel search strategy is applied to optimize the parameters of the impulsive wavelet based on the new criterion. Finally,envelope spectral analysis is applied to determine the incipient fault characteristic frequency. Both the simulation and experimental validation demonstrated the superiority of the proposed approach.

The Adaptive Wavelet Collocation Method and Its Application in Front Simulation

The adaptive wavelet collocation method （AWCM） is a variable grid technology for solving partial differential equations （PDEs） with high singularities. Based on interpolating wavelets, the AWCM adapts the grid so that a higher resolution is automatically attributed to domain regions with high singularities. Accuracy problems with the AWCM have been reported in the literature, and in this paper problems of efficiency with the AWCM are discussed in detail through a simple one-dimensional （1D） nonlinear advection equation whose analytic solution is easily obtained. A simple and efficient implementation of the AWCM is investigated. Through studying the maximum errors at the moment of frontogenesis of the 1D nonlinear advection equation with different initial values and a comparison with the finite difference method （FDM） on a uniform grid, the AWCM shows good potential for modeling the front efficiently. The AWCM is also applied to a two-dimensional （2D） unbalanced frontogenesis model in its first attempt at numerical simulation of a meteorological front. Some important characteristics about the model are revealed by the new scheme.

An Algorithm of Voice Activity Detection Based on EMD and Wavelet Entropy Ratio

A new method was proposed to identify speech-segment endpoints based on the empirical mode decomposition（EMD） and a new wavelet entropy ratio with improving the accuracy of voice activity detection.With the EMD, the noise signals can be decomposed into several intrinsic mode functions（IMFs）. Then the proposed wavelet energy entropy ratio can be used to extract the desired feature for each IMFs component. In view of the question that the method of voice endpoint detection based on the original wavelet entropy ratio cannot adapt to the low signal-to-noise ratio（SNR）condition, an appropriate positive constant was introduced to the basic wavelet energy entropy ratio with effectively improved discriminability between the speech and noise. After comparing the traditional wavelet energy entropy ratio with the proposed wavelet energy entropy ratio, the experiment results show that the proposed method is simple and fast. The speech endpoints can be accurately detected in low SNR environments.

Position detection of BLDC rotor based on adaptive wavelet neural network

Brushless DC(BLDC)motor is a complex nonlinear system,of which some parameters will also change during operation.Therefore,obtaining accurate rotor position directly through the line voltage becomes more difficult.So a new method is proposed in this paper which uses three line voltages as the input signal to identify the motor position based on adaptive wavelet neural network(WNN)and the differential evolution(DE)algorithm to optimize WNN structures,thus realizing the improvement of accuracy,exactness of the communication signals and convergence speed of the rotor position identification.Finally,both simulations and experimental results show that the proposed method has high accuracy of recognizing rotor position and strong orientation ability.

A NEW ADAPTIVE FILTERING SCHEME BASED ON WAVELET TRANSFORM

Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation properties and keep the advantage of quasi-orthogonal transform of the discrete wavelet, transform (DWT). The issue has been supported by computer simulations.

一种基于机器视觉的平面加工机床控制系统的设计

针对服装、包装等加工行业中须将人工测量的纸质图纸或模型样件的尺寸信息录入计算机并转换成电子加工图纸而导致的加工周期长、生产效率低的问题,提出了一种基于机器视觉的平面加工机床控制系统,以实现对纸质图纸或模型样件的快速检测。采用“ARM+DSP”方式搭建了主从式运动控制系统,设计了系统各部分功能模块。构建了“工控机+工业CCD (charge coupled device,电荷耦合器件)相机+光源控制”的视觉检测系统,结合FAWS (feature adaptive wavelet shrinkage,自适应特征的小波收缩)算法和麻雀搜索算法提出一种改进的FAWS算法进行图像降噪,并采用Canny算法进行图像边缘检测,实现图像轮廓的准确提取。设计了图像轮廓提取、轮廓数据转换为加工数据、数据通信等处理程序,实现了基于机器视觉的快速检测以及在系统加工过程中的人机交互。最后,对系统进行了实验测试,对实际加工效果进行了评价。结果表明,采用所研制的平面加工机床控制系统不仅能显著提高生产效率,而且能减小图像轮廓的误差。其性能稳定可靠,具有一定的工程实用价值。

IMPROVED METHOD FOR HILBERT INSTANTANEOUS FREQUENCY ESTIMATION

In the mechanical fault detection and diagnosis field, it is more and more important to analyze the instantaneous frequency （IF） character of complex vibration signal. The improved IF estimation method is put forward aiming at the shortage of traditional Hilbert transform. It is based on Hilbert transform in wavelet domain. With the help of relationship between the real part and the imaginary part obtained from the complex coefficient of continuous wavelet transform or the analyti- cal signal reconstructed in wavelet packet decomposition, the instantaneous phase function of the subcomponent is extracted. In order to improve the precise of IF estimated out, some means such as Linear regression, adaptive filtering, resampling are applied into the instantaneous phase obtained, then, the central differencing operator is used to get desired IF. Simulation results with synthetic and gearbox fault signals are included to illustrate the proposed method.

Study of Signal Processing Algorithm in the Receiving End of Through-the-Earth Communications of Elastic Wave

In the research of elastic wave signal detection algorithm, a method based on adaptive wavelet analysis and segmentation threshold processing of the channel noise removal methods is suggested to overcome the effect of noise, which is prcduced by absorption loss, scattering loss, reflection loss and multi-path effect during the elastic wave in the transmission undelgroound. The method helps to realize extraction and recovery of weak signal of elastic wave from the multi-path channel, and simulation study is carded out about wavelet de-noising effects of the elastic wave and obtained satisfactory results.

Fast Adaptive Wavelet for Remote Sensing Image Compression

Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm （FAWS）. Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.

Excitonic Eigenstates of Disordered Semiconductor Quantum Wires:Adaptive Wavelet Computation of Eigenvalues for the Electron-Hole Schrodinger Equation

A novel adaptive approach to compute the eigenenergies and eigenfunctions of the two-particle(electron-hole)Schrodinger equation including Coulomb attraction is presented.As an example,we analyze the energetically lowest exciton state of a thin one-dimensional semiconductor quantum wire in the presence of disorder which arises from the non-smooth interface between the wire and surrounding material.The eigenvalues of the corresponding Schrodinger equation,i.e.,the onedimensional exciton Wannier equation with disorder,correspond to the energies of excitons in the quantum wire.The wavefunctions,in turn,provide information on the optical properties of the wire.We reformulate the problem of two interacting particles that both can move in one dimension as a stationary eigenvalue problem with two spacial dimensions in an appropriate weak form whose bilinear form is arranged to be symmetric,continuous,and coercive.The disorder of the wire is modelled by adding a potential in the Hamiltonian which is generated by normally distributed random numbers.The numerical solution of this problem is based on adaptive wavelets.Our scheme allows for a convergence proof of the resulting scheme together with complexity estimates.Numerical examples demonstrate the behavior of the smallest eigenvalue,the ground state energies of the exciton,together with the eigenstates depending on the strength and spatial correlation of disorder.