Using wavelet multi-resolution nature to accelerate the identification of fractional order system

Because of the fractional order derivatives, the identification of the fractional order system(FOS) is more complex than that of an integral order system(IOS). In order to avoid high time consumption in the system identification, the leastsquares method is used to find other parameters by fixing the fractional derivative order. Hereafter, the optimal parameters of a system will be found by varying the derivative order in an interval. In addition, the operational matrix of the fractional order integration combined with the multi-resolution nature of a wavelet is used to accelerate the FOS identification, which is achieved by discarding wavelet coefficients of high-frequency components of input and output signals. In the end, the identifications of some known fractional order systems and an elastic torsion system are used to verify the proposed method.     

A multi-resolution wavelet algorithm for hand vein pattern recognition

A novel hand vein recognition algorithm is developed based on multi-resolution wavelet analysis. The texture feature of hand vein can be extracted by three-level wavelet decomposition. Furthermore, Knearest neighbor (KNN) with support vector machines (SVM) and minimum distance classifier (MDC) are employed for feature matching. Finally, the experiments are respectively performed in identification and verification modes using Tianjin University (TJU) hand vein image database constructed by our group.The results show the feasibility and effectiveness of the proposed method.     

基于多分辨分析的高炉铁水含硅量波动多重分形辨识

以包钢6号高炉、邯钢7号高炉和莱钢1号高炉在线采集的铁水含硅量(［Si］)的时间序列为样本, 利用多分辨分析剔除样本的长期趋势,对样本保留的波动趋势进行多重分形特征辨识. 通过计算广义Hurst指数、尺度函数、多重分形谱, 全面、细致量化了序列的局部及不同层次的波动奇异性. 计算结果表明: 去除长期趋势后, 三座高炉［Si］序列的波动呈现显著多重分形特征, 这样的波动过程仅用单一的Hurst指数或box维数来描述是不够的. 关键词： 多分辨分析 铁水含硅量 波动 多重分形     

基于小波包分析及支持向量机的超音速目标识别方法研究

提出了基于小波包分析及支持向量机的超音速目标识别方法 .通过 5 .5 6mm ,7.6 2mm和 12 .7mm三种枪弹试验获取信号 ,用小波包分解激波信号 ,统计每个频带的能量特征 ,用支持向量机方法训练测试样本 ,获得了很好的分类效果 .仿真结果表明基于超音速飞行体产生的激波信号来识别目标是可行的 .     

A new method of charged particle identification based on frequency spectrum analysis

A new frequency domain method for charged particle identification, called Frequency Ratio Analysis(FRA), is proposed by analyzing the frequency spectra of proton pulses and alpha pulses acquired from a totally depleted Si detector. Identification performance of the FRA method is evaluated and compared with two time domain methods, the current pulse amplitude method and the second moment method. The results show that the FRA method is not only feasible and effective but also superior to the two time domain methods, as it achieves an obvious increase in value of the figure-of-merit(FOM).     

一种基于小波分析的小目标检测算法

红外小目标检测技术由于其重要的军事意义成为研究热点。根据目标、噪声和背景边缘在小波域的不同特点,提出一种基于小波分析的红外小目标检测算法。该算法利用小波对奇异信号强有力的分析能力,消除了噪声和背景边缘对小目标检测的干扰,实现目标的检出。仿真实验证明该方法对红外图像中的小目标有比较理想的检测效果。     

一种新的基于小波变换的图像去噪方法

根据图像小波分解的特点和小波分解后高频小波系数的统计特性,构造了一种新阈值函数的去噪算法。与传统的硬阈值、软阈值函数相比,新阈值函数考虑了图像能量分布的特点,对于每一小波系数乘以一个与自身大小相关的降噪因子,并且新阈值函数简单易于计算,具有较强的自适应性。实验结果表明,采用新阈值函数的去噪结果能够有效地抑制图像的马赛克效应,无论在视觉效果上,还是在信噪比增益上均优于传统的软、硬阈值方法。     

Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain

To solve the problem that the magnetic resonance (MR) image has weak boundaries, large amount of information, and low signal-to-noise ratio, we propose an image segmentation method based on the multi-resolution Markov random field (MRMRF) model. The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales. The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm, and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation. The results are then segmented by the improved MRMRF model. In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model, it is proposed to introduce variable weight parameters in the segmentation process of each scale. Furthermore, the final segmentation results are optimized. We name this algorithm the variable-weight multi-resolution Markov random field (VWMRMRF). The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness, and can accurately and stably achieve low signal-to-noise ratio, weak boundary MR image segmentation.     

Joint multifractal analysis based on wavelet leaders

Mutually interacting components form complex systems and these components usually have long-range cross-correlated outputs. Using wavelet leaders, we propose a method for characterizing the joint multifractal nature of these long-range cross correlations; we call this method joint multifractal analysis based on wavelet leaders (MF-X-WL). We test the validity of the MF-X-WL method by performing extensive numerical experiments on dual binomial measures with multifractal cross correlations and bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. Both experiments indicate that MF-X-WL is capable of detecting cross correlations in synthetic data with acceptable estimating errors. We also apply the MF-X-WL method to pairs of series from financial markets (returns and volatilities) and online worlds (online numbers of different genders and different societies) and determine intriguing joint multifractal behavior.     

用改进的基因算法求大气压强公式

提出了一种利用改进的基因算法求大气压强精确公式的参数反演识别方法,运用基因算法反演求出由多态方程推导所得大气压强公式中的大气比热比γ,从而得到大气压强精确计算公式,并与有关文献给出的大气压强公式进行比较。结果表明,用基因算法所确定的大气压强公式所得计算值与实测值更加吻合,说明基因算法在反演问题中具有很高的应用价值,同时经数据分析得出改进基因算法比传统基因算法精度高,速度快。     

基于小波变换的激光主动成像图像去噪方法

针对激光主动成像图像的特点,提出了将小波变换和中值滤波相结合的图像去噪方法。在对小波分解后的水平、垂直和对角3个方向高频细节图像进行处理时,我们采用3种不同形状的模板进行均值滤波,为了保护图像的边缘和细节信息,采用边缘检测法来将高频中的边缘细节与噪声分开。实验结果表明:该方法在降低图像噪声的同时又较好地保留了图像的细节,去噪效果比较理想。     

基于小波神经网络的光纤陀螺误差补偿方法

为了提高光纤陀螺的测量精度,提出了一种基于小波神经网络的误差补偿方法。首先使用小波分析中的Mallat分解算法提取出陀螺信号中的主趋势项,对其误差余项进行重构。然后将重构信号作为小波神经网络的目标输出,将原始陀螺信号作为训练样本。为了提高小波神经网络的训练速度同时防止其陷入局部极小值,采用增加动量因子和自适应调整学习速率的方法来改进训练方法。训练后建立的神经网络模型对光纤陀螺误差具有良好的估计能力。结果表明,经过小波神经网络方法补偿后,光纤陀螺的输出精度达到了0. 019 4°/s,光纤陀螺的测量性能得到了提高。     

A novel area measurement scheme based on a multi-resolution dynamic contour

A novel scheme for an object's surface area measurement is proposed, which is suitable for area computation of an object with smooth and irregular edges. A multi-resolution dynamic contour is applied and this scheme consists of four steps. Firstly, a photoelectric image collimation system is applied to obtain a target image of a detected object. An image pyramid for the target image is constructed by wavelet decomposition and reconstruction. Secondly, in the image pyramid, a multi-resolution dynamic contour converges to the target's contour edge from coarse to fine scale, via feature search and an iteration algorithm. Thirdly, for the convergent dynamic contour, two formulas for the area and centroid computation of a closed B-spline curve are applied to compute the image target's area and centroid exactly. Finally, a novel centroid self-calibration technology is applied, which measures the pixel's size equivalence with the computed centroid and a dual-frequency laser to measure the true object's surface area exactly. Experiments indicate that this scheme's single-measurement error decreases to ±0.2%, when the number of control points is 20. Compared to conventional measurement approaches, this scheme is robust for a target image with noises or complicated edges.     

Modal identification based on continuous wavelet transform and ambient excitation tests

This paper deals with the use of the continuous wavelet transform for modal identification from ambient vibration tests. The wavelet analysis of the free responses of a linear mechanical system has been used to estimate its natural frequencies, viscous damping ratios, and mode shapes, using either the modulus or the phase of the wavelet transform with “ridge” and “skeleton” notions. This technique is extended in this paper, to the response from ambient excitation tests based on the equivalent formulation between the free response and the correlation function of a linear mechanical system under stationary stochastic excitation. The main novelties involve the new formulation of continuous wavelet transform and the use of the singular value decomposition algorithm for ridges and skeleton detection under the noisy conditions of ambient vibration tests. A complete procedure for modal identification of ambient response, including these new formulations, is presented and then applied to numerical examples and experimental test to illustrate its validity.     

A method for statistical wavelet analysis of ionospheric GPS signals

A technique of statistical wavelet analysis is proposed and tested for applicability to the data of radio sounding of the ionosphere by navigation signals of the global positioning system. Important advantages of wavelet analysis over spectral analysis are demonstrated, including performance under short-term fault conditions.     

基于HVS的小波域信息隐藏方法

为了提高信息隐藏的不可感知性和鲁棒性,结合人眼视觉系统(HVS)的相关知识,提出了一种新的小波域信息隐藏算法。将载体图像的中等亮度区域设为不可用区域,不在该区域隐藏信息,同时选择在载体图像小波变换的高频系数细节子图隐藏信息,并优先选择在对角高频系数细节子图中隐藏。计算局部子块的方差值作为隐藏信息的嵌入系数,并通过计算高频子图的噪声敏感模型ωθ(i,j)决定隐藏信息的位置。充分考虑了人眼的各种视觉掩蔽效应,使得隐藏信息后的结果图像相比隐藏之前没有出现明显的降质。实验结果表明该方法能够有效提高信息隐藏的不可知性,达到了很好的隐藏效果,同时能够有效地抗击噪声、裁剪和压缩等攻击影响,具有很好的鲁棒性,在信息安全领域有较好的应用前景。     

基于小波变换的共焦拉曼图像去噪方法

针对现有共焦拉曼光谱显微图像噪声去除不足的问题,提出一种基于小波变换的拉曼图像全面去噪方法。首先对拉曼光谱图像进行小波变换处理,然后再对拉曼图像进行双边滤波处理,进而实现拉曼光谱显微图像中噪声干扰的抑制。理论分析和实验表明:与现有拉曼光谱显微图像去噪方法相比,所提方法使拉曼光谱显微图像的峰值信噪比和结构相似度分别改善了92.99%和197%,其有效提高了拉曼光谱图像的信噪比和物质成分分析的准确率。     

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.     

小波计盒分形维数核爆电磁脉冲信号模糊识别

为了在现有禁核试验条件下开展核爆电磁脉冲探测技术研究,对实测的非均匀采样核爆电磁脉冲数据进行阈值检波、直流剔除、滤波、插值和归一化处理后,采用小波分析提取信号第四尺度小波系数能谱熵、第一尺度和第四尺度小波系数计盒分形维数特征值,在统计分析的基础上分别建立其专用隶属度函数;利用模糊识别理论,对核爆电磁脉冲信号和作为主要探测干扰信号源的闪电电磁脉冲信号进行了模糊识别,获得了总识别率为99.41%的识别效果。     

小波计盒分形维数核爆电磁脉冲信号模糊识别

为了在现有禁核试验条件下开展核爆电磁脉冲探测技术研究,对实测的非均匀采样核爆电磁脉冲数据进行阈值检波、直流剔除、滤波、插值和归一化处理后,采用小波分析提取信号第四尺度小波系数能谱熵、第一尺度和第四尺度小波系数计盒分形维数特征值,在统计分析的基础上分别建立其专用隶属度函数;利用模糊识别理论,对核爆电磁脉冲信号和作为主要探测干扰信号源的闪电电磁脉冲信号进行了模糊识别,获得了总识别率为99.41%的识别效果。