基于多尺度几何变换的导航雷达图象去噪仿真

导航雷达在采集、传输和显示过程中,由于多种因素的影响导致最终形成的图像中含有大量的噪声,影响了使用者对导航信息的分析和应用.传统的雷达图像去噪算法大多采用小波变换,但这种方法存在边缘模糊等问题.为了去除导航图像的噪声并解决小波变换中存在的边缘模糊问题,本文提出用基于多尺度几何变换的图像去噪方法对导航雷达图像进行处理,并利用基于多尺度几何变换的方法(包括基于Curvelet系数维纳滤波去噪方法和基于Contourlet域去噪方法)和基于小波变换的BayesShrink方法分别对含有模拟杂波和噪声的导航雷达图像进行仿真实验.实验结果表明:与基于小波变换的图像去噪方法相比,基于多尺度几何变换去噪方法能够更加有效去除雷达杂波和噪声.     

一种基于小波变换的故障诊断改进算法

为提高多传感器组合导航系统对各导航传感器的在线故障检测能力，提出了一种基于调频高斯小波变换的导航传感器故障诊断改进算法。该算法在分析调频高斯小波特性的基础上，采用高斯小波变换计算出观测量的小波系数后，然后利用带遗忘因子的数据平滑算法对小波系数进行平滑，通过判断平滑值来诊断导航工作正常与否。其优点是仅利用传感器的观测量来直接检测导航传感器故障，适当选择小波变换的拉伸因子和数据的衰减因子可以对方差突变等软故障进行有效的在线检测，并解决了误检问题。仿真结果证明了该算法的有效性。     

基于小波变换的状态x~2改进检测算法及其应用

在惯导器件的故障检测中,针对传统的状态x2检验法无法对缓变故障持续时间进行准确判定的问题,提出一种基于小波变换的状态x2改进检测算法。该方法首先利用小波变换后的模极大值在不同尺度上的衰减速度找出信号突变点;然后将检测结果反馈给Kalman滤波器,通过引入状态修正项,修正状态误差值,使故障检测曲线能够反映缓变故障的持续时间。仿真验证表明,采用基于小波变换的状态x2检测算法较好地解决了缓变故障持续时间无法准确判定的问题,大大提高了系统故障检测的准确性。     

基于曲率模态和小波奇异性的结构损伤识别

传统的傅立叶变换适合确定一个函数奇异性的整体性质,而难以确定奇异点在空间的位置及分布情况.小波变换具有在时域和频域局部放大的性质,在工程中获得广泛应用.由于曲率模态具有较高的灵敏度,仅仅需要较低阶的模态信息就可获得很好的识别效果.本文据此提出了采用损伤前后曲率模态残差小波变换系数方法对结构损伤进行识别,通过该小波变换系数的分布情况来确定结构的损伤指标.为验证该方法的有效性,通过选用具有线性相位的双正交样条小波,对一梁结构进行了数值模拟.结果表明,采用该方法不但对单一损伤而且对多损伤均能有效地识别出结构的损伤位置.     

基于改进猴群算法的传感器优化布置方法研究

提出了一种基于新型智能算法一改进猴群算法的传感器优化布置方法.考虑传感器优化布置的特点,采用整数编码的方式给出了猴群的位置,克服了猴群算法只能解决连续性变量优化的问题;在初始化猴群位置时通过引入欧氏距离来增强猴群的多样性,以提高其全局搜索能力;并在爬过程中加入和声算法中的随机扰动机制,来提高其局部搜索能力;文末以大连世贸大厦为例,进行了参数敏感性分析以及传感器优化布置方案的选择.结果表明,改进的猴群算法能较好地解决传感器优化布置问题,较经典的序列法有其明显的优越性.     

多尺度有限差分方法求解波动方程

小波分析是多尺度分析方法，本文利用具有紧支集的正交小波变换对有限差分方程进行空间多尺度近似，提出适合于层状介质波传问题数值计算的多尺度有限差分方法，将波动方程的求解转换到小波域中进行。利用小波基的自适应性与消失矩特性，有效减少了计算量、提高了稳定性，扩大了可求解的速度范围。地球物理勘探中的数值实例显示了算法具有良好效率。     

基于Haar小波变换的位移场测量方法

本文提出了一种应用小波变换对固体表面位移场进行测量的新方法，在图像分析时，用各点属于不同尺度的小波变换系数来表征该点周围的子区。将试件表面位移前后的两幅数字图像进行小波变换，通过变形前后两幅图像小波变换系数之间的相互匹配，使位移前后两幅图中的子区对应起来，从而确定图像的位移场。本文应用Haar小波变换进行了计算机模拟实验和实物位移实验。引入亚像素技术，获得了0．02的位移测量精度。     

基于加窗傅立叶变换的SR-CT滤波反投影重建

SR-CT(Synchrotron Radiation Computed Tomography同步辐射X射线计算机断层扫描)实验中,原始投影图像存在噪声,从而影响了滤波反投影重建图像的质量.为提高重建图像质量,提出了一种基于加窗傅立叶变换滤波改进重建图像质量的方法.通过对原始投影图像噪声和滤波反投影算法的讨论,对利用该方法提高重建图像质量的可行性进行了分析.利用加窗傅立叶滤波方法对两组SR-CT实验得到的投影图像分别进行了处理,抑制了原始投影图像的噪声,提高了重建图像的质量.实验的结果验证了该方法的有效性.     

连续小波变换离散化的爆炸振动特征分析

应用连续小波变换的离散化关系,针对一个改进的L-P（littlewood-paley）小波基函数,给出了一种实现频率完全分割的时频特征分析方法,并对爆炸振动时频特征进行了研究。80 kg TNT地面爆炸时地面垂向振动速度的时间能量密度分布情况表明,在质点振动峰值速度到达时刻爆炸振动的频率范围比较宽,而其他时刻的振动频率相对较为集中,时频能量分布的峰值正好对应于爆炸振动速度的峰值到达时间。基于小波变换的爆炸振动频谱特征与Fourier变换的结果具有良好的一致性。此外,还给出了利用小波变换结果建立爆炸振动随机演变理论模型的基本方法。     

基于卡尔曼滤波的信息融合算法优化研究

通过比较采用联邦卡尔曼滤波的状态向量融合和量测信息融合,得出量测信息融合优于状态向量融合,因为只有当卡尔曼滤波一致时状态向量融合才有效.采用基于最小均方差估计的观测值加权融合法融合了多传感器数据,保持了观测向量的维数.这种方法具有高效性.为了提高该算法的速度和精度,对系统的量测空间进行了等价变换,而等价系统的状态空间却没有改变.给出了等价变换前后的系统误差方差阵和状态估计均一致性的证明.把矩阵分析中的L-D分解算法运用到该算法中以避免计算矩阵的逆,从而改善了算法的稳定性和精度.举例验证了所设计算法的这些优点,给出了采用联邦卡尔曼滤波和所优化滤波算法的状态估计和误差的仿真结果,并依次进行了分析.经过这种优化,算法的精度和速度得到很大提高,已经应用到实际工程中.     

A new algorithm of the combination of image compression and encryption technology based on cross chaotic map

Since the size of the image file is not reduced after encryption, the flow rate of image file transmission cannot be reduced. Transmission time is wasted. To meet the requirements of security and real-time, wavelet transform and chaotic image encryption technology are combined. A cross-chaos map has been proposed based on Devaney’s theory. The image pixels are scrambled under the control of high-dimensional chaotic sequence, which is generated by the cross chaotic map. The image pixels are substituted by the ciphertext feedback algorithm. The algorithm can relate to encryption parameters with plaintext, and can make one plaintext byte affect more ciphertext bytes. In the process of image wavelet transform, image encryption and image compression are combined. The low frequency region of the wavelet transform is encrypted. Security analysis and experimental validation illuminated that the algorithm has high security, good encryption speed, and compression effect. The algorithm has good practical value.     

A novel spectrogram visual security encryption algorithm based on block compressed sensing and five-dimensional chaotic system

Based on block compressed sensing theory, combined with a five-dimensional chaotic system, we propose and analyze a novel spectrogram visual security encryption algorithm. This research is devoted to solving the compression, encryption and steganography problems of spectrograms involving large data volumes and high complexity. First, the discrete wavelet transform is applied to the spectrogram to generate the coefficient matrix. Then, block compressed sensing is applied to compress and preencrypt the spectrogram. Second, we design a new five-dimensional chaotic system. Then, several typical evaluation methods, such as the phase diagram, Lyapunov exponent, bifurcation diagram and sample entropy, are applied to deeply analyze the chaotic behavior and dynamic performance of the system. Moreover, the corresponding Simulink model has been built, which proves the realizability of the chaotic system. Importantly, the measurement matrix required for compressed sensing is constructed by the chaotic sequence. Third, dynamic Josephus scrambling and annular diffusion are performed on the secret image to obtain the cipher image. Finally, an improved least significant bit embedding method and alpha channel synchronous embedding are designed to obtain a steganographic image with visual security properties. To make the initial keys of each image completely different from other images, the required keys are produced using the SHA-256 algorithm. The experimental results confirm that the visual security cryptosystem designed in this study has better compression performance, visual security and reconstruction quality. Furthermore, it is able to effectively defend against a variety of conventional attack methods, such as statistical attacks and entropy attacks.

     

Wavelet‐based data compression for flow simulation on block‐structured Cartesian mesh

A data compression method based on image encoding techniques is presented for a flow simulation data set. An input flow field data set is converted into the octree structure by discrete wavelet transform, and then quantized finely or coarsely depending on its importance in the flow field. Embedded zerotree wavelet encoding as the image encoding technique and entropy encoding reduce the data size by making use of the octree structure created previously. The present compression method is incorporated in a block‐structured Cartesian mesh method called Building‐Cube method. The Building‐Cube method gives not only good performance in the flow simulation but also consistency with the embedded zerotree wavelet encoding in the data compression. Three compression cases for incompressible and compressible flow simulations, including a large‐scale simulation with O(10 ⁸) mesh points, demonstrate that the present compression method gives both high compression ratios and good qualities of compressed data. Copyright © 2013 John Wiley & Sons, Ltd.     

Novel hybrid fractal image encoding algorithm using standard deviation and DCT coefficients

In this paper, a fast fractal encoding algorithm using standard deviation (STD) and DCT coefficients is proposed. First, the STD values of domain blocks are calculated. Then, domain blocks are sorted by their STD values. During the encoding process, each range block is limited to search in the domain blocks with similar STD values. Since the searching space is reduced, the encoding speed is improved. Moreover, for improving the quality of the retrieved image, an auxiliary encoding algorithm (AEA) is proposed, and it could encode range blocks in which the pixel values fluctuate greatly. In the decoding step, a kind of simple line fitting on block boundaries is used to reduce the blocking effects. Simulation results show that the runtime of the proposed method is reduced greatly compared to the full search method. And the new algorithm could obtain good quality and higher compression ratio than the baseline encoding algorithm.     

Towards High Performance Digital Volume Correlation

We develop speed and efficiency improvements to a three-dimensional (3D) digital volume correlation (DVC) algorithm, which measures displacement and strain fields throughout the interior of a material. Our goal is to perform DVC with resolution comparable to that achieved in 2D digital image correlation, in time that is commensurate with the image acquisition time. This would represent a significant improvement over the current state-of-the-art available in the literature. Using an X-ray micro-CT scanner, we can resolve features at the 5 micron scale, generating 3D images with up to 36 billion voxels. We compute twelve degrees-of-freedom at each correlation point and utilize tricubic spline interpolation to achieve high accuracy. We improve the algorithm’s speed and robustness through an improved coarse search, efficient implementation of spline interpolation, and using smoothing splines to address noisy image data. For DVC, the volume of data, number of correlation points, and work to solve each correlation point grow cubically. We therefore employ parallel computing to handle this tremendous increase in computational and memory requirements. We demonstrate the application of DVC using simulated deformations of 3D micro-CT scans of polymer samples with embedded particles forming an internal pattern.     

Image compression and encryption algorithm based on uniform non-degeneracy chaotic system and fractal coding

This paper focuses on the design of chaotic image compression encryption algorithms. Firstly, we design a uniform non-degenerate chaotic system based on nonlinear filters and the feed-forward and feed-back structure. Theoretical and experimental analyses indicate that the system can avoid the drawbacks of the existing chaotic systems, such as chaos degradation, uneven trajectory distribution, and weak chaotic behavior. In addition, our chaotic system can produce chaotic sequences with good pseudo-random characteristics. Then, we propose a fractal image compression algorithm based on adaptive horizontal or vertical (HV) partition by improving the baseline HV partition and the time-consuming global matching algorithm. The algorithm does not need to implement time-consuming global matching operations. In addition, analysis results demonstrate that our fractal image compression algorithm can reconstruct the original image with high quality under ultra-high compression ratios. Finally, to protect the confidentiality of images, we propose a chaotic fractal image compression and encryption algorithm by using our chaotic system and fractal image compression algorithm. The algorithm achieves excellent diffusion and confusion abilities without using the hash value of plain images. Therefore, it avoids the failure of decryption caused by the tampering of hash value during the transmission process, and can well resist differential attacks and chosen-ciphertext attacks. In addition, simulation results show the algorithm is efficient and robust.

     

Discrete wavelet transform-based simple range classification strategies for fractal image coding

An efficient fractal method based on discrete wavelet transfer is proposed in this paper. Before encoding, images with one-level wavelet transform are first partitioned into four subbands, which classify the range blocks as four types. An accelerated fractal coding method called variance sorting (VS) scheme with high reconstructed quality is addressed for the low-frequency components. Hereafter, the low-frequency part rebuilt performs one-level wavelet decomposition once again. The first-level high-frequency subbands excluding the diagonal directions are predicted according to the next coarser frequency scale in the same directions. Simulation experimental results compared with other classification algorithms demonstrate that the proposed method can obtain high compression ratio and reduce the encoding time without significant loss in the reconstructed image quality.     

Dynamics explore of an improved HR neuron model under electromagnetic radiation and its applications

The influence of electromagnetic field to neuron firing rhythm is not negligible. In order to investigate the behavior mechanism, a five-dimensional neuron model based on the Faraday's law of electromagnetic induction is improved by introducing magnetic flux variables and electric field variables on the three-dimensional Hindmarsh–Rose (HR) neuron model, and then, its rich dynamics and application in image encryption are discussed. Specifically, the equilibrium point distribution is analyzed using Matcont software and it is found that there are subcritical Hopf bifurcation and coexisting mode firing first. Second, numerical simulations are performed in terms of two-parameter bifurcation, ISI bifurcation, the maximum Lyapunov exponent and firing sequences, and the experimental results show that the new model exhibits various firing rhythms. The rich dynamic behaviors make the model more suitable for application in image encryption. So in the end, a grayscale image encryption scheme containing five parts called sparse, compression calculation, forward diffusion, rank scrambling and backward diffusion is designed by combining with the compressive sensing theory. The security analysis results show that the designed encryption scheme not only has excellent compression performance and high security, but also displays faster encryption speed. That is to say, the algorithm can be applied to the field of real encryption owning to the advantages of the lower costs of data transmission and higher efficiency of encryption. It is worth mentioning that the influence of different dimensional compression methods on the encryption and reconstruction effects is analyzed for the first time. The research results of this paper provide some ideas for perfecting the neuron model, revealing the influence of electromagnetic field on biological nervous system, and the excellent performance of the new neuron model provides theoretical guidance and experimental basis for the practical application of digital image encryption.

     

有限空间爆炸静态压力的温度补偿方法

为改善压阻式压力传感器的温度漂移特性，构建了基于遗传算法和小波神经网络的压力传感器温度补偿模型。针对小波神经网络收敛速度慢且易陷入局部最优解的问题，采用遗传算法对小波神经网络的连接权值、伸缩参数和平移参数进行优化。基于压力传感器的标定数据，分别采用BP神经网络、小波神经网络和遗传小波神经网络对其进行温度补偿研究，结果表明:遗传小波神经网络兼容了小波分析的时频局部特性和神经网络的自学习能力，表现出良好的收敛速度和补偿精度，经补偿后传感器的输出值更接近于标定值，其最大误差由?17.44 kPa变至0.38 kPa，最大相对误差由?14.0%变至0.38%。将该模型应用于有限空间爆炸静态压力的温度补偿中，取得了较好的实际应用效果。     

基于前向线性预测算法的光纤陀螺零漂的神经网络建模

在详细分析光纤陀螺零漂的基础上,提出了先用滤波算法对光纤陀螺信号进行预处理,然后采用RBF神经网络对滤波后的信号进行建模的方法。针对光纤陀螺信号特点分别采用FLP算法、小波滤波算法、解相关变步长LMS自适应滤波算法对其进行了预处理,比较三种滤波方法,小波滤波算法效果优于其它两种预处理方法,但针对基于预处理后的陀螺信号采用RBF神经网络进行建模时,小波滤波预处理后的信号在建模精度上却是最差的,而对FLP算法滤波后的信号进行RBF建模,建模精度提高了两个数量级。结果表明:基于FLP算法的RBF神经网络在光纤陀螺中的建模是有效的,可大大提高建模的精度。