一种改进的可防欺骗的可视秘密分享方案

针对可视秘密分享方案存在的安全隐患,提出了一种改进的可防欺骗的可视秘密分享方案。本方案通过改变验证图像的分享方式,让构造者Dealer参与到验证过程,不仅可验证参与者的欺骗行为,还可以验证构造者Dealer和第三方的欺骗。提高了整个分享过程的安全性,并通过汉明重量与仿真结果证明了其可行性与实用性。     

小波阈值改进及在光谱信号去噪处理中的应用

针对宽光谱膜厚监控系统输出的光谱信号,通过改进Donoho阈值函数,对阈值加入微调因子,使与噪声幅度相近或小于噪声幅度的信号小波系数阈值减小,有利于其小波系数的保留。另一方面使噪声的小波系数的阈值增大,有利于其小波系数的滤除,使小波阈值滤波算法具有自适应性。通过实验表明该算法有效地抑制噪声,很好地保留了信号的细节信息,信号的峰值误差为0.7%—1.0%,峰位误差为0.1%—0.3%;提高了系统的监控准确度。     

基于R/S检验、ARFIMA模型和小波方差的人民币汇率长记忆性检验

分别应用R/S检验、ARFIMA模型和小波方差对人民币兑美元名义汇率收益率序列的长记忆性进行检验.根据R/S统计量计算出Hurst指数为0.573 745 1,采用ARFIMA(2,d,1)模型对人民币汇率收益率序列进行拟合的效果比较好,其分数差分参数为0.145 7,利用Haar小波对人民币汇率波动绝对值收益率序列进行最大重复离散小波变换,得出其长记忆性参数为0.393 1.3种方法的研究结果均表明人民币兑美元名义汇率收益率序列存在长记忆性.     

大气环境指标的二元极值分析

文中给出了利用多元阈值模型求解随机变量尾部联合分布的方法,并将其应用于大气环境指标中进行实证分析.通过选取合适的阈值和Copula函数,得到了上海市近十年的二氧化硫和二氧化氮的API指数的尾部联合分布以及条件尾部分布.利用这些尾部分布函数可以预测污染指标的变化趋势,从而为气象部门提供天气预报的科学依据.     

基于小波分析法与神经网络法的非平稳风速信号短期预测优化算法

为提高传统神经网络对非平稳风速的预测精度,提出一种基于小波分析法与神经网络法混合建模的优化算法。该优化方法引入小波分析法对实测非平稳风速信号进行分解,将非平稳性原始风速序列转化为多层较平稳分解风速序列,再利用BP神经网络对各分解层风速序列建立预测模型,最终加权各层预测结果获得风速超前多步预测结果。仿真结果表明:该优化算法实现了风速的高精度短期多步预测,将传统神经网络法对应超前步数的平均绝对相对误差分别提高了55.56%,32.43%和34.58%,其超前1步、3步和5步预测的风速平均相对误差分别为0.48%,1.50%和2.97%。优化网络具备信号分解与自学习能力。     

一种改进的小波滤噪算法及其在NMRL中的应用

本文针对核磁共振测井（NMRL）回波信号微弱且信号带宽较大的特点,将小波变换良好的时频局部化分析性能以及优良的噪声抑制能力与相敏检波算法相结合,设计并实现了一种基于小波阈值降噪的数字相敏检波（DPSD）算法。仿真结果表明,与FIR式DPSD算法相比,小波式DPSD算法在低孔隙度（T2＊较小）的情况下具有较低的峰值误差和良好的噪声抑制能力,在一定程度上解决了滤除噪声与保留信号完整性之间的矛盾,适用于低孔隙度地层的核磁共振测井应用。     

Satellite Image Adaptive Restoration Using Periodic Plus Smooth Image Decomposition and Complex Wavelet Packet Transforms

A satellite image adaptive restoration method was developed that avoids ringing artifacts at the image boundary and retains oriented features. The method combines periodic plus smooth image decomposition with complex wavelet packet transforms. The framework first decomposes a degraded satellite image into the sum of a "periodic component" and a "smooth component". The Bayesian method is then used to estimate the modulation transfer function degradation parameters and the noise. The periodic component is deconvoluted using complex wavelet packet transforms with the deconvolution result of the periodic component then combined with the smooth component to get the final recovered result. Tests show that this strategy effectively avoids ringing artifacts while preserving local image details (especially directional textures) without amplifying the noise. Quantitative comparisons illustrate that the results are comparable with previous methods. Another benefit is that this approach can process large satellite images with parallel processing, which is important for practical use.     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

向量值约化子空间超仿射小波对偶框架的构造

利用Fourier变换和周期化技巧,给出Fourier域上L~2(R~d,C~L)中约化子空间上的超仿射小波对偶框架的一个充分条件,从而将L~2(R~d)中约化子空间上的结果推广到向量值空间L~2(R~d,C~L)的约化子空间上.     

模糊模式识别中隶属函数选取的探讨

通过数值实例,论证了利用最大隶属度原则对三角形类型模糊模式识别时隶属函数选取的优越性,并提出了一种给定阈值且以三角形内角和差关系作为底数变量和指数变量的指数型隶属函数.