小波变换在金融数据分析中的应用

市场上的数据,从本质上讲都是一种时间序列。它和小波分析中的信号具有相同的特性。因此,完全可以将这些经济时间序列看成信号,应用小波变换进行分析和预测。     

Improved holographic particle sizing by using absolute values of the wavelet transform

A new method for sizing particle from in-line particle holograms by using absolute values of the wavelet transform is proposed in order to improve accuracy in measurements. The proposed method provides direct calculation of the particle size by using spatial frequency information of a chirp signal at minima position of an envelope function. Simulation and experimental results are presented.     

我国居民收入、人口、教育、财政政策和货币政策与居民消费模型的实证分析

本文从影响消费的各个因素:居民收入、人口、教育、国家宏观政策等着手,对各因素进行了相关分析,运用多元统计中的岭回归估计法建立消费模型。从定量和定性分析的角度,分析了我国居民消费水平、居民收入、人口增长率、各层次教育、国家财政支出和银行利率等相关因素之间相互影响的数量变动关系和内在规律,就如何提高居民消费水平促进经济协调发展提出若干对策。     

Wavelet and harmonic filtering for distortion-invariant volume holographic correlation

The wavelet and harmonic filtering method suggested by Zalevsky and Ouzieli is introduced in this paper and adopted in our volume holographic image recognition system. This composite filter combines several scaled versions of the cascaded wavelet and harmonic filter, obtaining high discrimination ability and wide dynamic range of rotation and scale deformations. Optical experiments are conducted to demonstrate the validity and practicability of the algorithm. To the best of our knowledge, this is the first report of using this algorithm in a volume holographic system. Moreover, the separate correlation approach proposed in this paper greatly simplifies the manufacturing process and reduces the cost of the system.     

SOME TOPICS ON WAVELET ANALYSIS

A review of the advance in the theory of wavelet analysis in recent years is given.     

Multivariate calibration of near infrared spectra by orthogonal WAVElet correction using a genetic algorithm

Orthogonal WAVElet correction (OWAVEC) is a pre-processing method aimed at simultaneously accomplishing two essential needs in multivariate calibration, signal correction and data compression, by combining the application of an orthogonal signal correction algorithm to remove information unrelated to a certain response with the great potential that wavelet analysis has shown for signal processing. In the previous version of the OWAVEC method, once the wavelet coefficients matrix had been computed from NIR spectra and deflated from irrelevant information in the orthogonalization step, effective data compression was achieved by selecting those largest correlation/variance wavelet coefficients serving as the basis for the development of a reliable regression model. This paper presents an evolution of the OWAVEC method, maintaining the first two stages in its application procedure (wavelet signal decomposition and direct orthogonalization) intact but incorporating genetic algorithms as a wavelet coefficients selection method to perform data compression and to improve the quality of the regression models developed later. Several specific applications dealing with diverse NIR regression problems are analyzed to evaluate the actual performance of the new OWAVEC method. Results provided by OWAVEC are also compared with those obtained with original data and with other orthogonal signal correction methods.     

病态分析体系有偏估计的研究

运用广义岭估计和Ｌｉｕｋｅｊｉａｎ提出的有偏估计，对病态分析体系进行了数值模拟和实际光度测定，结果表明，广义岭估计显优于最小二乘估计，Ｌｉｕｋｅｊｉａｎ法有功效，可和为解析病态分析体系的化学计量学方法。     

Simultaneous kinetic determination of thiocyanate and sulfide using eigenvalue ranking and correlation ranking in principal component-wavelet neural network

In this work, two toxic compound, sulfide and thiocyanate were determined simultaneously using kinetic spectrophotometry. These anions have shown the catalytic effects on the reaction between iodine and azide. Since the system was nonlinear, a nonlinear model, principal component-wavelet neural network (PC-WNN) was used as the multivariate calibration method. The principal component analysis was used to decrease the dimension of the original matrix. In other words, the scores of the PCs, 5, instead of the original variables, 301, were used as the input for the model. Two methods were used to select the most relevant principal components: eigenvalue ranking and correlation ranking. In this work, eigenvalue and correlation ranking methods have shown better results for thiocyanate and sulfide, respectively, and it can be concluded that these methods are complementary. The WNN has several advantages relative to other types of neural network such as better convergence ability. The data set was divided to calibration, prediction and validation sets. Each set was selected so that the concentrations of the analytes were approximately covered the entire ranges of the analytes. Mean relative error for thiocyanate and sulfide in validation set were 8.5 and 10.6, respectively. Thiocyanate and sulfide can be determined in the range of 60–700 ng ml⁻¹ and 20–400 ng ml⁻¹, respectively. The proposed method was applied for the determination of sulfide and thiocyanate in real samples such as tap, waste and river waters with satisfactory results.     

A Background and noise elimination method for quantitative calibration of near infrared spectra

A new hybrid algorithm is proposed to eliminate the varying background and noise simultaneously for multivariate calibration of near infrared (NIR) spectral signals. The method is based on the use of multi-resolution, which is one of the main advantages provided by wavelet transform. The signals are firstly split into different frequency components, which keep the same data points of the original signals. In conjunction with a modified uninformative variable elimination (mUVE) criterion, the new method can be used to remove the low-frequency varying background and the high-frequency noise simultaneously. The method is successfully applied to simulated spectral data set and experimental NIR spectral data, resulting in more parsimonious multivariate models with higher precision. In addition, the proposed strategy can be applied to other spectral signals as well.     

基于最小各向同性小波滤波的图像清晰度识别

提出了基于最小各向同性小波滤波的图像清晰度识别方法,对二维最小各向同性小波滤波提取图像特征进行了研究.直接将原始图像通过带通小波滤波器G0获得图像边缘信息,结合图像能量分析,建立了基于小波滤波的图像清晰度评价函数.利用构建的显微镜自动对焦实验平台,比较分析了基于小波滤波和拉普拉斯的评价方法.实验结果表明,采用基于各向同性小波滤波的自动对焦算法有更好的综合自动对焦性能.