On the regularization of dynamic data reconciliation problems

Dynamic data reconciliation problems are discussed from the perspective of the mathematical theory of ill-posed inverse problems. Regularization is of crucial importance to obtain satisfactory estimation quality of the reconciled variables. Usually, some penalty is added to the least-squares objective to achieve a well-posed problem. However, appropriate discretization schemes of the time-continuous problem act themselves as regularization, reducing the need of problem modification. Based on this property, we suggest to refine successively the discretization of the continuous problem starting from a coarse grid, to find a suitable regularization which renders a good compromise between (measurement) data and regularization error in the estimate. In particular, our experience supports the conjecture, that non-equidistant discretization grids offer advantages over uniform grids.     

Robust multi-scale principal components analysis with applications to process monitoring

Robust multi-scale principal component analysis (RMSPCA) improves multi-scale principal components analysis (MSPCA) techniques by incorporating the uncertainty of signal noise distributions and eliminating/down-weighting the effects of abnormal data in the training set. The novelty of the approach is to integrate MSPCA with the robustness to the typical normality assumption of noisy data. By using an M-estimator based on the generalized T distribution, RMSPCA adaptively transforms the data in the score space at each scale in order to eliminate/down-weight the effects of the outliers in the original data. The robust estimation of the covariance or correlation matrix at each scale is obtained by the proposed approach so that accurate MSPCA models can be obtained for process monitoring purposes. The performance of the proposed approach in process fault detection is illustrated and compared with that of the conventional MSPCA approach through a pilot-scale setting.     

Optical character recognition of the Orthodox Hellenic Byzantine Music notation

In this paper we present for the first time, the development of a new system for the off-line optical recognition of the characters used in the orthodox Hellenic Byzantine Music notation, that has been established since 1814. We describe the structure of the new system and propose algorithms for the recognition of the 71 distinct character classes, based on Wavelets, 4-projections and other structural and statistical features. Using a nearest neighbor classifier, combined with a post classification schema and a tree-structured classification philosophy, an accuracy of 99.4% was achieved, in a database of about 18,000 Byzantine character patterns that have been developed for the needs of the system.     

Wavelet-based analysis of multiscale phenomena: application to material porosity and identification of dominant scales

The paper presents a general process that utilizes wavelet analysis in order to link information on material properties at several scales. In the particular application addressed analytically and numerically, multiscale porosity is the source of material structure or heterogeneity, and the wavelet-based analysis of multiscale information shows clearly its role on properties such as resistance to mechanical failure. Furthermore, through the statistical properties of the heterogeneity at a hierarchy of scales, the process clearly identifies a dominant scale or range of scales. Special attention is paid to porosity appearing at two distinct scales far apart from each other since this demonstrates the process in a lucid fashion. Finally, the paper suggests ways to extend the process to general multiscale phenomena, including time scaling.     

数字通信中基于双正交子波的波形成形

本文讨论数字调制技术中的波形成形问题,提出利用双正交子波实现波形成形的思想.本文首先证明了利用双正交子波实现波形成形满足Nyquist准则;然后研究了利用双正交子波进行基带成形的具体数字化实现涉及的问题,并在频谱利用率和误码性能两个方面进行了对比分析,给出了计算机模拟结果,得到了有益的结论.     

基于小波变换的SAR图像相干斑噪声消除方法研究

本文提出了一种基于小波变换的合成孔径雷达(SAR)图像相干斑消除滤波器.这种滤波器通过在小波细节子图像中减少小波分解系数的幅度来抑制相干斑噪声,同时利用小波细节子图像中提供的边缘信息来检测边缘和纹理细节,并保留其对应的小波分解系数值.实验结果表明,此方法除了对相干斑噪声有很好的抑制作用外,还保留了尽可能多的目标特性和图像细节,有着良好的图像视觉解译效果.     

Novel wavelet-based QIM data hiding technique for tamper detection and correction of digital images

This paper proposes a tamper detection and correction technique using semi-fragile data hiding that aims to achieve high perceptual quality of images at the user-end even after malicious modifications. A binary signature and an image digest are embedded by modulating integer wavelet coefficients using dither modulation based quantization index modulation. Half-toning technique is used to obtain image digest from the low-resolution version of the host image itself. Decoder extracts the binary signature from the watermarked image for tamper detection, while the extracted image digest is used to correct the tamper region. Unlike previously proposed techniques, this novel approach distinguishes malicious changes from various common image processing operations more efficiently and also correct tapered regions effectively. Experimental results show that the proposed technique provides a superior performance in terms of probability of miss and false alarm as well as in tamper correction, compared to several existing semi-fragile watermarking techniques.     

A multiresolution representation scheme for multimedia databases

We present a compression scheme that is useful for interactive video applications such as browsing a multimedia database. The focus of our approach the development of a compression scheme (and a corresponding retrieval scheme) that is optimal for any data rate. To browse a multimedia database, such a compression scheme is essential. We use a multiresolution setting, but eliminate the need for wavelets. This results in much better compression. We show experimental results and explain in detail how to extend our approach to multidimensional data.     

Fast image segmentation based on multi-resolution analysis and wavelets

An efficient algorithm for image segmentation based on a multi-resolution application of a wavelets transform and feature distribution is presented. The original feature space is transformed into a lower resolution with a wavelets transform to derive fast computation of the optimum threshold value in a feature space. Based on this lower resolution version of the given feature space, a single feature value or multiple feature values are determined as the optimum threshold values. The optimum feature values, which are in the lower resolution, are projected onto the original feature space. In this step a refinement procedure may be added to detect the optimum threshold value. Experimental results for the proposed algorithm indicate feasibility and reliability for fast image segmentation.