Kernel Nearest-Neighbor Algorithm

The ‘kernel approach’ has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. It offers an alternative solution to increase the computational power of linear learning machines by mapping data into a high dimensional feature space. This ‘approach’ is extended to the well-known nearest-neighbor algorithm in this paper. It can be realized by substitution of a kernel distance metric for the original one in Hilbert space, and the corresponding algorithm is called kernel nearest-neighbor algorithm. Three data sets, an artificial data set, BUPA liver disorders database and USPS database, were used for testing. Kernel nearest-neighbor algorithm was compared with conventional nearest-neighbor algorithm and SVM Experiments show that kernel nearest-neighbor algorithm is more powerful than conventional nearest-neighbor algorithm, and it can compete with SVM. This revised version was published online in June 2006 with corrections to the Cover Date.     

MRF-based texture segmentation using wavelet decomposed images

In recent textured image segmentation, Bayesian approaches capitalizing on computational efficiency of multiresolution representations have received much attention. Most of the previous researches have been based on multiresolution stochastic models which use the Gaussian pyramid image decomposition. In this paper, motivated by nonredundant directional selectivity and highly discriminative nature of the wavelet representation, we present an unsupervised textured image segmentation algorithm based on a multiscale stochastic modeling over the wavelet decomposition of image. The model, using doubly stochastic Markov random fields, captures intrascale statistical dependencies over the wavelet decomposed image and intrascale and interscale dependencies over the corresponding multiresolution region image.     

电力系统暂态信号的小波分析方法及其应用(二)电力系统暂态信号的小波分析方法探讨

小波变换在电力系统一些领域的成功应用，展示了其应用前景，作为电力系统应用的一个重要分支－暂态信号分析，尚需深入研究和探索，基于此，本文从小波分析应用所面对的基本问题，采用，小波基选择（小波基构造）、后处理（特征提取）出发，系统探讨和综述了电力暂态信号的小波分析方法，为小波在电力系统暂态信号分析和暂态保护中的应用。     

小波变换在电力系统监控网络通信中的应用

电力系统故障数据的压缩与解压缩对于电力系统数字监控网络通信的实时性，分布性，同步性具有重要意义，基于提升策略的小波变换是实时实现电力系统故障数据的压缩与解压缩的有力工具。     

战略管理会计初探

阐述了战略管理会计的产生背景 ,分析了战略管理会计的基本特征 ,同时提出了战略管理会计的主要内容和基本方法     

A new wavelet-based measure of image focus

We present a new measure of image focus. It is based on wavelet transform of the image and is defined as a ratio of high-pass band and low-pass band norms. We show this measure is monotonic with respect to the degree of defocusation and sufficiently robust. We experimentally illustrate its performance on simulated as well as real data and compare it with existing focus measures (gray-level variance and energy of Laplacian). Finally, an application of the new measure in astronomical imaging is shown.     

Scattering Parameters Representing Imperfections in Precision Coaxial Air Lines

Scattering parameter expressions are developed for the principal mode of a coaxial air line. The model allows for skin-effect loss and dimensional variations in the inner and outer conductors. Small deviations from conductor circular cross sections are conformally mapped by the Bergman kernel technique. Numerical results are illustrated for a 7 mm air line. An error analysis reveals that the accuracy of the scattering parameters is limited primarily by the conductor radii measurement precision.     

多维多分辨率分析和图像序列的三维子波变换压缩编码

最近有文献报道图像序列的三维子波变换压缩编码。本文讨论了多维多分辨率分析和三维子带系统完全重构的充分必要条件，我们对8帧图像序列进行三级三维子波变换，然后进行零树量化和熵编码，文中给出了不同压缩比下的信噪比。编码器可以在要求的任意压缩比下停止编码．如同图像的二维子波零树编码。     

The Softmap Algorithm

A new unsupervised competitive learning rule is introduced, called the Self-organizing free-topology map (Softmap) algorithm, for nonparametric density estimation. The receptive fields of the formal neurons are overlapping, radially-symmetric kernels, the radii of which are adapted to the local input density together with the weight vectors which define the kernel centers. A fuzzy code membership function is introduced in order to encompass, in a novel way, the presence of overlapping receptive fields in the competitive learning scheme. Furthermore, a computationally simple heuristic is introduced for determining the overall degree of smoothness of the resulting density estimate. Finally, the density estimation performance is compared to that of the variable kernel method, VBAR and Kohonen's SOM algorithm.     

High-quality low-complexity wavelet-based compression algorithm for audio signals

Wavelets have recently emerged as a powerful tool for signal compression, particularly in the areas of image, video, and audio compression. In this paper, we present a low-complexity wavelet-based audio compression algorithm that is capable of handling fairly arbitrary audio sources. The algorithm transforms the incoming audio data into the wavelet domain, and compresses data by exploring redundancy in the wavelet coefficients and exploiting the large runs of zeros in the transformed signal. Also there is a possibility of applying a threshold to the non-zero coefficients, thus a further increase in the number of zeros is expected. The audio signal is first preprocessed to scale down the wavelet coefficients. Then the preprocessed signal is wavelet transformed using a bi-orthogonal discrete wavelet transform (DWT) and threshold by applying energy compaction strategy. Encoding represents the threshold coefficients in compact form. A new encoding technique that is easy to implement, and that provides a reasonable compression ratio for a certain acceptable distortion level has been developed to encode the threshold DWT. So, a bit rate can be controlled such that the algorithm operates at virtually any pre-selected bit rate. The motivation of using the bi-orthogonal wavelet transform is that it permits the use of a much broader class of filters, and this class includes symmetric linear phase filters. The superior performance of this algorithm is also demonstrated by comparing it with two other popular audio compression techniques and this meets the requirements of multimedia computing.