Spectral analysis of phylogenetic data

The spectral analysis of sequence and distance data is a new approach to phylogenetic analysis. For two-state character sequences, the character values at a given site split the set of taxa into two subsets, a bipartition of the taxa set. The vector which counts the relative numbers of each of these bipartitions over all sites is called a sequence spectrum. Applying a transformation called a Hadamard conjugation, the sequence spectrum is transformed to the conjugate spectrum. This conjugation corrects for unobserved changes in the data, independently from the choice of phylogenetic tree. For any given phylogenetic tree with edge weights (probabilities of state change), we define a corresponding tree spectrum. The selection of a weighted phylogenetic tree from the given sequence data is made by matching the conjugate spectrum with a tree spectrum. We develop an optimality selection procedure using a least squares best fit, to find the phylogenetic tree whose tree spectrum most closely matches the conjugate spectrum. An inferred sequence spectrum can be derived from the selected tree spectrum using the inverse Hadamard conjugation to allow a comparison with the original sequence spectrum. A possible adaptation for the analysis of four-state character sequences with unequal frequencies is considered. A corresponding spectral analysis for distance data is also introduced. These analyses are illustrated with biological examples for both distance and sequence data. Spectral analysis using the Fast Hadamard transform allows optimal trees to be found for at least 20 taxa and perhaps for up to 30 taxa. The development presented here is self contained, although some mathematical proofs available elsewhere have been omitted. The analysis of sequence data is based on methods reported earlier, but the terminology and the application to distance data are new.     

基于JMF实时传输的远程授课系统的设计与实现

在简要介绍多媒体实时传输关键技术的基础上,对在JMF平台上实现音视频数据实时处理传输技术的远程授课系统的硬件结构和软件层次结构设计以及RTP流的发送和接收作了详细阐述,提出了一套从硬件到软件的完整设计和实现方案。为保证系统高度的实时性,采用了对音频和视频建立不同的虚拟信道,以负责不同媒体信息的传输,每个信道根据其媒体信息特点,赋予相应的服务质量参数,保证了信息的高效传输。     

钻井布局方案最优化方法

本文研究钻井布局最优方案,提高已有井的使用率。通过建立非线性规划模型分别在欧式距离(l∞模距离)和纵横距离(l2模距离)下求解具体布局方案。     

基于线性均方误差估值的LPC距离量度

本文从LPC系数的统计特性出发,基于线性均方误差估值的思想,提出了一种用于语音识别的新的LPC距离量度——LMSE距离.在一个IBM-PC机语音处理系统和一个VAX-11/750机语音处理系统上,做了大量孤立词识别实俭,证实了这种距离的可行性.文中还将它与著名的Itakura距离进行了比较.     

WindowsMedia服务原理及在教学中的应用

WindowsMedia服务是Micmsoft提出的流信息播放方案，能够通过各类网络传送流式化多媒体内容，它由3个部分组成：MediaTools,Media Server和Media Player.Windows Media服务具有先进的编码技术、流技术以及媒体同步技术，对于目前正日渐兴起的远程教学提供了一种良好的解决方案。     

从成本曲线谈远程教育系统的成本效益优势

对远程教育系统和传统校园面授教育系统的成本曲线进行了分析,对传统校园面授教育和远程教育的成本效益进行对比,并探讨了远程教育的成本效益优势.     

网络远程教育发展与应用的思考

网络远程教育是近年来高等教育领域讨论的热点之一，对于网络远程教育的优势和不足、定位及发展前景众说纷纭，本文试从网络远程教育发生发展的背景出发，通过与传统教育进行对比分析，指出网络远程教育的特点与优势，并就现阶段在开展网络远程教育过程中出现的一些问题给出建议，旨在探讨网络远程教育良性发展的对策。     

求最佳生物序列并置排列的算法和程序

本文提出了计算最佳生物序列并置排列的程序和算法。程序使用计算进化距离的改进算法,在IBM-PC/AT上用Turbo-Pascal 4.0版开发。程序有友好的用户界面、操作简便、通用性强,可以移植到多种计算和操作环境。本文以蛋白质分子序列为例,给出了复杂赋权情形下计算进化距离和最佳并置排列的算法,证明了不同赋权方式本质上不改变改进算法的时间和空间复杂性。     

Hardware Euclidean Distance Transform Implementation

Based on an efficient algorithm of Euclidean distance transform for binary images, a circuit of O (N^2) size is proposed. With in-place calculation, both the intermediate data storing and the result output use the same menmory with the input data. This reduces the amount of menmory largely. By replacing multipliers with coumters, comparators, and adders, the circuit size is further reduced and its calculation speed is improved also.     

A Target Recognition Approach to Projecting HRR Profiles onto Subspace

Abstract: A array of the azimuthally averaged range-profile vectors and the inter-class and intra-class divergence matrixesare constructed iwth many frames of the high resolution range profiles which result from radar echoes of airplanes. Takingthe methods of whitening transformation and SVD produces a system of subspace vectors for target recognition. Where-upon, a template library for target recognition is built by the projection of a class-mean vector made from the radar dataonto the subspace for recognition. By Euclidean distance, a comparison is made between the above projection and eachtemplate in the library, to decide which class the target belongs to. Finally, simulations with the experimental radar dataarte given to show that the proposed method is robust to variation in azimuth and immune to additive Gaussian noisewhen SNR≥5dB.