GML空间数据压缩技术研究

针对GML空间数据冗余较大且存储和传输代价高的问题,提出一种GML空间数据压缩方法,采用VTD-XML解析GML文档,设计将树形结构的GML空间数据转换为2个线性结构数据的GBW变换,利用GZip压缩数据并输出。实验结果表明,该方法优于传统压缩方法,在提高GML空间数据压缩率的同时,并未明显增加压缩及解压缩时间。     

树形结构数据输入输出控件的设计与实现

为解决现有树形结构数据输入输出控件都不能很好地输入一个树形数据完整信息的问题,提出了对现有树形结构数据输入输出控件进行改进并与其它控件组合成复合控件的方法。该方法分析了树形结构数据在数据库中的存在形式,分别设计并封装了现有树形结构数据输入输出控件,实现了自定义单表和多表树形结构数据输出控件,然后在自定义输出控件的基础上结合其它控件设计了单表和多表树形结构数据输入控件。该控件是数据敏感的,封装后数据的显示只需填写相关的自定义属性。并通过备件编目实例验证了该方法的有效性。     

基于组合解压缩电路的多扫描链测试方法

提出一种采用组合电路实现解压缩电路的压缩方法,只需少量的输入管脚,可以驱动大量的内部扫描链·该方法利用确定性测试向量中存在的大量的不确定位(X位),采用对测试向量进行切片划分和兼容赋值的思想,通过分析扫描切片之间的兼容关系来寻找所需的外部扫描输入管脚的最小个数·实验结果表明,它能有效地降低测试数据量·此外,通过应用所提出的解压缩电路,扫描链的条数不再受到自动测试仪的限制,因此能充分发挥多扫描链设计降低测试应用时间的优点·     

部分向量奇偶位切分的LFSR重新播种方法

提出一种基于部分测试向量奇偶位切分的LFSR重新播种测试方法.针对确定测试集中各个测试向量包含确定位的位数有较大差异以及测试向量所含的确定位大多连续成块的特点,通过奇偶切分部分确定位较多的向量,使得编码压缩的LFSR度数得到有效降低,从而提高了测试数据压缩率.其解压缩电路仍然采用单个LFSR进行解码与切分向量的合并.与目前国际同类编码压缩方法相比,具有测试数据压缩率高、解压硬件开销低、测试数据传输协议简单等特点.     

一种二叉树结构型测试数据自动生成方法

目前关于软件测试数据自动生成的研究主要集中于数值和字符串型数据,对于指针和动态数据结构的测试生成研究较少.二叉树是一种广泛使用并具有代表性的动态数据结构,文中提出一种针对二叉树结构的测试数据自动生成方法,使用遗传搜索算法生成二叉树形态,同时利用约束求解确定其数据域的值.实验结果表明,该方法是行之有效的,其测试生成效果明显优于随机生成方法.     

软件可靠性测试中最小测试量的确定方法

分析了软件可靠性测试中确定最小测试量的重要意义,阐述了基于操作剖面的软件可靠性测试数据生成方法和软件可靠性测试数据统计特征原理下的确定最小测试量的理论依据和具体方法,提出了该理论指导下的测试数据生成流程,通过实例给出了方法的具体应用过程.     

DSP芯片核内高性能移位器设计与验证

移位类指令对于实现FIR、IIR等数字信号处理算法非常重要,为了快速的实现此类算法,介绍了一款16位嵌入式定点DSP芯片中核内移位器单元的设计。电路由一个移位阵列和指数提取单元、以及控制信号单元组成,采用改进的树形（Tree-Mux）结构,大大提高了其速度。使用0．18CMOS工艺,使得（16位输入40位输出）整个移位器得以实现;综合结果表明,最长时延为1．89ns。     

FRBF神经网络分类器设计新方法

提出了一种结合模糊径向基函数网络和稀疏V-SVM的二分类器构建方法。FRBF初始网络中的RBF隶属度函数中心由随机抽取的样本确定,而RBF隶属度函数的宽度由样本各个属性的分布方差确定。根据FRBF网络输出为模糊基函数线性组合的特点,在后件参数学习中引入具有结构风险最小化和属性选择功能的稀疏V-SVM方法,在对输出层的参数进行学习的同时进行模糊基函数的约简。若干UCI标准数据集分类测试结果验证了该分类器的有效性。     

如何使用解压缩软件Winzip8.0

下面我们来看看WinZip 8.0的具体使用方法。 一、快速压缩与解压缩 1.若想快速压缩或解压缩文件,只需在想要实行解、压缩的文件处点击鼠标右键,并在弹出的快捷菜单中选择“Add toZip”或“Extract To”命令即可。 2.从Zip压缩文件中解压缩所有的文件:在资源管理器窗口中,使用鼠标右键将要解压缩的文件从一个资源管理器窗口拖     

面向泛娱乐文本的层次多标签分类方法

针对泛娱乐领域文本情报预测类别标签具备有向无环图(DAG)结构的特点，提出一种考虑标签层次结构的基于最优路径层次多标签分类方法。根据现有标签构建DAG结构并将其转化为较易处理的树形结构；采用局部策略为树形结构中每个节点分别训练基分类器，同时为每个节点设置贡献值，贡献值由分类器输出概率与层次权重组合而成，贡献值大于阈值时该节点设置为1,否则为0;对树形结构进行深度优先遍历生成路径，计算各路径得分，选择满足层次约束并得分最高的路径作为最终预测集合。在泛娱乐公开文本信息数据集上进行了4组实验，结果表明该方法相较于分类器链、二元分析、SVM多标签分类和MLKNN算法，分类效果更优。     

Neural simulation of dynamic response of prefabricated buildings subjected to paraseismic excitations

On the basis of measurements on a group of 5-storey flat buildings experimental data were collected. Back-propagation neural networks (BPNNs) are formulated as replicators for data compression. The compressed values of the discretized displacement response spectrum are associated with paraseismic excitations caused by explosions in nearby quarries. The compressed excitations are completed by buildings parameters and used in the master BPNN for simulation of compressed outputs which correspond to the building displacement response on the 4th floor of buildings. The BPNN replicator is also formulated for the compression of the building displacement records in time domain into the target vectors of the master BPNN. After the training the compressed outputs of the master BPNN are decompressed by means of the BPNN decompressor into the building displacement records. In the paper it was proved that the discussed application of BPNNs gives satisfactory neural simulation of displacement records in time domain for vibrations with large amplitudes without analysis of the building motion equations.     

Use of lidar data to simulate forest net primary production

A method has been recently presented to predict the net primary production (NPP) of Mediterranean forests by integrating conventional and remote-sensing data. This method was based on the use of two models, C-Fix and BIOME-BGC, whose outputs are combined with estimates of stem volume and tree age to predict the NPP of the examined ecosystems. This article investigates the possibility of deriving these two forest attributes from airborne high-resolution lidar data. The research was carried out in the San Rossore pine forest, a test site in Central Italy where several investigations have been conducted. First, estimates of stand stem volume and tree age were obtained from lidar data by application of a simplified method based on existing literature and a few ground measurements. The accuracy of these stand attributes was assessed by comparison with the independent ground data derived from a recent forest inventory. Next, the stem volume and tree age estimates were used to drive the NPP modelling strategy, whose outputs were evaluated against the inventory measurements of current annual increment (CAI). The simplified lidar data processing method produces stand stem volume and tree age estimates having moderate accuracy, which are useful to feed the modelling strategy and predict CAI at a stand level. This method's success raises the possibility of integrating ecosystem modelling techniques and lidar data for the simulation of net forest carbon fluxes.     

On Test Data Compression Using Selective Don't-Care Identification

This paper proposes an effective method for reducing test data volume under multiple scan chain designs. The proposed method is based on reduction of distinct scan vectors using selective don't-care identification. Selective don't-care identification is repeatedly executed under condition that each bit of frequent scan vectors is fixed to binary values (0 or 1). Besides, a code extension technique is adopted for improving compression efficiency with keeping decompressor circuits simple in the manner that the code length for infrequent scan vectors is designed as double of that for frequent ones. The effectiveness of the proposed method is shown through experiments for ISCAS'89 and ITC'99 benchmark circuits.     

用决策树方法优化表决器

通过分析多数表决器的决策树,并根据决策树的分裂规则,对多数表决器的决策树进行扩展。扩展方法是在决策树的一个叶子节点上进行分裂,该叶子节点为多数表决器处于无法表决的环节。分裂属性是针对是否使用加权表决提出的阈值,该阈值用于选择性地使用加权表决算法进行表决。实际测试表明所改进的多数加权混合表决器在三模冗余系统中发生二模块错误或三模块错误时,能够提高表决正确率,降低无法表决情况的概率。     

A scheme of test data compression based on coding of even bits marking and selective output inversion

A new scheme of test data compression/decompression, namely coding of even bits marking and selective output inversion, is presented. It first uses a special kind of codewords, odd bits of which are used to represent the length of runs and even bits of which are used to represent whether the codewords finish. The characteristic of the codewords make the structure of decompressor simple. It then introduces a structure of selective output inversion to increase the probability of 0s. This scheme can obtain a better compression ratio than some already known schemes, but it only needs a very low hardware overhead. The performance of the scheme is experimentally confirmed on the larger examples of the ISCAS89 benchmark circuits.     

扩展相容性扫描树中的测试响应压缩器设计

在扫描树测试技术中,对相容单元扫描移入相同的测试向量值可以显著地减少测试应用时间,但会使测试需要的引脚数和测试响应数据量增大.为了减少扫描树测试结构需要的引脚数以及测试响应数据量,同时克服错误位扩散带来的困难,在异或网络的基础上,提出一种适用于扫描树结构的测试响应压缩器.该压缩器由扩散抑制电路和异或网络构成,通过抑制电路消除错误位扩散给测试响应压缩带来的困难.最后,用实验数据从性能上分析了该测试响应压缩器的适用性,对于ISCAS89标准电路,最高将输出压缩74倍,且没有混叠产生.     

Design and performance of a pixel-level pipelined-parallel architecture for high speed wavelet-based image compression

Wavelets have widely been used in many signal and image processing applications. In this paper, a new serial-parallel architecture for wavelet-based image compression is introduced. It is based on a 4-tap wavelet transform, which is realised using some FIFO memory modules implementing a pixel-level pipeline architecture to compress and decompress images. The real filter calculation over 4 × 4 window blocks is done using a tree of carry save adders to ensure the high speed processing required for many applications. The details of implementing both compressor and decompressor sub-systems are given. The primarily analysis reveals that the proposed architecture, implemented using current VLSI technologies, can process a video stream in real time.     

Not every domain of a plain decompressor contains the domain of a prefix-free one

C. Calude, A. Nies, L. Staiger, and F. Stephan posed the following question about the relation between plain and prefix Kolmogorov complexities (see their paper in DLT 2008 conference proceedings): does the domain of every optimal decompressor contain the domain of some optimal prefix-free decompressor? In this paper we provide a negative answer to this question.     

ANN-DT: an algorithm for extraction of decision trees fromartificial neural networks

Although artificial neural networks can represent a variety of complex systems with a high degree of accuracy, these connectionist models are difficult to interpret. This significantly limits the applicability of neural networks in practice, especially where a premium is placed on the comprehensibility or reliability of systems. A novel artificial neural-network decision tree algorithm (ANN-DT) is therefore proposed, which extracts binary decision trees from a trained neural network. The ANN-DT algorithm uses the neural network to generate outputs for samples interpolated from the training data set. In contrast to existing techniques, ANN-DT can extract rules from feedforward neural networks with continuous outputs. These rules are extracted from the neural network without making assumptions about the internal structure of the neural network or the features of the data. A novel attribute selection criterion based on a significance analysis of the variables on the neural-network output is examined. It is shown to have significant benefits in certain cases when compared with the standard criteria of minimum weighted variance over the branches. In three case studies the ANN-DT algorithm compared favorably with CART, a standard decision tree algorithm.