国产平台下可靠文件传输软件的设计及实现

在国产自主可控平台加速推广的当下,为实现文件在测控数据传输网不同软硬件平台之间的可靠、快速传输,本文根据相关技术规范,基于文件交换协议(File Exchange Protocol,简称FXP协议),设计并实现功能完备、运行稳定的可靠文件传输软件。软件具备模块化、可移植、基于TCP协议满足用户可靠性需求、基于UDP协议满足用户传输效率需求、兼容不同软硬件平台等的功能特点,并具备满足安全编程需求的软件特性,立足核心代码设计、验证,对软件功能和稳定运行两个方面做了进一步的改良和提升,以满足跨不同软硬件平台稳定运行、恶劣网络环境中可靠传输、畅通网络环境中尽力快速传输的新要求。     

大功率微波测试系统的构建及误差分析

本文分析和讨论了在构建波导大功率微波测试系统中所使用的大功率衰减器、定向耦合器和功率计的特性,并对相应的器件进行了研究和实际测试。构造出波导大功率微波测试系统,并在实际的大功率微波测试实验中得到验证,表明了该测试系统的正确性。并且根据衰减器、功率计的特性分析了其误差来源和相应的处理方法。     

Fast k most similar neighbor classifier for mixed data (tree k-MSN)

The k nearest neighbor (k-NN) classifier has been a widely used nonparametric technique in Pattern Recognition, because of its simplicity and good performance. In order to decide the class of a new prototype, the k-NN classifier performs an exhaustive comparison between the prototype to classify and the prototypes in the training set T. However, when T is large, the exhaustive comparison is expensive. For this reason, many fast k-NN classifiers have been developed, some of them are based on a tree structure, which is created during a preprocessing phase using the prototypes in T. Then, in a search phase, the tree is traversed to find the nearest neighbor. The speed up is obtained, while the exploration of some parts of the tree is avoided using pruning rules which are usually based on the triangle inequality. However, in soft sciences as Medicine, Geology, Sociology, etc., the prototypes are usually described by numerical and categorical attributes (mixed data), and sometimes the comparison function for computing the similarity between prototypes does not satisfy metric properties. Therefore, in this work an approximate fast k most similar neighbor classifier, for mixed data and similarity functions that do not satisfy metric properties, based on a tree structure (Tree k-MSN) is proposed. Some experiments with synthetic and real data are presented.     

Local normalized linear summation kernel for fast and robust recognition

Kernel-based methods are effective for object detection and recognition. However, the computational cost when using kernel functions is high, except when using linear kernels. To realize fast and robust recognition, we apply normalized linear kernels to local regions of a recognition target, and the kernel outputs are integrated by summation. This kernel is referred to as a local normalized linear summation kernel. Here, we show that kernel-based methods that employ local normalized linear summation kernels can be computed by a linear kernel of local normalized features. Thus, the computational cost of the kernel is nearly the same as that of a linear kernel and much lower than that of radial basis function (RBF) and polynomial kernels. The effectiveness of the proposed method is evaluated in face detection and recognition problems, and we confirm that our kernel provides higher accuracy with lower computational cost than RBF and polynomial kernels. In addition, our kernel is also robust to partial occlusion and shadows on faces since it is based on the summation of local kernels.     

A systematic method for efficient computation of full and subsets Zernike moments

A new method is proposed for fast and accurate computation of Zernike moments. This method presents a novel formula for computing exact Zernike moments by using exact complex moments where the exact values of complex moments are computed by mathematical integration of the monomials over digital image pixels. The proposed method is applicable to compute the full set of Zernike moments as well as the subsets of individual order, repetition and an individual moment. A comparison with other conventional methods is performed. The results show the superiority of the proposed method.     

一类非张量积区域快速傅立叶变换算法在匡产并行机上的可扩展性测试

本文给出了一类基于六边形非张量积区域上的广义离散快速傅立叶变换算法（HFFT）以及它在国产百万亿次超级计算机（曙光5000A）上的测试运行情况．文章介绍了该算法在曙光5000A上的大规模集群测试加速比和可扩展性特性,并通过分析,说明HFFT在国产超级计算机的大规模并行环境下拥有良好的可扩展性．在使用8192个处理器核的情况下,HFFT加速比达到了277倍．我们同样对FFTw软件包进行了测试．本文的分析为解决其他科学计算程序在国产百万亿次规模集群上的可扩展性问题,提供了一些先行的参考和建议．     

GPS软件接收机信号的快速捕获与跟踪

根据GPS软件接收机对数据块进行信号处理的工作方式,研究基于快速傅里叶变换的循环相关法伪码快速捕获与跟踪技术。基于实测GPS中频数据,采用循环相关法在频域计算输入信号与本地信号的相关值,由相位关系得到精细载频。仿真结果表明,该技术能减少运算量,缩短捕获时间。得到的载频能满足跟踪环对频率分辨率的要求。对跟踪结果进行子帧匹配和奇偶校验后,可以获得导航电文信     

eXist数据库结构连接算法的改进和实现

为了进一步提高eXist数据库的查询效率,针对数据库现有的数据存储特点和查询策略,提出一种快速定位的结构连接算法FL-Stack。算法采用栈缓存已遍历过的但仍可能与尚未遍历的后代结点匹配的祖先结点,并对能预先判断不可能满足结构连接匹配的祖先或后代结点,提出相应方法实现快速定位,以批量跳过这类结点。与现有算法必须逐个扫描祖先后代结点序列相比,这种快速定位的结构连接算法避免了逐个扫描带来的多次重复无意义的比较,可大大提高结构连接效率。     

Fast kernel conditional density estimation: A dual-tree Monte Carlo approach

We describe a fast, data-driven bandwidth selection procedure for kernel conditional density estimation (KCDE). Specifically, we give a Monte Carlo dual-tree algorithm for efficient, error-controlled approximation of a cross-validated likelihood objective. While exact evaluation of this objective has an unscalable O(n²) computational cost, our method is practical and shows speedup factors as high as 286,000 when applied to real multivariate datasets containing up to one million points. In absolute terms, computation times are reduced from months to minutes. This enables applications at much greater scale than previously possible. The core idea in our method is to first derive a standard deterministic dual-tree approximation, whose loose deterministic bounds we then replace with tight, probabilistic Monte Carlo bounds. The resulting Monte Carlo dual-tree algorithm exhibits strong error control and high speedup across a broad range of datasets several orders of magnitude greater in size than those reported in previous work. The cost of this high acceleration is the loss of the formal error guarantee of the deterministic dual-tree framework; however, our experiments show that error is still amply controlled by our Monte Carlo algorithm, and the many-order-of-magnitude speedups are worth this sacrifice in the large-data case, where cross-validated bandwidth selection for KCDE would otherwise be impractical.