基于Android平台的嵌入式调频广播系统设计

为了满足移动用户对智能终端更高的需求,开发了基于Android平台的调频广播系统。在研究嵌入式调频广播系统硬件设计的基础上,重点介绍了Android系统内核层驱动程序、硬件抽象层（HAL）接口及Java本地调用（JNI）函数的设计和Eclipse环境中应用程序的开发,实现了调频广播系统搜台、播放和切换频道等功能。     

层级潜变量空间中的三维人手跟踪方法

针对人手状态空间维数过高的问题,提出一种基于层级流形学习的三维人手跟踪方法.将人手状态空间划分成多个人手部分状态空间,采用层级高斯过程潜变量模型得到更能反映人手运动本质的树状低维流形空间,降低了粒子滤波器有效跟踪人手所需的粒子数量;使用径向基函数插值方法构建低维流形空间到图像空间的非线性映射,将低维粒子直接映射到图像空间中观测.实验结果表明,该方法可以鲁棒地跟踪关节人手.     

浅谈中文切词算法

如何高效率的获取满足个性化的需求成为了新时代的一个热门话题,搜索引擎在一定程度上体现了这一点。然而在搜索引擎中,内部分词算法机制是关键环节,它的目的在于选取好的关键字。一个好的分词算法会降低用户搜索信息的时间和难度,大大提高查询信息的效率。然而目前有很多分词算法,它们的性能和效率各不相同,本文的主要研究目的是探讨目前几种比较流行分词器算法的工作机制,根据它们自身的不同特点,在准确率和召回率这两个方面来比较它们的性能,并进一步研究它们是如何处理用户关键字的。     

基于DTW距离的伪周期数据流异常检测

伪周期数据流是一类常见的数据流,广泛出现于各种监测应用中.在这类数据流中出现的异常可能蕴涵了感兴趣的领域知识,因此有必要检测异常的发生以作为进一步深入分析的基础.DTW距离较之欧氏距离具有更好的鲁棒性,采用DTW距离作为伪周期数据流不同波段间相似性的度量可以有效检测出有较少历史相似波段的异常波段,继而在此基础上提出了一种基于聚类索引的快速近似异常波段检测方法用以加速检测过程,在真实数据集上的实验表明了所提方法的有效性.     

Fuzzy Identification of Nonlinear Systems via Orthogonal Transform

This paper is concerned with the application of orthogonal transforms and fuzzy competitive learning to extract fuzzy rules from data. The least square algorithm with orthogonal transforms is proposed to supervise the progress of fuzzy competitive learning. First of all, competitive learning takes place in the product space of system inputs and outputs and each cluster corresponds to a fuzzy IF–THEN rule. The fuzzy relation matrix, confirmed by fuzzy competitive learning, is studied by the orthogonal least square algorithm. The validity of fuzzy rules is obtained by analyzing the effect of orthogonal vectors in the fuzzy model, and subsequently removing less important ones. The structure identification and parameter identification of the fuzzy model are simultaneously confirmed in the proposed algorithm. Using simulation results as an example, the fuzzy model of non‐linear systems can be built by using the proposed algorithm. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

决策区间值信息系统的交互相容关系及属性约简

决策区间值信息系统的处理方法在解决实际问题中具有重要意义.定义了区间的相对包含度概念,进而提出区间的交互相似度、决策区间值信息系统的交互相容关系,从而克服了以往区间值信息系统方法忽略区间的交互关系的不足,提高了分类的准确性;讨论了交互相容关系下决策区间值信息系统的属性约简和相对属性约简,并给出了寻找约简和相对约简的具体操作方法.     

基于数据划分的海量数据查询优化研究与应用

针对海量数据查询效率低的问题,在比较和分析了多种海量数据查询优化解决方案的优缺点后,提出了一种基于数据划分的海量数据查询性能优化方法.该方法利用多数据库处理、表分区、分表技术将数据在三个维度上将数据划分存储,减少了海量数据的查询规模.经过实验该方法提高了大规模海量数据的查询效率.     

虚拟手术中软组织切割模型研究进展*

虚拟手术仿真训练系统是虚拟现实的重要应用,能够克服真实传统手术中资源少、训练不方便和可重复操作性差等问题。软组织切割是虚拟手术仿真训练系统中的重要组成部分,也是手术中最常见的操作。软组织切割模型建立的质量如何,直接决定了虚拟手术仿真的精度度和仿真度。为更好地对软组织切割模型进行深入探索,分析了虚拟手术仿真中软组织切割的研究难点。针对当前十四种有代表性的切割模型进行了研究,在此基础上对这些模型的优缺点进行了比较,并对软组织切割模型的发展趋势进行了展望。     

基于CORBA技术的分布式入侵监测系统模型

网络技术迅速发展,网络规模曰益扩大,传统的入侵监测系统的功能受到很大限制。基于分布式计算的分布式入侵监测技术是解决问题的好方法。本文首先简要介绍网络安全现状、入侵检测技术和CORBA技术,然后给出应用CORBA建立的分布式入侵监测系统体系结构。     

Differentially private histogram publication

Differential privacy (DP) is a promising scheme for releasing the results of statistical queries on sensitive data, with strong privacy guarantees against adversaries with arbitrary background knowledge. Existing studies on differential privacy mostly focus on simple aggregations such as counts. This paper investigates the publication of DP-compliant histograms, which is an important analytical tool for showing the distribution of a random variable, e.g., hospital bill size for certain patients. Compared to simple aggregations whose results are purely numerical, a histogram query is inherently more complex, since it must also determine its structure, i.e., the ranges of the bins. As we demonstrate in the paper, a DP-compliant histogram with finer bins may actually lead to significantly lower accuracy than a coarser one, since the former requires stronger perturbations in order to satisfy DP. Moreover, the histogram structure itself may reveal sensitive information, which further complicates the problem. Motivated by this, we propose two novel mechanisms, namely NoiseFirst and StructureFirst, for computing DP-compliant histograms. Their main difference lies in the relative order of the noise injection and the histogram structure computation steps. NoiseFirst has the additional benefit that it can improve the accuracy of an already published DP-compliant histogram computed using a naive method. For each of proposed mechanisms, we design algorithms for computing the optimal histogram structure with two different objectives: minimizing the mean square error and the mean absolute error, respectively. Going one step further, we extend both mechanisms to answer arbitrary range queries. Extensive experiments, using several real datasets, confirm that our two proposals output highly accurate query answers and consistently outperform existing competitors.