On the estimation of adequate test set size using fault failure rates

Test set size in terms of the number of test cases is an important consideration when testing software systems. Using too few test cases might result in poor fault detection and using too many might be very expensive and suffer from redundancy. We define the failure rate of a program as the fraction of test cases in an available test pool that result in execution failure on that program. This paper investigates the relationship between failure rates and the number of test cases required to detect the faults. Our experiments based on 11 sets of C programs suggest that an accurate estimation of failure rates of potential fault(s) in a program can provide a reliable estimate of adequate test set size with respect to fault detection and should therefore be one of the factors kept in mind during test set construction. Furthermore, the model proposed herein is fairly robust to incorrect estimations in failure rates and can still provide good predictive quality. Experiments are also performed to observe the relationship between multiple faults present in the same program using the concept of a failure rate. When predicting the effectiveness against a program with multiple faults, results indicate that not knowing the number of faults in the program is not a significant concern, as the predictive quality is typically not affected adversely.     

Input-output properties of the Page-Hinkley detector

We consider the stochastic input-output properties of a simple non-linear dynamical system, the so-called Page-Hinkley detector, playing a key role in change detection, and also in queuing theory. We show that for L-mixing inputs with negative expectation the output process of this system is L-mixing. The result is applied to get an upper bound for the false alarm rate. The proof is then adapted to get a similar result for the case of random i.i.d. inputs. Possible extensions and open problems are given in the discussion.     

Simulation-based two-stage estimation for multiple nonparametric regression

To reduce the curse of dimensionality arising from nonparametric estimation procedures for multiple nonparametric regression, in this paper we suggest a simulation-based two-stage estimation. We first introduce a simulation-based method to decompose the multiple nonparametric regression into two parts. The first part can be estimated with the parametric convergence rate and the second part is small enough so that it can be approximated by orthogonal basis functions with a small trade-off parameter. Then the linear combination of the first and second step estimators results in a two-stage estimator for the multiple regression function. Our method does not need any specified structural assumption on the regression function and it is proved that the newly proposed estimation is always consistent even if the trade-off parameter is designed to be small. Thus when the common nonparametric estimator such as local linear smoothing collapses because of the curse of dimensionality, our estimator still works well.     

One-sided multiple comparisons for treatment means with a control mean

We study comparisons of several treatments with a common control when it is believed a priori that the treatment means, μ_i, are at least as large as the control mean, μ₀. In this setting, which is called a tree ordering, we study multiple comparisons that determine whether μ_i>μ₀ or μ_i=μ₀ for each treatment. The classical procedure by Dunnett (1955) and the step-down and step-up techniques by [Dunnett and Tamhane, 1991] and [Dunnett and Tamhane, 1992] are well known. The results in Marcus and Talpaz (1992) provide multiple comparisons based on the maximum likelihood estimates restricted by the tree ordering. We also study two-stage procedures that consist of the likelihood ratio test of homogeneity with the alternative constrained by the tree ordering followed by two-sample t comparisons with possibly different critical values for the two-sample comparisons. Marcus et al. (1976) discuss the use of closed tests in such situations and propose using a closed version of the restricted likelihood ratio test. We describe step-down versions of the Marcus-Talpaz, the two-stage, and the likelihood ratio procedures, as well as a closed version of the Marcus-Talpaz multiple comparison procedure. Using Monte Carlo techniques, we study the familywise errors and powers of these procedures and make some recommendations concerning techniques that perform well for all tree ordered mean vectors.     

基于FPGA的四通道视频缩放引擎的研究及设计

设计了一种可实现4路视频信号缩放和帧率转换的电路架构.视频信号依次经过缩小模块、帧率转换模块以及放大模块,有效地减少了帧率转换对存储器带宽的需求.帧率变换模块采用输入和输出自适应调整的算法,同时在缩小模块采用加权均值算法,而放大模块则采用四点双三次插值算法.在满足视频放大质量要求的基础上,避免了采用过于复杂算法而消耗过...     

一种新型的科学健身训练仪的设计

随着科技技术的发展,人们的体力劳动越来越多的被现代化技术装置所取代,造成劳动者得了很多现代文明病。人们意识到健身运动的重要性。但不是任何形式、任何强度的运动都能达到健身的目的。针对这一问题设计了一种科学健身训练仪,通过监测人的心率和血氧饱和度,通过心率和血氧同运动强度的关系指导人们进行最佳健身训练,对过度运动预警,同时还具备了对运动效果评估的功能。系统电路具有小型化,模块化,低功耗的特点,较适合家庭健身训练时使用。     

半透膜仪采集空气中多氯联苯采样速度与其结构和性质定量构效关系研究

根据MOPAC2009软件包中PM6算法得到的分子描述符研究半透膜仪(SPMDs)采集大气中多氯联苯(PCBs)采样速度(R_(air))的定量构效关系(QSPR)模型,并分析影响R_(air)的关键因素。以半经验PM6算法得到的分子量子化学描述符作为预测变量,采用偏最小二乘算法(PLS)构建了R_(air)的QSPR模型。根据交叉验证,所得到的最佳模型中PLS成分解释的因变量的累积变异(Q~2_(cum))为0.683,这表明该模型具有良好的预测能力和稳健性。通过外部验证和将实验测得的R_(air)与预测得到的R_(air)进行比较,对所构建模型的稳定性和可靠性进行了验证,结果表明无论是训练组还是预测组,其预测值与实测值间均具有较好的线性关系,线性相关系数均大于0.8376。对PCBs采样速度R_(air)的主要影响因素为PCBs与SPMDs中甘油三油酸酯分子间的相互作用大小和为将PCBs溶解在甘油三油酸酯中形成洞穴所需能量要求。     

基于星地通信系统的变速率通信质量控制方法

为有效地提高星地通信系统的通信质量,提出了一种改变信息传输速率的方法VCTRM。该方法根据当前星地信道状况自适应地改变调制方式和码元传输速率,克服了采用变调制方式传输时在低信噪比时不能满足系统误比特率的缺陷,同时又解决了采用变码元传输速率在高信噪比时不能提高系统吞吐量的问题。通过Matlab对变调制方式、变码率及VCTRM三种传输方式进行了仿真,结果表明,VCTRM在不同信道状况下均能满足系统对误比特率(BER)的要求,且获得的系统吞吐量明显高于其他方法。     

基于各向异性质心Voronoi图的网络异常检测技术

网络异常检测技术是入侵检测研究领域中的重要内容,但在检测率和误报率上存在相互制约的问题,导致实际应用中性能不高。基于各向异性质心Voronoi图,提出一种新的网络异常检测算法。在新算法中,首先对数据集用各向异性质心Voronoi图进行聚类,然后计算每个数据点的点密度,判断数据点是否正常。通过KDD Cup1999数据集的实验测试表明,新算法具有较高的检测率和较低的误报率。     

卫星初始速度阻尼智能控制器设计

针对卫星入轨后的初始速率阻尼问题,提出一种基于人脑中情绪学习模型的在线自主自适应控制器。该控制器模拟人脑对感官输入和情绪刺激的处理过程,自主选择适当的控制信号,完成控制任务。设计了基于PID控制和PWPF调制的人脑情绪学习模型智能控制器用以完成卫星初始速率阻尼控制。仿真结果表明,该智能控制器对于卫星转动惯量的不确定性具有较强的鲁棒性,在线学习能力使得智能控制器的性能明显优于PID控制器。