利用梯度方向信息的随机Hough变换

针对随机Ｈｏｕｇｈ变换（ＲＨＴ）在处理复杂图像随机采样造成的大量无效积累提出一种改进的用于直线检测的ＲＨＴ，较发地解决了无效果累积问题，并使改进后的算法具有计算速度更快，占用内存更少以及检测性能更好等优点。     

Assessing the Error Probability of the Model Selection Test

The asymptotic error probability of Linhart's model selection test isevaluated, and compared with the nominal significance level. We examine thecase where the expected discrepancies of the candidate models from the truemodel are asymptotically equal. The local alternatives method is employed inthe limiting operation of the asymptotic evaluation. Although the errorprobability under the null hypothesis is actually shown to be equal to orless than the level for most situations, intolerable violations of the errorcontrol are observed for nested models: It is often erroneously concludedthat the smaller model is significantly better than the larger model. Toprevent this violation, a modification of Linhart's test statistic isproposed. The effectiveness of the proposed test is confirmed throughtheoretical analysis and numerical simulations.     

Monitoring the packet gap of real-time packet traffic

Real-time packet traffic is characterized by a strict deadline on the end-to-end time delay and an upper bound on the information loss. Due to the high correlation among consecutive packets, the individual packet loss does not well characterize the performance of real-time packet sessions. An additional measure of packet loss is necessary to adequately assess the quality of each real-time connection. The additional measure considered here is the average number of consecutively lost packets, also called the average packet gap. We derive a closed form for the average packet gap for the multiclassG/G/m/B queueing system in equilibrium and show that it only depends on the loss behavior of two consecutive packets. This result considerably simplifies the monitoring process of real-time packet traffic sessions. If the packet loss process is markovian, the consecutive packet loss has a geometric distribution.     

基于空间矩的灰度边缘亚像元度量精度分析

灰度图像中边缘定位受采样密度限制，亚像元精度度量方法在需要高精度的应用中受到重视，要想知识到底需要多高的分辨率才能达到定精度指标，就有必要分析特征定位方法的最坏情况的精度，文中用分析和实验的方法给出了基于空间矩的边缘定位方法，对于边缘点，边缘直线以及直边缘区域的最坏情况定位精度分析。分析和讨论的结果不但可以用来确定已知形状位置估计的最大误差，而且还可用于对准的形状几何参数，以获得较高的精度。     

高功率激光焊接光致等离子体的检测

系统地归纳了高功率激光焊接过程中光致等离子体的声、光、电、热等四种特征信号，综述了国外在等离子体信号检测方面的试验方法及研究进展，分析了未来的发展趋势     

一种基于Hough变换的声呐对目标航迹检测算法研究

为提高声呐对目标航迹的检测性能,从单部声呐时间融合的角度出发,提出了一种基于Hough变换的声呐信号检测算法。该算法把航迹上的观测点变换为参数空间的对应曲线,充分利用一段时间内的目标观测数据,在低信噪比下也能从背景噪声中提取目标的航迹特征,提高了声呐的检测概率,且对部分信息和数据的缺失不敏感。仿真试验证明了该算法的有效性。     

入侵检测系统技术现状及其发展趋势

阐述了入侵检测系统（IDS）的起源、发展和分类，介绍了它的结构和标准化工作，对入侵检测系统存在的问题及发展趋势作了概述。     

Estimating Invariant Probability Densities for Dynamical Systems

Knowing a probability density (ideally, an invariant density) for the trajectories of a dynamical system allows many significant estimates to be made, from the well-known dynamical invariants such as Lyapunov exponents and mutual information to conditional probabilities which are potentially more suitable for prediction than the single number produced by most predictors. Densities on typical attractors have properties, such as singularity with respect to Lebesgue measure, which make standard density estimators less useful than one would hope. In this paper we present a new method of estimating densities which can smooth in a way that tends to preserve fractal structure down to some level, and that also maintains invariance. We demonstrate with applications to real and artificial data.     

液体可视图象序列中杂质小目标的检测方法

在分析红外图象和视图象差异的基础上，研究了一种适用于可视图像序列的运动小目标检测算法。受目标影响的象素点具有较弱的时间相关性，即相对普通象素点有较大的时间方差，根据此特征，用管道更新的方法产生新方差图象序列，再用管道更新的方法累积，增加信噪比，然后用基于统计均值的自适应门限方法分割出目标，最后将原图象的梯度倒数作为象素点强度的加权，有效地抑制灰度突变边界的传感器噪音，保存目标点。     

Electrical detection of nuclear spin echoes

We show, via a straightforward calculation, that it is feasible to detect nuclear spin echoes in a ferromagnetic thin film by electrical means. The detection scheme is based on the deflection of the electronic magnetization when the nuclear magnetization reforms during an echo. This deflection is observed as a time-dependent magnetoresistive signal.