基于LMS自适应算法的随机信号去噪仿真与分析

分析了LMS自适应滤波的基本原理和固定步长算法,介绍了Auan方差分析法。基于Matlab实现了滤波系统的搭建,并针对某光纤陀螺原始数据设计了仿真实验,得到固定步长标度因数的可行取值范围为0．2～0．9。利用仿真实验比较了LMS自适应算法与小波分析、均值滤波的去噪效果,采用Allan方差给出了误差源分析。结果表明LMS自适应滤波能有效降低角度随机游走等随机噪声。     

参数自适应的条件随机场视频分割方法

针对已有算法需要采用一系列参数经验值得到最优视频分割结果的问题,提出根据视频特征自适应地计算视频邻域关系的特征强度函数,构造参数自适应的条件随机场视频分割模型。算法的核心思想是采用视频中像素之间的邻域关系自适应计算各个模型的特征函数,通过条件随机场模型对这些特征能量函数进行约束并利用Gibbs采样算法对该模型进行求解,获得全局优化的分割结果。针对不同环境下的视频分割实验表明,该算法能够很好地逼近最优经验参数所得到的视频分割结果,从而避免定义经验值所导致的算法局限性问题。     

用于计算机系统的铁电随机存储器的研究进展

综述了铁电存储器的存储原理、电路结构及应用、尚未解决的主要铁电材料的环保问题、疲劳失效问题、信息保持力和破坏性读出等问题.     

无线传感器网络中目标跟踪的马尔可夫模型与预测方法

针对目标运动是一个包含许多不确定因素的非线性非高斯随机过程,提出基于马尔可夫随机场模型与粒子滤波的WSN分布式目标跟踪方法(MRF-PF)。把目标跟踪过程看作是一个马尔可夫过程,基于贝叶斯规则,建立目标状态分布函数,用粒子滤波估计目标状态,实现目标跟踪。实验结果:对于泊松白噪声,MRF-PF方法的跟踪均方根误差RMSE相比卡尔曼滤波(KF)和扩展卡尔曼滤波(EKF)方法分别降低52.6%、49.2%;对于方差σ2由0.3→3的高斯噪声,GM-PF方法的RMSE相比KF、EKF分别降低54.5%～77.2%和23.5%～54.2%。这表明MRF-PF方法在非线性非高斯噪声或高斯噪声变化较大时具有较好的抗噪能力及跟踪性能。     

Using local transition probability models in Markov random fields for forest change detection

Change detection based on the comparison of independently classified images (i.e. post-classification comparison) is well-known to be negatively affected by classification errors of individual maps. Incorporating spatial-temporal contextual information in the classification helps to reduce the classification errors, thus improving change detection results. In this paper, spatial-temporal Markov Random Fields (MRF) models were used to integrate spatial-temporal information with spectral information for multi-temporal classification in an attempt to mitigate the impacts of classification errors on change detection. One important component in spatial-temporal MRF models is the specification of transition probabilities. Traditionally, a global transition probability model is used that assumes spatial stationarity of transition probabilities across an image scene, which may be invalid if areas have varying transition probabilities. By relaxing the stationarity assumption, we developed two local transition probability models to make the transition model locally adaptive to spatially varying transition probabilities. The first model called locally adjusted global transition model adapts to the local variation by multiplying a pixel-wise probability of change with the global transition model. The second model called pixel-wise transition model was developed as a fully local model based on the estimation of the pixel-wise joint probabilities. When applied to the forest change detection in Paraguay, the two local models showed significant improvements in the accuracy of identifying the change from forest to non-forest compared with traditional models. This indicates that the local transition probability models can present temporal information more accurately in change detection algorithms based on spatial-temporal classification of multi-temporal images. The comparison between the two local transition models showed that the fully local model better captured the spatial heterogeneity of the transition probabilities and achieved more stable and consistent results over different regions of a large image scene.     

Distributing test cases more evenly in adaptive random testing

Adaptive random testing (ART) has recently been proposed to enhance the failure-detection capability of random testing. In ART, test cases are not only randomly generated, but also evenly spread over the input domain. Various ART algorithms have been developed to evenly spread test cases in different ways. Previous studies have shown that some ART algorithms prefer to select test cases from the edge part of the input domain rather than from the centre part, that is, inputs do not have equal chance to be selected as test cases. Since we do not know where the failure-causing inputs are prior to testing, it is not desirable for inputs to have different chances of being selected as test cases. Therefore, in this paper, we investigate how to enhance some ART algorithms by offsetting the edge preference, and propose a new family of ART algorithms. A series of simulations have been conducted and it is shown that these new algorithms not only select test cases more evenly, but also have better failure detection capabilities.     

Nonparametric Bayesian Image Segmentation

Image segmentation algorithms partition the set of pixels of an image into a specific number of different, spatially homogeneous groups. We propose a nonparametric Bayesian model for histogram clustering which automatically determines the number of segments when spatial smoothness constraints on the class assignments are enforced by a Markov Random Field. A Dirichlet process prior controls the level of resolution which corresponds to the number of clusters in data with a unique cluster structure. The resulting posterior is efficiently sampled by a variant of a conjugate-case sampling algorithm for Dirichlet process mixture models. Experimental results are provided for real-world gray value images, synthetic aperture radar images and magnetic resonance imaging data.     

随机图点覆盖1度顶点核化算法分析

将随机图引入参数计算领域,利用随机图统计和概率分布等特性,从全局和整体上研究参数化点覆盖问题1度点核化过程中问题的核及度分布演变的内在机制和变化规律,并得出关于随机图1度点核化强度与顶点平均度关系及随机图点覆盖问题的决策与度分布关系的两个重要推论.最后分别从MIPS和BIND提取数据进行1度核化实验和分析.初步结果表明,对随机图点覆盖问题的分析方法不仅具有理论上的意义,而且随着问题随机度的大小而对问题有不同程度的把握能力.     

浅谈随机决策树

该文介绍了随机决策树分类模型及如何启发式选择随机决策树的深度及棵树,通过实验证明了该算法的有效性和高效性。     

非结构化P2P中搜索算法的性能分析

资源搜索是P2P应用所面临的最核心问题之一,相关的非结构化P2P系统主要采用了查询消息泛洪和信息索引机制.本文分析了非结构化P2P中几种典型的搜索算法,阐述了其搜索原理和搜索性能 .与其他研究不同,本文通过大量的模拟实验来验证搜索算法的性能;在不同网络规模中,通过对每一种搜索算法的查询成功率、搜索产生消息数和成功查询所用平均跳数进行比较分析,给出了相关结论.