A neural networks learning algorithm for minor component analysis and its convergence analysis

The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.     

基于虚拟多传感器信息融合的粮情预警系统

在粮食仓储过程中,通过温湿度传感器、图像传感器采集到的温湿度、图像的实时数据,经过信息处理算法的变换和特征提取,得到具有不同特征信息的虚拟多传感器数据,采用D-S证据理论对数据进行信息融合,对粮食在仓储过程中的霉变、虫害等粮情进行动态预测分析,当发现有不良变化时,该系统能够及时发出预警信息,以保证粮食的储藏安全。     

基于语义的VSM模型改进

文本聚类在很多领域都有广泛的应用,传统的文本聚类方法由于并不考虑语义因素,得出的聚类效果并不理想.利用语义对VSM模型进行变换,即基于语义对VSM模型的各维进行扭曲,将原本的正交坐标系基于语义变换为斜角坐标系,然后将文本的特征向量映射到变换后的VSM模型上再进行聚类,相对减小语义相关的特征向量间的语义距离,从而提高了文...     

投影深度向量分解融合PEMS的视角不变人体动作识别

针对摄像机内部参数的不确定性和投影平面选择难的问题,提出一种新的投影深度算法用于视角不变的动作识别,该算法采用对称镜面提取平面(PEMS)策略,有效解决了投影平面选择难的问题。首先通过摄像机组观察获得3D动作姿势,然后运用PEMS策略从场景中提取平面,相对于提取平面估计身体点的投影深度,最后使用这个信息进行动作识别。该算法的核心是投影平面的提取和投影深度组成向量的求解。利用本文算法在CMU MoCap数据集、TUM数据集和多视图IXMAS数据集上进行测试,精度可分别高达94%、91%和90%,且在较少动作实例情况下,仍然能够准确定义新动作。比较表明,本文算法的人体动作识别性能明显优于其他几种较新的算法。     

MR-SMP: A symmetric tridiagonal eigensolver for multi-core architectures

The computation of eigenvalues and eigenvectors of symmetric tridiagonal matrices arises frequently in applications; often as one of the steps in the solution of Hermitian and symmetric eigenproblems. While several accurate and efficient methods for the tridiagonal eigenproblem exist, their corresponding implementations usually target uni-processors or large distributed memory systems. Our new eigensolver MR³-SMP is instead specifically designed for multi-core and many-core general purpose processors, which today have effectively replaced uni-processors. We show that in most cases MR³-SMP is faster and achieves better speedups than state-of-the-art eigensolvers for uni-processors and distributed-memory systems.     

基于特征线段综合的图案元素生成

对于美术图案智能生成系统,其元素的获取,绘制及录入一直是一个瓶颈问题,为此提出了一种应用综合推理的思想来生成新的图案元素的方法,这种方法可以直接从已有图案元素的形象信息出发,根据一定的特征要求,迅速综合生成亲的图案元素或原的多种变化形态,因而大大丰富了图案元素库,并在一定程度上缓解了元素获取,绘制及录入问题。     

基于RBFCM和ECM的分层取证分析

为实现入侵证据的自动分析,设计一种基于取证图的分层取证分析方法。采用基于规则的模糊感知图模型,从局部识别出网络实体的状态,通过特征向量中心度计算得到重要的种子结点,再从大量攻击场景的关联结点中抽取攻击组。基于DARPA2000的实验结果表明,该方法在攻击组抽取和场景抽取方面具有较高的覆盖率和准确率。     

基于小波和奇异值分解的人脸识别方法

该文提出了一种基于小波和奇异值分解的人脸识别方法。首先对人脸图象进行小波分解，由于小波变换具有良好的多尺度特征表达能力，能将图象的大部分能量集中到低频子图中，使图象得到有效压缩。然后，对得到的每幅低频子图进行基于奇异值分解的特征提取，并将奇异值特征向量进行压缩，把压缩后的特征向量作为每幅人脸图象的特征，进而求出每一类人脸图象的特征向量中心。最后，将每一类的特征向量中心输入到分类器中进行识别。最终得到了令人满意的识别结果。     

a尺度Haar小波基的构造

对伸缩因子a为大于1的正整数的Haar小波基进行了统一构造，并给出了分解和重构算法。指出只需对一实对称矩阵求特征向量，便容易得到相应的a-1个母小波。     

Image denoising via sparse coding using eigenvectors of graph Laplacian

Image denoising plays an important role in image processing, which aims to separate clean images from the noisy images. A number of methods have been presented to deal with this practical problem in the past decades. In this paper, a sparse coding algorithm using eigenvectors of the graph Laplacian (EGL-SC) is proposed for image denoising by considering the global structures of images. To exploit the geometry attributes of images, the eigenvectors of the graph Laplacian, which are derived from the graph of noised patches, are incorporated in the sparse model as a set of basis functions. Sequently, the corresponding sparse coding problem is presented and efficiently solved with a relaxed iterative method in the framework of the double sparsity model. Meanwhile, as the denoising performance of the EGL-SC significantly depends on the number of the used eigenvectors, an optimal strategy for the number selection is employed. A parameter called as out-of-control rate is set to record the percentage of the denoised patches that suffer from serious residual errors in the sparse coding procedure. Thus, with the eigenvector number increasing, the appropriate number can be heuristically selected when the out-of-control rate falls below an empirical threshold. Experiments illustrate that the EGL-SC can achieve a better performance than some other well-developed denoising methods, especially in the structural similarity index for the noise of large deviations.