基于流形保持投影的驾驶疲劳识别

提出了一种基于流形保持投影的驾驶疲劳识别方法。利用光流技术计算人脸皮层的运动速度,并以此作为疲劳特征;为了有效地进行疲劳特征降维,在保局投影的基础上,将数据的非近邻信息引入目标函数中,提出了流形保持投影方法, 有效地保持了疲劳数据的局部流形结构和全局流形结构,同时利用格拉姆-施密特正交化过程解决了保局投影非正交问题。实验结果表明该方法具有很好的识别效果。     

基于双倍双精度施密特正交化方法的QR分解算法

当矩阵的规模较大或者条件数较高时，格拉姆-施密特(Gram-Schmidt)正交化算法和其相关修正算法时常表现出数值不稳定性的现象。为了解决该问题，探索了修正Gram-Schmidt算法(MGS)中舍入误差的累积效应，然后基于无误差变换技术和双倍双精度算法，设计并实现了双倍双精度修正Gram-Schmidt正交化算法(DDMGS)。该算法的精度测试中显示所提算法较分块施密特正交化(BMGS＿SVL,BMGS＿CWY,BCGS＿PIP与BCGS＿PIO)的变体算法具有更好的数值稳定性，证明了DDMGS算法能够有效地减少矩阵的正交性损失，提升数值精度，展示了所提算法的可靠性。在算法的性能测试中，首先计算并比较了不同算法的浮点计算量(flops),随后将所提DDMGS算法与修正施密特正交化算法在ARM和Intel两款处理器上作比较，虽然DDMGS算法的运行时间分别是MGS的5.03倍和18.06倍左右，但获得了明显的精度提升效果。     

一种核正交局部敏感辨别分析算法

为了发掘嵌入在人脸样本的非线性结构信息,把核方法和基向量正交化思想引入局部敏感分析算法中,提出一种新的人脸识别算法-核正交局部敏感辨别分析(Kernel based Orthogonal Locality Sensitive Discriminant Analysis).并给出了算法的推导过程及计算步骤.首先用核方法提取人脸样本的非线性信息,并将其投影至高维非线性空间,然后采用局部敏感辨别分析做线性映射,最后采用施密特正交化方法得到正交的基向量,从而使算法更好地描述人脸非线性流形结构特征.在ORL和YaleB人脸库的人脸识别实验证明了所提算法的有效性.     

室内可见光多用户MIMO系统改进BD预编码算法

块对角化(block diagonalization, BD)算法是一种多输入多输出的传统线性预编码算法, 其核心思想是通过奇异值分解(singular value decomposition, SVD)找到干扰矩阵零空间的正交基, 从而完全消除多用户干扰(multiuser interference, MUI), 但是随着收发端数目的增多, BD预编码算法所需的计算复杂也大大增加, 成为了制约其发展的关键因素之一. 为此, 本文提出了一种改进的低复杂度BD算法——基于正交分解中的施密特正交化求逆与格基规约操作的组合算法, 对传统BD算法两次高复杂度操作的奇异值分解用施密特正交化和格基规约操作进行替换, 从而降低算法复杂度. 结果表明, 本文改进算法的计算复杂度上降低了46.7%, 系统和容量上得到了2–10 bits/Hz的提高, 同时误码率上得到了2个量级的优化.     

正交降维内积及部分重新正交化

众所周知,经典的Gram-Schmidt正交化(GS)方法在计算上是不稳定的,为此提出了不少修改方案,Rice在[1]中提出了修正的Gram-Schmidt(MGS)方法,这是一个很成功的方法,尽管工作量没有改变,但此方法很稳定,本文将提出正交降维的内积法,分析其数值稳定性,然后再指出,为什么MGS方法有如此好的数值稳定性,第二节提出MGS的重新正交化方案及部分重新正交化技术,第三节提出部分正交降维内积部分重新正交化的新方案,最后对多种算法在PC-1500上作了数值试验,其结果与理论分析一致。     

基于自适应邻域选择的正交局部敏感判别分析

维数灾难是机器学习算法在高维数据上学习经常遇到的难题,基于局部敏感判别分析(locality sensitive discriminant analysis,LSDA),可以很好地解决维数灾难问题.且LSDA构建邻域时不能充分反映流形学习对邻域要求和克服测度扭曲问题,利用自适应邻域选择方法来度量邻域,同时,引入施密特正交化获得正交投影矩阵,提出一种自适应邻域选择的正交局部敏感判别分析算法.在ORL和YALE人脸数据库上进行实验,实验结果表明了该算法的有效性.     

一种新型CMOS施密特触发器

提出了一种新型的高速度全摆幅CMOS施密特触发器,其结构非常简单,只用了10个MOS管,其中包括两个开关管和两个反向器;通过分析计算给出了新型施密特触发器的阈值电平和磁滞宽度及传输延迟的计算方法,并且此施密特触发器具有磁滞宽度比较容易控制、转换速度快的特点;该电路采用0.6μm的CMOS工艺设计,并通过HSPICE验证,表明这种施密特触发器具有全摆幅输出、几乎不存在静态功耗、传输延迟几乎与负载无关等优点,比较适合在低电压、低功耗、高速便携设备中应用。     

基于施密特正交马田系统和φs转换的灰模糊积分关联度决策模型

针对传统灰关联度不能有效处理属性间存在的交互作用问题,定义了灰模糊积分关联度的概念,并给出了利用Фs函数将属性权重与属性间的交互度转换为λ模糊测度的方法;对于属性权重的计算,提出一种利用施密特正交马田系统计算属性权重的方法,该方法不但考虑了决策者的主观偏好,而且可以消除属性间的重叠信息,从而使权重的计算更加合理;构建了灰模糊积分关联度决策模型,并给出了详细的决策步骤.最后,通过实例验证了所提出的决策模型的可行性,并分析了不同交互度对决策结果的敏感性.     

CNDO／E—改进的CNDO分子轨道方法

本文提出一个适用于计算分子生成热的CNDO改进方法,该方法在计算核实Hamilton矩阵元中明确考虑了轨道基的正交化。在计算中将 s,p 轨道分开处理。同时,重新进行了参数化,并将计算的分子生成热与实验结果作了比较。     

正交化Fisher鉴别向量集及其应用

在正交化Fisher鉴别分析的基础上提出了正交化核Fisher鉴别分析方法。该方法具有理论简单、计算方便、特征表示能力强等优点。在CENPARMI手写体阿拉伯数字数据库、NUST603HW手写体汉字样本库和FERET人脸图像数据库上的仿真实验结果表明，正变化Fisher鉴别和正交化核Fisher鉴别方法在特征抽取能力和特征抽取效率方面均分别优于FoleySammon鉴别和核Foley-Sammon鉴别方法。     

Identification of errors-in-variables models with Faurre type realization algorithms

Identification of multiple input output discrete time linear dynamic systems operating in open or closed loop are considered in the time invariant case. Two methods have been used for such a purpose: the recursive prediction error method on the input-output data and the successive Gram-Schmidt orthogonalization of the spectral density function of the joint input-output variable.     

一种混合优化的结构自适应极限过程神经网络及应用

为解决过程神经网络的隐层结构和训练速度问题,在极限学习机的基础上,提出一种混合优化的结构自适应极限过程神经网络.首先,采用在隐层中逐次增加过程神经元节点直至满足输出误差的方式完成模型结构自适应;然后,为消除冗余节点,提出对新增临时节点输出实施Gram-Schmidt正交化完成相关性判别;最后,构建一种量子衍生布谷鸟算法,对新增节点输入权函数正交基展开系数实施寻优.仿真实验以Mackey-Glass和页岩油TOC预测为例,通过对比分析验证所提出方法的有效性,仿真结果表明所得模型的逼近效率和训练速度有明显提高.     

Accuracy of preconditioned CG-type methods for least squares problems

The conjugate gradient method with IMGS, an incomplete modified version of Gram-Schmidt orthogonalization to obtain an incomplete orthogonal factorization preconditioner, applied to the normal equations (PCGLS) is often used as the basic iterative method to solve the linear least squares problems. In this paper, a detailed analysis is given for understanding the effect of rounding errors on IMGS and determining the accuracy of computed solutions of PCGLS with IMGS for linear least squares problems in finite precision. It is shown that for a consistent system, the difference between the true residuals and the updated approximate residual vectors generated depends on the machine precision ε, on the maximum growth in norm of the iterates over their initial values, the norm of the true solution, and the condition number of R which is affected by the drop set in incomplete Gram-Schmidt factorization. Similar results are obtained for the difference between the true and computed solution for inconsistent systems. Numerical tests are carried out to confirm the theoretical conclusions.     

Face recognition using difference vector plus KPCA

In this paper, a novel approach for face recognition based on the difference vector plus kernel PCA is proposed. Difference vector is the difference between the original image and the common vector which is obtained by the images processed by the Gram-Schmidt orthogonalization and represents the common invariant properties of the class. The optimal feature vectors are obtained by KPCA procedure for the difference vectors. Recognition result is derived from finding the minimum distance between the test difference feature vectors and the training difference feature vectors. To test and evaluate the proposed approach performance, a series of experiments are performed on four face databases: ORL, Yale, FERET and AR face databases and the experimental results show that the proposed method is encouraging.     

Iterative algorithms for Gram-Schmidt orthogonalization

The algorithms that are treated in this paper are based on the classical and the modified Gram-Schmidt algorithms. It is shown that Gram-Schmidt orthogonalization for constructing aQR factorization should be carried out iteratively to obtain a matrixQ that is orthogonal in almost full working precision. In the formulation of the algorithms, the parts that express manipulations with matrices or vectors are clearly identified to enable an optimal implementation of the algorithms on parallel and/or vector machines. An extensive error analysis is presented. It shows, for instance, that the iterative classical algorithm is not inferior to the iterative modified algorithm when full precision ofQ is required. Experiments are reported to support the outcomes of the analysis.     

Vineyard identification in an oak woodland landscape with airborne digital camera imagery

Using airborne multispectral digital camera imagery, we compared a number of feature combination techniques in image classification to distinguish vineyard from non-vineyard land-cover types in northern California. Image processing techniques were applied to raw images to generate feature images including grey level co-occurrence based texture measures, low pass and Laplacian filtering results, Gram-Schmidt orthogonalization, principal components, and normalized difference vegetation index (NDVI). We used the maximum likelihood classifier for image classification. Accuracy assessment is performed using digitized boundaries of the vineyard blocks. The most successful classification as determined by t-tests of the Kappa coefficients was achieved based on the use of a texture image of homogeneity obtained from the near infrared image band, NDVI and brightness generated through orthogonalization analysis. This method averaged an overall accuracy of 81 per cent for six frames of images tested. With post-classification morphological processing (clumping and sieving) the overall accuracy was significantly increased to 87 per cent (with a confidence level of 0.99).     

基于正交非负矩阵分解的K-means聚类算法研究

为提高K-means聚类算法在高维数据下的聚类效果,提出了一种基于正交非负矩阵分解的K-means聚类算法。该算法对原始数据进行非负矩阵分解,并分别通过改进的Gram-Schmidt正交化和Householder正交化加入了正交约束,以保证低维特征的非负性,增加数据原型矩阵的正交性,然后进行K-means聚类。实验结果表明,基于IGS-ONMF和H-ONMF的K-means聚类算法在处理高维数据上具有更好的聚类效果。     

An Adaptive and Stable Method for Fitting Implicit Polynomial Curves and Surfaces

Representing 2D and 3D data sets with implicit polynomials (IPs) has been attractive because of its applicability to various computer vision issues. Therefore, many IP fitting methods have already been proposed. However, the existing fitting methods can be and need to be improved with respect to computational cost for deciding on the appropriate degree of the IP representation and to fitting accuracy, while still maintaining the stability of the fit. We propose a stable method for accurate fitting that automatically determines the moderate degree required. Our method increases the degree of IP until a satisfactory fitting result is obtained. The incrementability of QR decomposition with Gram-Schmidt orthogonalization gives our method computational efficiency. Furthermore, since the decomposition detects the instability element precisely, our method can selectively apply ridge regression-based constraints to that element only. As a result, our method achieves computational stability while maintaining fitting accuracy. Experimental results demonstrate the effectiveness of our method compared with prior methods.     

一种基于共同向量结合2DPCA的人脸识别方法

提出了一种基于共同向量结合2维主成分分析(2-dimen-sional principal component analysis, 2DPCA)的人脸识别方法. 共同向量由图像通过Gram-Schmidt正交变换而求得, 具有该类图像共同不变的性质. 原始图像与该类共同向量之间的差分向量通过2DPCA处理, 依据最小距离测试得到识别结果. 实验在ORL和Yale人脸数据库进行测试, 结果表明本文提出的方法有较好的识别性能.