基于拉普拉斯方向的差值线性判别分析

标准的LDA方法通常有3个问题:1)为了确保类内散度矩阵的非奇异性,必须首先通过PCA进行维数约简,这限制了对更多维数空间的使用;2)当每人只有单个训练样本时,类内散度矩阵必然奇异,此时LDA无法工作;3)缺乏对像素间的局部相关性的考虑。为了解决这些问题,提出一种基于拉普拉斯方向的差值线性判别分析方法。该方法通过拉普拉斯方向实现更鲁棒的图像相异性测度,通过引入差值散度矩阵来避免类内散度矩阵的奇异性。实验结果显示,该算法对表情变化、光照改变及不同遮挡情况获得了更高的识别率,尤其针对光照变化,效果更加显著。     

Multi-resolution feature fusion for face recognition

For face recognition, image features are first extracted and then matched to those features in a gallery set. The amount of information and the effectiveness of the features used will determine the recognition performance. In this paper, we propose a novel face recognition approach using information about face images at higher and lower resolutions so as to enhance the information content of the features that are extracted and combined at different resolutions. As the features from different resolutions should closely correlate with each other, we employ the cascaded generalized canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. To improve the performance and efficiency, we also employ “Gabor-feature hallucination”, which predicts the high-resolution (HR) Gabor features from the Gabor features of a face image directly by local linear regression. We also extend the algorithm to low-resolution (LR) face recognition, in which the medium-resolution (MR) and HR Gabor features of a LR input image are estimated directly. The LR Gabor features and the predicted MR and HR Gabor features are then fused using GCCA for LR face recognition. Our algorithm can avoid having to perform the interpolation/super-resolution of face images and having to extract HR Gabor features. Experimental results show that the proposed methods have a superior recognition rate and are more efficient than traditional methods.     

Graph regularized multiset canonical correlations with applications to joint feature extraction

Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.     

偏序时态模式下规范覆盖问题*

目前有效的多时间粒度时态数据库设计方法多针对全序时态模式,当时态模式的时态类型集为偏序集时,由于涉及时态类型间的最大下界等操作而使其难以实现。针对偏序时态模式的规范覆盖问题进行研究,提出了偏序TFD（时态函数依赖）集有限闭包、偏序模式属性集有限闭包、偏序无冗余覆盖和偏序规范TFD集等概念,解决了偏序TFD成员籍问题;并给出了偏序TFD集的规范覆盖算法,对其正确性进行了证明,对算法的时间复杂度进行了分析。为偏序时态模式的综合范式分解问题研究奠定基础。     

Analytic solution for Reissner plate bending based on new symplectic approach

This paper presents the analytic solution for Reissner plate bending derived by the symplectic geometry approach.Firstly,the basic equations for Reissner plate are transferred into Hamilton canonical equations.And then the whole state variables are separated.Finally,the solution is obtained according to the method of eigenfunction expansion in the symplectic geometry.Only the basic elasticity equations of Reissner plate are used in the present study and the pre-selection of the deformation function is abandoned,which is requisite in classical solution methods.Therefore,the utilized approach is completely reasonable and theoretical.To verify the accuracy and validity of the formulations derived,the numerical results are presented to compare with those available in the open literatures.     

基于典型相关分析法的顾客生命周期影响因素的实证研究

基于安徽省水泥行业B2B市场顾客的抽样调查数据,运用典型相关分析法实证研究水泥行业顾客生命周期的影响因素.利用SPSS13.0统计分析软件分析实证数据,通过因子分析、典型相关分析等方法研究顾客满意、顾客价值、顾客信任及其子因素与顾客生命周期之间的关系.结果表明,顾客满意、顾客价值和顾客信任是影响顾客生命周期的三大重要因素.     

Nonuniform sampling theorems for random signals in the linear canonical transform domain

Nonuniform sampling can be encountered in various practical processes because of random events or poor timebase. The analysis and applications of the nonuniform sampling for deterministic signals related to the linear canonical transform (LCT) have been well considered and researched, but up to now no papers have been published regarding the various nonuniform sampling theorems for random signals related to the LCT. The aim of this article is to explore the nonuniform sampling and reconstruction of random signals associated with the LCT. First, some special nonuniform sampling models are briefly introduced. Second, based on these models, some reconstruction theorems for random signals from various nonuniform samples associated with the LCT have been derived. Finally, the simulation results are made to prove the accuracy of the sampling theorems. In addition, the latent real practices of the nonuniform sampling for random signals have been also discussed.     

基于哈密顿解法的矩形厚板分析

建立了分析Reissner-Mindlin厚板问题的哈密顿解法。首先,以x坐标模拟时间坐标,选用互为对偶的混合变量作为基本变量,建立哈密顿正则微分方程组。然后,采用分离变量法和特征函数展开法在相应的边界条件下求出级数解。最后,给出矩形厚板典型例题的解答,分析了级数解的收敛性质。与常用的半逆解法相比,Hamilton解法有其优点:一是求解方法严密合理、有规可循;二是应用范围广,可用于求解系列问题。     

Real-time pixel based early apple bruise detection using short wave infrared hyperspectral imaging in combination with calibration and glare correction techniques

High speed data processing for online food quality inspection using hyperspectral imaging (HSI) is challenging as over hundred spectral images have to be analyzed simultaneously. In this study, a real-time pixel based early apple bruise detection system based on HSI in the shortwave infrared (SWIR) range has been developed. This systems consists of a novel, homogeneous SWIR illumination unit and a line scan camera. The system performance was tested on Jonagold apples bruised less than two hours before scanning. Partial least squares-discriminant analysis was used to discriminate bruised pixel spectra from sound pixel spectra. As the glossiness of many fruit and vegetables limits the accuracy in the detection of defects, several reflectance calibrations and pre-processing techniques were compared for glare correction and maximizing the signal to noise ratio. With the best combination of first derivative and mean centering, followed by image post-processing, this system was able to detect fresh bruises in thirty apples with 98% accuracy at the pixel level with a processing time per apple below 200 ms.     

CDMA时变色散信道的两级盲辨识算法

本文通过对时变信道的扩展函数在时频域进行正则采样，得到了CDMA时变系统的离散正则模型。针对该模型提出了一种具有较低运算量的两级盲辨识算法。为避免两级算法产生的误差传播效应，文章将理想SIMO模型等效为加性噪声模型来处理。与基展开模型方法相比，该算法具有不需利用观测数据的高阶统计量估计展开基频率的优点。文章通过仿真验证了该算法是可行的。