基于PCA和ICA的人脸识别

提出利用主成分分析(PCA)和独立成分分析(ICA)相结合的方法对人脸进行识别。首先对预处理后的图像进行降维,即利用PCA算法对图像进行去二阶相关和降维处理,然后再利用ICA算法获得人脸影像独立基成分,利用人脸影像独立基来构造一子空间,最后利用待识别图像在这个空间上的投影系数进行人脸识别。从两个不同的数据集,将传统的PCA人脸识别算法和提出的人脸识别算法进行比较。从实验数据结果看,提出的PCA和ICA结合人脸识别算法优于传统的PCA人脸识别算法。     

基于主成分分析和独立成分分析的调制分类算法

基于主成分分析(PCA)和独立成分分析(ICA),提出了一种新的调制分类算法。算法采用PCA对样本数据降维、去除冗余成分,采用FastICA方法提取分类特征;采用支持矢量机(SVM)作为分类器,以解决数据在低维空间中的不可分问题。该算法具有较低的复杂度和较高的训练速度。仿真表明,与最大似然(ML)算法相比,算法仅具有1.8 dB的信噪比损失,在Rayleigh慢衰落信道和中速运动的条件下,算法对5种QAM调制类型具有较好的分类性能。     

Bayesian kernel methods for analysis of functional neuroimages

We propose an approach to analyzing functional neuroimages in which 1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data and 2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). Kernel methods have become a staple of modern machine learning. Herein, we show that these techniques show promise for neuroimage analysis. In an on-off design, we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude, and/or size. We employ two Bayesian methods of estimating the kernel functions. The first is a maximum a posteriori (MAP) estimation method based on a Reversible-Jump Markov-chain Monte-Carlo (RJMCMC) algorithm that searches for both the appropriate model complexity and parameter values. The second is a relevance vector machine (RVM), a kernel machine that is known to be effective in controlling model complexity (and thus discouraging overfitting). In each method, after estimating the activation pattern, we test for local activation using a GLRT. We evaluate the results using receiver operating characteristic (ROC) curves for simulated neuroimaging data and example results for real fMRI data. We find that, while RVM and RJMCMC both produce good results, RVM requires far less computation time, and thus appears to be the more promising of the two approaches.     

Data compression on the illumination adjustable images by PCA and ICA

In the image-based relighting (IBL), tremendous reference images are needed to provide a high quality rendering. Therefore, a data compression is a must for its real applications. In this paper, two global analysis methods, the principal component analysis (PCA) and the independent component analysis (ICA), are used to compress the huge IBL data by exploiting its correlation properties. Both approaches approximate the raw data with a small number of global base images, and they follow a similar algorithm structure: base images extraction, raw data representation, and further compression on the base images and the representing coefficients. What differs is that PCA only removes the second-order data correlation, but ICA reduces nearly all order statistics data dependence, which should benefit the data compression. Simulations are given to evaluate their performance. Comparisons are also made between them and JPEG2000 and MPEG. The evaluation results show that both approaches are superior to JPEG2000 and MPEG. Although ICA tends to remove higher order dependence than PCA, it is a little inferior to PCA in terms of compression ratio/reconstruction error performance.     

基于仿生模式识别和PCA/ICA的DOA估计方法

本文提出了一种基于仿生模式识别和PCA/ICA的DOA估计方法.这种方法的建模过程是用在实际环境下采集的训练样本构造人工神经网络模型,对环境的适应能力较强;且这种方法采用PCA/ICA进行特征提取,使数据得到有效压缩,可以实现系统实时处理.实验结果表明:在信噪比为20dB和0dB时,该方法的正确估计率可达100%;在信噪比降为-20dB时,该方法仍有83%的可识别率.     

Projection spectral analysis: A unified approach to PCA and ICA with incremental learning

Projection spectral analysis is investigated and refined in this paper, in order to unify principal component analysis and independent component analysis. Singular value decomposition and spectral theorems are applied to nonsymmetric correlation or covariance matrices with multiplicities or singularities, where projections and nilpotents are obtained. Therefore, the suggested approach not only utilizes a sum-product of orthogonal projection operators and real distinct eigenvalues for squared singular values, but also reduces the dimension of correlation or covariance if there are multiple zero eigenvalues. Moreover, incremental learning strategies of projection spectral analysis are also suggested to improve the performance.     

MR盒子设计与基于特征点匹配的空间对准

文中设计了一款适用于智能手机的混合现实盒子,用于书画临摹训练。它通过一块半透半反镜将手机屏幕上的立体视差图像反射到观察者眼中,同时借助手机后置摄像头捕捉周围环境,借助特征点匹配方法监测手机位置的变化,进而改变所显示的虚拟立体景物的位置,使之与周围真实环境的空间关系维持不变。实验表明,文中提出的基于特征点匹配的方法可以较好地解决MR中的空间对准难题。     

配向膜对LCD对比度的影响

目前,LCD行业已经趋于成熟。各大面板企业都在提升自身产品的竞争力:高分辨率、高色域、轻质化、窄边框化以及高对比度等等。本文从聚酰亚胺取向层材料方面着手提高对比度进行了研究与阐述。研究过程中利用实验室与产线进行了实际测试,通过对比不同性质的取向膜材料所制作的平面电场宽视角(ADS)模式液晶面板,证明了配向能力越高的取向材料得到的面板对比度越高。高配向力的取向层可以使液晶分子的排列更加有序化,在常暗模式的FFS显示器中,初始位置的液晶分子预倾角越低,液晶面板的暗态越好,在亮态相同的情况下,对比度将会提高。     

Automatic identification of functional clusters in FMRI data using spatial dependence

In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a detailed functional segmentation of brain. However, such high-order segmentation does not establish the relationships among different brain networks, and also studying and classifying components can be challenging. In this study, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In our MICA framework, stable components are hierarchically grouped into clusters based on higher order statistical dependence--mutual information--among spatial components, instead of the typically used temporal correlation among time courses. The final cluster membership is determined using a statistical hypothesis testing method. Since ICA decomposition takes into account the modulation of the spatial maps, i.e., temporal information, our ICA-based approach incorporates both spatial and temporal information effectively. Our experimental results from both simulated and real fMRI datasets show that the use of spatial dependence leads to physiologically meaningful connectivity structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid, arteries, and large draining veins, are grouped together and encouragingly distinguished from other components of interest.     

Accurate alignment of functional EPI data to anatomical MRI using a physics-based distortion model

Mapping of functional magnetic resonance imaging (fMRI) to conventional anatomical MRI is a valuable step in the interpretation of fMRI activations. One of the main limits on the accuracy of this alignment arises from differences in the geometric distortion induced by magnetic field inhomogeneity. This paper describes an approach to the registration of echo planar image (EPI) data to conventional anatomical images which takes into account this difference in geometric distortion. We make use of an additional spin echo EPI image and use the known signal conservation in spin echo distortion to derive a specialized multimodality nonrigid registration algorithm. We also examine a plausible modification using log-intensity evaluation of the criterion to provide increased sensitivity in areas of low EPI signal. A phantom-based imaging experiment is used to evaluate the behavior of the different criteria, comparing nonrigid displacement estimates to those provided by a imagnetic field mapping acquisition. The algorithm is then applied to a range of nine brain imaging studies illustrating global and local improvement in the anatomical alignment and localization of fMRI activations.     

弹载环境时间延迟对传递对准影响及估计方法

弹载惯组传递对准过程中基准信息时延会制约对准性能的提高。基于常用的速度/姿态匹配模式,建立含时间延迟模型的观测方程,并提出时间延迟闭环滤波估计方法。分别从匹配信息精度、对准精度、惯性器件误差估计等方面分析时间延迟的影响情况。仿真结果表明,时间延迟会引起对准误差振荡,引起器件零偏、标度误差的有偏估计,通过闭环滤波方法可以准确估计时间延迟误差,同时能有效抑制时间延迟对传递对准及惯性器件误差估计精度的影响。     

取向层厚度及介电常数对TN-LCD影响的研究

在具有高驱动电压的液晶显示器中,为了对液晶器件起到保护作用,以采用增加取向层厚度的方法来解决某些显示器件由于驱动电压过高导致的问题。当取向层厚度不可忽略时,随着取向层厚度增加会导致器件驱动电压升高。本文利用扭曲向列相液晶显示器结构,通过模拟和实验分析了取向层厚度对LCD的影响以及不同介电常数的取向层对LCD的影响。结果表明:当取向层的介电常数大于20时,能够有效降低TN-LCD驱动电压。当其介电常数大于500时,其厚度变化对驱动电压的影响变得很小。本文结果对降低高驱动电压液晶器件的驱动电压有重要的指导性意义。     

Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases

Current trends in clinical applications demand automation in electrocardiogram (ECG) signal processing and heart beat classification. This paper examines the design of an effective recognition method to diagnose heart diseases. The proposed method consists of three main modules: de-noising module, feature extraction module, and classifier module. In the de-noising module, multiscale principal component analysis (MSPCA) is used for noise reduction of the ECG signals. In the feature extraction module, autoregressive (AR) modeling is used for extracting features. In the classifier module, different classifiers are examined such as simple logistic, k-nearest neighbor, multilayer perceptron, radial basis function networks, and support vector machines. Different experiments are carried out using the MIT-BIH arrhythmia database to classify different ECG heart beats and the performance of the proposed method is evaluated in terms of several standard metrics. The experimental results show that the proposed method is able to reduce noise from the noisy ECG signals more accurately in comparison to previous methods. The numerical results indicated that the proposed algorithm achieved 99.93 % of the classification accuracy using MSPCA de-noising and AR modeling.     

主成分分析法在学生评教中的应用研究

高校学生评教数据是反映教师教学质量的一项重要指标,基于此文中使用真实的评教样本,采用主成分分析方法对学生评教数据进行量化分析,确定决定教师教学质量的各主成分,然后计算出参与评价的每一位教师在各主成分上的得分,并根据得分进行排名。通过各主成分的排名和综合排名能够反映出教师在实际教学中存在的问题,为教师今后教学和教学管理部门制定教学改革措施提供参考依据。     

主成分分析的图像压缩与重构

针对图像占用空间大,特征表示时维数较高等的缺点,系统介绍了主成分分析（PCA）的基本原理。提出了利用PCA进行图像数据压缩与重建的基本模型。实验结果表明,利用PCA能有效的减少数据的维数,进行特征提取,实现图像压缩,同时并根据实际需要重建图像。     

Effect of temperature cycling on angular alignment in add/drop filter-module packaging

The thermally-induced, fiber-alignment shifts of ferrule-solder-ferrule (FSF) joints in add/drop filter-module packaging for multiplexer/demultiplexer applications have been studied experimentally and numerically. From experimental measurements of the module-insertion loss difference (0.3 dB) with and without temperature cycling, FSF angular-tilt misalignments of up to 0.05° were found after undergoing 42 temperature cycles. The effects of an imperfect soldering process along the two ferrules during the packaging of an add/drop filter module on the angular-tilt misalignment has been explored in this study. An elastic-plastic finite-element method (FEM) was performed to evaluate the variation of thermal stresses, the distribution of residual stresses, and the angular-tilt misalignment of the FSF joints. Experimental measurements of the FSF angular-tilt misalignment were in good agreement with the FEM calculations. The major angular-tilt misalignments of FSF joints in add/drop filter-module packaging during temperature cycling may come from the localized plastic-solder yielding introduced by the local thermal-stress variation and the redistribution of the residual stresses within the solder. The FSF angular-tilt misalignment and, hence, fiber-alignment shift of the add/drop filter module under temperature-cycling tests can be reduced significantly if a perfect soldering process along the two ferrules can be fabricated.     

IP网安全失控顽症和ICA进化

在“虚拟 开放”环境中运行的网络，网络诚信是不可回避的问题。网络缺乏安全保障，今已成为举世之忧。中解析了IP网为什么不可能克服其安全失控，阐述了对症治理措施，介绍了ICA进化。     

Testing for spatial heterogeneity in functional MRI using the multivariate general linear model

Much current research in functional magnetic resonance imaging (fMRI) employs multivariate machine learning approaches (e.g., support vector machines) to detect distributed spatial patterns from the temporal fluctuations of the neural signal. The aim of many studies is not classification, however, but investigation of multivariate spatial patterns, which pattern classifiers detect only indirectly. Here we propose a direct statistical measure for the existence of distributed spatial patterns (or spatial heterogeneity) applicable to fMRI datasets. We extend the univariate general linear model (GLM), typically used in fMRI analysis, to a multivariate case. We demonstrate that contrasting maximum likelihood estimations of different restrictions on this multivariate model can be used to estimate the extent of spatial heterogeneity in fMRI data. Under asymptotic assumptions inference can be made with reference to the χ(2) distribution. The test statistic is then assessed using simulated timecourses derived from real fMRI data followed by analyzing data from a real fMRI experiment. These analyses demonstrate the utility of the proposed measure of heterogeneity as well as considerations in its application. Measuring spatial heterogeneity in fMRI has important theoretical implications in its own right and may have potential uses for better characterising neurological conditions such as stroke and Alzheimer's disease.     

卫星混合信号分离中的ICA应用技术

针对与卫星有用信号频谱相混叠的干扰信号不易检测识别的问题．本文提出一种利用独立分量分析（ICA）理论将有用信号与干扰信号分离的卫星干扰信号检测识别方法,并在Matlab中采用FastlCA算法对混合卫星干扰信号进行了仿真。仿真结果表明该方法能有效地将有用信号与干扰信号进行分离．为进一步采取相应抗干扰措施提供了依据。     

光束偏移量对金刚石锯片激光焊接质量的影响

光束偏移量是金刚石锯片激光焊接的一个重要的工艺参数,它直接影响焊缝中的气孔、成分及其分布和组织。