Polarized fluorescence spectroscopy of human tissues

We report the results of a study carried out to investigate the effect of blood absorption on polarized and unpolarized fluorescence from resected tissue samples and tissue phantoms. The signatures of blood absorption were found to be significantly smaller in polarized fluorescence than in unpolarized fluorescence spectra. The reduced effect of blood absorption on polarized fluorescence also leads to reduced site-to-site variability in polarized fluorescence intensity and line shape compared with unpolarized fluorescence.     

基于主分量分析法的小波域三维目标序列图像隐藏技术

针对在一幅载体图像中隐藏三维目标序列图像的问题,本文利用主分量分析法获取三维目标的本征图像,将本征图像的小波域系数嵌入到载体图像的小波域系数中,利用分解系数和提取得到的本征图像重构出三维目标的系列图像.本文所提出的方法不是直接存储目标图像,而是存储能够反映三维目标特征的一组本征图像.研究结果表明,该方法有效地将三维目标的特征隐藏在了载体图像中,隐藏信息量大. 关键词： 信息隐藏 三维目标 主分量分析 小波变换     

Application of wavelet transform in-ray spectra analysis

The frequency distribution of different ingredients in-ray spectra,e.g.,photo-peak,fluctuations of counts and Compton region,is separately analyzed.After wavelet transform of-ray spectra,the wavelet coefficients of a photo-peak increase with transforming scales and these coefficients show direct proportion with intensity of peak at determinate scale.A novel algorithm based on wavelet transform is proposed and studied.The results indicate that most of the photo-peaks in multi-spectra can be determined accurately,the-rays energy and intensity of the peak can also be determined.This method has the prospect of being applied in on-line multi-spectra analysis in such fields as radioprotection and nuclear safety monitoring.     

Error analysis of the principal component analysis demodulation algorithm

In this work, we present suitable phase accuracy indicators, which are obtained from the first three obtained eigenvalues of the principal component analysis (PCA) demodulation algorithm. These indicators can be used in the measuring process to determine a blind phase goodness assessment, without the need of using any ground truth phase information. Therefore, it is possible to perform further actions if required, as obtaining more interferograms or repeat the measure. Additionally, we present simulated and experimental results that support our mathematical analysis and conclusions. A complete MATLAB software package reproducing any result and figure shown in this work is provided in (http://goo.gl/fy5EC).     

Moment-wavelet quantization: a first principles analysis of quantum mechanics through continuous wavelet transform theory

The space of polynomials is invariant under affine maps. This suggests that a moment based analysis can facilitate a first principles incorporation of continuous wavelet transform (CWT) theory into quantum mechanics. We show that this is indeed the case for a large class of Hamiltonians and mother wavelet functions. We establish the equivalence between moment quantization (MQ) and CWT. By so doing, we clearly demonstrate the inherent multiscale structure of MQ analysis with regards to determining the physical energies and corresponding wavefunctions.     

NMR spectral quantitation by principal component analysis. III. A generalized procedure for determination of lineshape variations

We present a general procedure for automatic quantitation of a series of spectral peaks based on principal component analysis (PCA). PCA has been previously used for spectral quantitation of a single resonant peak of constant shape but variable amplitude. Here we extend this procedure to estimate all of the peak parameters: amplitude, position (frequency), phase and linewidth. The procedure consists of a series of iterative steps in which the estimates of position and phase from one stage of iteration are used to correct the spectra prior to the next stage. The process is convergent to a stable result, typically in less than 5 iterations. If desired, remaining linewidth variations can then be corrected. Correction of (typically) unwanted variations of these types is important not only for direct peak quantitation, but also as a preprocessing step for spectral data prior to application of pattern recognition/classification techniques. The procedure is demonstrated on simulated data and on a set of 992 (31)P NMR in vivo spectra taken from a kinetic study of rat muscle energetics. The proposed procedure is robust, makes very limited assumptions about the lineshape, and performs well with data of low signal-to-noise ratio.     

汉语塞音选择性特征自动萃取的小波变换方法

本文提出了汉语语音导引特征的概念,讨论了语音导引特征在自动语音识别中用于导引匹配搜索的运用方式和重要作用;提出并设计了汉语塞音选择性特征自动萃取的小波变换方法和数字信号处理算法。本文方法和算法从声学信号处理和检测的角度,将汉语清辅音声波输入信号自动分为塞音子集BDG:{b,d,g}、塞音子集ZZHJGPTcCHQK:{z,zh,j,g,p,t,c,ch,q,k}和擦音集FsSHhX:{f,s,sh,x,h};对输入的合清辅音的音节,计算检测并输出汉语自动语音识别系统可以利用的清辅音类属标记b.d.g、STOP/BD和f.s.sh.x.h以及它们的音段起始时标;从声学信息计算检测的角度为汉语自动语音识别系统提供一种新的\     

Measurement of transparence topography based on principal component analysis

An algorithm based on the principal component analysis (PCA) is proposed to measure transparent surface topography. First, many frames of random phase shifted fringe patterns are captured. Second, the first and second principal components are extracted from the phase shifted fringe patterns by PCA. Then, the phase is calculated from principal components. Finally, the correct global phase sign is determined by the clustering method considering local fringe phase monotonicity. The experimental results show that the proposed method can directly extract the three-dimensional (3D) shape, which does not need to determine the amount of phase shift, without precise phase shifter. It reduces the experimental hardware requirements and improves its adaptability in profilometry.     

基于特征点的整数小波目标跟踪方法的研究

基于提升方法的整数小波变换的诸多优点,以Harr整数小波变换为例,提出了动态目标跟踪算法。对标准图像进行整数Harr小波变换,并将提升项取整,对提升项的参数用一定数量训练图像进行学习。选取在训练参数平方和为最小意义上的、使整数小波变换后的图像高频部分具有较大值的点作为特征点。对包含有目标的参考图像进行整数小波变换,选择高频分量具有较大值的点,利用训练过的提升项参数使目标和基准图像配准。由于算法采用整数小波变换,使提取具有较好的鲁棒性,从而实现目标发生旋转、平移及尺度变化等的跟踪过程。仿真实验表明,该方法能对动态运动目标进行跟踪。     

Diagnostic features for the condition monitoring of hypoid gear utilizing the wavelet transform

The present paper contributes to the development of new efficient solutions aimed at improving vehicle functional safety through the implementation of new transmission diagnostic methods. The study focuses on a new perspective of the diagnostic frequency range for hypoid gear condition monitoring, using acoustic emission and vibro-acoustic signal measurements and the Discrete Wavelet Transform for data analysis. The identification of the most sensitive diagnostic parameters and determination of frequency intervals using the Discrete Wavelet Transform, in which the most significant increase in the values of diagnostic parameters can be seen, has been presented. In addition, the identification of the sensitivity of the above-introduced non-destructive methods is presented. Diagnostic results in the extended frequency range were compared with the results of the classical method, and it was found that the selected parameter in the proposed measuring range could better characterize the condition of the gear unit.     

主成分分析在舰船辐射噪声分类识别中的应用

主成分分析(PCA)是经典的多元统计分析方法,在处理多变量综合问题方面有比较突出的优势。本文主要探讨了主成分分析在舰船辐射噪声信号分类识别中的应用。在经典功率谱的基础上尝试将PCA技术运用在两种不同的方法中,对两种舰船辐射噪声进行了特征提取和分类识别,得到了较好的效果。     

Spectral data compression using weighted principal component analysis with consideration of human visual system and light sources

This paper proposed two weight functions based on principal component analysis (PCA) to reserve more colorimetric information in spectral data compression process. One weight function consisted of the CIE XYZ color-matching functions representing the characteristic of the human visual system, while another was made up of the CIE XYZ color-matching functions of human visual system and relative spectral power distribution of the CIE standard illuminant D65. The improvement obtained from the proposed two methods were tested to compress and reconstruct the reflectance spectra of 1600 glossy Munsell color chips and 1950 Natural Color System color chips as well as six multispectral images. The performance was evaluated by the mean values of color difference under the CIE 1931 standard colorimetric observer and the CIE standard illuminant D65 and A. The mean values of root mean square errors between the original and reconstructed spectra were also calculated. The experimental results show that the proposed two methods significantly outperform the standard PCA and another two weighted PCA in the aspects of colorimetric reconstruction accuracy with very slight degradation in spectral reconstruction accuracy. In addition, weight functions with the CIE standard illuminant D65 can improve the colorimetric reconstruction accuracy compared to weight functions without the CIE standard illuminant D65.     

A principal component analysis of transmission spectra of wine distillates

A chemometric method of decomposing multidimensional data into a small-sized space, the principal component method, has been applied to the transmission spectra of vintage Moldovan wine distillates. A sample of 42 distillates aged from four to 7 years from six producers has been used to show the possibility of identifying a producer in a two-dimensional space of principal components describing 94.5% of the data-matrix dispersion. Analysis of the loads into the first two principal components has shown that, in order to measure the optical characteristics of the samples under study using only two wavelengths, it is necessary to select 380 and 540 nm, instead of the standard 420 and 520 nm, to describe the variability of the distillates by one principal component or 370 and 520 nm to describe the variability by two principal components.     

Instantaneous intensity analysis of impulsive sound by the wavelet transform

The method for measuring instantaneous intensity by using the wavelet transform is proposed. We also conducted an analysis of the experimental data by using the hammering noise and confirmed the applicability of this calculation method.     

Hybrid two-dimensional continuous wavelet transform for analysis of phase-shifted interferograms

In interferogram or fringe pattern analysis, a technique capable of automatically analyzing interferograms that contain complex fringes and high-level noise as well as defects is highly demanded. In this paper, a hybrid technique combining the concepts of the two-dimensional continuous wavelet transform technique and the phase-shifting technique is proposed. The novel technique takes the advantages of the two existing techniques, and has the ability to accurately and automatically analyze multiple phase-shifted complex interferograms involving noise and defects. The validity of the technique is demonstrated by both computer simulation and real experiment.     

基于多尺度加权主成分分析的SF6红外光谱分析

利用红外光谱法分析SF6气体及其衍生物是判断气体绝缘组合电器(GIS)运行状态和故障的一种重要手段。传统的诊断方法过程繁琐、效率低下,而且受主观因素的影响较大。本文指出可以采用机器学习的方法实现GIS设备的故障诊断,并提出了多尺度加权主成分分析的特征提取方法。多尺度加权主成分分析结合了主成分分析和多尺度分解的特点,保证了尺度特征信息的最大化,并且修正了特征向量在数据分类时的权重。通过对广西电力研究院提供的SF6及其衍生物的红外光谱进行分析,证明了多尺度加权主成分分析算法对训练样本的分类效果要比标准的主成分分析算法好3～4倍。     

A principal component analysis of precipitation patterns over northern Italy

Summary A principal component analysis of rainfalls recorded during five winters (1981–1985) at about 70 stations in Northern Italy has been performed. The spatial distribution of the first four eigenvectors (whose principal components cumulatively account for about 81% of the total variance) is presented and discussed. A subsequent rotation of the first four principal components led to new spatial distributions which are are also described. Moreover, the rotation allowed associating the first three rotated principal components with atmospheric circulation patterns typically occurring in the area and belonging to a classification scheme of weather types on the Po Valley previously introduced by other authors on the base of a visual scanning of three years of daily maps.

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Riassunto è stata eseguita una analisi in componenti principali di dati di precipitazione raccolti durante cinque inverni (1981–1985) in circa 70 stazioni dell'Italia settentrionale. Vengono presentate e discusse le distribuzioni spaziali dei primi quattro autovettori, le cui componenti principali spiegano cumulativamente circa 81% della varianza totale. Una successiva rotazione delle prime quattro componenti principali ha condotto a nuove distribuzioni spaziali, che vengono anch'esse descritte. Inoltre la rotazione ha permesso di associare alle prime tre componenti principali ruotate dei modelli di circolazione atmosferica, tipicamente ricorrenti sull'area in questione e facenti parte di uno schema di classificazione dei tipi di tempo sulla Valle Padana introdotto precedentemente da altri autori sulla base di uno ?scanning? visuale di tre anni di mappe giornaliere.

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Резюме Проводится анализ главхых компонент количества вьавших осадков в течение пяти зим (1981–1985), зарегестрированных на 70 станциях в Северной Италии. Предлагается и обсуждается пространственное распределение первых четырех собственных векторов. Последующее вращение первых четырех главных компонент приводит к новым пространственным распределениям, которые также описываются и анализируются. Кроме того, указанное вращение позволяет связать первые три вращающиеся главные компоненты с диаграммами атмосферной цирркуляции, которая является типичной для этой области и пренадлежит к скхеме классификации погоды в долине По, введенной ранее друими авторами на основе визуального сканирования суточных карт в течение трех лет.

     

Finger crease pattern recognition using Legendre moments and principal component analysis

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper,we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the preprocessing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.     

小波变换重建超短脉冲光谱相位的误差分析

分析了噪声对小波变换重建超短脉冲光谱相位的影响. 在理想情况和附加噪声、倍增噪声， 以及干涉条纹采样量化干扰的情况下，分析了小波变换对几种光谱相位的重建精度. 结果显 示，在理想情况和噪声干扰的情况下，小波变换的相位重建精度都在003rad范围以内. 这 说明小波变换是一种精确可信的光谱相位重建方法，具有很强的抗噪声干扰能力. 另外，将 小波变换的轨迹图类比SHG FROG的轨迹图，可以直接定性判断脉冲的啁啾特征. 关键词： 超短脉冲 相位重建 小波变换 噪声     

Differentiation between human normal colon mucosa and colon cancer tissue using ToF-SIMS imaging technique and principal component analysis

Human normal colon mucosa and colon cancer tissue were studied using the time-of-flight secondary ion mass spectroscopy (ToF-SIMS) and principal component analysis (PCA) techniques. The surfaces of the tissues were successfully cleaned by C₆₀²⁺ cluster-ion beams before the ToF-SIMS images were obtained. A PCA on the spectra and images were performed to compare differences in the peaks and images of normal and cancer tissues. Significant differences in principal component 1 (PC 1) score values for normal and cancer tissues were observed, and each PC 1 loadings had a specific peak profile of proteins. In addition, the PC images obtained from the ToF-SIMS images for normal and cancer tissues were clearly distinguishable, and the amino acid fragments associated with normal and cancer tissues were found to have originated from the lamina propria region and the epithelium cells, respectively. Based on the PCA results, structural distortion of the crypts in the cancer colon tissue could be attributed to the proliferation of the cancerous epithelium cells. This work shows that the application of the ToF-SIMS imaging technique with PCA could be a useful method of obtaining valuable information for cancer analysis.