基于独立成分分析的高光谱图像异常检测

针对高光谱图像中背景及目标先验知识未知条件下的异常目标检测问题,提出了一种基于独立成分分析(ICA)的异常探测算法.首先估计原始数据的虚拟维(VD)以确定要分离的独立成分个数,在此基础上进行快速独立成分分析(FastICA),然后基于平均局部奇异度选择含异常信息较多的独立成分,最后使用丰度量化算法得到异常目标的丰度图像...     

The many faces of radiation‐induced changes

During diffraction experiments even cryo‐cooled protein crystals can be significantly damaged due to chemical and physical changes induced by absorbed X‐ray photons. The character and scale of the observed effects depend strongly on the temperature and the composition of crystals. The absorption of radiation energy results in incremental regular changes to the crystal structure, making its impact on the process of solving the structure strongly correlated with other experimental variables. An understanding of all the dependencies is still limited and does not allow for a precise prediction of the outcome of a particular diffraction experiment. Results are presented of diffraction experiments performed under different experimental conditions. The influence of temperature and crystal composition on different characteristics of radiation damage is analyzed. The observed effects are discussed in terms of their impact on data processing and phasing procedures.     

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

In this paper, a new method to reduce noises within chaotic signals based on ICA(independent component analysis)and EMD(empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions(IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals.Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor.     

基于独立成分分析和经验模态分解的混沌信号降噪

基于经验模态分解和独立成分分析去噪的特点,提出了一种联合独立成分分析和经验模态分解的混沌信号降噪方法. 利用经验模态分解对混沌信号进行分解,根据平移不变经验模态分解的思想构造多维输入向量, 通过所构造的多维输入向量和独立成分分析对混沌信号的各层内蕴模态函数进行自适应去噪处理; 将处理后的所有内蕴模态函数进行累加重构,从而得到降噪后的混沌信号. 仿真实验中分别对叠加不同强度高斯噪声的Lorenz混沌信号及实际观测的月太阳黑子混沌序列进行了研究, 结果表明本文方法能够对混沌信号进行有效的降噪,而且能够较好地校正相空间中点的位置, 逼近真实的混沌吸引子轨迹. 关键词： 独立成分分析 经验模态分解 混沌信号 降噪     

A scanning Kelvin probe for synchrotron investigations: the in situ detection of radiation‐induced potential changes

A wide range of high‐performance X‐ray surface/interface characterization techniques are implemented nowadays at every synchrotron radiation source. However, these techniques are not always `non‐destructive' because possible beam‐induced electronic or structural changes may occur during X‐ray irradiation. As these changes may be at least partially reversible, an in situ technique is required for assessing their extent. Here the integration of a scanning Kelvin probe (SKP) set‐up with a synchrotron hard X‐ray interface scattering instrument for the in situ detection of work function variations resulting from X‐ray irradiation is reported. First results, obtained on bare sapphire and sapphire covered by a room‐temperature ionic liquid, are presented. In both cases a potential change was detected, which decayed and vanished after switching off the X‐ray beam. This demonstrates the usefulness of a SKP for in situ monitoring of surface/interface potentials during X‐ray materials characterization experiments.     

Novel graphical representation of genome sequence and its applications in similarity analysis

In order to compare different genome sequences, an alignment-free method has been proposed. Considering the essential property of sequence is sequentiality, we define a compound transformation which transforms a genome sequence into a sparse 16 by L−1$L-1$ matrix M based on 16 kinds of 2-mer (dinucleotides). Furthermore, we found the transformation above-mentioned is an order-preserving transformation (OPT). Based on the theory of matrix analysis, we derive a 16-dimensional vector to characterize a genome sequence via singular value decomposition (SVD) of M. Finally, we analyze the similarities among multiple sequences from 20 eutherian species. The experiment results show that our approach performs well in the field of sequence analysis.     

Analysis of functional networks involved in motor execution and motor imagery using combined hierarchical clustering analysis and independent component analysis

Cognitive experiments involving motor execution (ME) and motor imagery (MI) have been intensively studied using functional magnetic resonance imaging (fMRI). However, the functional networks of a multitask paradigm which include ME and MI were not widely explored. In this article, we aimed to investigate the functional networks involved in MI and ME using a method combining the hierarchical clustering analysis (HCA) and the independent component analysis (ICA). Ten right-handed subjects were recruited to participate a multitask experiment with conditions such as visual cue, MI, ME and rest. The results showed that four activation clusters were found including parts of the visual network, ME network, the MI network and parts of the resting state network. Furthermore, the integration among these functional networks was also revealed. The findings further demonstrated that the combined HCA with ICA approach was an effective method to analyze the fMRI data of multitasks.     

Noise source identification of diesel engine based on variational mode decomposition and robust independent component analysis

Through separating and identifying the noise sources of diesel engine, each independent noise obtained can be used as reference for the noise reduction, condition monitoring and fault diagnosis. In the noise source identification of diesel engine, the combustion noise and the piston slap noise are found to be overlapped in time domain and frequency domain. So it is intricate to accurately separate them. Therefore, the noise source identification method which is based on variational mode decomposition (VMD), robust independent component analysis (RobustICA) and continuous wavelet transform (CWT) is proposed. In the test, a 6-cylinder diesel engine was tested in a semi-anechoic chamber. The lead wrapped method was adopted to wrap No. 1–5 cylinders so as to isolate the interference noises, only the No. 6 cylinder part was bared. The single channel noise signal of cylinder head was measured. Then the variational mode decomposition algorithm is utilized to decompose the noise signal into several variational mode components. The RobustICA algorithm is adopted to extract the independent components. Finally, the continuous wavelet transform and the prior knowledge of diesel engine are applied to further identify the separated results. The results show that by using the proposed method to separate and identify the radiation noise of the cylinder head of the diesel engine, the independent components obtained are respectively the combustion noise and the piston slap noise. Comparing with the EEMD-RobustICA-CWT method, each independent noises obtained through the proposed method are more accurate and pure with less other interference noises.     

Separating 4D multi-task fMRI data of multiple subjects by independent component analysis with projection

Independent component analysis (ICA) is a widely accepted method to extract brain networks underlying cognitive processes from functional magnetic resonance imaging (fMRI) data. However, the application of ICA to multi-task fMRI data is limited due to the potential non-independency between task-related components. The ICA with projection (ICAp) method proposed by our group (Hum Brain Mapp 2009;30:417–31) is demonstrated to be able to solve the interactions among task-related components for single subject fMRI data. However, it still must be determined if ICAp is capable of processing multi-task fMRI data over a group of subjects. Moreover, it is unclear whether ICAp can be reliably applied to event-related (ER) fMRI data. In this study, we combined the projection method with the temporal concatenation method reported by Calhoun (Hum Brain Mapp 2008;29:828–38), referred to as group ICAp, to perform the group analysis of multi-task fMRI data. Both a human fMRI rest data-based simulation and real fMRI experiments, of block design and ER design, verified the feasibility and reliability of group ICAp, as well as demonstrated that ICAp had the strength to separate 4D multi-task fMRI data into multiple brain networks engaged in each cognitive task and to adequately find the commonalities and differences among multiple tasks.     

一种改进的微弱NQR信号检测算法

核四极矩共振（NQR）是一种固态射频谱分析技术,可用于检测高危险爆炸物. 然而NQR信号本身非常弱,并且易受线圈的热噪声和外部射频干扰的影响,低信噪比限制了NQR的实际应用. 该文提出一种改进的微弱NQR信号检测算法. 首先利用Hankel矩阵方式下奇异值分解的方法,有效地抑制射频干扰和噪声,并将NQR信号分离出来. 然后提出了一种基于MUSIC谱估计的非线性最小二乘检测器,它既保证了高的频率分辨率,又大大降低了运算量. 仿真数据和实测数据结果表明该算法的有效性.     

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.     

Cosmic rays of the highest energies

The article describes the study of cosmic rays above 10 19 eV. Following a brief introduction, the reasons for the current interest in this topic are discussed. The methods used to measure the properties of the very rare ultrahigh-energy cosmic rays are explained and the current data described. The data lead to an enigma and the many theories proposed to explain it are summarized. The properties and potential of new detectors are outlined.     

基于主元分析与动态时间弯曲的故障诊断方法及应用研究

轴承是工程实际中常用而又极易损坏的部件,特别是对其早期微弱响应的辨识,具有重要的社会价值和意义。为提高运转轴承的安全可靠性和可维护性,提出了基于主元分析与动态时间弯曲距离的故障诊断方法,它可以准确对早期微弱动态响应辨识、诊断。该方法首先将典型故障样本信号与待测信号小波去噪并EMD分解,并对若干固有模态分量主元分析求取主元,然后对主元分量进行分析,获得相关特征值组成特征向量,计算待测信号与已知故障样本信号特征向量的弯曲距离,弯曲距离越小表明两信号越相似,从而辨识故障。此外,还可将其应用于转子、碰磨、齿轮故障诊断中,工程应用实例表明该方法可以准确故障分类,高效故障诊断。     

Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

Laser-induced breakdown spectroscopy(LIBS) is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis(PCA) and support vector machine(SVM) is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy(91.38% for the real rock) is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated.     

Principal component analysis of non-uniformity in electrical characteristics of HgCdTe photodiode arrays

We present a method of analyzing the non-uniformity in electrical characteristics of HgCdTe photodiode arrays for infrared imaging applications. We have selected dynamic resistance–voltage (R–V) characteristics for analyzing electrical behavior of HgCdTe photodiodes because the dynamic resistance at a given operating voltage directly governs the imager performance and being derivative of current–voltage (I–V) characteristics, it has little impact of the constant shifts due to stray illumination during dark measurements, relaxing the stringent requirement of perfect dark conditions to some extent for performance analysis. We have demonstrated that by using statistical analysis such as correlation of the selected signatures and their principal component analysis, we can identify the root cause of the high non-uniformity among sensor pixels in the array. The method has been implemented using theoretical I–V model of MWIR HgCdTe photodiodes, but it is generic and may be implemented on any other types of diode arrays for theoretical or experimental analysis of their non-uniformity.     

Automatic frequency alignment and quantitation of single resonances in multiple magnetic resonance spectra via complex principal component analysis

Several algorithms for automatic frequency alignment and quantitation of single resonances in multiple magnetic resonance (MR) spectra are investigated. First, a careful comparison between the complex principal component analysis (PCA) and the Hankel total least squares-based methods for quantifying the resonances in the spectral sets of magnetic resonance spectroscopy imaging (MRSI) spectra is presented. Afterward, we discuss a method based on complex PCA plus linear regression and a method based on cross correlation of the magnitude spectra for correcting frequency shifts of resonances in sets of MR spectra. Their advantages and limitations are demonstrated on simulated MR data sets as well as on an in vivo MRSI data set of the human brain.     

Effects of soft X‐ray radiation damage on paraffin‐embedded rat tissues supported on ultralene: a chemical perspective

Radiation damage is an important aspect to be considered when analysing biological samples with X‐ray techniques as it can induce chemical and structural changes in the specimens. This work aims to provide new insights into the soft X‐ray induced radiation damage of the complete sample, including not only the biological tissue itself but also the substrate and embedding medium, and the tissue fixation procedure. Sample preparation and handling involves an unavoidable interaction with the sample matrix and could play an important role in the radiation‐damage mechanism. To understand the influence of sample preparation and handling on radiation damage, the effects of soft X‐ray exposure at different doses on ultralene, paraffin and on paraffin‐embedded rat tissues were studied using Fourier‐transform infrared (FTIR) microspectroscopy and X‐ray microscopy. Tissues were preserved with three different commonly used fixatives: formalin, glutaraldehyde and Karnovsky. FTIR results showed that ultralene and paraffin undergo a dose‐dependent degradation of their vibrational profiles, consistent with radiation‐induced oxidative damage. In addition, formalin fixative has been shown to improve the preservation of the secondary structure of proteins in tissues compared with both glutaraldehyde and Karnovsky fixation. However, conclusive considerations cannot be drawn on the optimal fixation protocol because of the interference introduced by both substrate and embedding medium in the spectral regions specific to tissue lipids, nucleic acids and carbohydrates. Notably, despite the detected alterations affecting the chemical architecture of the sample as a whole, composed of tissue, substrate and embedding medium, the structural morphology of the tissues at the micrometre scale is essentially preserved even at the highest exposure dose.     

Fourier‐transform Raman analysis of milk powder: a potential method for rapid quality screening

In this work, FT‐Raman spectroscopy was explored as a fast and reliable screening method for the assessment of milk powder quality and the identification of samples adulterated with whey (1–40% w/w). Raman measurements can easily differentiate milk powders without the need of sample preparation, whereas the traditional methods of quality control, including high‐performance liquid chromatography, are laborious and slow. The FT‐Raman spectra of whole, low‐fat, and skimmed milk powder samples were obtained and distinguished from commercial milk powder samples. In addition, the exploratory analysis employing data from Raman spectroscopy and principal component analysis (PCA)allowed the separation of milk powder samples according to type,identifying differences between samples in the same group. Multivariate analysis was also developed to classify the adulterated milk powder samples using PCA and partial least squares discriminate analysis (PLS‐DA). The resulting PLS‐DA model correctly classified 100% of the adulterated samples. These results clearly demonstrate the utility of FT‐Raman spectroscopy combined with chemometrics as a rapid method for screening milk powder. Copyright © 2011 John Wiley & Sons, Ltd.     

In vivo high‐resolution synchrotron radiation imaging of collagen‐induced arthritis in a rodent model

In vivo microstructures of the affected feet of collagen‐induced arthritic (CIA) mice were examined using a high‐resolution synchrotron radiation (SR) X‐ray refraction technique with a polychromatic beam issued from a bending magnet. The CIA models were obtained from six‐week‐old DBA/1J mice that were immunized with bovine type II collagen and grouped as grades 0–3 according to a clinical scoring for the severity of arthritis. An X‐ray shadow of a specimen was converted into a visual image on the surface of a CdWO₄ scintillator that was magnified using a microscopic objective lens before being captured with a digital charge‐coupled‐device camera. Various changes in the joint microstructure, including cartilage destruction, periosteal born formation, articular bone thinning and erosion, marrow invasion by pannus progression, and widening joint space, were clearly identified at each level of arthritis severity with an equivalent pixel size of 2.7 µm. These high‐resolution features of destruction in the CIA models have not previously been available from any other conventional imaging modalities except histological light microscopy. However, thickening of the synovial membrane was not resolved in composite images by the SR refraction imaging method. In conclusion, in vivo SR X‐ray microscopic imaging may have potential as a diagnostic tool in small animals that does not require a histochemical preparation stage in examining microstructural changes in joints affected with arthritis. The findings from the SR images are comparable with standard histopathology findings.     

Detailed spectroscopic analysis of complex multi‐component materials using a combination of Raman mapping with BTEM

The combined application of Raman microscopy and self‐modeling curve resolution techniques can address a wide range of material characterization problems. In particular, the combination of Raman microscopy and the Band‐Target Entropy Minimization (BTEM) algorithm has been applied to various organic, inorganic, pharmaceutical and bio‐material related problems. In the present contribution, the principles behind this type of analysis are reviewed, followed by a number of case‐by‐case studies. For each of these examples, a Raman microscopic mapping measurement (consisting of 100 s up to 1000 s of spectra) is performed, followed by BTEM analysis which provides the underlying pure component spectra of the constituents present in the system without the use of any a priori information. In most cases, outstanding signal‐to‐noise ratios for components at the 0.1‐1.0 % level can be obtained, and sometimes trace constituents can also be detected. Subsequently, the identity of the components can be determined by comparison to spectral libraries. Finally, the reconstructed pure component spectra can be further used to obtain the spatial distribution of the constituents present in the sample. Copyright © 2012 John Wiley & Sons, Ltd.