Science.gov

Sample records for addition principal component

  1. Kernel Near Principal Component Analysis

    SciTech Connect

    MARTIN, SHAWN B.

    2002-07-01

    We propose a novel algorithm based on Principal Component Analysis (PCA). First, we present an interesting approximation of PCA using Gram-Schmidt orthonormalization. Next, we combine our approximation with the kernel functions from Support Vector Machines (SVMs) to provide a nonlinear generalization of PCA. After benchmarking our algorithm in the linear case, we explore its use in both the linear and nonlinear cases. We include applications to face data analysis, handwritten digit recognition, and fluid flow.

  2. Fast Steerable Principal Component Analysis

    PubMed Central

    Zhao, Zhizhen; Shkolnisky, Yoel; Singer, Amit

    2016-01-01

    Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2-D images as large as a few hundred pixels in each direction. Here, we introduce an algorithm that efficiently and accurately performs principal component analysis (PCA) for a large set of 2-D images, and, for each image, the set of its uniform rotations in the plane and their reflections. For a dataset consisting of n images of size L × L pixels, the computational complexity of our algorithm is O(nL3 + L4), while existing algorithms take O(nL4). The new algorithm computes the expansion coefficients of the images in a Fourier–Bessel basis efficiently using the nonuniform fast Fourier transform. We compare the accuracy and efficiency of the new algorithm with traditional PCA and existing algorithms for steerable PCA. PMID:27570801

  3. Nonlinear principal component analysis of climate data

    SciTech Connect

    Boyle, J.; Sengupta, S.

    1995-06-01

    This paper presents the details of the nonlinear principal component analysis of climate data. Topic discussed include: connection with principal component analysis; network architecture; analysis of the standard routine (PRINC); and results.

  4. PROJECTED PRINCIPAL COMPONENT ANALYSIS IN FACTOR MODELS

    PubMed Central

    Fan, Jianqing; Liao, Yuan; Wang, Weichen

    2016-01-01

    This paper introduces a Projected Principal Component Analysis (Projected-PCA), which employees principal component analysis to the projected (smoothed) data matrix onto a given linear space spanned by covariates. When it applies to high-dimensional factor analysis, the projection removes noise components. We show that the unobserved latent factors can be more accurately estimated than the conventional PCA if the projection is genuine, or more precisely, when the factor loading matrices are related to the projected linear space. When the dimensionality is large, the factors can be estimated accurately even when the sample size is finite. We propose a flexible semi-parametric factor model, which decomposes the factor loading matrix into the component that can be explained by subject-specific covariates and the orthogonal residual component. The covariates’ effects on the factor loadings are further modeled by the additive model via sieve approximations. By using the newly proposed Projected-PCA, the rates of convergence of the smooth factor loading matrices are obtained, which are much faster than those of the conventional factor analysis. The convergence is achieved even when the sample size is finite and is particularly appealing in the high-dimension-low-sample-size situation. This leads us to developing nonparametric tests on whether observed covariates have explaining powers on the loadings and whether they fully explain the loadings. The proposed method is illustrated by both simulated data and the returns of the components of the S&P 500 index. PMID:26783374

  5. Complex Principal Components for Robust Motion Estimation

    PubMed Central

    Mauldin, F. William; Viola, Francesco; Walker, William F.

    2010-01-01

    Bias and variance errors in motion estimation result from electronic noise, decorrelation, aliasing, and inherent algorithm limitations. Unlike most error sources, decorrelation is coherent over time and has the same power spectrum as the signal. Thus, reducing decorrelation is impossible through frequency domain filtering or simple averaging and must be achieved through other methods. In this paper, we present a novel motion estimator, termed the principal component displacement estimator (PCDE), which takes advantage of the signal separation capabilities of principal component analysis (PCA) to reject decorrelation and noise. Furthermore, PCDE only requires the computation of a single principal component, enabling computational speed that is on the same order of magnitude or faster than the commonly used Loupas algorithm. Unlike prior PCA strategies, PCDE uses complex data to generate motion estimates using only a single principal component. The use of complex echo data is critical because it allows for separation of signal components based on motion, which is revealed through phase changes of the complex principal components. PCDE operates on the assumption that the signal component of interest is also the most energetic component in an ensemble of echo data. This assumption holds in most clinical ultrasound environments. However, in environments where electronic noise SNR is less than 0 dB or in blood flow data for which the wall signal dominates the signal from blood flow, the calculation of more than one PC is required to obtain the signal of interest. We simulated synthetic ultrasound data to assess the performance of PCDE over a wide range of imaging conditions and in the presence of decorrelation and additive noise. Under typical ultrasonic elasticity imaging conditions (0.98 signal correlation, 25 dB SNR, 1 sample shift), PCDE decreased estimation bias by more than 10% and standard deviation by more than 30% compared with the Loupas method and normalized

  6. Principal component analysis for designed experiments

    PubMed Central

    2015-01-01

    Background Principal component analysis is used to summarize matrix data, such as found in transcriptome, proteome or metabolome and medical examinations, into fewer dimensions by fitting the matrix to orthogonal axes. Although this methodology is frequently used in multivariate analyses, it has disadvantages when applied to experimental data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the data set. Second, the method is sensitive to experimental noise and bias between sample groups. It cannot reflect the experimental design that is planned to manage the noise and bias; rather, it estimates the same weight and independence to all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. First, the principal axes were identified using training data sets and shared across experiments. These training data reflect the design of experiments, and their preparation allows noise to be reduced and group bias to be removed. Second, the center of the rotation was determined in accordance with the experimental design. Third, the resulting components were scaled to unify their size unit. Results The effects of these options were observed in microarray experiments, and showed an improvement in the separation of groups and robustness to noise. The range of scaled scores was unaffected by the number of items. Additionally, unknown samples were appropriately classified using pre-arranged axes. Furthermore, these axes well reflected the characteristics of groups in the experiments. As was observed, the scaling of the components and sharing of axes enabled comparisons of the components beyond experiments. The use of training data reduced the effects of noise and bias in the data, facilitating the physical interpretation of the

  7. Nonlinear Principal Components Analysis: Introduction and Application

    ERIC Educational Resources Information Center

    Linting, Marielle; Meulman, Jacqueline J.; Groenen, Patrick J. F.; van der Koojj, Anita J.

    2007-01-01

    The authors provide a didactic treatment of nonlinear (categorical) principal components analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the observed variables to a number of uncorrelated principal components. The most important advantages of nonlinear over linear PCA are that it incorporates nominal and ordinal…

  8. Fault detection with principal component pursuit method

    NASA Astrophysics Data System (ADS)

    Pan, Yijun; Yang, Chunjie; Sun, Youxian; An, Ruqiao; Wang, Lin

    2015-11-01

    Data-driven approaches are widely applied for fault detection in industrial process. Recently, a new method for fault detection called principal component pursuit(PCP) is introduced. PCP is not only robust to outliers, but also can accomplish the objectives of model building, fault detection, fault isolation and process reconstruction simultaneously. PCP divides the data matrix into two parts: a fault-free low rank matrix and a sparse matrix with sensor noise and process fault. The statistics presented in this paper fully utilize the information in data matrix. Since the low rank matrix in PCP is similar to principal components matrix in PCA, a T2 statistic is proposed for fault detection in low rank matrix. And this statistic can illustrate that PCP is more sensitive to small variations in variables than PCA. In addition, in sparse matrix, a new monitored statistic performing the online fault detection with PCP-based method is introduced. This statistic uses the mean and the correlation coefficient of variables. Monte Carlo simulation and Tennessee Eastman (TE) benchmark process are provided to illustrate the effectiveness of monitored statistics.

  9. EP component identification and measurement by principal components analysis.

    PubMed

    Chapman, R M; McCrary, J W

    1995-04-01

    Between the acquisition of Evoked Potential (EP) data and their interpretation lies a major problem: What to measure? An approach to this kind of problem is outlined here in terms of Principal Components Analysis (PCA). An important second theme is that experimental manipulation is important to functional interpretation. It would be desirable to have a system of EP measurement with the following characteristics: (1) represent the data in a concise, parsimonous way; (2) determine EP components from the data without assuming in advance any particular waveforms for the components; (3) extract components which are independent of each other; (4) measure the amounts (contributions) of various components in observed EPs; (5) use measures that have greater reliability than measures at any single time point or peak; and (6) identify and measure components that overlap in time. PCA has these desirable characteristics. Simulations are illustrated. PCA's beauty also has some warts that are discussed. In addition to discussing the usual two-mode model of PCA, an extension of PCA to a three-mode model is described that provides separate parameters for (1) waveforms over time, (2) coefficients for spatial distribution, and (3) scores telling the amount of each component in each EP. PCA is compared with more traditional approaches. Some biophysical considerations are briefly discussed. Choices to be made in applying PCA are considered. Other issues include misallocation of variance, overlapping components, validation, and latency changes. PMID:7626278

  10. Principal Component Analysis of Thermographic Data

    NASA Technical Reports Server (NTRS)

    Winfree, William P.; Cramer, K. Elliott; Zalameda, Joseph N.; Howell, Patricia A.; Burke, Eric R.

    2015-01-01

    Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. While a reliable technique for enhancing the visibility of defects in thermal data, PCA can be computationally intense and time consuming when applied to the large data sets typical in thermography. Additionally, PCA can experience problems when very large defects are present (defects that dominate the field-of-view), since the calculation of the eigenvectors is now governed by the presence of the defect, not the "good" material. To increase the processing speed and to minimize the negative effects of large defects, an alternative method of PCA is being pursued where a fixed set of eigenvectors, generated from an analytic model of the thermal response of the material under examination, is used to process the thermal data from composite materials. This method has been applied for characterization of flaws.

  11. Principal component analysis of phenolic acid spectra

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Phenolic acids are common plant metabolites that exhibit bioactive properties and have applications in functional food and animal feed formulations. The ultraviolet (UV) and infrared (IR) spectra of four closely related phenolic acid structures were evaluated by principal component analysis (PCA) to...

  12. Principal component analysis implementation in Java

    NASA Astrophysics Data System (ADS)

    Wójtowicz, Sebastian; Belka, Radosław; Sławiński, Tomasz; Parian, Mahnaz

    2015-09-01

    In this paper we show how PCA (Principal Component Analysis) method can be implemented using Java programming language. We consider using PCA algorithm especially in analysed data obtained from Raman spectroscopy measurements, but other applications of developed software should also be possible. Our goal is to create a general purpose PCA application, ready to run on every platform which is supported by Java.

  13. Real-Time Principal-Component Analysis

    NASA Technical Reports Server (NTRS)

    Duong, Vu; Duong, Tuan

    2005-01-01

    A recently written computer program implements dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN), which was described in Method of Real-Time Principal-Component Analysis (NPO-40034) NASA Tech Briefs, Vol. 29, No. 1 (January 2005), page 59. To recapitulate: DOGEDYN is a method of sequential principal-component analysis (PCA) suitable for such applications as data compression and extraction of features from sets of data. In DOGEDYN, input data are represented as a sequence of vectors acquired at sampling times. The learning algorithm in DOGEDYN involves sequential extraction of principal vectors by means of a gradient descent in which only the dominant element is used at each iteration. Each iteration includes updating of elements of a weight matrix by amounts proportional to a dynamic initial learning rate chosen to increase the rate of convergence by compensating for the energy lost through the previous extraction of principal components. In comparison with a prior method of gradient-descent-based sequential PCA, DOGEDYN involves less computation and offers a greater rate of learning convergence. The sequential DOGEDYN computations require less memory than would parallel computations for the same purpose. The DOGEDYN software can be executed on a personal computer.

  14. A principal components model of soundscape perception.

    PubMed

    Axelsson, Östen; Nilsson, Mats E; Berglund, Birgitta

    2010-11-01

    There is a need for a model that identifies underlying dimensions of soundscape perception, and which may guide measurement and improvement of soundscape quality. With the purpose to develop such a model, a listening experiment was conducted. One hundred listeners measured 50 excerpts of binaural recordings of urban outdoor soundscapes on 116 attribute scales. The average attribute scale values were subjected to principal components analysis, resulting in three components: Pleasantness, eventfulness, and familiarity, explaining 50, 18 and 6% of the total variance, respectively. The principal-component scores were correlated with physical soundscape properties, including categories of dominant sounds and acoustic variables. Soundscape excerpts dominated by technological sounds were found to be unpleasant, whereas soundscape excerpts dominated by natural sounds were pleasant, and soundscape excerpts dominated by human sounds were eventful. These relationships remained after controlling for the overall soundscape loudness (Zwicker's N(10)), which shows that 'informational' properties are substantial contributors to the perception of soundscape. The proposed principal components model provides a framework for future soundscape research and practice. In particular, it suggests which basic dimensions are necessary to measure, how to measure them by a defined set of attribute scales, and how to promote high-quality soundscapes. PMID:21110579

  15. Hockey sticks, principal components, and spurious significance

    NASA Astrophysics Data System (ADS)

    McIntyre, Stephen; McKitrick, Ross

    2005-02-01

    The ``hockey stick'' shaped temperature reconstruction of Mann et al. (1998, 1999) has been widely applied. However it has not been previously noted in print that, prior to their principal components (PCs) analysis on tree ring networks, they carried out an unusual data transformation which strongly affects the resulting PCs. Their method, when tested on persistent red noise, nearly always produces a hockey stick shaped first principal component (PC1) and overstates the first eigenvalue. In the controversial 15th century period, the MBH98 method effectively selects only one species (bristlecone pine) into the critical North American PC1, making it implausible to describe it as the ``dominant pattern of variance''. Through Monte Carlo analysis, we show that MBH98 benchmarks for significance of the Reduction of Error (RE) statistic are substantially under-stated and, using a range of cross-validation statistics, we show that the MBH98 15th century reconstruction lacks statistical significance.

  16. Multilevel sparse functional principal component analysis.

    PubMed

    Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S

    2014-01-29

    We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions. PMID:24872597

  17. Multilevel sparse functional principal component analysis

    PubMed Central

    Di, Chongzhi; Crainiceanu, Ciprian M.; Jank, Wolfgang S.

    2014-01-01

    We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions. PMID:24872597

  18. Radar fall detection using principal component analysis

    NASA Astrophysics Data System (ADS)

    Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

    2016-05-01

    Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

  19. The principal components of response strength.

    PubMed Central

    Killeen, P R; Hall, S S

    2001-01-01

    As Skinner (1938) described it, response strength is the "state of the reflex with respect to all its static properties" (p. 15), which include response rate, latency, probability, and persistence. The relations of those measures to one another was analyzed by probabilistically reinforcing, satiating, and extinguishing pigeons' key pecking in a trials paradigm. Reinforcement was scheduled according to variable-interval, variable-ratio, and fixed-interval contingencies. Principal components analysis permitted description in terms of a single latent variable, strength, and this was validated with confirmatory factor analyses. Overall response rate was an excellent predictor of this state variable. PMID:11394483

  20. BBH Classification Using Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Shoemaker, Deirdre; Cadonati, Laura; Clark, James; Day, Brian; Jeng, Ik Siong; Lombardi, Alexander; London, Lionel; Mangini, Nicholas; Logue, Josh

    2015-04-01

    Binary black holes will inspiral, merge and ringdown in the LIGO/VIRGO band for an interesting range of total masses. We present an update on our approach of using Principal Component Analysis to build models of NR BBH waveforms that focus on the merger for generic BBH signals. These models are intended to be used to conduct coarse parameter estimation for gravitational wave burst candidate events. The proposed benefit is a fast, optimized catalog that classifies bulk features in the signal. NSFPHY-0955773, 0955825, SUPA and STFC UK. Simulations by NSF XSEDE PHY120016 and PHY090030.

  1. The principal components of response strength.

    PubMed

    Killeen, P R; Hall, S S

    2001-03-01

    As Skinner (1938) described it, response strength is the "state of the reflex with respect to all its static properties" (p. 15), which include response rate, latency, probability, and persistence. The relations of those measures to one another was analyzed by probabilistically reinforcing, satiating, and extinguishing pigeons' key pecking in a trials paradigm. Reinforcement was scheduled according to variable-interval, variable-ratio, and fixed-interval contingencies. Principal components analysis permitted description in terms of a single latent variable, strength, and this was validated with confirmatory factor analyses. Overall response rate was an excellent predictor of this state variable. PMID:11394483

  2. Principal components analysis of Jupiter VIMS spectra

    USGS Publications Warehouse

    Bellucci, G.; Formisano, V.; D'Aversa, E.; Brown, R.H.; Baines, K.H.; Bibring, J.-P.; Buratti, B.J.; Capaccioni, F.; Cerroni, P.; Clark, R.N.; Coradini, A.; Cruikshank, D.P.; Drossart, P.; Jaumann, R.; Langevin, Y.; Matson, D.L.; McCord, T.B.; Mennella, V.; Nelson, R.M.; Nicholson, P.D.; Sicardy, B.; Sotin, C.; Chamberlain, M.C.; Hansen, G.; Hibbits, K.; Showalter, M.; Filacchione, G.

    2004-01-01

    During Cassini - Jupiter flyby occurred in December 2000, Visual-Infrared mapping spectrometer (VIMS) instrument took several image cubes of Jupiter at different phase angles and distances. We have analysed the spectral images acquired by the VIMS visual channel by means of a principal component analysis technique (PCA). The original data set consists of 96 spectral images in the 0.35-1.05 ??m wavelength range. The product of the analysis are new PC bands, which contain all the spectral variance of the original data. These new components have been used to produce a map of Jupiter made of seven coherent spectral classes. The map confirms previously published work done on the Great Red Spot by using NIMS data. Some other new findings, presently under investigation, are presented. ?? 2004 Published by Elsevier Ltd on behalf of COSPAR.

  3. Sparse principal component analysis in cancer research

    PubMed Central

    Hsu, Ying-Lin; Huang, Po-Yu; Chen, Dung-Tsa

    2015-01-01

    A critical challenging component in analyzing high-dimensional data in cancer research is how to reduce the dimension of data and how to extract relevant features. Sparse principal component analysis (PCA) is a powerful statistical tool that could help reduce data dimension and select important variables simultaneously. In this paper, we review several approaches for sparse PCA, including variance maximization (VM), reconstruction error minimization (REM), singular value decomposition (SVD), and probabilistic modeling (PM) approaches. A simulation study is conducted to compare PCA and the sparse PCAs. An example using a published gene signature in a lung cancer dataset is used to illustrate the potential application of sparse PCAs in cancer research. PMID:26719835

  4. Investigating dark energy experiments with principal components

    SciTech Connect

    Crittenden, Robert G.; Zhao, Gong-Bo; Pogosian, Levon E-mail: levon@sfu.ca

    2009-12-01

    We use a principal component approach to contrast different kinds of probes of dark energy, and to emphasize how an array of probes can work together to constrain an arbitrary equation of state history w(z). We pay particular attention to the role of the priors in assessing the information content of experiments and propose using an explicit prior on the degree of smoothness of w(z) that is independent of the binning scheme. We also show how a figure of merit based on the mean squared error probes the number of new modes constrained by a data set, and use it to examine how informative various experiments will be in constraining the evolution of dark energy.

  5. Principal Components Analysis Studies of Martian Clouds

    NASA Astrophysics Data System (ADS)

    Klassen, D. R.; Bell, J. F., III

    2001-11-01

    We present the principal components analysis (PCA) of absolutely calibrated multi-spectral images of Mars as a function of Martian season. The PCA technique is a mathematical rotation and translation of the data from a brightness/wavelength space to a vector space of principal ``traits'' that lie along the directions of maximal variance. The first of these traits, accounting for over 90% of the data variance, is overall brightness and represented by an average Mars spectrum. Interpretation of the remaining traits, which account for the remaining ~10% of the variance, is not always the same and depends upon what other components are in the scene and thus, varies with Martian season. For example, during seasons with large amounts of water ice in the scene, the second trait correlates with the ice and anti-corrlates with temperature. We will investigate the interpretation of the second, and successive important PCA traits. Although these PCA traits are orthogonal in their own vector space, it is unlikely that any one trait represents a singular, mineralogic, spectral end-member. It is more likely that there are many spectral endmembers that vary identically to within the noise level, that the PCA technique will not be able to distinguish them. Another possibility is that similar absorption features among spectral endmembers may be tied to one PCA trait, for example ''amount of 2 \\micron\\ absorption''. We thus attempt to extract spectral endmembers by matching linear combinations of the PCA traits to USGS, JHU, and JPL spectral libraries as aquired through the JPL Aster project. The recovered spectral endmembers are then linearly combined to model the multi-spectral image set. We present here the spectral abundance maps of the water ice/frost endmember which allow us to track Martian clouds and ground frosts. This work supported in part through NASA Planetary Astronomy Grant NAG5-6776. All data gathered at the NASA Infrared Telescope Facility in collaboration with

  6. Principal component analysis of scintimammographic images.

    PubMed

    Bonifazzi, Claudio; Cinti, Maria Nerina; Vincentis, Giuseppe De; Finos, Livio; Muzzioli, Valerio; Betti, Margherita; Nico, Lanconelli; Tartari, Agostino; Pani, Roberto

    2006-01-01

    The recent development of new gamma imagers based on scintillation array with high spatial resolution, has strongly improved the possibility of detecting sub-centimeter cancer in Scintimammography. However, Compton scattering contamination remains the main drawback since it limits the sensitivity of tumor detection. Principal component image analysis (PCA), recently introduced in scintimam nographic imaging, is a data reduction technique able to represent the radiation emitted from chest, breast healthy and damaged tissues as separated images. From these images a Scintimammography can be obtained where the Compton contamination is "removed". In the present paper we compared the PCA reconstructed images with the conventional scintimammographic images resulting from the photopeak (Ph) energy window. Data coming from a clinical trial were used. For both kinds of images the tumor presence was quantified by evaluating the t-student statistics for independent sample as a measure of the signal-to-noise ratio (SNR). Since the absence of Compton scattering, the PCA reconstructed images shows a better noise suppression and allows a more reliable diagnostics in comparison with the images obtained by the photopeak energy window, reducing the trend in producing false positive. PMID:17646004

  7. Principal Components Analysis of Population Admixture

    PubMed Central

    Ma, Jianzhong; Amos, Christopher I.

    2012-01-01

    With the availability of high-density genotype information, principal components analysis (PCA) is now routinely used to detect and quantify the genetic structure of populations in both population genetics and genetic epidemiology. An important issue is how to make appropriate and correct inferences about population relationships from the results of PCA, especially when admixed individuals are included in the analysis. We extend our recently developed theoretical formulation of PCA to allow for admixed populations. Because the sampled individuals are treated as features, our generalized formulation of PCA directly relates the pattern of the scatter plot of the top eigenvectors to the admixture proportions and parameters reflecting the population relationships, and thus can provide valuable guidance on how to properly interpret the results of PCA in practice. Using our formulation, we theoretically justify the diagnostic of two-way admixture. More importantly, our theoretical investigations based on the proposed formulation yield a diagnostic of multi-way admixture. For instance, we found that admixed individuals with three parental populations are distributed inside the triangle formed by their parental populations and divide the triangle into three smaller triangles whose areas have the same proportions in the big triangle as the corresponding admixture proportions. We tested and illustrated these findings using simulated data and data from HapMap III and the Human Genome Diversity Project. PMID:22808102

  8. Principal components of CMB non-Gaussianity

    NASA Astrophysics Data System (ADS)

    Regan, Donough; Munshi, Dipak

    2015-04-01

    The skew-spectrum statistic introduced by Munshi & Heavens has recently been used in studies of non-Gaussianity from diverse cosmological data sets including the detection of primary and secondary non-Gaussianity of cosmic microwave background (CMB) radiation. Extending previous work, focused on independent estimation, here we deal with the question of joint estimation of multiple skew-spectra from the same or correlated data sets. We consider the optimum skew-spectra for various models of primordial non-Gaussianity as well as secondary bispectra that originate from the cross-correlation of secondaries and lensing of CMB: coupling of lensing with the Integrated Sachs-Wolfe effect, coupling of lensing with thermal Sunyaev-Zeldovich, as well as from unresolved point sources. For joint estimation of various types of non-Gaussianity, we use the principal component analysis (PCA) to construct the linear combinations of amplitudes of various models of non-Gaussianity, e.g. f^loc_NL,f^eq_NL,f^ortho_NL that can be estimated from CMB maps. We describe how the bias induced in the estimation of primordial non-Gaussianity due to secondary non-Gaussianity may be evaluated for arbitrary primordial models using a PCA analysis. The PCA approach allows one to infer approximate (but generally accurate) constraints using CMB data sets on any reasonably smooth model by use of a look-up table and performing a simple computation. This principle is validated by computing constraints on the Dirac-Born-Infeld bispectrum using a PCA analysis of the standard templates.

  9. Data structure characterization of miltispectral data using principal component and principal factor analysis

    NASA Technical Reports Server (NTRS)

    Lee, Jae K.; Mausel, Paul W.; Lulla, Kamlesh P.

    1989-01-01

    Both principal component analysis (PCA) and principal factor analysis (PFA) were used to analyze an experimental multispectral data structure in terms of common and unique variance. Only the common variance of the multispectral data was associated with the principal factor, while higher-order principal components were associated with both common and unique variance. The unique variance was found to represent small spectral variations within each cover type as well as noise vectors, and was most abundant in the lower-order principal components. The lower-order principal components can be useful in research designed to discriminate minor physical variations within features, and to highlight localized change when using multitemporal-multispectral data. Conversely, PFA of the multispectral data provided an insight into a great potential for discriminating basic land-cover types by excluding the unique variance which was related to the noise and minor spectral variations.

  10. An Introductory Application of Principal Components to Cricket Data

    ERIC Educational Resources Information Center

    Manage, Ananda B. W.; Scariano, Stephen M.

    2013-01-01

    Principal Component Analysis is widely used in applied multivariate data analysis, and this article shows how to motivate student interest in this topic using cricket sports data. Here, principal component analysis is successfully used to rank the cricket batsmen and bowlers who played in the 2012 Indian Premier League (IPL) competition. In…

  11. A principal component analysis of transmission spectra of wine distillates

    NASA Astrophysics Data System (ADS)

    Rogovaya, M. V.; Sinitsyn, G. V.; Khodasevich, M. A.

    2014-11-01

    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.

  12. Principal Component Analysis of Long-Lag,Wide-Pulse Gamma-Ray Burst Data

    NASA Astrophysics Data System (ADS)

    Peng, Zhao-Yang; Liu, Wen-Shuai

    2014-09-01

    We have carried out a Principal Component Analysis (PCA) of the temporal and spectral variables of 24 long-lag, wide-pulse gamma-ray bursts (GRBs) presented by Norris et al. (2005). Taking all eight temporal and spectral parameters into account, our analysis shows that four principal components are enough to describe the variation of the temporal and spectral data of long-lag bursts. In addition, the first-two principal components are dominated by the temporal variables while the third and fourth principal components are dominated by the spectral parameters.

  13. Principal Component Analysis for Enhancement of Infrared Spectra Monitoring

    NASA Astrophysics Data System (ADS)

    Haney, Ricky Lance

    The issue of air quality within the aircraft cabin is receiving increasing attention from both pilot and flight attendant unions. This is due to exposure events caused by poor air quality that in some cases may have contained toxic oil components due to bleed air that flows from outside the aircraft and then through the engines into the aircraft cabin. Significant short and long-term medical issues for aircraft crew have been attributed to exposure. The need for air quality monitoring is especially evident in the fact that currently within an aircraft there are no sensors to monitor the air quality and potentially harmful gas levels (detect-to-warn sensors), much less systems to monitor and purify the air (detect-to-treat sensors) within the aircraft cabin. The specific purpose of this research is to utilize a mathematical technique called principal component analysis (PCA) in conjunction with principal component regression (PCR) and proportionality constant calculations (PCC) to simplify complex, multi-component infrared (IR) spectra data sets into a reduced data set used for determination of the concentrations of the individual components. Use of PCA can significantly simplify data analysis as well as improve the ability to determine concentrations of individual target species in gas mixtures where significant band overlap occurs in the IR spectrum region. Application of this analytical numerical technique to IR spectrum analysis is important in improving performance of commercial sensors that airlines and aircraft manufacturers could potentially use in an aircraft cabin environment for multi-gas component monitoring. The approach of this research is two-fold, consisting of a PCA application to compare simulation and experimental results with the corresponding PCR and PCC to determine quantitatively the component concentrations within a mixture. The experimental data sets consist of both two and three component systems that could potentially be present as air

  14. Sparse principal component analysis by choice of norm

    PubMed Central

    Luo, Ruiyan; Zhao, Hongyu

    2012-01-01

    Recent years have seen the developments of several methods for sparse principal component analysis due to its importance in the analysis of high dimensional data. Despite the demonstration of their usefulness in practical applications, they are limited in terms of lack of orthogonality in the loadings (coefficients) of different principal components, the existence of correlation in the principal components, the expensive computation needed, and the lack of theoretical results such as consistency in high-dimensional situations. In this paper, we propose a new sparse principal component analysis method by introducing a new norm to replace the usual norm in traditional eigenvalue problems, and propose an efficient iterative algorithm to solve the optimization problems. With this method, we can efficiently obtain uncorrelated principal components or orthogonal loadings, and achieve the goal of explaining a high percentage of variations with sparse linear combinations. Due to the strict convexity of the new norm, we can prove the convergence of the iterative method and provide the detailed characterization of the limits. We also prove that the obtained principal component is consistent for a single component model in high dimensional situations. As illustration, we apply this method to real gene expression data with competitive results. PMID:23524453

  15. PRINCIPAL COMPONENTS ANALYSIS AND PARTIAL LEAST SQUARES REGRESSION

    EPA Science Inventory

    The mathematics behind the techniques of principal component analysis and partial least squares regression is presented in detail, starting from the appropriate extreme conditions. he meaning of the resultant vectors and many of their mathematical interrelationships are also pres...

  16. Physical parameter effects on radar backscatter using principal component analysis

    NASA Astrophysics Data System (ADS)

    Chuah, Hean T.; Teh, K. B.

    1994-12-01

    This paper contains a sensitivity analysis of the effects of physical parameters on radar backscatter coefficients from a vegetation canopy using the method of principal component analysis. A Monte Carlo forward scattering model is used to generate the necessary data set for such analysis. The vegetation canopy is modeled as a layer of randomly distributed circular disks bounded below by a Kirchhoff rough surface. Data reduction is accomplished by the statistical principal component analysis technique in which only three principal components are found to be sufficient, containing 97% of the information in the original set. The first principal component can be interpreted as volume-volume backscatter, while the second and the third as surface backscatter and surface-volume backscatter, respectively. From the correlation matrix obtained, the sensitivity of radar backscatter due to various physical parameters is investigated. These include wave frequency, moisture content, scatterer's size, volume fraction, ground permittivity and surface roughness.

  17. EXAFS and principal component analysis : a new shell game.

    SciTech Connect

    Wasserman, S.

    1998-10-28

    The use of principal component (factor) analysis in the analysis EXAFS spectra is described. The components derived from EXAFS spectra share mathematical properties with the original spectra. As a result, the abstract components can be analyzed using standard EXAFS methodology to yield the bond distances and other coordination parameters. The number of components that must be analyzed is usually less than the number of original spectra. The method is demonstrated using a series of spectra from aqueous solutions of uranyl ions.

  18. Principal Component Analysis for Normal-Distribution-Valued Symbolic Data.

    PubMed

    Wang, Huiwen; Chen, Meiling; Shi, Xiaojun; Li, Nan

    2016-02-01

    This paper puts forward a new approach to principal component analysis (PCA) for normal-distribution-valued symbolic data, which has a vast potential of applications in the economic and management field. We derive a full set of numerical characteristics and variance-covariance structure for such data, which forms the foundation for our analytical PCA approach. Our approach is able to use all of the variance information in the original data than the prevailing representative-type approach in the literature which only uses centers, vertices, etc. The paper also provides an accurate approach to constructing the observations in a PC space based on the linear additivity property of normal distribution. The effectiveness of the proposed method is illustrated by simulated numerical experiments. At last, our method is applied to explain the puzzle of risk-return tradeoff in China's stock market. PMID:25095276

  19. Principal Components Analysis of Triaxial Vibration Data From Helicopter Transmissions

    NASA Technical Reports Server (NTRS)

    Tumer, Irem Y.; Huff, Edward M.

    2001-01-01

    Research on the nature of the vibration data collected from helicopter transmissions during flight experiments has led to several crucial observations believed to be responsible for the high rates of false alarms and missed detections in aircraft vibration monitoring systems. This work focuses on one such finding, namely, the need to consider additional sources of information about system vibrations. In this light, helicopter transmission vibration data, collected using triaxial accelerometers, were explored in three different directions, analyzed for content, and then combined using Principal Components Analysis (PCA) to analyze changes in directionality. In this paper, the PCA transformation is applied to 176 test conditions/data sets collected from an OH58C helicopter to derive the overall experiment-wide covariance matrix and its principal eigenvectors. The experiment-wide eigenvectors. are then projected onto the individual test conditions to evaluate changes and similarities in their directionality based on the various experimental factors. The paper will present the foundations of the proposed approach, addressing the question of whether experiment-wide eigenvectors accurately model the vibration modes in individual test conditions. The results will further determine the value of using directionality and triaxial accelerometers for vibration monitoring and anomaly detection.

  20. Principal Components Analysis of a JWST NIRSpec Detector Subsystem

    NASA Technical Reports Server (NTRS)

    Arendt, Richard G.; Fixsen, D. J.; Greenhouse, Matthew A.; Lander, Matthew; Lindler, Don; Loose, Markus; Moseley, S. H.; Mott, D. Brent; Rauscher, Bernard J.; Wen, Yiting; Wilson, Donna V.; Xenophontos, Christos

    2013-01-01

    We present principal component analysis (PCA) of a flight-representative James Webb Space Telescope NearInfrared Spectrograph (NIRSpec) Detector Subsystem. Although our results are specific to NIRSpec and its T - 40 K SIDECAR ASICs and 5 m cutoff H2RG detector arrays, the underlying technical approach is more general. We describe how we measured the systems response to small environmental perturbations by modulating a set of bias voltages and temperature. We used this information to compute the systems principal noise components. Together with information from the astronomical scene, we show how the zeroth principal component can be used to calibrate out the effects of small thermal and electrical instabilities to produce cosmetically cleaner images with significantly less correlated noise. Alternatively, if one were designing a new instrument, one could use a similar PCA approach to inform a set of environmental requirements (temperature stability, electrical stability, etc.) that enabled the planned instrument to meet performance requirements

  1. Natural movement generation using hidden Markov models and principal components.

    PubMed

    Kwon, Junghyun; Park, Frank C

    2008-10-01

    Recent studies have shown that the perception of natural movements-in the sense of being "humanlike"-depends on both joint and task space characteristics of the movement. This paper proposes a movement generation framework that merges two established techniques from gesture recognition and motion generation-hidden Markov models (HMMs) and principal components-into an efficient and reliable means of generating natural movements, which uniformly considers joint and task space characteristics. Given human motion data that are classified into several movement categories, for each category, the principal components extracted from the joint trajectories are used as basis elements. An HMM is, in turn, designed and trained for each movement class using the human task space motion data. Natural movements are generated as the optimal linear combination of principal components, which yields the highest probability for the trained HMM. Experimental case studies with a prototype humanoid robot demonstrate the various advantages of our proposed framework. PMID:18784005

  2. Application of Principal Component Analysis to EUV multilayer defect printing

    NASA Astrophysics Data System (ADS)

    Xu, Dongbo; Evanschitzky, Peter; Erdmann, Andreas

    2015-09-01

    This paper proposes a new method for the characterization of multilayer defects on EUV masks. To reconstruct the defect geometry parameters from the intensity and phase of a defect, the Principal Component Analysis (PCA) is employed to parametrize the intensity and phase distributions into principal component coefficients. In order to construct the base functions of PCA, a combination of a reference multilayer defect and appropriate pupil filters is introduced to obtain the designed sets of intensity and phase distributions. Finally, an Artificial Neural Network (ANN) is applied to correlate the principal component coefficients of the intensity and the phase of the defect with the defect geometry parameters and to reconstruct the unknown defect geometry parameters.

  3. Insights Into Categorization Of Solar Flares Using Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Balasubramaniam, K. S.; Norquist, D. C.

    2012-05-01

    Using time sequences of solar chromospheric images acquired using the USAF/NSO Improved Solar Observing Network (ISOON) prototype telescope, we have applied principal component analysis (PCA) to time-series of both erupting and non-erupting active regions. Our primary purpose is to develop an advanced data driven model for solar flare prediction using machine learning algorithms, with principal components as the input. Using the principal components we show a clear separation in the Eigen vectors. Eigen vectors fall into three major flaring categories: weak flares (GOES peak intensity < C4.0; intermediary flares (GOES peak intensity between C4.0 and C8.0) and, strong flares (GOES peak intensity > C8.0). In this paper, we will provide insights into implications for the underlying physical mechanisms that describe these three distinct categories. This work funded by the U. S. Air Force Office of Scientific Research (AFOSR).

  4. Improvements for Image Compression Using Adaptive Principal Component Extraction (APEX)

    NASA Technical Reports Server (NTRS)

    Ziyad, Nigel A.; Gilmore, Erwin T.; Chouikha, Mohamed F.

    1997-01-01

    The issues of image compression and pattern classification have been a primary focus of researchers among a variety of fields including signal and image processing, pattern recognition, data classification, etc. These issues depend on finding an efficient representation of the source data. In this paper we collate our earlier results where we introduced the application of the. Hilbe.rt scan to a principal component algorithm (PCA) with Adaptive Principal Component Extraction (APEX) neural network model. We apply these technique to medical imaging, particularly image representation and compression. We apply the Hilbert scan to the APEX algorithm to improve results

  5. Demixed principal component analysis of neural population data

    PubMed Central

    Kobak, Dmitry; Brendel, Wieland; Constantinidis, Christos; Feierstein, Claudia E; Kepecs, Adam; Mainen, Zachary F; Qi, Xue-Lian; Romo, Ranulfo; Uchida, Naoshige; Machens, Christian K

    2016-01-01

    Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure. DOI: http://dx.doi.org/10.7554/eLife.10989.001 PMID:27067378

  6. Additive Manufacturing of Aerospace Propulsion Components

    NASA Technical Reports Server (NTRS)

    Misra, Ajay K.; Grady, Joseph E.; Carter, Robert

    2015-01-01

    The presentation will provide an overview of ongoing activities on additive manufacturing of aerospace propulsion components, which included rocket propulsion and gas turbine engines. Future opportunities on additive manufacturing of hybrid electric propulsion components will be discussed.

  7. Applications of Nonlinear Principal Components Analysis to Behavioral Data.

    ERIC Educational Resources Information Center

    Hicks, Marilyn Maginley

    1981-01-01

    An empirical investigation of the statistical procedure entitled nonlinear principal components analysis was conducted on a known equation and on measurement data in order to demonstrate the procedure and examine its potential usefulness. This method was suggested by R. Gnanadesikan and based on an early paper of Karl Pearson. (Author/AL)

  8. Spatially Weighted Principal Component Analysis for Imaging Classification

    PubMed Central

    Guo, Ruixin; Ahn, Mihye; Zhu, Hongtu

    2014-01-01

    The aim of this paper is to develop a supervised dimension reduction framework, called Spatially Weighted Principal Component Analysis (SWPCA), for high dimensional imaging classification. Two main challenges in imaging classification are the high dimensionality of the feature space and the complex spatial structure of imaging data. In SWPCA, we introduce two sets of novel weights including global and local spatial weights, which enable a selective treatment of individual features and incorporation of the spatial structure of imaging data and class label information. We develop an e cient two-stage iterative SWPCA algorithm and its penalized version along with the associated weight determination. We use both simulation studies and real data analysis to evaluate the finite-sample performance of our SWPCA. The results show that SWPCA outperforms several competing principal component analysis (PCA) methods, such as supervised PCA (SPCA), and other competing methods, such as sparse discriminant analysis (SDA). PMID:26089629

  9. Removing Milky Way from airglow images using principal component analysis

    NASA Astrophysics Data System (ADS)

    Li, Zhenhua; Liu, Alan; Sivjee, Gulamabas G.

    2014-04-01

    Airglow imaging is an effective way to obtain atmospheric gravity wave information in the airglow layers in the upper mesosphere and the lower thermosphere. Airglow images are often contaminated by the Milky Way emission. To extract gravity wave parameters correctly, the Milky Way must be removed. The paper demonstrates that principal component analysis (PCA) can effectively represent the dominant variation patterns of the intensity of airglow images that are associated with the slow moving Milky Way features. Subtracting this PCA reconstructed field reveals gravity waves that are otherwise overwhelmed by the strong spurious waves associated with the Milky Way. Numerical experiments show that nonstationary gravity waves with typical wave amplitudes and persistences are not affected by the PCA removal because the variances contributed by each wave event are much smaller than the ones in the principal components.

  10. Principal component analysis: a review and recent developments.

    PubMed

    Jolliffe, Ian T; Cadima, Jorge

    2016-04-13

    Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application. PMID:26953178

  11. Applications Of Nonlinear Principal Components Analysis To Behavioral Data.

    PubMed

    Hicks, M M

    1981-07-01

    A quadratic function was derived from variables believed to be nonlinearly related. The method was suggested by Gnanadesikan (1977) and based on an early paper of Karl Pearson (1901) (which gave rise to principal components), in which Pearson demonstrated that a plane of best fit to a system of points could be elicited from the elements of the eigenvector associated with the smallest eigenvalue of the covariance matrix. PMID:26815595

  12. Principal Component Analysis of Arctic Solar Irradiance Spectra

    NASA Technical Reports Server (NTRS)

    Rabbette, Maura; Pilewskie, Peter; Gore, Warren J. (Technical Monitor)

    2000-01-01

    During the FIRE (First ISCPP Regional Experiment) Arctic Cloud Experiment and coincident SHEBA (Surface Heat Budget of the Arctic Ocean) campaign, detailed moderate resolution solar spectral measurements were made to study the radiative energy budget of the coupled Arctic Ocean - Atmosphere system. The NASA Ames Solar Spectral Flux Radiometers (SSFRs) were deployed on the NASA ER-2 and at the SHEBA ice camp. Using the SSFRs we acquired continuous solar spectral irradiance (380-2200 nm) throughout the atmospheric column. Principal Component Analysis (PCA) was used to characterize the several tens of thousands of retrieved SSFR spectra and to determine the number of independent pieces of information that exist in the visible to near-infrared solar irradiance spectra. It was found in both the upwelling and downwelling cases that almost 100% of the spectral information (irradiance retrieved from 1820 wavelength channels) was contained in the first six extracted principal components. The majority of the variability in the Arctic downwelling solar irradiance spectra was explained by a few fundamental components including infrared absorption, scattering, water vapor and ozone. PCA analysis of the SSFR upwelling Arctic irradiance spectra successfully separated surface ice and snow reflection from overlying cloud into distinct components.

  13. GPR anomaly detection with robust principal component analysis

    NASA Astrophysics Data System (ADS)

    Masarik, Matthew P.; Burns, Joseph; Thelen, Brian T.; Kelly, Jack; Havens, Timothy C.

    2015-05-01

    This paper investigates the application of Robust Principal Component Analysis (RPCA) to ground penetrating radar as a means to improve GPR anomaly detection. The method consists of a preprocessing routine to smoothly align the ground and remove the ground response (haircut), followed by mapping to the frequency domain, applying RPCA, and then mapping the sparse component of the RPCA decomposition back to the time domain. A prescreener is then applied to the time-domain sparse component to perform anomaly detection. The emphasis of the RPCA algorithm on sparsity has the effect of significantly increasing the apparent signal-to-clutter ratio (SCR) as compared to the original data, thereby enabling improved anomaly detection. This method is compared to detrending (spatial-mean removal) and classical principal component analysis (PCA), and the RPCA-based processing is seen to provide substantial improvements in the apparent SCR over both of these alternative processing schemes. In particular, the algorithm has been applied to both field collected impulse GPR data and has shown significant improvement in terms of the ROC curve relative to detrending and PCA.

  14. Point-process principal components analysis via geometric optimization.

    PubMed

    Solo, Victor; Pasha, Syed Ahmed

    2013-01-01

    There has been a fast-growing demand for analysis tools for multivariate point-process data driven by work in neural coding and, more recently, high-frequency finance. Here we develop a true or exact (as opposed to one based on time binning) principal components analysis for preliminary processing of multivariate point processes. We provide a maximum likelihood estimator, an algorithm for maximization involving steepest ascent on two Stiefel manifolds, and novel constrained asymptotic analysis. The method is illustrated with a simulation and compared with a binning approach. PMID:23020106

  15. APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO RELAXOGRAPHIC IMAGES

    SciTech Connect

    STOYANOVA,R.S.; OCHS,M.F.; BROWN,T.R.; ROONEY,W.D.; LI,X.; LEE,J.H.; SPRINGER,C.S.

    1999-05-22

    Standard analysis methods for processing inversion recovery MR images traditionally have used single pixel techniques. In these techniques each pixel is independently fit to an exponential recovery, and spatial correlations in the data set are ignored. By analyzing the image as a complete dataset, improved error analysis and automatic segmentation can be achieved. Here, the authors apply principal component analysis (PCA) to a series of relaxographic images. This procedure decomposes the 3-dimensional data set into three separate images and corresponding recovery times. They attribute the 3 images to be spatial representations of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) content.

  16. Self-aggregation in scaled principal component space

    SciTech Connect

    Ding, Chris H.Q.; He, Xiaofeng; Zha, Hongyuan; Simon, Horst D.

    2001-10-05

    Automatic grouping of voluminous data into meaningful structures is a challenging task frequently encountered in broad areas of science, engineering and information processing. These data clustering tasks are frequently performed in Euclidean space or a subspace chosen from principal component analysis (PCA). Here we describe a space obtained by a nonlinear scaling of PCA in which data objects self-aggregate automatically into clusters. Projection into this space gives sharp distinctions among clusters. Gene expression profiles of cancer tissue subtypes, Web hyperlink structure and Internet newsgroups are analyzed to illustrate interesting properties of the space.

  17. Multivariate concentration determination using principal component regression with residual analysis

    PubMed Central

    Keithley, Richard B.; Heien, Michael L.; Wightman, R. Mark

    2009-01-01

    Data analysis is an essential tenet of analytical chemistry, extending the possible information obtained from the measurement of chemical phenomena. Chemometric methods have grown considerably in recent years, but their wide use is hindered because some still consider them too complicated. The purpose of this review is to describe a multivariate chemometric method, principal component regression, in a simple manner from the point of view of an analytical chemist, to demonstrate the need for proper quality-control (QC) measures in multivariate analysis and to advocate the use of residuals as a proper QC method. PMID:20160977

  18. Principal Component Analysis of Terrestrial and Venusian Topography

    NASA Astrophysics Data System (ADS)

    Stoddard, P. R.; Jurdy, D. M.

    2015-12-01

    We use Principal Component Analysis (PCA) as an objective tool in analyzing, comparing, and contrasting topographic profiles of different/similar features from different locations and planets. To do so, we take average profiles of a set of features and form a cross-correlation matrix, which is then diagonalized to determine its principal components. These components, not merely numbers, represent actual profile shapes that give a quantitative basis for comparing different sets of features. For example, PCA for terrestrial hotspots shows the main component as a generic dome shape. Secondary components show a more sinusoidal shape, related to the lithospheric loading response, and thus give information about the nature of the lithosphere setting of the various hotspots. We examine a range of terrestrial spreading centers: fast, slow, ultra-slow, incipient, and extinct, and compare these to several chasmata on Venus (including Devana, Ganis, Juno, Parga, and Kuanja). For upwelling regions, we consider the oceanic Hawaii, Reunion, and Iceland hotspots and Yellowstone, a prototypical continental hotspot. Venus has approximately one dozen broad topographic and geoid highs called regiones. Our analysis includes Atla, Beta, and W. Eistla regiones. Atla and Beta are widely thought to be the most likely to be currently or recently active. Analysis of terrestrial rifts suggests shows increasing uniformity of shape among rifts with increasing spreading rates. Venus' correlations of uniformity rank considerably lower than the terrestrial ones. Extrapolating the correlation/spreading rate suggests that Venus' chasmata, if analogous to terrestrial spreading centers, most resemble the ultra-slow spreading level (less than 12mm/yr) of the Arctic Gakkel ridge. PCA will provide an objective measurement of this correlation.

  19. Temporal variations in ozone concentrations derived from Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Yonemura, S.; Kawashima, S.; Matsueda, H.; Sawa, Y.; Inoue, S.; Tanimoto, H.

    2008-03-01

    The application of principal components and cluster analysis to vertical ozone concentration profiles in Tsukuba, Japan, has been explored. Average monthly profiles and profiles of the ratio between standard deviation and the absolute ozone concentration (SDPR) of 1 km data were calculated from the original ozone concentration data. Mean (first) and gradient (second) components explained more than 80% of the variation in both the 0-6 km tropospheric and 11-20 km troposphere-stratosphere (interspheric) layers. The principal components analysis not only reproduced the expected inverse relationship between mean ozone concentration and tropopause height ( r 2 = 0.41) and that in the tropospheric layer this is larger in spring and summer, but also yielded new information as follows. The larger gradient component score in summer for the interspheric layer points to the seasonal variation of the troposphere-stratosphere exchange. The minimum SDPR was at about 3 km in the tropospheric layer and the maximum was at about 17 km in the interspheric layer. The tropospheric SDPR mean component score was larger in summer, possibly reflecting the mixing of Pacific maritime air masses with urban air masses. The cluster analysis of the monthly ozone profiles for the 1970s and 2000s revealed different patterns for winter and summer. The month of May was part of the winter pattern in the 1970s but part of the summer pattern during the 2000s. This statistically detected change likely reflects the influence of global warming. Thus, these two statistical analysis techniques can be powerful tools for identifying features of ozone concentration profiles.

  20. Water reuse systems: A review of the principal components

    USGS Publications Warehouse

    Lucchetti, G.; Gray, G.A.

    1988-01-01

    Principal components of water reuse systems include ammonia removal, disease control, temperature control, aeration, and particulate filtration. Effective ammonia removal techniques include air stripping, ion exchange, and biofiltration. Selection of a particular technique largely depends on site-specific requirements (e.g., space, existing water quality, and fish densities). Disease control, although often overlooked, is a major problem in reuse systems. Pathogens can be controlled most effectively with ultraviolet radiation, ozone, or chlorine. Simple and inexpensive methods are available to increase oxygen concentration and eliminate gas supersaturation, these include commercial aerators, air injectors, and packed columns. Temperature control is a major advantage of reuse systems, but the equipment required can be expensive, particularly if water temperature must be rigidly controlled and ambient air temperature fluctuates. Filtration can be readily accomplished with a hydrocyclone or sand filter that increases overall system efficiency. Based on criteria of adaptability, efficiency, and reasonable cost, we recommend components for a small water reuse system.

  1. Ice-cloud particle habit classification using principal components

    NASA Astrophysics Data System (ADS)

    Lindqvist, H.; Muinonen, K.; Nousiainen, T.; Um, J.; McFarquhar, G. M.; Haapanala, P.; Makkonen, R.; Hakkarainen, H.

    2012-08-01

    A novel automatic classification method is proposed for identifying the habits of large ice-cloud particles and deriving the shape distribution of particle ensembles. This IC-PCA (Ice-crystal Classification with Principal Component Analysis) tool is based on a principal component analysis of selected physical and statistical features of ice-crystal perimeters. The method is developed and tested using image data obtained with a Cloud Particle Imager, but can be applied to other silhouette data as well. For three randomly selected test cases of 222, 200, and 201 crystals from tropical, midlatitude, and arctic ice clouds, the combined classification accuracy of the IC-PCA is 81.1%. Since previous, semiautomatic classification methods are more time-consuming and include a subjective phase, the automatic and objective IC-PCA offers a notable improvement in retrieving the shapes of the individual crystals. As the habit distributions of ice-cloud particles can be applied to computations of radiative impact of cirrus, it is also demonstrated how classification uncertainties propagate into the radiative transfer computations by using the arctic test case as an example. Computations of shortwave radiative fluxes show that the flux differences between clouds of manually and automatically classified crystals can be as large as 10 Wm-2 but also that two manual classifications of the same image data result in even larger differences, implying the need for a systematic and repeatable classification method.

  2. Level-1C Product from AIRS: Principal Component Filtering

    NASA Technical Reports Server (NTRS)

    Manning, Evan M.; Jiang, Yibo; Aumann, Hartmut H.; Elliott, Denis A.; Hannon, Scott

    2012-01-01

    The Atmospheric Infrared Sounder (AIRS), launched on the EOS Aqua spacecraft on May 4, 2002, is a grating spectrometer with 2378 channels in the range 3.7 to 15.4 microns. In a grating spectrometer each individual radiance measurement is largely independent of all others. Most measurements are extremely accurate and have very low noise levels. However, some channels exhibit high noise levels or other anomalous behavior, complicating applications needing radiances throughout a band, such as cross-calibration with other instruments and regression retrieval algorithms. The AIRS Level-1C product is similar to Level-1B but with instrument artifacts removed. This paper focuses on the "cleaning" portion of Level-1C, which identifies bad radiance values within spectra and produces substitute radiances using redundant information from other channels. The substitution is done in two passes, first with a simple combination of values from neighboring channels, then with principal components. After results of the substitution are shown, differences between principal component reconstructed values and observed radiances are used to investigate detailed noise characteristics and spatial misalignment in other channels.

  3. Principal components granulometric analysis of tidally dominated depositional environments

    SciTech Connect

    Mitchell, S.W. ); Long, W.T. ); Friedrich, N.E. )

    1991-02-01

    Sediments often are investigated by using mechanical sieve analysis (at 1/4 or 1/2{phi} intervals) to identify differences in weight-percent distributions between related samples, and thereby, to deduce variations in sediment sources and depositional processes. Similar granulometric data from groups of surface samples from two siliciclastic estuaries and one carbonate tidal creek have been clustered using principal components analysis. Subtle geographic trends in tidally dominated depositional processes and in sediment sources can be inferred from the clusters. In Barnstable Harbor, Cape Cod, Massachusetts, the estuary can be subdivided into five major subenvironments, with tidal current intensities/directions and sediment sources (longshore transport or sediments weathering from the Sandwich Moraine) as controls. In Morro Bay, San Luis Obispo county, California, all major environments (beach, dune, bay, delta, and fluvial) can be easily distinguished; a wide variety of subenvironments can be recognized. On Pigeon Creek, San Salvador Island, Bahamas, twelve subenvironments can be recognized. Biogenic (Halimeda, Peneroplios, mixed skeletal), chemogenic (pelopids, aggregates), and detrital (lithoclastis skeletal), chemogenic (pelopids, aggregates), and detrital (lithoclastis of eroding Pleistocene limestone) are grain types which dominate. When combined with tidal current intensities/directions, grain sources produce subenvironments distributed parallel to tidal channels. The investigation of the three modern environments indicates that principal components granulometric analysis is potentially a useful tool in recognizing subtle changes in transport processes and sediment sources preserved in ancient depositional sequences.

  4. CMB constraints on principal components of the inflaton potential

    SciTech Connect

    Dvorkin, Cora; Hu, Wayne

    2010-08-15

    We place functional constraints on the shape of the inflaton potential from the cosmic microwave background through a variant of the generalized slow-roll approximation that allows large amplitude, rapidly changing deviations from scale-free conditions. Employing a principal component decomposition of the source function G{sup '{approx_equal}}3(V{sup '}/V){sup 2}-2V{sup ''}/V and keeping only those measured to better than 10% results in 5 nearly independent Gaussian constraints that may be used to test any single-field inflationary model where such deviations are expected. The first component implies <3% variations at the 100 Mpc scale. One component shows a 95% CL preference for deviations around the 300 Mpc scale at the {approx}10% level but the global significance is reduced considering the 5 components examined. This deviation also requires a change in the cold dark matter density which in a flat {Lambda}CDM model is disfavored by current supernova and Hubble constant data and can be tested with future polarization or high multipole temperature data. Its impact resembles a local running of the tilt from multipoles 30-800 but is only marginally consistent with a constant running beyond this range. For this analysis, we have implemented a {approx}40x faster WMAP7 likelihood method which we have made publicly available.

  5. Principal Semantic Components of Language and the Measurement of Meaning

    PubMed Central

    Samsonovic, Alexei V.; Ascoli, Giorgio A.

    2010-01-01

    Metric systems for semantics, or semantic cognitive maps, are allocations of words or other representations in a metric space based on their meaning. Existing methods for semantic mapping, such as Latent Semantic Analysis and Latent Dirichlet Allocation, are based on paradigms involving dissimilarity metrics. They typically do not take into account relations of antonymy and yield a large number of domain-specific semantic dimensions. Here, using a novel self-organization approach, we construct a low-dimensional, context-independent semantic map of natural language that represents simultaneously synonymy and antonymy. Emergent semantics of the map principal components are clearly identifiable: the first three correspond to the meanings of “good/bad” (valence), “calm/excited” (arousal), and “open/closed” (freedom), respectively. The semantic map is sufficiently robust to allow the automated extraction of synonyms and antonyms not originally in the dictionaries used to construct the map and to predict connotation from their coordinates. The map geometric characteristics include a limited number (∼4) of statistically significant dimensions, a bimodal distribution of the first component, increasing kurtosis of subsequent (unimodal) components, and a U-shaped maximum-spread planar projection. Both the semantic content and the main geometric features of the map are consistent between dictionaries (Microsoft Word and Princeton's WordNet), among Western languages (English, French, German, and Spanish), and with previously established psychometric measures. By defining the semantics of its dimensions, the constructed map provides a foundational metric system for the quantitative analysis of word meaning. Language can be viewed as a cumulative product of human experiences. Therefore, the extracted principal semantic dimensions may be useful to characterize the general semantic dimensions of the content of mental states. This is a fundamental step toward a

  6. Raw Data Maximum Likelihood Estimation for Common Principal Component Models: A State Space Approach.

    PubMed

    Gu, Fei; Wu, Hao

    2016-09-01

    The specifications of state space model for some principal component-related models are described, including the independent-group common principal component (CPC) model, the dependent-group CPC model, and principal component-based multivariate analysis of variance. Some derivations are provided to show the equivalence of the state space approach and the existing Wishart-likelihood approach. For each model, a numeric example is used to illustrate the state space approach. In addition, a simulation study is conducted to evaluate the standard error estimates under the normality and nonnormality conditions. In order to cope with the nonnormality conditions, the robust standard errors are also computed. Finally, other possible applications of the state space approach are discussed at the end. PMID:27364333

  7. Application of principal component analysis in phase-shifting photoelasticity.

    PubMed

    Quiroga, Juan A; Gómez-Pedrero, José A

    2016-03-21

    Principal component analysis phase shifting (PCA) is a useful tool for fringe pattern demodulation in phase shifting interferometry. The PCA has no restrictions on background intensity or fringe modulation, and it is a self-calibrating phase sampling algorithm (PSA). Moreover, the technique is well suited for analyzing arbitrary sets of phase-shifted interferograms due to its low computational cost. In this work, we have adapted the standard phase shifting algorithm based on the PCA to the particular case of photoelastic fringe patterns. Compared with conventional PSAs used in photoelasticity, the PCA method does not need calibrated phase steps and, given that it can deal with an arbitrary number of images, it presents good noise rejection properties, even for complicated cases such as low order isochromatic photoelastic patterns. PMID:27136792

  8. Principal component analysis based methodology to distinguish protein SERS spectra

    NASA Astrophysics Data System (ADS)

    Das, G.; Gentile, F.; Coluccio, M. L.; Perri, A. M.; Nicastri, A.; Mecarini, F.; Cojoc, G.; Candeloro, P.; Liberale, C.; De Angelis, F.; Di Fabrizio, E.

    2011-05-01

    Surface-enhanced Raman scattering (SERS) substrates were fabricated using electro-plating and e-beam lithography techniques. Nano-structures were obtained comprising regular arrays of gold nanoaggregates with a diameter of 80 nm and a mutual distance between the aggregates (gap) ranging from 10 to 30 nm. The nanopatterned SERS substrate enabled to have better control and reproducibility on the generation of plasmon polaritons (PPs). SERS measurements were performed for various proteins, namely bovine serum albumin (BSA), myoglobin, ferritin, lysozyme, RNase-B, α-casein, α-lactalbumin and trypsin. Principal component analysis (PCA) was used to organize and classify the proteins on the basis of their secondary structure. Cluster analysis proved that the error committed in the classification was of about 14%. In the paper, it was clearly shown that the combined use of SERS measurements and PCA analysis is effective in categorizing the proteins on the basis of secondary structure.

  9. Undersampled dynamic magnetic resonance imaging using kernel principal component analysis.

    PubMed

    Wang, Yanhua; Ying, Leslie

    2014-01-01

    Compressed sensing (CS) is a promising approach to accelerate dynamic magnetic resonance imaging (MRI). Most existing CS methods employ linear sparsifying transforms. The recent developments in non-linear or kernel-based sparse representations have been shown to outperform the linear transforms. In this paper, we present an iterative non-linear CS dynamic MRI reconstruction framework that uses the kernel principal component analysis (KPCA) to exploit the sparseness of the dynamic image sequence in the feature space. Specifically, we apply KPCA to represent the temporal profiles of each spatial location and reconstruct the images through a modified pre-image problem. The underlying optimization algorithm is based on variable splitting and fixed-point iteration method. Simulation results show that the proposed method outperforms conventional CS method in terms of aliasing artifact reduction and kinetic information preservation. PMID:25570262

  10. Method of Real-Time Principal-Component Analysis

    NASA Technical Reports Server (NTRS)

    Duong, Tuan; Duong, Vu

    2005-01-01

    Dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN) is a method of sequential principal-component analysis (PCA) that is well suited for such applications as data compression and extraction of features from sets of data. In comparison with a prior method of gradient-descent-based sequential PCA, this method offers a greater rate of learning convergence. Like the prior method, DOGEDYN can be implemented in software. However, the main advantage of DOGEDYN over the prior method lies in the facts that it requires less computation and can be implemented in simpler hardware. It should be possible to implement DOGEDYN in compact, low-power, very-large-scale integrated (VLSI) circuitry that could process data in real time.

  11. Functional principal components analysis of workload capacity functions.

    PubMed

    Burns, Devin M; Houpt, Joseph W; Townsend, James T; Endres, Michael J

    2013-12-01

    Workload capacity, an important concept in many areas of psychology, describes processing efficiency across changes in workload. The capacity coefficient is a function across time that provides a useful measure of this construct. Until now, most analyses of the capacity coefficient have focused on the magnitude of this function, and often only in terms of a qualitative comparison (greater than or less than one). This work explains how a functional extension of principal components analysis can capture the time-extended information of these functional data, using a small number of scalar values chosen to emphasize the variance between participants and conditions. This approach provides many possibilities for a more fine-grained study of differences in workload capacity across tasks and individuals. PMID:23475829

  12. Iris recognition based on robust principal component analysis

    NASA Astrophysics Data System (ADS)

    Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

    2014-11-01

    Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

  13. [Research into simultaneous spectrophotometric determination of components in cough syrup by principal component regression method].

    PubMed

    Zhang, Li-qing; Wu, Xiao-hua; Tang, Xi; Zhu, Xian-liang; Su, Wen-ting

    2002-06-01

    Principal component regression (PCR) method is used to analyse five components: acetaminophen, p-aminophenol, caffeine, chlorphenamine maleate and guaifenesin. The basic principle and the analytical step of the approach are described in detail. The computer program of LHG is based on VB language. The experimental result shows that the PCR method has no systematical error as compared to classical method. The experimental result shows that the average recovery of each component is all in the range from 96.43% to 107.14%. Each component obtains satisfactory result without any pre-separation. The approach is simple, rapid and suitable for the computer-aid analysis. PMID:12938324

  14. Revisiting AVHRR tropospheric aerosol trends using principal component analysis

    NASA Astrophysics Data System (ADS)

    Li, Jing; Carlson, Barbara E.; Lacis, Andrew A.

    2014-03-01

    The advanced very high resolution radiometer (AVHRR) satellite instruments provide a nearly 25 year continuous record of global aerosol properties over the ocean. It offers valuable insights into the long-term change in global aerosol loading. However, the AVHRR data record is heavily influenced by two volcanic eruptions, El Chichon on March 1982 and Mount Pinatubo on June 1991. The gradual decay of volcanic aerosols may last years after the eruption, which potentially masks the estimation of aerosol trends in the lower troposphere, especially those of anthropogenic origin. In this study, we show that a principal component analysis approach effectively captures the bulk of the spatial and temporal variability of volcanic aerosols into a single mode. The spatial pattern and time series of this mode provide a good match to the global distribution and decay of volcanic aerosols. We further reconstruct the data set by removing the volcanic aerosol component and reestimate the global and regional aerosol trends. Globally, the reconstructed data set reveals an increase of aerosol optical depth from 1985 to 1990 and decreasing trend from 1994 to 2006. Regionally, in the 1980s, positive trends are observed over the North Atlantic and North Arabian Sea, while negative tendencies are present off the West African coast and North Pacific. During the 1994 to 2006 period, the Gulf of Mexico, North Atlantic close to Europe, and North Africa exhibit negative trends, while the coastal regions of East and South Asia, the Sahel region, and South America show positive trends.

  15. Derivation of Boundary Manikins: A Principal Component Analysis

    NASA Technical Reports Server (NTRS)

    Young, Karen; Margerum, Sarah; Barr, Abbe; Ferrer, Mike A.; Rajulu, Sudhakar

    2008-01-01

    When designing any human-system interface, it is critical to provide realistic anthropometry to properly represent how a person fits within a given space. This study aimed to identify a minimum number of boundary manikins or representative models of subjects anthropometry from a target population, which would realistically represent the population. The boundary manikin anthropometry was derived using, Principal Component Analysis (PCA). PCA is a statistical approach to reduce a multi-dimensional dataset using eigenvectors and eigenvalues. The measurements used in the PCA were identified as those measurements critical for suit and cockpit design. The PCA yielded a total of 26 manikins per gender, as well as their anthropometry from the target population. Reduction techniques were implemented to reduce this number further with a final result of 20 female and 22 male subjects. The anthropometry of the boundary manikins was then be used to create 3D digital models (to be discussed in subsequent papers) intended for use by designers to test components of their space suit design, to verify that the requirements specified in the Human Systems Integration Requirements (HSIR) document are met. The end-goal is to allow for designers to generate suits which accommodate the diverse anthropometry of the user population.

  16. Revisiting AVHRR Tropospheric Aerosol Trends Using Principal Component Analysis

    NASA Technical Reports Server (NTRS)

    Li, Jing; Carlson, Barbara E.; Lacis, Andrew A.

    2014-01-01

    The advanced very high resolution radiometer (AVHRR) satellite instruments provide a nearly 25 year continuous record of global aerosol properties over the ocean. It offers valuable insights into the long-term change in global aerosol loading. However, the AVHRR data record is heavily influenced by two volcanic eruptions, El Chichon on March 1982 and Mount Pinatubo on June 1991. The gradual decay of volcanic aerosols may last years after the eruption, which potentially masks the estimation of aerosol trends in the lower troposphere, especially those of anthropogenic origin. In this study, we show that a principal component analysis approach effectively captures the bulk of the spatial and temporal variability of volcanic aerosols into a single mode. The spatial pattern and time series of this mode provide a good match to the global distribution and decay of volcanic aerosols. We further reconstruct the data set by removing the volcanic aerosol component and reestimate the global and regional aerosol trends. Globally, the reconstructed data set reveals an increase of aerosol optical depth from 1985 to 1990 and decreasing trend from 1994 to 2006. Regionally, in the 1980s, positive trends are observed over the North Atlantic and North Arabian Sea, while negative tendencies are present off the West African coast and North Pacific. During the 1994 to 2006 period, the Gulf of Mexico, North Atlantic close to Europe, and North Africa exhibit negative trends, while the coastal regions of East and South Asia, the Sahel region, and South America show positive trends.

  17. Principal Component Analysis for pattern recognition in volcano seismic spectra

    NASA Astrophysics Data System (ADS)

    Unglert, Katharina; Jellinek, A. Mark

    2016-04-01

    Variations in the spectral content of volcano seismicity can relate to changes in volcanic activity. Low-frequency seismic signals often precede or accompany volcanic eruptions. However, they are commonly manually identified in spectra or spectrograms, and their definition in spectral space differs from one volcanic setting to the next. Increasingly long time series of monitoring data at volcano observatories require automated tools to facilitate rapid processing and aid with pattern identification related to impending eruptions. Furthermore, knowledge transfer between volcanic settings is difficult if the methods to identify and analyze the characteristics of seismic signals differ. To address these challenges we have developed a pattern recognition technique based on a combination of Principal Component Analysis and hierarchical clustering applied to volcano seismic spectra. This technique can be used to characterize the dominant spectral components of volcano seismicity without the need for any a priori knowledge of different signal classes. Preliminary results from applying our method to volcanic tremor from a range of volcanoes including K¯ı lauea, Okmok, Pavlof, and Redoubt suggest that spectral patterns from K¯ı lauea and Okmok are similar, whereas at Pavlof and Redoubt spectra have their own, distinct patterns.

  18. The Relation between Factor Score Estimates, Image Scores, and Principal Component Scores

    ERIC Educational Resources Information Center

    Velicer, Wayne F.

    1976-01-01

    Investigates the relation between factor score estimates, principal component scores, and image scores. The three methods compared are maximum likelihood factor analysis, principal component analysis, and a variant of rescaled image analysis. (RC)

  19. Principal components analysis of Mars in the near-infrared

    NASA Astrophysics Data System (ADS)

    Klassen, David R.

    2009-11-01

    Principal components analysis and target transformation are applied to near-infrared image cubes of Mars in a study to disentangle the spectra into a small number of spectral endmembers and characterize the spectral information. The image cubes are ground-based telescopic data from the NASA Infrared Telescope Facility during the 1995 and 1999 near-aphelion oppositions when ice clouds were plentiful [ Clancy, R.T., Grossman, A.W., Wolff, M.J., James, P.B., Rudy, D.J., Billawala, Y.N., Sandor, B.J., Lee, S.W., Muhleman, D.O., 1996. Icarus 122, 36-62; Wolff, M.J., Clancy, R.T., Whitney, B.A., Christensen, P.R., Pearl, J.C., 1999b. In: The Fifth International Conference on Mars, July 19-24, 1999, Pasadena, CA, pp. 6173], and the 2003 near-perihelion opposition when ice clouds are generally limited to topographically high regions (volcano cap clouds) but airborne dust is more common [ Martin, L.J., Zurek, R.W., 1993. J. Geophys. Res. 98 (E2), 3221-3246]. The heart of the technique is to transform the data into a vector space along the dimensions of greatest spectral variance and then choose endmembers based on these new "trait" dimensions. This is done through a target transformation technique, comparing linear combinations of the principal components to a mineral spectral library. In general Mars can be modeled, on the whole, with only three spectral endmembers which account for almost 99% of the data variance. This is similar to results in the thermal infrared with Mars Global Surveyor Thermal Emission Spectrometer data [Bandfield, J.L., Hamilton, V.E., Christensen, P.R., 2000. Science 287, 1626-1630]. The globally recovered surface endmembers can be used as inputs to radiative transfer modeling in order to measure ice abundance in martian clouds [Klassen, D.R., Bell III, J.F., 2002. Bull. Am. Astron. Soc. 34, 865] and a preliminary test of this technique is also presented.

  20. Identifying sources of emerging organic contaminants in a mixed use watershed using principal components analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Principal components analysis (PCA) was used to identify sources of emerging organic contaminants in the Zumbro River watershed in southeastern Minnesota. Two main principal components (PCs) were identified, which together explained more than 50% of the variance in the data. Principal Component 1 (P...

  1. Quince (Cydonia oblonga miller) fruit characterization using principal component analysis.

    PubMed

    Silva, Branca M; Andrade, Paula B; Martins, Rui C; Valentão, Patrícia; Ferreres, Federico; Seabra, Rosa M; Ferreira, Margarida A

    2005-01-12

    This paper presents a large amount of data on the composition of quince fruit with regard to phenolic compounds, organic acids, and free amino acids. Subsequently, principal component analysis (PCA) is carried out to characterize this fruit. The main purposes of this study were (i) the clarification of the interactions among three factors-quince fruit part, geographical origin of the fruits, and harvesting year-and the phenolic, organic acid, and free amino acid profiles; (ii) the classification of the possible differences; and (iii) the possible correlation among the contents of phenolics, organic acids, and free amino acids in quince fruit. With these aims, quince pulp and peel from nine geographical origins of Portugal, harvested in three consecutive years, for a total of 48 samples, were studied. PCA was performed to assess the relationship among the different components of quince fruit phenolics, organic acids, and free amino acids. Phenolics determination was the most interesting. The difference between pulp and peel phenolic profiles was more apparent during PCA. Two PCs accounted for 81.29% of the total variability, PC1 (74.14%) and PC2 (7.15%). PC1 described the difference between the contents of caffeoylquinic acids (3-O-, 4-O-, and 5-O-caffeoylquinic acids and 3,5-O-dicaffeoylquinic acid) and flavonoids (quercetin 3-galactoside, rutin, kaempferol glycoside, kaempferol 3-glucoside, kaempferol 3-rutinoside, quercetin glycosides acylated with p-coumaric acid, and kaempferol glycosides acylated with p-coumaric acid). PC2 related the content of 4-O-caffeoylquinic acid with the contents of 5-O-caffeoylquinic and 3,5-O-dicaffeoylquinic acids. PCA of phenolic compounds enables a clear distinction between the two parts of the fruit. The data presented herein may serve as a database for the detection of adulteration in quince derivatives. PMID:15631517

  2. PRINCIPAL COMPONENT ANALYSIS OF SLOAN DIGITAL SKY SURVEY STELLAR SPECTRA

    SciTech Connect

    McGurk, Rosalie C.; Kimball, Amy E.; Ivezic, Zeljko

    2010-03-15

    We apply Principal Component Analysis (PCA) to {approx}100,000 stellar spectra obtained by the Sloan Digital Sky Survey (SDSS). In order to avoid strong nonlinear variation of spectra with effective temperature, the sample is binned into 0.02 mag wide intervals of the g - r color (-0.20 < g - r < 0.90, roughly corresponding to MK spectral types A3-K3), and PCA is applied independently for each bin. In each color bin, the first four eigenspectra are sufficient to describe the observed spectra within the measurement noise. We discuss correlations of eigencoefficients with metallicity and gravity estimated by the Sloan Extension for Galactic Understanding and Exploration Stellar Parameters Pipeline. The resulting high signal-to-noise mean spectra and the other three eigenspectra are made publicly available. These data can be used to generate high-quality spectra for an arbitrary combination of effective temperature, metallicity, and gravity within the parameter space probed by the SDSS. The SDSS stellar spectroscopic database and the PCA results presented here offer a convenient method to classify new spectra, to search for unusual spectra, to train various spectral classification methods, and to synthesize accurate colors in arbitrary optical bandpasses.

  3. Biological agent detection based on principal component analysis

    NASA Astrophysics Data System (ADS)

    Mudigonda, Naga R.; Kacelenga, Ray

    2006-05-01

    This paper presents an algorithm, based on principal component analysis for the detection of biological threats using General Dynamics Canada's 4WARN Sentry 3000 biodetection system. The proposed method employs a statistical method for estimating background biological activity so as to make the algorithm adaptive to varying background situations. The method attempts to characterize the pattern of change that occurs in the fluorescent particle counts distribution and uses the information to suppress false-alarms. The performance of the method was evaluated using a total of 68 tests including 51 releases of Bacillus Globigii (BG), six releases of BG in the presence of obscurants, six releases of obscurants only, and five releases of ovalbumin at the Ambient Breeze Tunnel Test facility, Battelle, OH. The peak one-minute average concentration of BG used in the tests ranged from 10 - 65 Agent Containing Particles per Liter of Air (ACPLA). The obscurants used in the tests included diesel smoke, white grenade smoke, and salt solution. The method successfully detected BG at a sensitivity of 10 ACPLA and resulted in an overall probability of detection of 94% for BG without generating any false-alarms for obscurants at a detection threshold of 0.6 on a scale of 0 to 1. Also, the method successfully detected BG in the presence of diesel smoke and salt water fumes. The system successfully responded to all the five ovalbumin releases with noticeable trends in algorithm output and alarmed for two releases at the selected detection threshold.

  4. Principal Component Noise Filtering for NAST-I Radiometric Calibration

    NASA Technical Reports Server (NTRS)

    Tian, Jialin; Smith, William L., Sr.

    2011-01-01

    The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed- Interferometer (NAST-I) instrument is a high-resolution scanning interferometer that measures emitted thermal radiation between 3.3 and 18 microns. The NAST-I radiometric calibration is achieved using internal blackbody calibration references at ambient and hot temperatures. In this paper, we introduce a refined calibration technique that utilizes a principal component (PC) noise filter to compensate for instrument distortions and artifacts, therefore, further improve the absolute radiometric calibration accuracy. To test the procedure and estimate the PC filter noise performance, we form dependent and independent test samples using odd and even sets of blackbody spectra. To determine the optimal number of eigenvectors, the PC filter algorithm is applied to both dependent and independent blackbody spectra with a varying number of eigenvectors. The optimal number of PCs is selected so that the total root-mean-square (RMS) error is minimized. To estimate the filter noise performance, we examine four different scenarios: apply PC filtering to both dependent and independent datasets, apply PC filtering to dependent calibration data only, apply PC filtering to independent data only, and no PC filters. The independent blackbody radiances are predicted for each case and comparisons are made. The results show significant reduction in noise in the final calibrated radiances with the implementation of the PC filtering algorithm.

  5. Principal Component Analysis Studies of Turbulence in Optically Thick Gas

    NASA Astrophysics Data System (ADS)

    Correia, C.; Lazarian, A.; Burkhart, B.; Pogosyan, D.; De Medeiros, J. R.

    2016-02-01

    In this work we investigate the sensitivity of principal component analysis (PCA) to the velocity power spectrum in high-opacity regimes of the interstellar medium (ISM). For our analysis we use synthetic position-position-velocity (PPV) cubes of fractional Brownian motion and magnetohydrodynamics (MHD) simulations, post-processed to include radiative transfer effects from CO. We find that PCA analysis is very different from the tools based on the traditional power spectrum of PPV data cubes. Our major finding is that PCA is also sensitive to the phase information of PPV cubes and this allows PCA to detect the changes of the underlying velocity and density spectra at high opacities, where the spectral analysis of the maps provides the universal -3 spectrum in accordance with the predictions of the Lazarian & Pogosyan theory. This makes PCA a potentially valuable tool for studies of turbulence at high opacities, provided that proper gauging of the PCA index is made. However, we found the latter to not be easy, as the PCA results change in an irregular way for data with high sonic Mach numbers. This is in contrast to synthetic Brownian noise data used for velocity and density fields that show monotonic PCA behavior. We attribute this difference to the PCA's sensitivity to Fourier phase information.

  6. Spatially Weighted Principal Component Regression for High-dimensional Prediction

    PubMed Central

    Shen, Dan; Zhu, Hongtu

    2015-01-01

    We consider the problem of using high dimensional data residing on graphs to predict a low-dimensional outcome variable, such as disease status. Examples of data include time series and genetic data measured on linear graphs and imaging data measured on triangulated graphs (or lattices), among many others. Many of these data have two key features including spatial smoothness and intrinsically low dimensional structure. We propose a simple solution based on a general statistical framework, called spatially weighted principal component regression (SWPCR). In SWPCR, we introduce two sets of weights including importance score weights for the selection of individual features at each node and spatial weights for the incorporation of the neighboring pattern on the graph. We integrate the importance score weights with the spatial weights in order to recover the low dimensional structure of high dimensional data. We demonstrate the utility of our methods through extensive simulations and a real data analysis based on Alzheimer’s disease neuroimaging initiative data. PMID:26213452

  7. Principal component analysis for LISA: The time delay interferometry connection

    SciTech Connect

    Romano, J.D.; Woan, G.

    2006-05-15

    Data from the Laser Interferometer Space Antenna (LISA) is expected to be dominated by frequency noise from its lasers. However, the noise from any one laser appears more than once in the data and there are combinations of the data that are insensitive to this noise. These combinations, called time delay interferometry (TDI) variables, have received careful study and point the way to how LISA data analysis may be performed. Here we approach the problem from the direction of statistical inference, and show that these variables are a direct consequence of a principal component analysis of the problem. We present a formal analysis for a simple LISA model and show that there are eigenvectors of the noise covariance matrix that do not depend on laser frequency noise. Importantly, these orthogonal basis vectors correspond to linear combinations of TDI variables. As a result we show that the likelihood function for source parameters using LISA data can be based on TDI combinations of the data without loss of information.

  8. Inverse spatial principal component analysis for geophysical survey data interpolation

    NASA Astrophysics Data System (ADS)

    Li, Qingmou; Dehler, Sonya A.

    2015-04-01

    The starting point for data processing, visualization, and overlay with other data sources in geological applications often involves building a regular grid by interpolation of geophysical measurements. Typically, the sampling interval along survey lines is much higher than the spacing between survey lines because the geophysical recording system is able to operate with a high sampling rate, while the costs and slower speeds associated with operational platforms limit line spacing. However, currently available interpolating methods often smooth data observed with higher sampling rate along a survey line to accommodate the lower spacing across lines, and much of the higher resolution information is not captured in the interpolation process. In this approach, a method termed as the inverse spatial principal component analysis (isPCA) is developed to address this problem. In the isPCA method, a whole profile observation as well as its line position is handled as an entity and a survey collection of line entities is analyzed for interpolation. To test its performance, the developed isPCA method is used to process a simulated airborne magnetic survey from an existing magnetic grid offshore the Atlantic coast of Canada. The interpolation results using the isPCA method and other methods are compared with the original survey grid. It is demonstrated that the isPCA method outperforms the Inverse Distance Weighting (IDW), Kriging (Geostatistical), and MINimum Curvature (MINC) interpolation methods in retaining detailed anomaly structures and restoring original values. In a second test, a high resolution magnetic survey offshore Cape Breton, Nova Scotia, Canada, was processed and the results are compared with other geological information. This example demonstrates the effective performance of the isPCA method in basin structure identification.

  9. Hurricane properties by principal component analysis of Doppler radar data

    NASA Astrophysics Data System (ADS)

    Harasti, Paul Robert

    A novel approach based on Principal Component Analysis (PCA) of Doppler radar data establishes hurricane properties such as the positions of the circulation centre and wind maxima. The method was developed in conjunction with a new Doppler radar wind model for both mature and weak hurricanes. The tangential wind (Vt) is modeled according to Vtζx = constant, where ζ is the radius, and x is an exponent. The maximum Vt occurs at the Radius of Maximum Wind (RMW). For the mature (weak) hurricane case, x = 1 ( x < 1) within the RMW, and x = 0.5 ( x = 0) beyond the RMW. The radial wind is modeled in a similar fashion in the radial direction with up to two transition radii but it is also varied linearly in the vertical direction. This is the first Doppler radar wind model to account for the vertical variations in the radial wind. The new method employs an S2-mode PCA on the Doppler velocity data taken from a single PPI scan and arranged sequentially in a matrix according to their azimuth and range coordinates. The first two eigenvectors of both the range and azimuth eigenspaces represent over 95% of the total variance in the modeled data; one eigenvector from each pair is analyzed separately to estimate particular hurricane properties. These include the bearing and range to the hurricane's circulation centre, the RMW, and the transition radii of the radial wind. Model results suggest that greater accuracy is achievable and fewer restrictions apply in comparison to other methods. The PCA method was tested on the Doppler velocity data of Hurricane Erin (1995) and Typhoon Alex (1987). In both cases, the similarity of the eigenvectors to their theoretical counterparts was striking even in the presence of significant missing data. Results from Hurricane Erin were in agreement with concurrent aircraft observations of the wind centre corrected for the storm motion. Such information was not available for Typhoon Alex, however, the results agreed with those from other methods

  10. Principal Component Analyses of Topographic Profiles of Terrestrial and Venusian Uplifts

    NASA Astrophysics Data System (ADS)

    Stoddard, P. R.; Jurdy, D. M.

    2013-12-01

    highs) characterizes how uplifts on Earth and Venus differ. Previously, we established that Yellowstone more closely resembles Venus' regiones, Atla, Beta and W. Eistla, than it does Earth's oceanic hotspots. Perhaps this results from Venus' thicker lithosphere, more like that of the North American continent than oceanic lithosphere. Corresponding principal component analyses for rifts on the two planets found that Venus' chasmata most closely match the topography of the ultra-slow spreading Arctic Ocean. On the other hand, active terrestrial spreading centers, whether fast or slow, display little difference between them. However, only four Venus chasmata were considered in our analysis, and crustal thickness may play an important role in the development and response of chasmata. Similarly, topographic profiles may also be sensitive to the local lithospheric thickness. An expanded set of chasmata, ones in various settings, warrants further study. Principal component analysis may reveal further similarities. While PCA does not definitively answer the question of whether lithospheric spreading is occurring on Venus, it does strongly suggest that any spreading happens at very slow rates. Additionally, comparisons of uplift features on the two planets reaffirm presence of a thick lithosphere for Venus, and of rifting as the dominant process for Atla and Beta regiones.

  11. Detecting Genomic Signatures of Natural Selection with Principal Component Analysis: Application to the 1000 Genomes Data

    PubMed Central

    Duforet-Frebourg, Nicolas; Luu, Keurcien; Laval, Guillaume; Bazin, Eric; Blum, Michael G.B.

    2016-01-01

    To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult. PMID:26715629

  12. Detecting Genomic Signatures of Natural Selection with Principal Component Analysis: Application to the 1000 Genomes Data.

    PubMed

    Duforet-Frebourg, Nicolas; Luu, Keurcien; Laval, Guillaume; Bazin, Eric; Blum, Michael G B

    2016-04-01

    To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult. PMID:26715629

  13. Generalized multilevel function-on-scalar regression and principal component analysis.

    PubMed

    Goldsmith, Jeff; Zipunnikov, Vadim; Schrack, Jennifer

    2015-06-01

    This manuscript considers regression models for generalized, multilevel functional responses: functions are generalized in that they follow an exponential family distribution and multilevel in that they are clustered within groups or subjects. This data structure is increasingly common across scientific domains and is exemplified by our motivating example, in which binary curves indicating physical activity or inactivity are observed for nearly 600 subjects over 5 days. We use a generalized linear model to incorporate scalar covariates into the mean structure, and decompose subject-specific and subject-day-specific deviations using multilevel functional principal components analysis. Thus, functional fixed effects are estimated while accounting for within-function and within-subject correlations, and major directions of variability within and between subjects are identified. Fixed effect coefficient functions and principal component basis functions are estimated using penalized splines; model parameters are estimated in a Bayesian framework using Stan, a programming language that implements a Hamiltonian Monte Carlo sampler. Simulations designed to mimic the application have good estimation and inferential properties with reasonable computation times for moderate datasets, in both cross-sectional and multilevel scenarios; code is publicly available. In the application we identify effects of age and BMI on the time-specific change in probability of being active over a 24-hour period; in addition, the principal components analysis identifies the patterns of activity that distinguish subjects and days within subjects. PMID:25620473

  14. Multivariate analysis of intracranial pressure (ICP) signal using principal component analysis.

    PubMed

    Al-Zubi, N; Momani, L; Al-Kharabsheh, A; Al-Nuaimy, W

    2009-01-01

    The diagnosis and treatment of hydrocephalus and other neurological disorders often involve the acquisition and analysis of large amount of intracranial pressure (ICP) signal. Although the analysis and subsequent interpretation of this data is an essential part of the clinical management of the disorders, it is typically done manually by a trained clinician, and the difficulty in interpreting some of the features of this complex time series can sometimes lead to issues of subjectivity and reliability. This paper presents a method for the quantitative analysis of this data using a multivariate approach based on principal component analysis, with the aim of optimising symptom diagnosis, patient characterisation and treatment simulation and personalisation. In this method, 10 features are extracted from the ICP signal and principal components that represent these features are defined and analysed. Results from ICP traces of 40 patients show that the chosen features have relevant information about the ICP signal and can be represented with a few components of the PCA (approximately 91% of the total variance of the data is represented by the first four components of the PCA) and that these components can be helpful in characterising subgroups in the patient population that would otherwise not have been apparent. The introduction of supplementaty (non-ICP) variables has offered insight into additional groupings and relationships which may prove to be a fruitful avenue for exploration. PMID:19964826

  15. A novel approach to study human posture control: "Principal movements" obtained from a principal component analysis of kinematic marker data.

    PubMed

    Federolf, Peter A

    2016-02-01

    Human upright posture is maintained by postural movements, which can be quantified by "principal movements" (PMs) obtained through a principal component analysis (PCA) of kinematic marker data. The current study expands the concept of "principal movements" in analogy to Newton's mechanics by defining "principal position" (PP), "principal velocity" (PV), and "principal acceleration" (PA) and demonstrates that a linear combination of PPs and PAs determines the center of pressure (COP) variance in upright standing. Twenty-one subjects equipped with 27-markers distributed over all body segments stood on a force plate while their postural movements were recorded using a standard motion tracking system. A PCA calculated on normalized and weighted posture vectors yielded the PPs and their time derivatives, the PVs and PAs. COP variance explained by the PPs and PAs was obtained through a regression analysis. The first 15 PMs quantified 99.3% of the postural variance and explained 99.60% ± 0.22% (mean ± SD) of the anterior-posterior and 98.82 ± 0.74% of the lateral COP variance in the 21 subjects. Calculation of the PMs thus provides a data-driven definition of variables that simultaneously quantify the state of the postural system (PPs and PVs) and the activity of the neuro-muscular controller (PAs). Since the definition of PPs and PAs is consistent with Newton's mechanics, these variables facilitate studying how mechanical variables, such as the COP motion, are governed by the postural control system. PMID:26768228

  16. Localization of the event-related potential novelty response as defined by principal components analysis.

    PubMed

    Dien, Joseph; Spencer, Kevin M; Donchin, Emanuel

    2003-10-01

    Recent research indicates that novel stimuli elicit at least two distinct components, the Novelty P3 and the P300. The P300 is thought to be elicited when a context updating mechanism is activated by a wide class of deviant events. The functional significance of the Novelty P3 is uncertain. Identification of the generator sources of the two components could provide additional information about their functional significance. Previous localization efforts have yielded conflicting results. The present report demonstrates that the use of principal components analysis (PCA) results in better convergence with knowledge about functional neuroanatomy than did previous localization efforts. The results are also more convincing than that obtained by two alternative methods, MUSIC-RAP and the Minimum Norm. Source modeling on 129-channel data with BESA and BrainVoyager suggests the P300 has sources in the temporal-parietal junction whereas the Novelty P3 has sources in the anterior cingulate. PMID:14561451

  17. Identifying apple surface defects using principal components analysis and artifical neural networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

  18. Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

  19. Hypothesis Generation in Latent Growth Curve Modeling Using Principal Components

    ERIC Educational Resources Information Center

    Davison, Mark L.

    2008-01-01

    While confirmatory latent growth curve analyses provide procedures for testing hypotheses about latent growth curves underlying data, one must first derive hypotheses to be tested. It is argued that such hypotheses should be generated from a combination of theory and exploratory data analyses. An exploratory components analysis is described and…

  20. Properties of dark matter haloes and their correlations: the lesson from principal component analysis

    NASA Astrophysics Data System (ADS)

    Skibba, Ramin A.; Macciò, Andrea V.

    2011-09-01

    We study the correlations between the structural parameters of dark matter haloes using principal component analysis. We consider a set of eight parameters, six of which are commonly used to characterize dark matter halo properties: mass, concentration, spin, shape, overdensity and the angle (ΦL) between the major axis and the angular momentum vector. Two additional parameters (xoff and ρrms) are used to describe the degree of 'relaxedness' of the halo. We find that we can account for much of the variance of these properties with halo mass and concentration, on the one hand, and halo relaxedness on the other. None the less, three principal components are usually required to account for most of the variance. We argue that halo mass is not as dominant as expected, which is a challenge for halo occupation models and semi-analytic models that assume that mass determines other halo (and galaxy) properties. In addition, we find that the angle ΦL is not significantly correlated with other halo parameters, which may present a difficulty for models in which galaxy discs are oriented in haloes in a particular way. Finally, at fixed mass, we find that a halo's environment (quantified by the large-scale overdensity) is relatively unimportant.

  1. Identification of the isomers using principal component analysis (PCA) method

    NASA Astrophysics Data System (ADS)

    Kepceoǧlu, Abdullah; Gündoǧdu, Yasemin; Ledingham, Kenneth William David; Kilic, Hamdi Sukur

    2016-03-01

    In this work, we have carried out a detailed statistical analysis for experimental data of mass spectra from xylene isomers. Principle Component Analysis (PCA) was used to identify the isomers which cannot be distinguished using conventional statistical methods for interpretation of their mass spectra. Experiments have been carried out using a linear TOF-MS coupled to a femtosecond laser system as an energy source for the ionisation processes. We have performed experiments and collected data which has been analysed and interpreted using PCA as a multivariate analysis of these spectra. This demonstrates the strength of the method to get an insight for distinguishing the isomers which cannot be identified using conventional mass analysis obtained through dissociative ionisation processes on these molecules. The PCA results dependending on the laser pulse energy and the background pressure in the spectrometers have been presented in this work.

  2. Spectral discrimination of bleached and healthy submerged corals based on principal components analysis

    SciTech Connect

    Holden, H.; LeDrew, E.

    1997-06-01

    Remote discrimination of substrate types in relatively shallow coastal waters has been limited by the spatial and spectral resolution of available sensors. An additional limiting factor is the strong attenuating influence of the water column over the substrate. As a result, there have been limited attempts to map submerged ecosystems such as coral reefs based on spectral characteristics. Both healthy and bleached corals were measured at depth with a hand-held spectroradiometer, and their spectra compared. Two separate principal components analyses (PCA) were performed on two sets of spectral data. The PCA revealed that there is indeed a spectral difference based on health. In the first data set, the first component (healthy coral) explains 46.82%, while the second component (bleached coral) explains 46.35% of the variance. In the second data set, the first component (bleached coral) explained 46.99%; the second component (healthy coral) explained 36.55%; and the third component (healthy coral) explained 15.44 % of the total variance in the original data. These results are encouraging with respect to using an airborne spectroradiometer to identify areas of bleached corals thus enabling accurate monitoring over time.

  3. Principal Component and Linkage Analysis of Cardiovascular Risk Traits in the Norfolk Isolate

    PubMed Central

    Cox, Hannah C.; Bellis, Claire; Lea, Rod A.; Quinlan, Sharon; Hughes, Roger; Dyer, Thomas; Charlesworth, Jac; Blangero, John; Griffiths, Lyn R.

    2009-01-01

    Objective(s) An individual's risk of developing cardiovascular disease (CVD) is influenced by genetic factors. This study focussed on mapping genetic loci for CVD-risk traits in a unique population isolate derived from Norfolk Island. Methods This investigation focussed on 377 individuals descended from the population founders. Principal component analysis was used to extract orthogonal components from 11 cardiovascular risk traits. Multipoint variance component methods were used to assess genome-wide linkage using SOLAR to the derived factors. A total of 285 of the 377 related individuals were informative for linkage analysis. Results A total of 4 principal components accounting for 83% of the total variance were derived. Principal component 1 was loaded with body size indicators; principal component 2 with body size, cholesterol and triglyceride levels; principal component 3 with the blood pressures; and principal component 4 with LDL-cholesterol and total cholesterol levels. Suggestive evidence of linkage for principal component 2 (h2 = 0.35) was observed on chromosome 5q35 (LOD = 1.85; p = 0.0008). While peak regions on chromosome 10p11.2 (LOD = 1.27; p = 0.005) and 12q13 (LOD = 1.63; p = 0.003) were observed to segregate with principal components 1 (h2 = 0.33) and 4 (h2 = 0.42), respectively. Conclusion(s): This study investigated a number of CVD risk traits in a unique isolated population. Findings support the clustering of CVD risk traits and provide interesting evidence of a region on chromosome 5q35 segregating with weight, waist circumference, HDL-c and total triglyceride levels. PMID:19339786

  4. Wavelet decomposition based principal component analysis for face recognition using MATLAB

    NASA Astrophysics Data System (ADS)

    Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

    2016-03-01

    For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

  5. Adjusting for population stratification in a fine scale with principal components and sequencing data.

    PubMed

    Zhang, Yiwei; Shen, Xiaotong; Pan, Wei

    2013-12-01

    Population stratification is of primary interest in genetic studies to infer human evolution history and to avoid spurious findings in association testing. Although it is well studied with high-density single nucleotide polymorphisms (SNPs) in genome-wide association studies (GWASs), next-generation sequencing brings both new opportunities and challenges to uncovering population structures in finer scales. Several recent studies have noticed different confounding effects from variants of different minor allele frequencies (MAFs). In this paper, using a low-coverage sequencing dataset from the 1000 Genomes Project, we compared a popular method, principal component analysis (PCA), with a recently proposed spectral clustering technique, called spectral dimensional reduction (SDR), in detecting and adjusting for population stratification at the level of ethnic subgroups. We investigated the varying performance of adjusting for population stratification with different types and sets of variants when testing on different types of variants. One main conclusion is that principal components based on all variants or common variants were generally most effective in controlling inflations caused by population stratification; in particular, contrary to many speculations on the effectiveness of rare variants, we did not find much added value with the use of only rare variants. In addition, SDR was confirmed to be more robust than PCA, especially when applied to rare variants. PMID:24123217

  6. Improving land cover classification using input variables derived from a geographically weighted principal components analysis

    NASA Astrophysics Data System (ADS)

    Comber, Alexis J.; Harris, Paul; Tsutsumida, Narumasa

    2016-09-01

    This study demonstrates the use of a geographically weighted principal components analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy. A principal components analysis (PCA) is commonly applied in remote sensing but generates global, spatially-invariant results. GWPCA is a local adaptation of PCA that locally transforms the image data, and in doing so, can describe spatial change in the structure of the multi-band imagery, thus directly reflecting that many landscape processes are spatially heterogenic. In this research the GWPCA localised loadings of MODIS data are used as textural inputs, along with GWPCA localised ranked scores and the image bands themselves to three supervised classification algorithms. Using a reference data set for land cover to the west of Jakarta, Indonesia the classification procedure was assessed via training and validation data splits of 80/20, repeated 100 times. For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy. Further, but more moderate improvements in accuracy were found by additionally including GWPCA ranked scores as textural inputs, data that provide information on spatial anomalies in the imagery. The critical importance of considering both spatial structure and spatial anomalies of the imagery in the classification is discussed, together with the transferability of the new method to other studies. Research topics for method refinement are also suggested.

  7. Applications of principal component analysis to breath air absorption spectra profiles classification

    NASA Astrophysics Data System (ADS)

    Kistenev, Yu. V.; Shapovalov, A. V.; Borisov, A. V.; Vrazhnov, D. A.; Nikolaev, V. V.; Nikiforova, O. Y.

    2015-12-01

    The results of numerical simulation of application principal component analysis to absorption spectra of breath air of patients with pulmonary diseases are presented. Various methods of experimental data preprocessing are analyzed.

  8. Relationship between gilt behavior and meat quality using principal component analysis.

    PubMed

    Ros-Freixedes, R; Sadler, L J; Onteru, S K; Smith, R M; Young, J M; Johnson, A K; Lonergan, S M; Huff-Lonergan, E; Dekkers, J C M; Rothschild, M F

    2014-01-01

    Pig on-farm behavior has important repercussions on pig welfare and performance, but generally its relationship with meat quality is not well understood. We used principal component analysis to determine the relationship between meat quality traits, feeding patterns, scale activity, and number of conflict-avoidance interactions. The first principal component indicated that gilts with greater daily feed intake stayed longer in the feeder and their meat had increased intramuscular fat (IMF), was lighter in color, and, in the second principal component, had better juiciness, tenderness, chewiness, and flavor. Meat from gilts with lower scale activity scores appeared to have more IMF but greater drip losses (DL). The third principal component suggested that dominant gilts could gain priority access to the feeder, eating more and growing fatter. In conclusion, except for the slight associations with IMF and DL, gilt scale activity and conflict-avoidance behaviors were not good indicators of final meat quality attributes. PMID:23921217

  9. Reduction of a collisional-radiative mechanism for argon plasma based on principal component analysis

    SciTech Connect

    Bellemans, A.; Munafò, A.; Magin, T. E.; Degrez, G.; Parente, A.

    2015-06-15

    This article considers the development of reduced chemistry models for argon plasmas using Principal Component Analysis (PCA) based methods. Starting from an electronic specific Collisional-Radiative model, a reduction of the variable set (i.e., mass fractions and temperatures) is proposed by projecting the full set on a reduced basis made up of its principal components. Thus, the flow governing equations are only solved for the principal components. The proposed approach originates from the combustion community, where Manifold Generated Principal Component Analysis (MG-PCA) has been developed as a successful reduction technique. Applications consider ionizing shock waves in argon. The results obtained show that the use of the MG-PCA technique enables for a substantial reduction of the computational time.

  10. Dimension reduction of non-equilibrium plasma kinetic models using principal component analysis

    NASA Astrophysics Data System (ADS)

    Peerenboom, Kim; Parente, Alessandro; Kozák, Tomáš; Bogaerts, Annemie; Degrez, Gérard

    2015-04-01

    The chemical complexity of non-equilibrium plasmas poses a challenge for plasma modeling because of the computational load. This paper presents a dimension reduction method for such chemically complex plasmas based on principal component analysis (PCA). PCA is used to identify a low-dimensional manifold in chemical state space that is described by a small number of parameters: the principal components. Reduction is obtained since continuity equations only need to be solved for these principal components and not for all the species. Application of the presented method to a CO2 plasma model including state-to-state vibrational kinetics of CO2 and CO demonstrates the potential of the PCA method for dimension reduction. A manifold described by only two principal components is able to predict the CO2 to CO conversion at varying ionization degrees very accurately.

  11. Burst and Principal Components Analyses of MEA Data Separates Chemicals by Class

    EPA Science Inventory

    Microelectrode arrays (MEAs) detect drug and chemical induced changes in action potential "spikes" in neuronal networks and can be used to screen chemicals for neurotoxicity. Analytical "fingerprinting," using Principal Components Analysis (PCA) on spike trains recorded from prim...

  12. A reduction in ag/residential signature conflict using principal components analysis of LANDSAT temporal data

    NASA Technical Reports Server (NTRS)

    Williams, D. L.; Borden, F. Y.

    1977-01-01

    Methods to accurately delineate the types of land cover in the urban-rural transition zone of metropolitan areas were considered. The application of principal components analysis to multidate LANDSAT imagery was investigated as a means of reducing the overlap between residential and agricultural spectral signatures. The statistical concepts of principal components analysis were discussed, as well as the results of this analysis when applied to multidate LANDSAT imagery of the Washington, D.C. metropolitan area.

  13. Genetic Evaluation of Dual-Purpose Buffaloes (Bubalus bubalis) in Colombia Using Principal Component Analysis

    PubMed Central

    Agudelo-Gómez, Divier; Pineda-Sierra, Sebastian; Cerón-Muñoz, Mario Fernando

    2015-01-01

    Genealogy and productive information of 48621 dual-purpose buffaloes born in Colombia between years 1996 and 2014 was used. The following traits were assessed using one-trait models: milk yield at 270 days (MY270), age at first calving (AFC), weaning weight (WW), and weights at the following ages: first year (W12), 18 months (W18), and 2 years (W24). Direct additive genetic and residual random effects were included in all the traits. Maternal permanent environmental and maternal additive genetic effects were included for WW and W12. The fixed effects were: contemporary group (for all traits), sex (for WW, W12, W18, and W24), parity (for WW, W12, and MY270). Age was included as covariate for WW, W12, W18 and W24. Principal component analysis (PCA) was conducted using the genetic values of 133 breeding males whose breeding-value reliability was higher than 50% for all the traits in order to define the number of principal components (PC) which would explain most of the variation. The highest heritabilities were for W18 and MY270, and the lowest for AFC; with 0.53, 0.23, and 0.17, respectively. The first three PCs represented 66% of the total variance. Correlation of the first PC with meat production traits was higher than 0.73, and it was -0.38 with AFC. Correlations of the second PC with maternal genetic component traits for WW and W12 were above 0.75. The third PC had 0.84 correlation with MY270. PCA is an alternative approach for analyzing traits in dual-purpose buffaloes and reduces the dimension of the traits. PMID:26230093

  14. Genetic Evaluation of Dual-Purpose Buffaloes (Bubalus bubalis) in Colombia Using Principal Component Analysis.

    PubMed

    Agudelo-Gómez, Divier; Pineda-Sierra, Sebastian; Cerón-Muñoz, Mario Fernando

    2015-01-01

    Genealogy and productive information of 48621 dual-purpose buffaloes born in Colombia between years 1996 and 2014 was used. The following traits were assessed using one-trait models: milk yield at 270 days (MY270), age at first calving (AFC), weaning weight (WW), and weights at the following ages: first year (W12), 18 months (W18), and 2 years (W24). Direct additive genetic and residual random effects were included in all the traits. Maternal permanent environmental and maternal additive genetic effects were included for WW and W12. The fixed effects were: contemporary group (for all traits), sex (for WW, W12, W18, and W24), parity (for WW, W12, and MY270). Age was included as covariate for WW, W12, W18 and W24. Principal component analysis (PCA) was conducted using the genetic values of 133 breeding males whose breeding-value reliability was higher than 50% for all the traits in order to define the number of principal components (PC) which would explain most of the variation. The highest heritabilities were for W18 and MY270, and the lowest for AFC; with 0.53, 0.23, and 0.17, respectively. The first three PCs represented 66% of the total variance. Correlation of the first PC with meat production traits was higher than 0.73, and it was -0.38 with AFC. Correlations of the second PC with maternal genetic component traits for WW and W12 were above 0.75. The third PC had 0.84 correlation with MY270. PCA is an alternative approach for analyzing traits in dual-purpose buffaloes and reduces the dimension of the traits. PMID:26230093

  15. Directly Reconstructing Principal Components of Heterogeneous Particles from Cryo-EM Images

    PubMed Central

    Tagare, Hemant D.; Kucukelbir, Alp; Sigworth, Fred J.; Wang, Hongwei; Rao, Murali

    2015-01-01

    Structural heterogeneity of particles can be investigated by their three-dimensional principal components. This paper addresses the question of whether, and with what algorithm, the three-dimensional principal components can be directly recovered from cryo-EM images. The first part of the paper extends the Fourier slice theorem to covariance functions showing that the three-dimensional covariance, and hence the principal components, of a heterogeneous particle can indeed be recovered from two-dimensional cryo-EM images. The second part of the paper proposes a practical algorithm for reconstructing the principal components directly from cryo-EM images without the intermediate step of calculating covariances. This algorithm is based on maximizing the (posterior) likelihood using the Expectation-Maximization algorithm. The last part of the paper applies this algorithm to simulated data and to two real cryo-EM data sets: a data set of the 70S ribosome with and without Elongation Factor-G (EF-G), and a data set of the inluenza virus RNA dependent RNA Polymerase (RdRP). The first principal component of the 70S ribosome data set reveals the expected conformational changes of the ribosome as the EF-G binds and unbinds. The first principal component of the RdRP data set reveals a conformational change in the two dimers of the RdRP. PMID:26049077

  16. Magnetic unmixing of first-order reversal curve diagrams using principal component analysis

    NASA Astrophysics Data System (ADS)

    Lascu, Ioan; Harrison, Richard; Li, Yuting; Piotrowski, Alexander; Channell, James; Muraszko, Joy; Hodell, David

    2015-04-01

    We have developed a magnetic unmixing method based on principal component analysis (PCA) of entire first-order reversal curve (FORC) diagrams. FORC diagrams are an advanced hysteresis technique that allows the quantitative characterisation of magnetic grain size, domain state, coercivity and spatial distribution of ensembles of particles within a sample. PCA has been previously applied on extracted central ridges from FORC diagrams of sediment samples containing single domain (SD) magnetite produced by magnetotactic bacteria (Heslop et al., 2014). We extend this methodology to the entire FORC space, which incorporates additional SD signatures, pseudo-single domain (PSD) and multi domain (MD) magnetite signatures, as well as fingerprints of other minerals, such as hematite (HEM). We apply the PCA by resampling the FORC distribution on a regular grid designed to encompass all significant features. Typically 80-90% of the variability within the FORC dataset is described by one or two principal components. Individual FORCs are recast as linear combinations of physically distinct end-member FORCs defined using the principal components and constraints derived from physical modelling. In a first case study we quantify the spatial variation of end-member components in surficial sediments along the North Atlantic Deep Water (NADW) from Iceland to Newfoundland. The samples have been physically separated into granulometric fractions, which added a further constraint in determining three end members used to model the magnetic ensemble, namely a coarse silt-sized MD component, a fine silt-sized PSD component, and a mixed clay-sized component containing both SD magnetite and hematite (SD+HEM). Sediments from core tops proximal to Iceland are dominated by the SD+HEM component, whereas those closer to Greenland and Canada are increasingly dominated by MD grains. Iceland sediments follow a PSD to SD+HEM trend with increasing grain-size fraction, whereas the Greenland and North

  17. Capturing multidimensionality in stroke aphasia: mapping principal behavioural components to neural structures

    PubMed Central

    Butler, Rebecca A.

    2014-01-01

    Stroke aphasia is a multidimensional disorder in which patient profiles reflect variation along multiple behavioural continua. We present a novel approach to separating the principal aspects of chronic aphasic performance and isolating their neural bases. Principal components analysis was used to extract core factors underlying performance of 31 participants with chronic stroke aphasia on a large, detailed battery of behavioural assessments. The rotated principle components analysis revealed three key factors, which we labelled as phonology, semantic and executive/cognition on the basis of the common elements in the tests that loaded most strongly on each component. The phonology factor explained the most variance, followed by the semantic factor and then the executive-cognition factor. The use of principle components analysis rendered participants’ scores on these three factors orthogonal and therefore ideal for use as simultaneous continuous predictors in a voxel-based correlational methodology analysis of high resolution structural scans. Phonological processing ability was uniquely related to left posterior perisylvian regions including Heschl’s gyrus, posterior middle and superior temporal gyri and superior temporal sulcus, as well as the white matter underlying the posterior superior temporal gyrus. The semantic factor was uniquely related to left anterior middle temporal gyrus and the underlying temporal stem. The executive-cognition factor was not correlated selectively with the structural integrity of any particular region, as might be expected in light of the widely-distributed and multi-functional nature of the regions that support executive functions. The identified phonological and semantic areas align well with those highlighted by other methodologies such as functional neuroimaging and neurostimulation. The use of principle components analysis allowed us to characterize the neural bases of participants’ behavioural performance more robustly and

  18. Genetic principal components for reproductive and productive traits in dual-purpose buffaloes in Colombia.

    PubMed

    Agudelo-Gómez, D A; Pelicioni Savegnago, R; Buzanskas, M E; Ferraudo, A S; Prado Munari, D; Cerón-Muñoz, M F

    2015-08-01

    A multitrait model (MC) and 5 reduced-rank models with principal component structure (components PC, PC, PC, PC, and PC) were compared. The objectives were to determine the most appropriate model for estimating genetic parameters and to evaluate the genetic progress of dual-purpose buffaloes in Colombia using that model. The traits evaluated were weaning weight (WW), yearling weight (W12), weight at 18 mo of age (W18), weight at 2 yr of age (W24), age at first calving (AFC), and milk yield at 270 d of first lactation (MY270). Genealogy and productive information from 34,326 buffaloes born in Colombia between 1997 and 2014 were used. Colombian Association of Buffalo Breeders (ACB) provided the data. Direct additive genetic and residual random effects were included for all the traits. In addition, the maternal additive genetic effect and permanent environmental random effect were included for WW, while a maternal additive genetic effect was included for W12. The fixed effects were contemporary group (farm, year, and calving season: January to April, May to August, or September to December; for all traits) and sex (for WW, W12, W18, and W24). Additionally, parity was included as a fixed effect for WW and W12. Age at weighing was used as a covariate for WW, W12, W18, and W24. Genetic progress of all traits was analyzed using a generalized smooth model (GAM). According to the Akaike information criteria (AIC), the best model was the one with reduced rank and first 3 principal components (PC). This model maintained 100% of the original variance. Genetic parameters estimated with this model were similar to those estimated by MC, but with smaller standard errors. Heritability for weight-related traits ranged between 0.23 and 0.44. Heritabilities for AFC and MY270 were 0.14 and 0.24, respectively. The genetic correlations obtained between all weights (WW, W12, W18, and W24) were positive and high. Correlations between all weights with AFC were negative and moderate

  19. A critical evaluation of the principal component analysis detection of polarized signatures using real stellar data

    NASA Astrophysics Data System (ADS)

    Paletou, F.

    2012-08-01

    The general context of this study is the post-processing of multiline spectropolarimetric observations of stars, and in particular the numerical analysis techniques aiming at detecting and characterizing polarized signatures. Using real observational data, we compare and clarify several points concerning various methods of analysis. We applied and compared the results of simple line addition, least-squares deconvolution, and denoising by principal component analysis to polarized stellar spectra available from the TBLegacy database of the Narval spectropolarimeter. This comparison of various approaches of distinct sophistication levels allows us to make a safe choice for the next implementation of on-line post-processing of our unique database for the stellar physics community.

  20. Comparing the effects of weathering and microbial degradation on gasoline using principal components analysis.

    PubMed

    Turner, Dee A; Goodpaster, John V

    2012-01-01

    Ignitable liquid residues recovered from a fire scene will often show signs of weathering as a result of exposure to the heat of the fire. In addition, when the substrate is rich in organic matter, both weathering and microbial degradation may be observed. In this study, 20 μL aliquots of fresh gasoline samples were intentionally weathered and also subjected to microbial degradation in potting soil. These samples were then analyzed using a passive adsorption-elution recovery method and gas chromatography/mass spectrometry. Peak areas from compounds of interest were normalized and autoscaled and then subjected to principal components analysis. This analysis showed that while lower boiling compounds are subject to weathering, a different set of compounds are subject to microbial degradation. Of the compounds studied, heptane, octane, toluene, and ethylbenzene were the most vulnerable to both weathering and microbial degradation. In contrast, 1,3,5-trimethylbenzene and 2-ethyltoluene were the most resistant to both phenomena. PMID:22150510

  1. Principal component analysis of satellite passive microwave data over sea ice

    NASA Astrophysics Data System (ADS)

    Rothrock, D. A.; Thomas, Donald R.; Thorndike, Alan S.

    1988-03-01

    The 10 channels of scanning multichannel microwave radiometer data for the Arctic are examined by correlation, multiple regression, and principal component analyses. Data from April, August, and December 1979 are analyzed separately. Correlations are greater than 0.8 for all pairs of channels except some of those involving the 37-GHz channels. Multiple regression shows a high degree of redundancy in the data; three channels can explain between 94.0 and 99.6% of the total variance. A principal component analysis of the covariance matrix shows that the first two eigenvalues contain 99.7% of the variance. Only the first two principal components contain variance due to the mixture of surface types. Three component mixtures (water, first-year ice, and multiyear ice) can be resolved in two dimensions. The presence of other ice types, such as second-year ice or wet ice, makes determination of ice age ambiguous in some geographic regions. Winds and surface temperature variations cause variations in the first three principal components. The confounding of these variables with mixture of surface types is a major source of error in resolving the mixture. The variance in principal components 3 through 10 is small and entirely due to variability in the pure type signatures. Determination of winds and surface temperature, as well as other variables, from this information is limited by instrument noise and presently unknown large-scale variability in the emissivity of sea ice.

  2. Determining the Number of Principal Components to Retain via Parallel Analysis: Alternatives to Monte Carlo Analyses.

    ERIC Educational Resources Information Center

    Lautenschlager, Gary J.

    The parallel analysis method for determining the number of components to retain in a principal components analysis has received a recent resurgence of support and interest. However, researchers and practitioners desiring to use this criterion have been hampered by the required Monte Carlo analyses needed to develop the criteria. Two recent…

  3. 40 CFR 60.2998 - What are the principal components of the model rule?

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule? The model rule contains nine major components, as follows: (a) Compliance schedule. (b) Waste... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION...

  4. 40 CFR 60.2998 - What are the principal components of the model rule?

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule? The model rule contains nine major components, as follows: (a) Compliance schedule. (b)...

  5. 40 CFR 60.2570 - What are the principal components of the model rule?

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... the model rule? 60.2570 Section 60.2570 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Construction On or Before November 30, 1999 Use of Model Rule § 60.2570 What are the principal components of the model rule? The model rule contains the eleven major components listed in paragraphs (a)...

  6. Performance evaluation of principal component analysis in dynamic FDG-PET studies of recurrent colorectal cancer.

    PubMed

    Thireou, Trias; Strauss, Ludwig G; Dimitrakopoulou-Strauss, Antonia; Kontaxakis, George; Pavlopoulos, Sotiris; Santos, Andres

    2003-01-01

    Performance evaluation of principal component analysis (PCA) of dynamic F-18-FDG-PET studies of patients with recurrent colorectal cancer. Principal component images (PCI) of 17 iteratively reconstructed data sets were visually and quantitatively evaluated. The F-18-FDG compartment model parameters were estimated using polynomial regression. All structures were present in PCI1. PCI2 was correlated with the vascular component and PCI3 with the tumor. The vessel density in the tumor was estimated with a correlation coefficient equal to 0.834. PCA supports the visual interpretation of dynamic F-18-FDG-PET studies, facilitates the application of compartment modeling and is a promising quantification technique. PMID:12573889

  7. Negative impact of noise on the principal component analysis of NMR data

    NASA Astrophysics Data System (ADS)

    Halouska, Steven; Powers, Robert

    2006-01-01

    Principal component analysis (PCA) is routinely applied to the study of NMR based metabolomic data. PCA is used to simplify the examination of complex metabolite mixtures obtained from biological samples that may be composed of hundreds or thousands of chemical components. PCA is primarily used to identify relative changes in the concentration of metabolites to identify trends or characteristics within the NMR data that permits discrimination between various samples that differ in their source or treatment. A common concern with PCA of NMR data is the potential over emphasis of small changes in high concentration metabolites that would over-shadow significant and large changes in low-concentration components that may lead to a skewed or irrelevant clustering of the NMR data. We have identified an additional concern, very small and random fluctuations within the noise of the NMR spectrum can also result in large and irrelevant variations in the PCA clustering. Alleviation of this problem is obtained by simply excluding the noise region from the PCA by a judicious choice of a threshold above the spectral noise.

  8. Optimized principal component analysis on coronagraphic images of the fomalhaut system

    SciTech Connect

    Meshkat, Tiffany; Kenworthy, Matthew A.; Quanz, Sascha P.; Amara, Adam

    2014-01-01

    We present the results of a study to optimize the principal component analysis (PCA) algorithm for planet detection, a new algorithm complementing angular differential imaging and locally optimized combination of images (LOCI) for increasing the contrast achievable next to a bright star. The stellar point spread function (PSF) is constructed by removing linear combinations of principal components, allowing the flux from an extrasolar planet to shine through. The number of principal components used determines how well the stellar PSF is globally modeled. Using more principal components may decrease the number of speckles in the final image, but also increases the background noise. We apply PCA to Fomalhaut Very Large Telescope NaCo images acquired at 4.05 μm with an apodized phase plate. We do not detect any companions, with a model dependent upper mass limit of 13-18 M {sub Jup} from 4-10 AU. PCA achieves greater sensitivity than the LOCI algorithm for the Fomalhaut coronagraphic data by up to 1 mag. We make several adaptations to the PCA code and determine which of these prove the most effective at maximizing the signal-to-noise from a planet very close to its parent star. We demonstrate that optimizing the number of principal components used in PCA proves most effective for pulling out a planet signal.

  9. Fast and sensitive recognition of various explosive compounds using Raman spectroscopy and principal component analysis

    NASA Astrophysics Data System (ADS)

    Hwang, Joonki; Park, Aaron; Chung, Jin Hyuk; Choi, Namhyun; Park, Jun-Qyu; Cho, Soo Gyeong; Baek, Sung-June; Choo, Jaebum

    2013-06-01

    Recently, the development of methods for the identification of explosive materials that are faster, more sensitive, easier to use, and more cost-effective has become a very important issue for homeland security and counter-terrorism applications. However, limited applicability of several analytical methods such as, the incapability of detecting explosives in a sealed container, the limited portability of instruments, and false alarms due to the inherent lack of selectivity, have motivated the increased interest in the application of Raman spectroscopy for the rapid detection and identification of explosive materials. Raman spectroscopy has received a growing interest due to its stand-off capacity, which allows samples to be analyzed at distance from the instrument. In addition, Raman spectroscopy has the capability to detect explosives in sealed containers such as glass or plastic bottles. We report a rapid and sensitive recognition technique for explosive compounds using Raman spectroscopy and principal component analysis (PCA). Seven hundreds of Raman spectra (50 measurements per sample) for 14 selected explosives were collected, and were pretreated with noise suppression and baseline elimination methods. PCA, a well-known multivariate statistical method, was applied for the proper evaluation, feature extraction, and identification of measured spectra. Here, a broad wavenumber range (200- 3500 cm-1) on the collected spectra set was used for the classification of the explosive samples into separate classes. It was found that three principal components achieved 99.3 % classification rates in the sample set. The results show that Raman spectroscopy in combination with PCA is well suited for the identification and differentiation of explosives in the field.

  10. Image-based pupil plane characterization via principal component analysis for EUVL tools

    NASA Astrophysics Data System (ADS)

    Levinson, Zac; Burbine, Andrew; Verduijn, Erik; Wood, Obert; Mangat, Pawitter; Goldberg, Kenneth A.; Benk, Markus P.; Wojdyla, Antoine; Smith, Bruce W.

    2016-03-01

    We present an approach to image-based pupil plane amplitude and phase characterization using models built with principal component analysis (PCA). PCA is a statistical technique to identify the directions of highest variation (principal components) in a high-dimensional dataset. A polynomial model is constructed between the principal components of through-focus intensity for the chosen binary mask targets and pupil amplitude or phase variation. This method separates model building and pupil characterization into two distinct steps, thus enabling rapid pupil characterization following data collection. The pupil plane variation of a zone-plate lens from the Semiconductor High-NA Actinic Reticle Review Project (SHARP) at Lawrence Berkeley National Laboratory will be examined using this method. Results will be compared to pupil plane characterization using a previously proposed methodology where inverse solutions are obtained through an iterative process involving least-squares regression.

  11. Principal component analysis of geodetically measured deformation in Long Valley caldera, eastern California, 1983-1987

    USGS Publications Warehouse

    Savage, J.C.

    1988-01-01

    Geodetic measurements of deformation at Long Valley caldera provide two examples of the application of principal component analysis. A 40-line trilateration network surrounding the caldera was surveyed in midsummer 1983, 1984, 1985, 1986, and 1987. Principal component analysis indicates that the observed deformation can be represented by a single coherent source. The time dependence for that source displays a rapid rate of deformation in 1983-1984 followed by less rapid but uniform rate in the 1984-1987 interval. The spatial factor seems consistent with expansion of a magma chamber beneath the caldera plus some shallow right-lateral slip on a vertical fault in the south moat of the caldera. An independent principal component analysis of the 1982, 1983, 1984, 1985, 1986, and 1987 leveling across the caldera requires two self-coherent sources to explain the deformation. -from Author

  12. Application of principal component analysis (PCA) as a sensory assessment tool for fermented food products.

    PubMed

    Ghosh, Debasree; Chattopadhyay, Parimal

    2012-06-01

    The objective of the work was to use the method of quantitative descriptive analysis (QDA) to describe the sensory attributes of the fermented food products prepared with the incorporation of lactic cultures. Panellists were selected and trained to evaluate various attributes specially color and appearance, body texture, flavor, overall acceptability and acidity of the fermented food products like cow milk curd and soymilk curd, idli, sauerkraut and probiotic ice cream. Principal component analysis (PCA) identified the six significant principal components that accounted for more than 90% of the variance in the sensory attribute data. Overall product quality was modelled as a function of principal components using multiple least squares regression (R (2) = 0.8). The result from PCA was statistically analyzed by analysis of variance (ANOVA). These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring the fermented food product attributes that are important for consumer acceptability. PMID:23729852

  13. Metabolomic differentiation of Cannabis sativa cultivars using 1H NMR spectroscopy and principal component analysis.

    PubMed

    Choi, Young Hae; Kim, Hye Kyong; Hazekamp, Arno; Erkelens, Cornelis; Lefeber, Alfons W M; Verpoorte, Robert

    2004-06-01

    The metabolomic analysis of 12 Cannabis sativa cultivars was carried out by 1H NMR spectroscopy and multivariate analysis techniques. Principal component analysis (PCA) of the 1H NMR spectra showed a clear discrimination between those samples by principal component 1 (PC1) and principal component 3 (PC3) in cannabinoid fraction. The loading plot of PC value obtained from all 1)H NMR signals shows that Delta9-tetrahydrocannabinolic acid (THCA) and cannabidiolic acid (CBDA) are important metabolites to differentiate the cultivars from each other. The discrimination of the cultivars could also be obtained from a water extract containing carbohydrates and amino acids. The level of sucrose, glucose, asparagine, and glutamic acid are found to be major discriminating metabolites of these cultivars. This method allows an efficient differentiation between cannabis cultivars without any prepurification steps. PMID:15217272

  14. PATHWAY-BASED ANALYSIS FOR GENOME-WIDE ASSOCIATION STUDIES USING SUPERVISED PRINCIPAL COMPONENTS

    PubMed Central

    Chen, Xi; Wang, Lily; Hu, Bo; Guo, Mingsheng; Barnard, John; Zhu, Xiaofeng

    2012-01-01

    Many complex diseases are influenced by genetic variations in multiple genes, each with only a small marginal effect on disease susceptibility. Pathway analysis, which identifies biological pathways associated with disease outcome, has become increasingly popular for genome-wide association studies (GWAS). In addition to combining weak signals from a number of SNPs in the same pathway, results from pathway analysis also shed light on the biological processes underlying disease. We propose a new pathway-based analysis method for GWAS, the supervised principal component analysis (SPCA) model. In the proposed SPCA model, a selected subset of SNPs most associated with disease outcome is used to estimate the latent variable for a pathway. The estimated latent variable for each pathway is an optimal linear combination of a selected subset of SNPs; therefore, the proposed SPCA model provides the ability to borrow strength across the SNPs in a pathway. In addition to identifying pathways associated with disease outcome, SPCA also carries out additional within-category selection to identify the most important SNPs within each gene set. The proposed model operates in a well-established statistical framework and can handle design information such as covariate adjustment and matching information in GWAS. We compare the proposed method with currently available methods using data with realistic linkage disequilibrium structures and we illustrate the SPCA method using the Wellcome Trust Case-Control Consortium Crohn Disease (CD) dataset. PMID:20842628

  15. Fungus covered insulator materials studied with laser-induced fluorescence and principal component analysis.

    PubMed

    Bengtsson, M; Wallström, S; Sjöholm, M; Grönlund, R; Anderson, B; Larsson, A; Karlsson, S; Kröll, S; Svanberg, S

    2005-08-01

    A method combining laser-induced fluorescence and principal component analysis to detect and discriminate between algal and fungal growth on insulator materials has been studied. Eight fungal cultures and four insulator materials have been analyzed. Multivariate classifications were utilized to characterize the insulator material, and fungal growth could readily be distinguished from a clean surface. The results of the principal component analyses make it possible to distinguish between algae infected, fungi infected, and clean silicone rubber materials. The experiments were performed in the laboratory using a fiber-optic fluorosensor that consisted of a nitrogen laser and an optical multi-channel analyzer system. PMID:16105213

  16. Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration

    PubMed Central

    Xiao, Jinjun; Li, Min; Zhang, Haipeng

    2015-01-01

    This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images. PMID:25960739

  17. Using Principal Component Analysis And Choqet Integral To Establish A Diagnostic Model of Parkinson Disease

    NASA Astrophysics Data System (ADS)

    cao, Xiuming; Song, Jinjie; Zhang, Caipo

    This work focused on principal component analysis and Choquet integral to structure a model of diagnose Parkinson disease. The proper value of Sugeno measure is vital to a diagnostic model. This paper aims at providing a method of using principal component analysis to obtain the sugeno measure. In this diagnostic model, there are two key elements. One is the goodness of fit that the degrees of evidential support for attribute. The other is the importance of attribute itself. The instances of Parkinson disease illuminate that the method is effective.

  18. Analysis of heavy metal sources in soil using kriging interpolation on principal components.

    PubMed

    Ha, Hoehun; Olson, James R; Bian, Ling; Rogerson, Peter A

    2014-05-01

    Anniston, Alabama has a long history of operation of foundries and other heavy industry. We assessed the extent of heavy metal contamination in soils by determining the concentrations of 11 heavy metals (Pb, As, Cd, Cr, Co, Cu, Mn, Hg, Ni, V, and Zn) based on 2046 soil samples collected from 595 industrial and residential sites. Principal Component Analysis (PCA) was adopted to characterize the distribution of heavy metals in soil in this region. In addition, a geostatistical technique (kriging) was used to create regional distribution maps for the interpolation of nonpoint sources of heavy metal contamination using geographical information system (GIS) techniques. There were significant differences found between sampling zones in the concentrations of heavy metals, with the exception of the levels of Ni. Three main components explaining the heavy metal variability in soils were identified. The results suggest that Pb, Cd, Cu, and Zn were associated with anthropogenic activities, such as the operations of some foundries and major railroads, which released these heavy metals, whereas the presence of Co, Mn, and V were controlled by natural sources, such as soil texture, pedogenesis, and soil hydrology. In general terms, the soil levels of heavy metals analyzed in this study were higher than those reported in previous studies in other industrial and residential communities. PMID:24693925

  19. Optimal principal component analysis-based numerical phase aberration compensation method for digital holography.

    PubMed

    Sun, Jiasong; Chen, Qian; Zhang, Yuzhen; Zuo, Chao

    2016-03-15

    In this Letter, an accurate and highly efficient numerical phase aberration compensation method is proposed for digital holographic microscopy. Considering that most parts of the phase aberration resides in the low spatial frequency domain, a Fourier-domain mask is introduced to extract the aberrated frequency components, while rejecting components that are unrelated to the phase aberration estimation. Principal component analysis (PCA) is then performed only on the reduced-sized spectrum, and the aberration terms can be extracted from the first principal component obtained. Finally, by oversampling the reduced-sized aberration terms, the precise phase aberration map is obtained and thus can be compensated by multiplying with its conjugation. Because the phase aberration is estimated from the limited but more relevant raw data, the compensation precision is improved and meanwhile the computation time can be significantly reduced. Experimental results demonstrate that our proposed technique could achieve both high compensating accuracy and robustness compared with other developed compensation methods. PMID:26977692

  20. Statistical intercomparison of global climate models: A common principal component approach with application to GCM data

    SciTech Connect

    Sengupta, S.K.; Boyle, J.S.

    1993-05-01

    Variables describing atmospheric circulation and other climate parameters derived from various GCMs and obtained from observations can be represented on a spatio-temporal grid (lattice) structure. The primary objective of this paper is to explore existing as well as some new statistical methods to analyze such data structures for the purpose of model diagnostics and intercomparison from a statistical perspective. Among the several statistical methods considered here, a new method based on common principal components appears most promising for the purpose of intercomparison of spatio-temporal data structures arising in the task of model/model and model/data intercomparison. A complete strategy for such an intercomparison is outlined. The strategy includes two steps. First, the commonality of spatial structures in two (or more) fields is captured in the common principal vectors. Second, the corresponding principal components obtained as time series are then compared on the basis of similarities in their temporal evolution.

  1. A Principal Components Analysis and Validation of the Coping with the College Environment Scale (CWCES)

    ERIC Educational Resources Information Center

    Ackermann, Margot Elise; Morrow, Jennifer Ann

    2008-01-01

    The present study describes the development and initial validation of the Coping with the College Environment Scale (CWCES). Participants included 433 college students who took an online survey. Principal Components Analysis (PCA) revealed six coping strategies: planning and self-management, seeking support from institutional resources, escaping…

  2. Gabor feature-based apple quality inspection using kernel principal component analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Automated inspection of apple quality involves computer recognition of good apples and blemished apples based on geometric or statistical features derived from apple images. This paper introduces a Gabor feature-based kernel, principal component analysis (PCA) method; by combining Gabor wavelet rep...

  3. Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis

    ERIC Educational Resources Information Center

    Brusco, Michael J.; Singh, Renu; Steinley, Douglas

    2009-01-01

    The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the…

  4. Principal component analysis of UV-VIS-NIR transmission spectra of Moldavian matured wine distillates

    NASA Astrophysics Data System (ADS)

    Khodasevich, Mikhail A.; Trofimova, Darya V.; Nezalzova, Elena I.

    2011-02-01

    Principal component analysis of UV-VIS-NIR transmission spectra of matured wine distillates (1-40 years aged) produced by three Moldavian manufacturers allows to characterize with sufficient certainty the eleven chemical parameters of considered alcoholic beverages: contents of acetaldehyde, ethyl acetate, furfural, vanillin, syringic aldehyde and acid, etc.

  5. Principal component analysis of UV-VIS-NIR transmission spectra of Moldavian matured wine distillates

    NASA Astrophysics Data System (ADS)

    Khodasevich, Mikhail A.; Trofimova, Darya V.; Nezalzova, Elena I.

    2010-09-01

    Principal component analysis of UV-VIS-NIR transmission spectra of matured wine distillates (1-40 years aged) produced by three Moldavian manufacturers allows to characterize with sufficient certainty the eleven chemical parameters of considered alcoholic beverages: contents of acetaldehyde, ethyl acetate, furfural, vanillin, syringic aldehyde and acid, etc.

  6. Relationships between Association of Research Libraries (ARL) Statistics and Bibliometric Indicators: A Principal Components Analysis

    ERIC Educational Resources Information Center

    Hendrix, Dean

    2010-01-01

    This study analyzed 2005-2006 Web of Science bibliometric data from institutions belonging to the Association of Research Libraries (ARL) and corresponding ARL statistics to find any associations between indicators from the two data sets. Principal components analysis on 36 variables from 103 universities revealed obvious associations between…

  7. PRINCIPAL COMPONENT REGRESSION OF NEAR-INFRARED REFLECTANCE SPECTRA FOR BEEF TENDERNESS PREDICTION

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Tenderness is the most important factor affecting consumer perception of eating quality of meat. In this paper, the development of the principal component regression (PCR) models to relate near-infrared (NIR) reflectance spectra of raw meat to Warner-Bratzler (WB) shear force measurement of cooked m...

  8. 40 CFR 60.2570 - What are the principal components of the model rule?

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... (k) of this section. (a) Increments of progress toward compliance. (b) Waste management plan. (c... the model rule? 60.2570 Section 60.2570 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Construction On or Before November 30, 1999 Use of Model Rule § 60.2570 What are the principal components...

  9. Adaptive Spatial Filtering with Principal Component Analysis for Biomedical Photoacoustic Imaging

    NASA Astrophysics Data System (ADS)

    Nagaoka, Ryo; Yamazaki, Rena; Saijo, Yoshifumi

    Photoacoustic (PA) signal is very sensitive to noise generated by peripheral equipment such as power supply, stepping motor or semiconductor laser. Band-pass filter is not effective because the frequency bandwidth of the PA signal also covers the noise frequency. The objective of the present study is to reduce the noise by using an adaptive spatial filter with principal component analysis (PCA).

  10. 40 CFR 60.1580 - What are the principal components of the model rule?

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 40 Protection of Environment 6 2010-07-01 2010-07-01 false What are the principal components of the model rule? 60.1580 Section 60.1580 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small...

  11. Hip fracture risk estimation based on principal component analysis of QCT atlas: a preliminary study

    NASA Astrophysics Data System (ADS)

    Li, Wenjun; Kornak, John; Harris, Tamara; Lu, Ying; Cheng, Xiaoguang; Lang, Thomas

    2009-02-01

    We aim to capture and apply 3-dimensional bone fragility features for fracture risk estimation. Using inter-subject image registration, we constructed a hip QCT atlas comprising 37 patients with hip fractures and 38 age-matched controls. In the hip atlas space, we performed principal component analysis to identify the principal components (eigen images) that showed association with hip fracture. To develop and test a hip fracture risk model based on the principal components, we randomly divided the 75 QCT scans into two groups, one serving as the training set and the other as the test set. We applied this model to estimate a fracture risk index for each test subject, and used the fracture risk indices to discriminate the fracture patients and controls. To evaluate the fracture discrimination efficacy, we performed ROC analysis and calculated the AUC (area under curve). When using the first group as the training group and the second as the test group, the AUC was 0.880, compared to conventional fracture risk estimation methods based on bone densitometry, which had AUC values ranging between 0.782 and 0.871. When using the second group as the training group, the AUC was 0.839, compared to densitometric methods with AUC values ranging between 0.767 and 0.807. Our results demonstrate that principal components derived from hip QCT atlas are associated with hip fracture. Use of such features may provide new quantitative measures of interest to osteoporosis.

  12. Evaluation of skin melanoma in spectral range 450-950 nm using principal component analysis

    NASA Astrophysics Data System (ADS)

    Jakovels, D.; Lihacova, I.; Kuzmina, I.; Spigulis, J.

    2013-06-01

    Diagnostic potential of principal component analysis (PCA) of multi-spectral imaging data in the wavelength range 450- 950 nm for distant skin melanoma recognition is discussed. Processing of the measured clinical data by means of PCA resulted in clear separation between malignant melanomas and pigmented nevi.

  13. 40 CFR 60.1580 - What are the principal components of the model rule?

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... the model rule? 60.1580 Section 60.1580 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY..., 1999 Use of Model Rule § 60.1580 What are the principal components of the model rule? The model rule.... (d) Monitoring and stack testing. (e) Recordkeeping and reporting. Model Rule—Increments of Progress...

  14. 40 CFR 60.5080 - What are the principal components of the model rule?

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... the model rule? 60.5080 Section 60.5080 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... and Compliance Times for Existing Sewage Sludge Incineration Units Use of Model Rule § 60.5080 What are the principal components of the model rule? The model rule contains the nine major...

  15. Principal Component Analysis: Resources for an Essential Application of Linear Algebra

    ERIC Educational Resources Information Center

    Pankavich, Stephen; Swanson, Rebecca

    2015-01-01

    Principal Component Analysis (PCA) is a highly useful topic within an introductory Linear Algebra course, especially since it can be used to incorporate a number of applied projects. This method represents an essential application and extension of the Spectral Theorem and is commonly used within a variety of fields, including statistics,…

  16. Principal Perspectives about Policy Components and Practices for Reducing Cyberbullying in Urban Schools

    ERIC Educational Resources Information Center

    Hunley-Jenkins, Keisha Janine

    2012-01-01

    This qualitative study explores large, urban, mid-western principal perspectives about cyberbullying and the policy components and practices that they have found effective and ineffective at reducing its occurrence and/or negative effect on their schools' learning environments. More specifically, the researcher was interested in learning more…

  17. Stability of Nonlinear Principal Components Analysis: An Empirical Study Using the Balanced Bootstrap

    ERIC Educational Resources Information Center

    Linting, Marielle; Meulman, Jacqueline J.; Groenen, Patrick J. F.; van der Kooij, Anita J.

    2007-01-01

    Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate…

  18. UNIPALS: SOFTWARE FOR PRINCIPAL COMPONENTS ANALYSIS AND PARTIAL LEAST SQUARES REGRESSION

    EPA Science Inventory

    Software for the analysis of multivariate chemical data by principal components and partial least squares methods is included on disk. he methods extract latent variables from the chemical data with the UNIversal PArtial Least Squares or UNIPALS algorithm. he software is written ...

  19. The Use of Exploratory Factor Analysis and Principal Components Analysis in Communication Research.

    ERIC Educational Resources Information Center

    Park, Hee Sun; Dailey, Rene; Lemus, Daisy

    2002-01-01

    Discusses the distinct purposes of principal components analysis (PCA) and exploratory factor analysis (EFA), using two data sets as examples. Reviews the use of each technique in three major communication journals: "Communication Monographs,""Human Communication Research," and "Communication Research." Finds that the use of EFA and PCA indicates…

  20. Checking Dimensionality in Item Response Models with Principal Component Analysis on Standardized Residuals

    ERIC Educational Resources Information Center

    Chou, Yeh-Tai; Wang, Wen-Chung

    2010-01-01

    Dimensionality is an important assumption in item response theory (IRT). Principal component analysis on standardized residuals has been used to check dimensionality, especially under the family of Rasch models. It has been suggested that an eigenvalue greater than 1.5 for the first eigenvalue signifies a violation of unidimensionality when there…

  1. A Case of Extreme Simplicity of the Core Matrix in Three-Mode Principal Components Analysis.

    ERIC Educational Resources Information Center

    Murakami, Takashi; ten Berge, Jos M. F.; Kiers, Henk A. L.

    1998-01-01

    In three-mode principal components analysis, the P x Q x R core matrix "G" can be transformed to simple structure before it is interpreted. This paper shows that, when P=QR-1, G can be transformed to have nearly all the elements equal to values specified a priori. A closed-form solution for this transformation is offered. (SLD)

  2. Bright-field cell image segmentation by principal component pursuit with an Ncut penalization

    NASA Astrophysics Data System (ADS)

    Chen, Yuehuan; Wan, Justin W. L.

    2015-03-01

    Segmentation of cells in time-lapse bright-field microscopic images is crucial in understanding cell behaviours for oncological research. However, the complex nature of the cells makes it difficult to segment cells accurately. Furthermore, poor contrast, broken cell boundaries and the halo artifact pose additional challenges to this problem. Standard segmentation techniques such as edged-based methods, watershed, or active contours result in poor segmentation. Other existing methods for bright-field images cannot provide good results without localized segmentation steps. In this paper, we present two robust mathematical models to segment bright-field cells automatically for the entire image. These models treat cell image segmentation as a background subtraction problem, which can be formulated as a Principal Component Pursuit (PCP) problem. Our first segmentation model is formulated as a PCP with nonnegative constraints. We exploit the sparse component of the PCP solution for identifying the cell pixels. However, there is no control on the quality of the sparse component and the nonzero entries can scatter all over the image, resulting in a noisy segmentation. The second model is an improvement of the first model by combining PCP with spectral clustering. Seemingly unrelated approaches, we combine the two techniques by incorporating normalized-cut in the PCP as a measure for the quality of the segmentation. These two models have been applied to a set of C2C12 cells obtained from bright-field microscopy. Experimental results demonstrate that the proposed models are effective in segmenting cells from bright-field images.

  3. Design and Validation of a Morphing Myoelectric Hand Posture Controller Based on Principal Component Analysis of Human Grasping

    PubMed Central

    Segil, Jacob L.; Weir, Richard F. ff.

    2015-01-01

    An ideal myoelectric prosthetic hand should have the ability to continuously morph between any posture like an anatomical hand. This paper describes the design and validation of a morphing myoelectric hand controller based on principal component analysis of human grasping. The controller commands continuously morphing hand postures including functional grasps using between two and four surface electromyography (EMG) electrodes pairs. Four unique maps were developed to transform the EMG control signals in the principal component domain. A preliminary validation experiment was performed by 10 nonamputee subjects to determine the map with highest performance. The subjects used the myoelectric controller to morph a virtual hand between functional grasps in a series of randomized trials. The number of joints controlled accurately was evaluated to characterize the performance of each map. Additional metrics were studied including completion rate, time to completion, and path efficiency. The highest performing map controlled over 13 out of 15 joints accurately. PMID:23649286

  4. Principal component analysis of indocyanine green fluorescence dynamics for diagnosis of vascular diseases

    NASA Astrophysics Data System (ADS)

    Seo, Jihye; An, Yuri; Lee, Jungsul; Choi, Chulhee

    2015-03-01

    Indocyanine green (ICG), a near-infrared fluorophore, has been used in visualization of vascular structure and non-invasive diagnosis of vascular disease. Although many imaging techniques have been developed, there are still limitations in diagnosis of vascular diseases. We have recently developed a minimally invasive diagnostics system based on ICG fluorescence imaging for sensitive detection of vascular insufficiency. In this study, we used principal component analysis (PCA) to examine ICG spatiotemporal profile and to obtain pathophysiological information from ICG dynamics. Here we demonstrated that principal components of ICG dynamics in both feet showed significant differences between normal control and diabetic patients with vascula complications. We extracted the PCA time courses of the first three components and found distinct pattern in diabetic patient. We propose that PCA of ICG dynamics reveal better classification performance compared to fluorescence intensity analysis. We anticipate that specific feature of spatiotemporal ICG dynamics can be useful in diagnosis of various vascular diseases.

  5. Principal component analysis based carrier removal approach for Fourier transform profilometry

    NASA Astrophysics Data System (ADS)

    Feng, Shijie; Chen, Qian; Zuo, Chao

    2015-05-01

    To handle the issue of the nonlinear carrier phase due to the divergent illumination commonly adopted in the fringe projection measurement, we propose a principal component analysis (PCA) based carrier removal method for Fourier transform profilometry. By PCA, the method can decompose the nonlinear carrier phase map into several principal components, where the phase of the carrier can be extracted from the first dominant component acquired. It is effective and requires less human intervention since no data points need to be collected from the reference plane in advance compared with traditional methods. Further, the influence of the lens distortion is considered thus the carrier can be determined more accurately. Our experiment shows the validity of the proposed approach.

  6. Principal Component Analysis of Spectroscopic Imaging Data in Scanning Probe Microscopy

    SciTech Connect

    Jesse, Stephen; Kalinin, Sergei V

    2009-01-01

    The approach for data analysis in band excitation family of scanning probe microscopies based on principal component analysis (PCA) is explored. PCA utilizes the similarity between spectra within the image to select the relevant response components. For small signal variations within the image, the PCA components coincide with the results of deconvolution using simple harmonic oscillator model. For strong signal variations, the PCA allows effective approach to rapidly process, de-noise and compress the data. The extension of PCA for correlation function analysis is demonstrated. The prospects of PCA as a universal tool for data analysis and representation in multidimensional SPMs are discussed.

  7. Principal Component Analysis reveals correlation of cavities evolution and functional motions in proteins.

    PubMed

    Desdouits, Nathan; Nilges, Michael; Blondel, Arnaud

    2015-02-01

    Protein conformation has been recognized as the key feature determining biological function, as it determines the position of the essential groups specifically interacting with substrates. Hence, the shape of the cavities or grooves at the protein surface appears to drive those functions. However, only a few studies describe the geometrical evolution of protein cavities during molecular dynamics simulations (MD), usually with a crude representation. To unveil the dynamics of cavity geometry evolution, we developed an approach combining cavity detection and Principal Component Analysis (PCA). This approach was applied to four systems subjected to MD (lysozyme, sperm whale myoglobin, Dengue envelope protein and EF-CaM complex). PCA on cavities allows us to perform efficient analysis and classification of the geometry diversity explored by a cavity. Additionally, it reveals correlations between the evolutions of the cavities and structures, and can even suggest how to modify the protein conformation to induce a given cavity geometry. It also helps to perform fast and consensual clustering of conformations according to cavity geometry. Finally, using this approach, we show that both carbon monoxide (CO) location and transfer among the different xenon sites of myoglobin are correlated with few cavity evolution modes of high amplitude. This correlation illustrates the link between ligand diffusion and the dynamic network of internal cavities. PMID:25424655

  8. Modified principal component analysis: an integration of multiple similarity subspace models.

    PubMed

    Fan, Zizhu; Xu, Yong; Zuo, Wangmeng; Yang, Jian; Tang, Jinhui; Lai, Zhihui; Zhang, David

    2014-08-01

    We modify the conventional principal component analysis (PCA) and propose a novel subspace learning framework, modified PCA (MPCA), using multiple similarity measurements. MPCA computes three similarity matrices exploiting the similarity measurements: 1) mutual information; 2) angle information; and 3) Gaussian kernel similarity. We employ the eigenvectors of similarity matrices to produce new subspaces, referred to as similarity subspaces. A new integrated similarity subspace is then generated using a novel feature selection approach. This approach needs to construct a kind of vector set, termed weak machine cell (WMC), which contains an appropriate number of the eigenvectors spanning the similarity subspaces. Combining the wrapper method and the forward selection scheme, MPCA selects a WMC at a time that has a powerful discriminative capability to classify samples. MPCA is very suitable for the application scenarios in which the number of the training samples is less than the data dimensionality. MPCA outperforms the other state-of-the-art PCA-based methods in terms of both classification accuracy and clustering result. In addition, MPCA can be applied to face image reconstruction. MPCA can use other types of similarity measurements. Extensive experiments on many popular real-world data sets, such as face databases, show that MPCA achieves desirable classification results, as well as has a powerful capability to represent data. PMID:25050950

  9. Infrared small target and background separation via column-wise weighted robust principal component analysis

    NASA Astrophysics Data System (ADS)

    Dai, Yimian; Wu, Yiquan; Song, Yu

    2016-07-01

    When facing extremely complex infrared background, due to the defect of l1 norm based sparsity measure, the state-of-the-art infrared patch-image (IPI) model would be in a dilemma where either the dim targets are over-shrinked in the separation or the strong cloud edges remains in the target image. In order to suppress the strong edges while preserving the dim targets, a weighted infrared patch-image (WIPI) model is proposed, incorporating structural prior information into the process of infrared small target and background separation. Instead of adopting a global weight, we allocate adaptive weight to each column of the target patch-image according to its patch structure. Then the proposed WIPI model is converted to a column-wise weighted robust principal component analysis (CWRPCA) problem. In addition, a target unlikelihood coefficient is designed based on the steering kernel, serving as the adaptive weight for each column. Finally, in order to solve the CWPRCA problem, a solution algorithm is developed based on Alternating Direction Method (ADM). Detailed experiment results demonstrate that the proposed method has a significant improvement over the other nine classical or state-of-the-art methods in terms of subjective visual quality, quantitative evaluation indexes and convergence rate.

  10. Integrative and regularized principal component analysis of multiple sources of data.

    PubMed

    Liu, Binghui; Shen, Xiaotong; Pan, Wei

    2016-06-15

    Integration of data of disparate types has become increasingly important to enhancing the power for new discoveries by combining complementary strengths of multiple types of data. One application is to uncover tumor subtypes in human cancer research in which multiple types of genomic data are integrated, including gene expression, DNA copy number, and DNA methylation data. In spite of their successes, existing approaches based on joint latent variable models require stringent distributional assumptions and may suffer from unbalanced scales (or units) of different types of data and non-scalability of the corresponding algorithms. In this paper, we propose an alternative based on integrative and regularized principal component analysis, which is distribution-free, computationally efficient, and robust against unbalanced scales. The new method performs dimension reduction simultaneously on multiple types of data, seeking data-adaptive sparsity and scaling. As a result, in addition to feature selection for each type of data, integrative clustering is achieved. Numerically, the proposed method compares favorably against its competitors in terms of accuracy (in identifying hidden clusters), computational efficiency, and robustness against unbalanced scales. In particular, compared with a popular method, the new method was competitive in identifying tumor subtypes associated with distinct patient survival patterns when applied to a combined analysis of DNA copy number, mRNA expression, and DNA methylation data in a glioblastoma multiforme study. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26756854

  11. A new simple /spl infin/OH neuron model as a biologically plausible principal component analyzer.

    PubMed

    Jankovic, M V

    2003-01-01

    A new approach to unsupervised learning in a single-layer neural network is discussed. An algorithm for unsupervised learning based upon the Hebbian learning rule is presented. A simple neuron model is analyzed. A dynamic neural model, which contains both feed-forward and feedback connections between the input and the output, has been adopted. The, proposed learning algorithm could be more correctly named self-supervised rather than unsupervised. The solution proposed here is a modified Hebbian rule, in which the modification of the synaptic strength is proportional not to pre- and postsynaptic activity, but instead to the presynaptic and averaged value of postsynaptic activity. It is shown that the model neuron tends to extract the principal component from a stationary input vector sequence. Usually accepted additional decaying terms for the stabilization of the original Hebbian rule are avoided. Implementation of the basic Hebbian scheme would not lead to unrealistic growth of the synaptic strengths, thanks to the adopted network structure. PMID:18238065

  12. Principal component analysis of ensemble recordings reveals cell assemblies at high temporal resolution

    PubMed Central

    Benchenane, Karim; Khamassi, Mehdi; Wiener, Sidney I.; Battaglia, Francesco P.

    2009-01-01

    Simultaneous recordings of many single neurons reveals unique insights into network processing spanning the timescale from single spikes to global oscillations. Neurons dynamically self-organize in subgroups of coactivated elements referred to as cell assemblies. Furthermore, these cell assemblies are reactivated, or replayed, preferentially during subsequent rest or sleep episodes, a proposed mechanism for memory trace consolidation. Here we employ Principal Component Analysis to isolate such patterns of neural activity. In addition, a measure is developed to quantify the similarity of instantaneous activity with a template pattern, and we derive theoretical distributions for the null hypothesis of no correlation between spike trains, allowing one to evaluate the statistical significance of instantaneous coactivations. Hence, when applied in an epoch different from the one where the patterns were identified, (e.g. subsequent sleep) this measure allows to identify times and intensities of reactivation. The distribution of this measure provides information on the dynamics of reactivation events: in sleep these occur as transients rather than as a continuous process. PMID:19529888

  13. Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers.

    PubMed

    Elsawy, Amr S; Eldawlatly, Seif; Taher, Mohamed; Aly, Gamal M

    2014-01-01

    The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method. PMID:25571123

  14. Applying robust variant of Principal Component Analysis as a damage detector in the presence of outliers

    NASA Astrophysics Data System (ADS)

    Gharibnezhad, Fahit; Mujica, Luis E.; Rodellar, José

    2015-01-01

    Using Principal Component Analysis (PCA) for Structural Health Monitoring (SHM) has received considerable attention over the past few years. PCA has been used not only as a direct method to identify, classify and localize damages but also as a significant primary step for other methods. Despite several positive specifications that PCA conveys, it is very sensitive to outliers. Outliers are anomalous observations that can affect the variance and the covariance as vital parts of PCA method. Therefore, the results based on PCA in the presence of outliers are not fully satisfactory. As a main contribution, this work suggests the use of robust variant of PCA not sensitive to outliers, as an effective way to deal with this problem in SHM field. In addition, the robust PCA is compared with the classical PCA in the sense of detecting probable damages. The comparison between the results shows that robust PCA can distinguish the damages much better than using classical one, and even in many cases allows the detection where classic PCA is not able to discern between damaged and non-damaged structures. Moreover, different types of robust PCA are compared with each other as well as with classical counterpart in the term of damage detection. All the results are obtained through experiments with an aircraft turbine blade using piezoelectric transducers as sensors and actuators and adding simulated damages.

  15. Part III: Principal component analysis: bridging the gap between strain, sex and drug effects.

    PubMed

    Keeley, R J; McDonald, R J

    2015-07-15

    Previous work has identified the adolescent period as particularly sensitive to the short- and long-term effects of marijuana and its main psychoactive component Δ9-tetrahydrocannabinol (THC). However, other studies have identified certain backgrounds as more sensitive than others, including the sex of the individual or the strain of the rat used. Further, the effects of THC may be specific to certain behavioural tasks (e.g. measures of anxiety), and the consequences of THC are not seen equally across all behavioural measures. Here, data obtained from adolescent male and female Long-Evans and Wistar rats exposed to THC and tested as adults, which, using standard ANOVA testing, showed strain- and sex-specific effects of THC, was analyzed using principal component analysis (PCA). PCA allowed for the examination of the relative contribution of our variables of interest to the variance in the data obtained from multiple behavioural tasks, including the skilled reaching task, the Morris water task, the discriminative fear-conditioning to context task, the elevated plus maze task and the conditioned place preference task to a low dose of amphetamine, as well as volumetric estimates of brain volumes and cfos activation. We observed that early life experience accounted for a large proportion of variance across data sets, although its relative contribution varied across tasks. Additionally, THC accounted for a very small proportion of the variance across all behavioural tasks. We demonstrate here that by using PCA, we were able to describe the main variables of interest and demonstrate that THC exposure had a negligible effect on the variance in the data set. PMID:25813745

  16. Sex-based differences in lifting technique under increasing load conditions: A principal component analysis.

    PubMed

    Sheppard, P S; Stevenson, J M; Graham, R B

    2016-05-01

    The objective of the present study was to determine if there is a sex-based difference in lifting technique across increasing-load conditions. Eleven male and 14 female participants (n = 25) with no previous history of low back disorder participated in the study. Participants completed freestyle, symmetric lifts of a box with handles from the floor to a table positioned at 50% of their height for five trials under three load conditions (10%, 20%, and 30% of their individual maximum isometric back strength). Joint kinematic data for the ankle, knee, hip, and lumbar and thoracic spine were collected using a two-camera Optotrak motion capture system. Joint angles were calculated using a three-dimensional Euler rotation sequence. Principal component analysis (PCA) and single component reconstruction were applied to assess differences in lifting technique across the entire waveforms. Thirty-two PCs were retained from the five joints and three axes in accordance with the 90% trace criterion. Repeated-measures ANOVA with a mixed design revealed no significant effect of sex for any of the PCs. This is contrary to previous research that used discrete points on the lifting curve to analyze sex-based differences, but agrees with more recent research using more complex analysis techniques. There was a significant effect of load on lifting technique for five PCs of the lower limb (PC1 of ankle flexion, knee flexion, and knee adduction, as well as PC2 and PC3 of hip flexion) (p < 0.005). However, there was no significant effect of load on the thoracic and lumbar spine. It was concluded that when load is standardized to individual back strength characteristics, males and females adopted a similar lifting technique. In addition, as load increased male and female participants changed their lifting technique in a similar manner. PMID:26851478

  17. [The principal components analysis--method to classify the statistical variables with applications in medicine].

    PubMed

    Dascălu, Cristina Gena; Antohe, Magda Ecaterina

    2009-01-01

    Based on the eigenvalues and the eigenvectors analysis, the principal component analysis has the purpose to identify the subspace of the main components from a set of parameters, which are enough to characterize the whole set of parameters. Interpreting the data for analysis as a cloud of points, we find through geometrical transformations the directions where the cloud's dispersion is maximal--the lines that pass through the cloud's center of weight and have a maximal density of points around them (by defining an appropriate criteria function and its minimization. This method can be successfully used in order to simplify the statistical analysis on questionnaires--because it helps us to select from a set of items only the most relevant ones, which cover the variations of the whole set of data. For instance, in the presented sample we started from a questionnaire with 28 items and, applying the principal component analysis we identified 7 principal components--or main items--fact that simplifies significantly the further data statistical analysis. PMID:21495371

  18. EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

    PubMed Central

    Jirayucharoensak, Suwicha; Pan-Ngum, Setha; Israsena, Pasin

    2014-01-01

    Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers. PMID:25258728

  19. EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation.

    PubMed

    Jirayucharoensak, Suwicha; Pan-Ngum, Setha; Israsena, Pasin

    2014-01-01

    Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers. PMID:25258728

  20. Matrix partitioning and EOF/principal component analysis of Antarctic Sea ice brightness temperatures

    NASA Technical Reports Server (NTRS)

    Murray, C. W., Jr.; Mueller, J. L.; Zwally, H. J.

    1984-01-01

    A field of measured anomalies of some physical variable relative to their time averages, is partitioned in either the space domain or the time domain. Eigenvectors and corresponding principal components of the smaller dimensioned covariance matrices associated with the partitioned data sets are calculated independently, then joined to approximate the eigenstructure of the larger covariance matrix associated with the unpartitioned data set. The accuracy of the approximation (fraction of the total variance in the field) and the magnitudes of the largest eigenvalues from the partitioned covariance matrices together determine the number of local EOF's and principal components to be joined by any particular level. The space-time distribution of Nimbus-5 ESMR sea ice measurement is analyzed.

  1. Application of principal component analysis in protein unfolding: An all-atom molecular dynamics simulation study

    NASA Astrophysics Data System (ADS)

    Das, Atanu; Mukhopadhyay, Chaitali

    2007-10-01

    We have performed molecular dynamics (MD) simulation of the thermal denaturation of one protein and one peptide—ubiquitin and melittin. To identify the correlation in dynamics among various secondary structural fragments and also the individual contribution of different residues towards thermal unfolding, principal component analysis method was applied in order to give a new insight to protein dynamics by analyzing the contribution of coefficients of principal components. The cross-correlation matrix obtained from MD simulation trajectory provided important information regarding the anisotropy of backbone dynamics that leads to unfolding. Unfolding of ubiquitin was found to be a three-state process, while that of melittin, though smaller and mostly helical, is more complicated.

  2. Research on content measurement of textile mixture by near infrared spectroscopy based on principal component regression

    NASA Astrophysics Data System (ADS)

    Yan, Li; Liu, Li

    2010-07-01

    A new method for accurate measurement of content of textile mixture by use of Fourier transform near infrared spectroscopy is put forward. The near infrared spectra of 56 samples with different cotton and polyester contents were obtained, in which 41 samples, 10 samples and 5 samples were used for the calibration set, validation set and prediction set respectively. Principal component analysis (PCA) was utilized for the spectra data compression. Principal component regression (PCR) model was developed. It indicates that the MAE is within 2.9% and the RMSE is less than 3.6% for the validation samples, which is suitable for the prediction of unknown samples. The PCR model was applied to predict unknown samples. Experimental results show that this approach by use of Fourier transform Near Infrared Spectroscopy can be used to quantitative analysis for textile fiber.

  3. Extracting the core indicators of pulverized coal for blast furnace injection based on principal component analysis

    NASA Astrophysics Data System (ADS)

    Guo, Hong-wei; Su, Bu-xin; Zhang, Jian-liang; Zhu, Meng-yi; Chang, Jian

    2013-03-01

    An updated approach to refining the core indicators of pulverized coal used for blast furnace injection based on principal component analysis is proposed in view of the disadvantages of the existing performance indicator system of pulverized coal used in blast furnaces. This presented method takes into account all the performance indicators of pulverized coal injection, including calorific value, igniting point, combustibility, reactivity, flowability, grindability, etc. Four core indicators of pulverized coal injection are selected and studied by using principal component analysis, namely, comprehensive combustibility, comprehensive reactivity, comprehensive flowability, and comprehensive grindability. The newly established core index system is not only beneficial to narrowing down current evaluation indices but also effective to avoid previous overlapping problems among indicators by mutually independent index design. Furthermore, a comprehensive property indicator is introduced on the basis of the four core indicators, and the injection properties of pulverized coal can be overall evaluated.

  4. Obtaining a linear combination of the principal components of a matrix on quantum computers

    NASA Astrophysics Data System (ADS)

    Daskin, Ammar

    2016-07-01

    Principal component analysis is a multivariate statistical method frequently used in science and engineering to reduce the dimension of a problem or extract the most significant features from a dataset. In this paper, using a similar notion to the quantum counting, we show how to apply the amplitude amplification together with the phase estimation algorithm to an operator in order to procure the eigenvectors of the operator associated to the eigenvalues defined in the range [ a, b] , where a and b are real and 0 ≤ a ≤ b ≤ 1 . This makes possible to obtain a combination of the eigenvectors associated with the largest eigenvalues and so can be used to do principal component analysis on quantum computers.

  5. Magnetic anomaly detection (MAD) of ferromagnetic pipelines using principal component analysis (PCA)

    NASA Astrophysics Data System (ADS)

    Sheinker, Arie; Moldwin, Mark B.

    2016-04-01

    The magnetic anomaly detection (MAD) method is used for detection of visually obscured ferromagnetic objects. The method exploits the magnetic field originating from the ferromagnetic object, which constitutes an anomaly in the ambient earth’s magnetic field. Traditionally, MAD is used to detect objects with a magnetic field of a dipole structure, where far from the object it can be considered as a point source. In the present work, we expand MAD to the case of a non-dipole source, i.e. a ferromagnetic pipeline. We use principal component analysis (PCA) to calculate the principal components, which are then employed to construct an effective detector. Experiments conducted in our lab with real-world data validate the above analysis. The simplicity, low computational complexity, and the high detection rate make the proposed detector attractive for real-time, low power applications.

  6. Effect of washing on identification of Bacillus spores by principal-component analysis of fluorescence data

    NASA Astrophysics Data System (ADS)

    Kunnil, Joseph; Sarasanandarajah, Sivananthan; Chacko, Easaw; Reinisch, Lou

    2006-05-01

    The fluorescence spectra of Bacillus spores are measured at excitation wavelengths of 280, 310, 340, 370, and 400 nm. When cluster analysis is used with the principal-component analysis, the Bacillus globigii spores can be distinguished from the other species of Bacillus spores (B. cereus, B. popilliae, and B. thuringiensis). To test how robust the identification process is with the fluorescence spectra, the B. globigii is obtained from three separate preparations in different laboratories. Furthermore the fluorescence is measured before and after washing and redrying the B. globigii spores. Using the cluster analysis of the first two or three principal components of the fluorescence spectra, one is able to distinguish B. globigii spores from the other species, independent of preparing or washing the spores.

  7. Principal component analysis of Birkeland currents determined by the Active Magnetosphere and Planetary Electrodynamics Response Experiment

    NASA Astrophysics Data System (ADS)

    Milan, S. E.; Carter, J. A.; Korth, H.; Anderson, B. J.

    2015-12-01

    Principal component analysis is performed on Birkeland or field-aligned current (FAC) measurements from the Active Magnetosphere and Planetary Electrodynamics Response Experiment. Principal component analysis (PCA) identifies the patterns in the FACs that respond coherently to different aspects of geomagnetic activity. The regions 1 and 2 current system is shown to be the most reproducible feature of the currents, followed by cusp currents associated with magnetic tension forces on newly reconnected field lines. The cusp currents are strongly modulated by season, indicating that their strength is regulated by the ionospheric conductance at the foot of the field lines. PCA does not identify a pattern that is clearly characteristic of a substorm current wedge. Rather, a superposed epoch analysis of the currents associated with substorms demonstrates that there is not a single mode of response, but a complicated and subtle mixture of different patterns.

  8. [Infrared spectroscopy analysis of SF6 using multiscale weighted principal component analysis].

    PubMed

    Peng, Xi; Wang, Xian-Pei; Huang, Yun-Guang

    2012-06-01

    Infrared spectroscopy analysis of SF6 and its derivative is an important method for operating state assessment and fault diagnosis of the gas insulated switchgear (GIS). Traditional methods are complicated and inefficient, and the results can vary with different subjects. In the present work, the feature extraction methods in machine learning are recommended to solve such diagnosis problem, and a multiscale weighted principal component analysis method is proposed. The proposed method combines the advantage of standard principal component analysis and multiscale decomposition to maximize the feature information in different scales, and modifies the importance of the eigenvectors in classification. The classification performance of the proposed method was demonstrated to be 3 to 4 times better than that of the standard PCA for the infrared spectra of SF6 and its derivative provided by Guangxi Research Institute of Electric Power. PMID:22870634

  9. Detecting cervical cancer progression through extracted intrinsic fluorescence and principal component analysis

    NASA Astrophysics Data System (ADS)

    Devi, Seema; Panigrahi, Prasanta K.; Pradhan, Asima

    2014-12-01

    Intrinsic fluorescence spectra of the human normal, cervical intraepithelial neoplasia 1 (CIN1), CIN2, and cervical cancer tissue have been extracted by effectively combining the measured polarized fluorescence and polarized elastic scattering spectra. The efficacy of principal component analysis (PCA) to disentangle the collective behavior from smaller correlated clusters in a dimensionally reduced space in conjunction with the intrinsic fluorescence is examined. This combination unambiguously reveals the biochemical changes occurring with the progression of the disease. The differing activities of the dominant fluorophores, collagen, nicotinamide adenine dinucleotide, flavins, and porphyrin of different grades of precancers are clearly identified through a careful examination of the sectorial behavior of the dominant eigenvectors of PCA. To further classify the different grades, the Mahalanobis distance has been calculated using the scores of selected principal components.

  10. An efficient classification method based on principal component and sparse representation.

    PubMed

    Zhai, Lin; Fu, Shujun; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang

    2016-01-01

    As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition. PMID:27386281

  11. Assessment of models for pedestrian dynamics with functional principal component analysis

    NASA Astrophysics Data System (ADS)

    Chraibi, Mohcine; Ensslen, Tim; Gottschalk, Hanno; Saadi, Mohamed; Seyfried, Armin

    2016-06-01

    Many agent based simulation approaches have been proposed for pedestrian flow. As such models are applied e.g. in evacuation studies, the quality and reliability of such models is of vital interest. Pedestrian trajectories are functional data and thus functional principal component analysis is a natural tool to assess the quality of pedestrian flow models beyond average properties. In this article we conduct functional Principal Component Analysis (PCA) for the trajectories of pedestrians passing through a bottleneck. In this way it is possible to assess the quality of the models not only on basis of average values but also by considering its fluctuations. We benchmark two agent based models of pedestrian flow against the experimental data using PCA average and stochastic features. Functional PCA proves to be an efficient tool to detect deviation between simulation and experiment and to assess quality of pedestrian models.

  12. Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

    PubMed Central

    Gao, H; Zhang, T; Wu, Y; Wu, Y; Jiang, L; Zhan, J; Li, J; Yang, R

    2014-01-01

    Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent ‘super traits' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle. PMID:24984606

  13. Processing of X-ray Microcalorimeter Data with Pulse Shape Variation using Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Yan, D.; Cecil, T.; Gades, L.; Jacobsen, C.; Madden, T.; Miceli, A.

    2016-01-01

    We present a method using principal component analysis (PCA) to process x-ray pulses with severe shape variation where traditional optimal filter methods fail. We demonstrate that PCA is able to noise-filter and extract energy information from x-ray pulses despite their different shapes. We apply this method to a dataset from an x-ray thermal kinetic inductance detector which has severe pulse shape variation arising from position-dependent absorption.

  14. APPLICATION OF PRINCIPAL COMPONENT ANALYSIS AND BAYESIAN DECOMPOSITION TO RELAXOGRAPHIC IMAGING

    SciTech Connect

    OCHS,M.F.; STOYANOVA,R.S.; BROWN,T.R.; ROONEY,W.D.; LI,X.; LEE,J.H.; SPRINGER,C.S.

    1999-05-22

    Recent developments in high field imaging have made possible the acquisition of high quality, low noise relaxographic data in reasonable imaging times. The datasets comprise a huge amount of information (>>1 million points) which makes rigorous analysis daunting. Here, the authors present results demonstrating that Principal Component Analysis (PCA) and Bayesian Decomposition (BD) provide powerful methods for relaxographic analysis of T{sub 1} recovery curves and editing of tissue type in resulting images.

  15. Processing of X-ray Microcalorimeter Data with Pulse Shape Variation using Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Yan, D.; Cecil, T.; Gades, L.; Jacobsen, C.; Madden, T.; Miceli, A.

    2016-07-01

    We present a method using principal component analysis (PCA) to process x-ray pulses with severe shape variation where traditional optimal filter methods fail. We demonstrate that PCA is able to noise-filter and extract energy information from x-ray pulses despite their different shapes. We apply this method to a dataset from an x-ray thermal kinetic inductance detector which has severe pulse shape variation arising from position-dependent absorption.

  16. Application of principal component analysis to multispectral imaging data for evaluation of pigmented skin lesions

    NASA Astrophysics Data System (ADS)

    Jakovels, Dainis; Lihacova, Ilze; Kuzmina, Ilona; Spigulis, Janis

    2013-11-01

    Non-invasive and fast primary diagnostics of pigmented skin lesions is required due to frequent incidence of skin cancer - melanoma. Diagnostic potential of principal component analysis (PCA) for distant skin melanoma recognition is discussed. Processing of the measured clinical multi-spectral images (31 melanomas and 94 nonmalignant pigmented lesions) in the wavelength range of 450-950 nm by means of PCA resulted in 87 % sensitivity and 78 % specificity for separation between malignant melanomas and pigmented nevi.

  17. Unsupervised change detection in satellite images using fuzzy c-means clustering and principal component analysis

    NASA Astrophysics Data System (ADS)

    Kesikoğlu, M. H.; Atasever, Ü. H.; Özkan, C.

    2013-10-01

    Change detection analyze means that according to observations made in different times, the process of defining the change detection occurring in nature or in the state of any objects or the ability of defining the quantity of temporal effects by using multitemporal data sets. There are lots of change detection techniques met in literature. It is possible to group these techniques under two main topics as supervised and unsupervised change detection. In this study, the aim is to define the land cover changes occurring in specific area of Kayseri with unsupervised change detection techniques by using Landsat satellite images belonging to different years which are obtained by the technique of remote sensing. While that process is being made, image differencing method is going to be applied to the images by following the procedure of image enhancement. After that, the method of Principal Component Analysis is going to be applied to the difference image obtained. To determine the areas that have and don't have changes, the image is grouped as two parts by Fuzzy C-Means Clustering method. For achieving these processes, firstly the process of image to image registration is completed. As a result of this, the images are being referred to each other. After that, gray scale difference image obtained is partitioned into 3 × 3 nonoverlapping blocks. With the method of principal component analysis, eigenvector space is gained and from here, principal components are reached. Finally, feature vector space consisting principal component is partitioned into two clusters using Fuzzy C-Means Clustering and after that change detection process has been done.

  18. Differentiation of the Chemical Profile of Piper arboreum Tissues Using NIR Spectrometry and Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Duarte, M. S.; Pontes, M. J. C.; Ramos, C. S.

    2016-01-01

    The differentiation of chemical profiles from Piper arboreum tissues using near infrared (NIR) spectrometry and principal component analysis (PCA) was addressed. The NIR analyses were performed with a small quantity of dried and ground tissues. Differences in the chemical composition of leaf, stem, and root tissues were observed. The results obtained were compared to those produced by gas chromatography-mass spectrometry (GC-MS) as the reference method, confirming the NIR results.

  19. Principal Component-Based Radiative Transfer Model (PCRTM) for Hyperspectral Sensors. Part I; Theoretical Concept

    NASA Technical Reports Server (NTRS)

    Liu, Xu; Smith, William L.; Zhou, Daniel K.; Larar, Allen

    2005-01-01

    Modern infrared satellite sensors such as Atmospheric Infrared Sounder (AIRS), Cosmic Ray Isotope Spectrometer (CrIS), Thermal Emission Spectrometer (TES), Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and Infrared Atmospheric Sounding Interferometer (IASI) are capable of providing high spatial and spectral resolution infrared spectra. To fully exploit the vast amount of spectral information from these instruments, super fast radiative transfer models are needed. This paper presents a novel radiative transfer model based on principal component analysis. Instead of predicting channel radiance or transmittance spectra directly, the Principal Component-based Radiative Transfer Model (PCRTM) predicts the Principal Component (PC) scores of these quantities. This prediction ability leads to significant savings in computational time. The parameterization of the PCRTM model is derived from properties of PC scores and instrument line shape functions. The PCRTM is very accurate and flexible. Due to its high speed and compressed spectral information format, it has great potential for super fast one-dimensional physical retrievals and for Numerical Weather Prediction (NWP) large volume radiance data assimilation applications. The model has been successfully developed for the National Polar-orbiting Operational Environmental Satellite System Airborne Sounder Testbed - Interferometer (NAST-I) and AIRS instruments. The PCRTM model performs monochromatic radiative transfer calculations and is able to include multiple scattering calculations to account for clouds and aerosols.

  20. Vibrational spectroscopy and principal component analysis for conformational study of virus nucleic acids

    NASA Astrophysics Data System (ADS)

    Dovbeshko, G. I.; Repnytska, O. P.; Pererva, T.; Miruta, A.; Kosenkov, D.

    2004-07-01

    Conformation analysis of mutated DNA-bacteriophages (PLys-23, P23-2, P47- the numbers have been assigned by T. Pererva) induced by MS2 virus incorporated in Ecoli AB 259 Hfr 3000 has been done. Surface enhanced infrared absorption (SEIRA) spectroscopy and principal component analysis has been applied for solving this problem. The nucleic acids isolated from the mutated phages had a form of double stranded DNA with different modifications. The nucleic acid from phage P47 was undergone the structural rearrangement in the most degree. The shape and position ofthe fine structure of the Phosphate asymmetrical band at 1071cm-1 as well as the stretching OH vibration at 3370-3390 cm-1 has indicated to the appearance ofadditional OH-groups. The Z-form feature has been found in the base vibration region (1694 cm-1) and the sugar region (932 cm-1). A supposition about modification of structure of DNA by Z-fragments for P47 phage has been proposed. The P23-2 and PLys-23 phages have showed the numerous minor structural changes also. On the basis of SEIRA spectra we have determined the characteristic parameters of the marker bands of nucleic acid used for construction of principal components. Contribution of different spectral parameters of nucleic acids to principal components has been estimated.

  1. Inverting geodetic time series with a principal component analysis-based inversion method

    NASA Astrophysics Data System (ADS)

    Kositsky, A. P.; Avouac, J.-P.

    2010-03-01

    The Global Positioning System (GPS) system now makes it possible to monitor deformation of the Earth's surface along plate boundaries with unprecedented accuracy. In theory, the spatiotemporal evolution of slip on the plate boundary at depth, associated with either seismic or aseismic slip, can be inferred from these measurements through some inversion procedure based on the theory of dislocations in an elastic half-space. We describe and test a principal component analysis-based inversion method (PCAIM), an inversion strategy that relies on principal component analysis of the surface displacement time series. We prove that the fault slip history can be recovered from the inversion of each principal component. Because PCAIM does not require externally imposed temporal filtering, it can deal with any kind of time variation of fault slip. We test the approach by applying the technique to synthetic geodetic time series to show that a complicated slip history combining coseismic, postseismic, and nonstationary interseismic slip can be retrieved from this approach. PCAIM produces slip models comparable to those obtained from standard inversion techniques with less computational complexity. We also compare an afterslip model derived from the PCAIM inversion of postseismic displacements following the 2005 8.6 Nias earthquake with another solution obtained from the extended network inversion filter (ENIF). We introduce several extensions of the algorithm to allow statistically rigorous integration of multiple data sources (e.g., both GPS and interferometric synthetic aperture radar time series) over multiple timescales. PCAIM can be generalized to any linear inversion algorithm.

  2. Principal Component Analysis Characterizes Shared Pathogenetics from Genome-Wide Association Studies

    PubMed Central

    Chang, Diana; Keinan, Alon

    2014-01-01

    Genome-wide association studies (GWASs) have recently revealed many genetic associations that are shared between different diseases. We propose a method, disPCA, for genome-wide characterization of shared and distinct risk factors between and within disease classes. It flips the conventional GWAS paradigm by analyzing the diseases themselves, across GWAS datasets, to explore their “shared pathogenetics”. The method applies principal component analysis (PCA) to gene-level significance scores across all genes and across GWASs, thereby revealing shared pathogenetics between diseases in an unsupervised fashion. Importantly, it adjusts for potential sources of heterogeneity present between GWAS which can confound investigation of shared disease etiology. We applied disPCA to 31 GWASs, including autoimmune diseases, cancers, psychiatric disorders, and neurological disorders. The leading principal components separate these disease classes, as well as inflammatory bowel diseases from other autoimmune diseases. Generally, distinct diseases from the same class tend to be less separated, which is in line with their increased shared etiology. Enrichment analysis of genes contributing to leading principal components revealed pathways that are implicated in the immune system, while also pointing to pathways that have yet to be explored before in this context. Our results point to the potential of disPCA in going beyond epidemiological findings of the co-occurrence of distinct diseases, to highlighting novel genes and pathways that unsupervised learning suggest to be key players in the variability across diseases. PMID:25211452

  3. Modeling PCB dechlorination in aquatic sediments by principal component based factor analysis and positive matrix factorization

    NASA Astrophysics Data System (ADS)

    Christensen, E. R.; Bzdusek, P. A.

    2003-04-01

    Anaerobic PCB dechlorination in aquatic sediments is a naturally occurring process that reduces the dioxin-like PCB toxicity. The PCB biphenyl structure is kept intact but the number of substituted chlorine atoms is reduced, primarily from the para and meta positions. Flanked para and meta chlorine dechlorination, as in process H/H', appears to be more common in-situ than flanked and unflanked para, and meta dechlorination as in process Q. Aroclors that are susceptible to these reactions include 1242, 1248, 1254, and 1260. These dechlorination reactions have recently been modeled by a least squares method for Ashtabula River, Ohio, and Fox River, Wisconsin sediments. Prior to modeling the dechlorination reactions for an ecosystem it is desirable to generate overall PCB source functions. One method to determine source functions is to use loading matrices of a factor analytical model. We have developed such models based both on a principal component approach including nonnegative oblique rotations, and positive matrix factorization (PMF). While the principal component method first requires an eigenvalue analysis of a covariance matrix, the PMF method is based on a direct least squares analysis considering simultaneously the loading and score matrices. Loading matrices obtained from the PMF method are somewhat sensitive to the initial guess of source functions. Preliminary work indicates that a hybrid approach considering first principal components and then PMF may offer an optimum solution. The relationship of PMF to conventional chemical mass balance modeling with or without some prior knowledge of source functions is also discussed.

  4. Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

    PubMed

    Jankovic, Marko; Ogawa, Hidemitsu

    2004-10-01

    Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method. PMID:15593379

  5. An extended echo state network using Volterra filtering and principal component analysis.

    PubMed

    Boccato, Levy; Lopes, Amauri; Attux, Romis; Von Zuben, Fernando J

    2012-08-01

    Echo state networks (ESNs) can be interpreted as promoting an encouraging compromise between two seemingly conflicting objectives: (i) simplicity of the resulting mathematical model and (ii) capability to express a wide range of nonlinear dynamics. By imposing fixed weights to the recurrent connections, the echo state approach avoids the well-known difficulties faced by recurrent neural network training strategies, but still preserves, to a certain extent, the potential of the underlying structure due to the existence of feedback loops within the dynamical reservoir. Moreover, the overall training process is relatively simple, as it amounts essentially to adapting the readout, which usually corresponds to a linear combiner. However, the linear nature of the output layer may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. In this work, we present a novel architecture for an ESN in which the linear combiner is replaced by a Volterra filter structure. Additionally, the principal component analysis technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. The proposed architecture is then analyzed in the context of a set of representative information extraction problems, more specifically supervised and unsupervised channel equalization, and blind separation of convolutive mixtures. The obtained results, when compared to those produced by already proposed ESN versions, highlight the benefits brought by the novel network proposal and characterize it as a promising tool to deal with challenging signal processing tasks. PMID:22386782

  6. Differences in Human Meibum Lipid Composition with Meibomian Gland Dysfunction Using NMR and Principal Component Analysis

    PubMed Central

    Foulks, Gary N.; Yappert, Marta C.; Milliner, Sarah E.

    2012-01-01

    Purpose. Nuclear magnetic resonance (NMR) spectroscopy has been used to quantify lipid wax, cholesterol ester terpenoid and glyceride composition, saturation, oxidation, and CH2 and CH3 moiety distribution. This tool was used to measure changes in human meibum composition with meibomian gland dysfunction (MGD). Methods. 1H-NMR spectra of meibum from 39 donors with meibomian gland dysfunction (Md) were compared to meibum from 33 normal donors (Mn). Results. Principal component analysis (PCA) was applied to the CH2/CH3 regions of a set of training NMR spectra of human meibum. PCA discriminated between Mn and Md with an accuracy of 86%. There was a bias toward more accurately predicting normal samples (92%) compared with predicting MGD samples (78%). When the NMR spectra of Md were compared with those of Mn, three statistically significant decreases were observed in the relative amounts of CH3 moieties at 1.26 ppm, the products of lipid oxidation above 7 ppm, and the ═CH moieties at 5.2 ppm associated with terpenoids. Conclusions. Loss of the terpenoids could be deleterious to meibum since they exhibit a plethora of mostly positive biological functions and could account for the lower level of cholesterol esters observed in Md compared with Mn. All three changes could account for the higher degree of lipid order of Md compared with age-matched Mn. In addition to the power of NMR spectroscopy to detect differences in the composition of meibum, it is promising that NMR can be used as a diagnostic tool. PMID:22131391

  7. Seismic data interpretation using the Hough transform and principal component analysis

    NASA Astrophysics Data System (ADS)

    Orozco-del-Castillo, M. G.; Ortiz-Alemán, C.; Martin, R.; Ávila-Carrera, R.; Rodríguez-Castellanos, A.

    2011-03-01

    In this work two novel image processing techniques are applied to detect and delineate complex salt bodies from seismic exploration profiles: Hough transform and principal component analysis (PCA). It is well recognized by the geophysical community that the lack of resolution and poor structural identification in seismic data recorded at sub-salt plays represent severe technical and economical problems. Under such circumstances, seismic interpretation based only on the human-eye is inaccurate. Additionally, petroleum field development decisions and production planning depend on good-quality seismic images that generally are not feasible in salt tectonics areas. In spite of this, morphological erosion, region growing and, especially, a generalization of the Hough transform (closely related to the Radon transform) are applied to build parabolic shapes that are useful in the idealization and recognition of salt domes from 2D seismic profiles. In a similar way, PCA is also used to identify shapes associated with complex salt bodies in seismic profiles extracted from 3D seismic data. To show the validity of the new set of seismic results, comparisons between both image processing techniques are exhibited. It is remarkable that the main contribution of this work is oriented in providing the seismic interpreters with new semi-automatic computational tools. The novel image processing approaches presented here may be helpful in the identification of diapirs and other complex geological features from seismic images. Conceivably, in the near future, a new branch of seismic attributes could be recognized by geoscientists and engineers based on the encouraging results reported here.

  8. Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy.

    PubMed

    Seo, Jihye; An, Yuri; Lee, Jungsul; Ku, Taeyun; Kang, Yujung; Ahn, Chulwoo; Choi, Chulhee

    2016-04-30

    Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features. PMID:27071414

  9. Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy

    NASA Astrophysics Data System (ADS)

    Seo, Jihye; An, Yuri; Lee, Jungsul; Ku, Taeyun; Kang, Yujung; Ahn, Chulwoo; Choi, Chulhee

    2016-04-01

    Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features.

  10. PCA of PCA: principal component analysis of partial covering absorption in NGC 1365

    NASA Astrophysics Data System (ADS)

    Parker, M. L.; Walton, D. J.; Fabian, A. C.; Risaliti, G.

    2014-06-01

    We analyse 400 ks of XMM-Newton data on the active galactic nucleus NGC 1365 using principal component analysis (PCA) to identify model-independent spectral components. We find two significant components and demonstrate that they are qualitatively different from those found in MCG-6-30-15 using the same method. As the variability in NGC 1365 is known to be due to changes in the parameters of a partial covering neutral absorber, this shows that the same mechanism cannot be the driver of variability in MCG-6-30-15. By examining intervals where the spectrum shows relatively low absorption we separate the effects of intrinsic source variability, including signatures of relativistic reflection, from variations in the intervening absorption. We simulate the principal components produced by different physical variations, and show that PCA provides a clear distinction between absorption and reflection as the drivers of variability in AGN spectra. The simulations are shown to reproduce the PCA spectra of both NGC 1365 and MCG-6-30-15, and further demonstrate that the dominant cause of spectral variability in these two sources requires a qualitatively different mechanism.

  11. Computed Tomography Inspection and Analysis for Additive Manufacturing Components

    NASA Technical Reports Server (NTRS)

    Beshears, Ronald D.

    2016-01-01

    Computed tomography (CT) inspection was performed on test articles additively manufactured from metallic materials. Metallic AM and machined wrought alloy test articles with programmed flaws were inspected using a 2MeV linear accelerator based CT system. Performance of CT inspection on identically configured wrought and AM components and programmed flaws was assessed using standard image analysis techniques to determine the impact of additive manufacturing on inspectability of objects with complex geometries.

  12. Fast and Accurate Radiative Transfer Calculations Using Principal Component Analysis for Climate Modeling

    NASA Astrophysics Data System (ADS)

    Kopparla, P.; Natraj, V.; Spurr, R. J. D.; Shia, R. L.; Yung, Y. L.

    2014-12-01

    Radiative transfer (RT) computations are an essential component of energy budget calculations in climate models. However, full treatment of RT processes is computationally expensive, prompting usage of 2-stream approximations in operational climate models. This simplification introduces errors of the order of 10% in the top of the atmosphere (TOA) fluxes [Randles et al., 2013]. Natraj et al. [2005, 2010] and Spurr and Natraj [2013] demonstrated the ability of a technique using principal component analysis (PCA) to speed up RT simulations. In the PCA method for RT performance enhancement, empirical orthogonal functions are developed for binned sets of inherent optical properties that possess some redundancy; costly multiple-scattering RT calculations are only done for those (few) optical states corresponding to the most important principal components, and correction factors are applied to approximate radiation fields. Here, we extend the PCA method to a broadband spectral region from the ultraviolet to the shortwave infrared (0.3-3 micron), accounting for major gas absorptions in this region. Comparisons between the new model, called Universal Principal Component Analysis model for Radiative Transfer (UPCART), 2-stream models (such as those used in climate applications) and line-by-line RT models are performed, in order for spectral radiances, spectral fluxes and broadband fluxes. Each of these are calculated at the TOA for several scenarios with varying aerosol types, extinction and scattering optical depth profiles, and solar and viewing geometries. We demonstrate that very accurate radiative forcing estimates can be obtained, with better than 1% accuracy in all spectral regions and better than 0.1% in most cases as compared to an exact line-by-line RT model. The model is comparable in speeds to 2-stream models, potentially rendering UPCART useful for operational General Circulation Models (GCMs). The operational speed and accuracy of UPCART can be further

  13. A comparative study of principal component analysis and independent component analysis in eddy current pulsed thermography data processing.

    PubMed

    Bai, Libing; Gao, Bin; Tian, Shulin; Cheng, Yuhua; Chen, Yifan; Tian, Gui Yun; Woo, W L

    2013-10-01

    Eddy Current Pulsed Thermography (ECPT), an emerging Non-Destructive Testing and Evaluation technique, has been applied for a wide range of materials. The lateral heat diffusion leads to decreasing of temperature contrast between defect and defect-free area. To enhance the flaw contrast, different statistical methods, such as Principal Component Analysis and Independent Component Analysis, have been proposed for thermography image sequences processing in recent years. However, there is lack of direct and detailed independent comparisons in both algorithm implementations. The aim of this article is to compare the two methods and to determine the optimized technique for flaw contrast enhancement in ECPT data. Verification experiments are conducted on artificial and thermal fatigue nature crack detection. PMID:24182145

  14. Principal components analysis of reward prediction errors in a reinforcement learning task.

    PubMed

    Sambrook, Thomas D; Goslin, Jeremy

    2016-01-01

    Models of reinforcement learning represent reward and punishment in terms of reward prediction errors (RPEs), quantitative signed terms describing the degree to which outcomes are better than expected (positive RPEs) or worse (negative RPEs). An electrophysiological component known as feedback related negativity (FRN) occurs at frontocentral sites 240-340ms after feedback on whether a reward or punishment is obtained, and has been claimed to neurally encode an RPE. An outstanding question however, is whether the FRN is sensitive to the size of both positive RPEs and negative RPEs. Previous attempts to answer this question have examined the simple effects of RPE size for positive RPEs and negative RPEs separately. However, this methodology can be compromised by overlap from components coding for unsigned prediction error size, or "salience", which are sensitive to the absolute size of a prediction error but not its valence. In our study, positive and negative RPEs were parametrically modulated using both reward likelihood and magnitude, with principal components analysis used to separate out overlying components. This revealed a single RPE encoding component responsive to the size of positive RPEs, peaking at ~330ms, and occupying the delta frequency band. Other components responsive to unsigned prediction error size were shown, but no component sensitive to negative RPE size was found. PMID:26196667

  15. A principal component analysis to interpret the spectral electrical behaviour of sediments

    NASA Astrophysics Data System (ADS)

    Inzoli, Silvia; Giudici, Mauro; Huisman, Johan Alexander

    2015-04-01

    Spectral Induced Polarization (SIP) measurements provide the opportunity to evaluate both conduction and polarization processes occurring in a porous medium. Conduction properties are related to the pore volume (for coarse grained materials) and also to the pore surface (for fine grained materials), whereas polarization properties are mainly controlled by the pore surface. Thus, SIP is a valuable survey method and its applicability ranges from aquifer characterization to organic and inorganic contaminant detection. However, the high number of factors affecting the spectral electrical behaviour still prevents an easy and unambiguous interpretation of SIP measurements. Controlled laboratory experiments by different research groups have shown that the resistivity phase depends on pore/grain size distribution, clay percentage, specific surface area, water saturation/conductivity and packing, among other factors. In the analysis of natural samples, all these variables are often simultaneously unknown and the direct application of the laboratory-derived empirical relationships between geophysical and sedimentological properties is not trivial. In this framework, we performed SIP laboratory measurements on unconsolidated alluvial samples of the Po river and Lambro river depositional units (Northern Italy). These samples were fully saturated with NaCl solutions with increasing electrical conductivity. SIP measurements were analysed using a Debye Decomposition technique and by fitting two Cole-Cole-type models (i.e. the Cole-Cole and the Generalized Cole-Cole). A principal component analysis was then applied separately on the three different subsets of model parameters. The main aims of this analysis were: i) to cluster the samples according to their spectral properties; ii) to evaluate differences and similarities of the fitting models in terms of the most significant combinations of parameters able to describe the overall variability within the dataset; iii) to analyse

  16. Lippia origanoides chemotype differentiation based on essential oil GC-MS and principal component analysis.

    PubMed

    Stashenko, Elena E; Martínez, Jairo R; Ruíz, Carlos A; Arias, Ginna; Durán, Camilo; Salgar, William; Cala, Mónica

    2010-01-01

    Chromatographic (GC/flame ionization detection, GC/MS) and statistical analyses were applied to the study of essential oils and extracts obtained from flowers, leaves, and stems of Lippia origanoides plants, growing wild in different Colombian regions. Retention indices, mass spectra, and standard substances were used in the identification of 139 substances detected in these essential oils and extracts. Principal component analysis allowed L. origanoides classification into three chemotypes, characterized according to their essential oil major components. Alpha- and beta-phellandrenes, p-cymene, and limonene distinguished chemotype A; carvacrol and thymol were the distinctive major components of chemotypes B and C, respectively. Pinocembrin (5,7-dihydroxyflavanone) was found in L. origanoides chemotype A supercritical fluid (CO(2)) extract at a concentration of 0.83+/-0.03 mg/g of dry plant material, which makes this plant an interesting source of an important bioactive flavanone with diverse potential applications in cosmetic, food, and pharmaceutical products. PMID:19950347

  17. Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis

    NASA Astrophysics Data System (ADS)

    Wang, Zhuozheng; Deller, J. R.; Fleet, Blair D.

    2016-01-01

    Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness.

  18. Principal component analysis of global maps of the total electronic content

    NASA Astrophysics Data System (ADS)

    Maslennikova, Yu. S.; Bochkarev, V. V.

    2014-03-01

    In this paper we present results of the spatial distribution analysis of the total electron content (TEC) performed by the Principal Component Analysis (PCA) with the use of global maps of TEC provided by the JPL laboratory (Jet Propulsion Laboratory, NASA, USA) for the period from 2004 to 2010. We show that the obtained components of the decomposition of TEC essentially depend on the representation of the initial data and the method of their preliminary processing. We propose a technique for data centering that allows us to take into account the influence of diurnal and seasonal factors. We establish a correlation between amplitudes of the first components of the decomposition of TEC (connected with the equatorial anomaly) and the solar activity index F10.7, as well as with the flow of high energy particles of the solar wind.

  19. A methodology to estimate probability of occurrence of floods using principal component analysis

    NASA Astrophysics Data System (ADS)

    castro Heredia, L. M.; Gironas, J. A.

    2014-12-01

    Flood events and debris flows are characterized by a very rapid response of basins to precipitation, often resulting in loss of life and property damage. Complex topography with steep slopes and narrow valleys increase the likelihood of having these events. An early warning system (EWS) is a tool that allows anticipating a hazardous event, which in turns provides time for an early response to reduce negative impacts. These EWS's can rely on very powerful and computer-demanding models to predict flow discharges and inundation zones, which require data typically unavailable. Instead, simpler EWŚs based on a statistical analysis of observed hydro-meteorological data could be a good alternative. In this work we propose a methodology for estimating the probability of exceedance of maximum flowdischarges using principal components analysis (PCA). In the method we first perform a spatio-temporal cross-correlation analysis between extreme flows data and daily meteorological records for the last 15 days prior to the day of the flood event. We then use PCA to create synthetic variables which are representative of the meteorological variables associated with the flood event (i.e. cumulative rainfall and minimum temperature). Finally, we developed a model to explain the probability of exceedance using the principal components. The methodology was applied to a basin in the foothill area of Santiago, Chile, for which all the extreme events between 1970 and 2013 were analyzed.Results show that elevation rather than distance or location within the contributing basin is what mainly explains the statistical correlation between meteorologicalrecords and flood events. Two principal components were found that explain more than 90% of the total variance of the accumulated rainfalls and minimum temperatures. One component was formed with cumulative rainfall from 3 to 15 days prior to the event, whereas the other one was formed with the minimum temperatures for the last 2 days preceding

  20. Application of Additively Manufactured Components in Rocket Engine Turbopumps

    NASA Technical Reports Server (NTRS)

    Calvert, Marty, Jr.; Hanks, Andrew; Schmauch, Preston; Delessio, Steve

    2015-01-01

    The use of additive manufacturing technology has the potential to revolutionize the development of turbopump components in liquid rocket engines. When designing turbomachinery with the additive process there are several benefits and risks that are leveraged relative to a traditional development cycle. This topic explores the details and development of a 90,000 RPM Liquid Hydrogen Turbopump from which 90% of the parts were derived from the additive process. This turbopump was designed, developed and will be tested later this year at Marshall Space Flight Center.

  1. Additive Manufacturing Design Considerations for Liquid Engine Components

    NASA Technical Reports Server (NTRS)

    Whitten, Dave; Hissam, Andy; Baker, Kevin; Rice, Darron

    2014-01-01

    The Marshall Space Flight Center's Propulsion Systems Department has gained significant experience in the last year designing, building, and testing liquid engine components using additive manufacturing. The department has developed valve, duct, turbo-machinery, and combustion device components using this technology. Many valuable lessons were learned during this process. These lessons will be the focus of this presentation. We will present criteria for selecting part candidates for additive manufacturing. Some part characteristics are 'tailor made' for this process. Selecting the right parts for the process is the first step to maximizing productivity gains. We will also present specific lessons we learned about feature geometry that can and cannot be produced using additive manufacturing machines. Most liquid engine components were made using a two-step process. The base part was made using additive manufacturing and then traditional machining processes were used to produce the final part. The presentation will describe design accommodations needed to make the base part and lessons we learned about which features could be built directly and which require the final machine process. Tolerance capabilities, surface finish, and material thickness allowances will also be covered. Additive Manufacturing can produce internal passages that cannot be made using traditional approaches. It can also eliminate a significant amount of manpower by reducing part count and leveraging model-based design and analysis techniques. Information will be shared about performance enhancements and design efficiencies we experienced for certain categories of engine parts.

  2. Material Characterization of Additively Manufactured Components for Rocket Propulsion

    NASA Technical Reports Server (NTRS)

    Carter, Robert; Draper, Susan; Locci, Ivan; Lerch, Bradley; Ellis, David; Senick, Paul; Meyer, Michael; Free, James; Cooper, Ken; Jones, Zachary

    2015-01-01

    To advance Additive Manufacturing (AM) technologies for production of rocket propulsion components the NASA Glenn Research Center (GRC) is applying state of the art characterization techniques to interrogate microstructure and mechanical properties of AM materials and components at various steps in their processing. The materials being investigated for upper stage rocket engines include titanium, copper, and nickel alloys. Additive manufacturing processes include laser powder bed, electron beam powder bed, and electron beam wire fed processes. Various post build thermal treatments, including Hot Isostatic Pressure (HIP), have been studied to understand their influence on microstructure, mechanical properties, and build density. Micro-computed tomography, electron microscopy, and mechanical testing in relevant temperature environments has been performed to develop relationships between build quality, microstructure, and mechanical performance at temperature. A summary of GRCs Additive Manufacturing roles and experimental findings will be presented.

  3. Materials Characterization of Additively Manufactured Components for Rocket Propulsion

    NASA Technical Reports Server (NTRS)

    Carter, Robert; Draper, Susan; Locci, Ivan; Lerch, Bradley; Ellis, David; Senick, Paul; Meyer, Michael; Free, James; Cooper, Ken; Jones, Zachary

    2015-01-01

    To advance Additive Manufacturing (AM) technologies for production of rocket propulsion components the NASA Glenn Research Center (GRC) is applying state of the art characterization techniques to interrogate microstructure and mechanical properties of AM materials and components at various steps in their processing. The materials being investigated for upper stage rocket engines include titanium, copper, and nickel alloys. Additive manufacturing processes include laser powder bed, electron beam powder bed, and electron beam wire fed processes. Various post build thermal treatments, including Hot Isostatic Pressure (HIP), have been studied to understand their influence on microstructure, mechanical properties, and build density. Micro-computed tomography, electron microscopy, and mechanical testing in relevant temperature environments has been performed to develop relationships between build quality, microstructure, and mechanical performance at temperature. A summary of GRC's Additive Manufacturing roles and experimental findings will be presented.

  4. The comparison of robust partial least squares regression with robust principal component regression on a real

    NASA Astrophysics Data System (ADS)

    Polat, Esra; Gunay, Suleyman

    2013-10-01

    One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.

  5. Measuring farm sustainability using data envelope analysis with principal components: the case of Wisconsin cranberry.

    PubMed

    Dong, Fengxia; Mitchell, Paul D; Colquhoun, Jed

    2015-01-01

    Measuring farm sustainability performance is a crucial component for improving agricultural sustainability. While extensive assessments and indicators exist that reflect the different facets of agricultural sustainability, because of the relatively large number of measures and interactions among them, a composite indicator that integrates and aggregates over all variables is particularly useful. This paper describes and empirically evaluates a method for constructing a composite sustainability indicator that individually scores and ranks farm sustainability performance. The method first uses non-negative polychoric principal component analysis to reduce the number of variables, to remove correlation among variables and to transform categorical variables to continuous variables. Next the method applies common-weight data envelope analysis to these principal components to individually score each farm. The method solves weights endogenously and allows identifying important practices in sustainability evaluation. An empirical application to Wisconsin cranberry farms finds heterogeneity in sustainability practice adoption, implying that some farms could adopt relevant practices to improve the overall sustainability performance of the industry. PMID:25277860

  6. The application of principal component analysis to quantify technique in sports.

    PubMed

    Federolf, P; Reid, R; Gilgien, M; Haugen, P; Smith, G

    2014-06-01

    Analyzing an athlete's "technique," sport scientists often focus on preselected variables that quantify important aspects of movement. In contrast, coaches and practitioners typically describe movements in terms of basic postures and movement components using subjective and qualitative features. A challenge for sport scientists is finding an appropriate quantitative methodology that incorporates the holistic perspective of human observers. Using alpine ski racing as an example, this study explores principal component analysis (PCA) as a mathematical method to decompose a complex movement pattern into its main movement components. Ski racing movements were recorded by determining the three-dimensional coordinates of 26 points on each skier which were subsequently interpreted as a 78-dimensional posture vector at each time point. PCA was then used to determine the mean posture and principal movements (PMk ) carried out by the athletes. The first four PMk contained 95.5 ± 0.5% of the variance in the posture vectors which quantified changes in body inclination, vertical or fore-aft movement of the trunk, and distance between skis. In summary, calculating PMk offered a data-driven, quantitative, and objective method of analyzing human movement that is similar to how human observers such as coaches or ski instructors would describe the movement. PMID:22436088

  7. Principal components and iterative regression analysis of geophysical series: Application to Sunspot number (1750 2004)

    NASA Astrophysics Data System (ADS)

    Nordemann, D. J. R.; Rigozo, N. R.; de Souza Echer, M. P.; Echer, E.

    2008-11-01

    We present here an implementation of a least squares iterative regression method applied to the sine functions embedded in the principal components extracted from geophysical time series. This method seems to represent a useful improvement for the non-stationary time series periodicity quantitative analysis. The principal components determination followed by the least squares iterative regression method was implemented in an algorithm written in the Scilab (2006) language. The main result of the method is to obtain the set of sine functions embedded in the series analyzed in decreasing order of significance, from the most important ones, likely to represent the physical processes involved in the generation of the series, to the less important ones that represent noise components. Taking into account the need of a deeper knowledge of the Sun's past history and its implication to global climate change, the method was applied to the Sunspot Number series (1750-2004). With the threshold and parameter values used here, the application of the method leads to a total of 441 explicit sine functions, among which 65 were considered as being significant and were used for a reconstruction that gave a normalized mean squared error of 0.146.

  8. NGC 7097: THE ACTIVE GALACTIC NUCLEUS AND ITS MIRROR, REVEALED BY PRINCIPAL COMPONENT ANALYSIS TOMOGRAPHY

    SciTech Connect

    Ricci, T. V.; Steiner, J. E.; Menezes, R. B.

    2011-06-10

    Three-dimensional spectroscopy techniques are becoming more and more popular, producing an increasing number of large data cubes. The challenge of extracting information from these cubes requires the development of new techniques for data processing and analysis. We apply the recently developed technique of principal component analysis (PCA) tomography to a data cube from the center of the elliptical galaxy NGC 7097 and show that this technique is effective in decomposing the data into physically interpretable information. We find that the first five principal components of our data are associated with distinct physical characteristics. In particular, we detect a low-ionization nuclear-emitting region (LINER) with a weak broad component in the Balmer lines. Two images of the LINER are present in our data, one seen through a disk of gas and dust, and the other after scattering by free electrons and/or dust particles in the ionization cone. Furthermore, we extract the spectrum of the LINER, decontaminated from stellar and extended nebular emission, using only the technique of PCA tomography. We anticipate that the scattered image has polarized light due to its scattered nature.

  9. Functional Principal Component Analysis of Spatio-Temporal Point Processes with Applications in Disease Surveillance

    PubMed Central

    Li, Yehua; Guan, Yongtao

    2014-01-01

    In disease surveillance applications, the disease events are modeled by spatio-temporal point processes. We propose a new class of semiparametric generalized linear mixed model for such data, where the event rate is related to some known risk factors and some unknown latent random effects. We model the latent spatio-temporal process as spatially correlated functional data, and propose Poisson maximum likelihood and composite likelihood methods based on spline approximations to estimate the mean and covariance functions of the latent process. By performing functional principal component analysis to the latent process, we can better understand the correlation structure in the point process. We also propose an empirical Bayes method to predict the latent spatial random effects, which can help highlight hot areas with unusually high event rates. Under an increasing domain and increasing knots asymptotic framework, we establish the asymptotic distribution for the parametric components in the model and the asymptotic convergence rates for the functional principal component estimators. We illustrate the methodology through a simulation study and an application to the Connecticut Tumor Registry data. PMID:25368436

  10. Isolating climatic and paleomagnetic imbricated signals in two marine cores using principal component analysis

    NASA Astrophysics Data System (ADS)

    Valet, Jean-Pierre; Moreno, Eva; Bassinot, Franck; Johannes, Lola; Dewilde, Fabien; Bastos, Tiago; Lefort, Apolline; Venec-Peyre, Marie-ThéRèSe

    2011-08-01

    High resolution measurements of climatic and magnetic parameters have been performed on two cores from the eastern China Sea and the western Caroline Basin. On both cores, magnetic parameters show a strong imprint of climatic changes but the absence of relationship between the inclination and the bulk density indicates that the directional changes do not depend on lithology. A weak 100 ka cycle is present in the China sea inclination variations, but this period is not in phase with the orbital eccentricity and thus not relevant. All normalization parameters yielded similar estimates of relative paleointensity (RPI), but we have noticed the persistence of climatic components in the signal. Principal Component Analysis (PCA) applied to different parameters related to climate, lithology and paleointensity has allowed to extract a "clean" magnetic signal that we refer as "principal component of paleointensity (PCP)" which is in better agreement with the Sint-2000 composite curve and provides a reliable record of relative paleointensity. The presence of climatic frequencies in RPIs most likely reflects the influence of lithology on the response of magnetization to field intensity. We suggest that PCA analysis can be very useful to approach these problems. Not only can the calculation separate overlapping climatic and magnetic signals, but it indicates what confidence should be given to the data. Incidentally, the present results emphasize the importance of carrying out detailed paleoclimatic analyses along with paleointensity studies.

  11. Two-component Structure of the Hβ Broad-line Region in Quasars. I. Evidence from Spectral Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Hu, Chen; Wang, Jian-Min; Ho, Luis C.; Ferland, Gary J.; Baldwin, Jack A.; Wang, Ye

    2012-12-01

    We report on a spectral principal component analysis (SPCA) of a sample of 816 quasars, selected to have small Fe II velocity shifts with spectral coverage in the rest wavelength range 3500-5500 Å. The sample is explicitly designed to mitigate spurious effects on SPCA induced by Fe II velocity shifts. We improve the algorithm of SPCA in the literature and introduce a new quantity, the fractional-contribution spectrum, that effectively identifies the emission features encoded in each eigenspectrum. The first eigenspectrum clearly records the power-law continuum and very broad Balmer emission lines. Narrow emission lines dominate the second eigenspectrum. The third eigenspectrum represents the Fe II emission and a component of the Balmer lines with kinematically similar intermediate-velocity widths. Correlations between the weights of the eigenspectra and parametric measurements of line strength and continuum slope confirm the above interpretation for the eigenspectra. Monte Carlo simulations demonstrate the validity of our method to recognize cross talk in SPCA and firmly rule out a single-component model for broad Hβ. We also present the results of SPCA for four other samples that contain quasars in bins of larger Fe II velocity shift; similar eigenspectra are obtained. We propose that the Hβ-emitting region has two kinematically distinct components: one with very large velocities whose strength correlates with the continuum shape and another with more modest, intermediate velocities that is closely coupled to the gas that gives rise to Fe II emission.

  12. Fast and Accurate Radiative Transfer Calculations Using Principal Component Analysis for (Exo-)Planetary Retrieval Models

    NASA Astrophysics Data System (ADS)

    Kopparla, P.; Natraj, V.; Shia, R. L.; Spurr, R. J. D.; Crisp, D.; Yung, Y. L.

    2015-12-01

    Radiative transfer (RT) computations form the engine of atmospheric retrieval codes. However, full treatment of RT processes is computationally expensive, prompting usage of two-stream approximations in current exoplanetary atmospheric retrieval codes [Line et al., 2013]. Natraj et al. [2005, 2010] and Spurr and Natraj [2013] demonstrated the ability of a technique using principal component analysis (PCA) to speed up RT computations. In the PCA method for RT performance enhancement, empirical orthogonal functions are developed for binned sets of inherent optical properties that possess some redundancy; costly multiple-scattering RT calculations are only done for those few optical states corresponding to the most important principal components, and correction factors are applied to approximate radiation fields. Kopparla et al. [2015, in preparation] extended the PCA method to a broadband spectral region from the ultraviolet to the shortwave infrared (0.3-3 micron), accounting for major gas absorptions in this region. Here, we apply the PCA method to a some typical (exo-)planetary retrieval problems. Comparisons between the new model, called Universal Principal Component Analysis Radiative Transfer (UPCART) model, two-stream models and line-by-line RT models are performed, for spectral radiances, spectral fluxes and broadband fluxes. Each of these are calculated at the top of the atmosphere for several scenarios with varying aerosol types, extinction and scattering optical depth profiles, and stellar and viewing geometries. We demonstrate that very accurate radiance and flux estimates can be obtained, with better than 1% accuracy in all spectral regions and better than 0.1% in most cases, as compared to a numerically exact line-by-line RT model. The accuracy is enhanced when the results are convolved to typical instrument resolutions. The operational speed and accuracy of UPCART can be further improved by optimizing binning schemes and parallelizing the codes, work

  13. Assessing the effect of data pretreatment procedures for principal components analysis of chromatographic data.

    PubMed

    McIlroy, John W; Smith, Ruth Waddell; McGuffin, Victoria L

    2015-12-01

    Following publication of the National Academy of Sciences report "Strengthening Forensic Science in the United States: A Path Forward", there has been increasing interest in the application of multivariate statistical procedures for the evaluation of forensic evidence. However, prior to statistical analysis, variance from sources other than the sample must be minimized through application of data pretreatment procedures. This is necessary to ensure that subsequent statistical analysis of the data provides meaningful results. The purpose of this work was to evaluate the effect of pretreatment procedures on multivariate statistical analysis of chromatographic data obtained for a reference set of diesel fuels. Diesel was selected due to its chemical complexity and forensic relevance, both for fire debris and environmental forensic applications. Principal components analysis (PCA) was applied to the untreated chromatograms to assess association of replicates and discrimination among the different diesel samples. The chromatograms were then pretreated by sequentially applying the following procedures: background correction, smoothing, retention-time alignment, and normalization. The effect of each procedure on association and discrimination was evaluated based on the association of replicates in the PCA scores plot. For these data, background correction and smoothing offered minimal improvement, whereas alignment and normalization offered the greatest improvement in the association of replicates and discrimination among highly similar samples. Further, prior to pretreatment, the first principal component accounted for only non-sample sources of variance. Following pretreatment, these sources were minimized and the first principal component accounted for significant chemical differences among the diesel samples. These results highlight the need for pretreatment procedures and provide a metric to assess the effect of pretreatment on subsequent multivariate statistical

  14. Spectral principal component analysis of mid-infrared spectra of a sample of PG QSOs

    NASA Astrophysics Data System (ADS)

    Bian, Wei-Hao; He, Zhi-Cheng; Green, Richard; Shi, Yong; Ge, Xue; Liu, Wen-Shuai

    2016-03-01

    A spectral principal component (SPC) analysis of a sample of 87 Palomar-Green (PG) QSOs at z < 0.5 is presented for their mid-infrared spectra from Spitzer Space Telescope. We have derived the first five eigenspectra, which account for 85.2 per cent of the mid-infrared spectral variation. It is found that the first eigenspectrum represents the mid-infrared slope, forbidden emission line strength and 9.7 μm silicate feature; the 3rd and 4th eigenspectra represent the silicate features at 18 and 9.7 μm, respectively. With the principal components (PC) from optical principal component analysis, we find that there is a medium strong correlation between spectral SPC1 and PC2 (accretion rate). It suggests that more nuclear contribution to the near-IR spectrum leads to the change of mid-IR slope. We find mid-IR forbidden lines are suppressed with higher accretion rate. A medium strong correlation between SPC3 and PC1 (Eddington ratio) suggests a connection between the silicate feature at 18 μm and the Eddington ratio. For the ratio of the silicate strength at 9.7 μm to that at 18 μm, we find a strong correlation with PC2 (accretion rate or QSO luminosity). We also find that there is a medium strong correlation between the star formation rate (SFR) and PC2. It implies a correlation between SFR and the central accretion rate in PG QSOs.

  15. FPGA-based real-time blind source separation with principal component analysis

    NASA Astrophysics Data System (ADS)

    Wilson, Matthew; Meyer-Baese, Uwe

    2015-05-01

    Principal component analysis (PCA) is a popular technique in reducing the dimension of a large data set so that more informed conclusions can be made about the relationship between the values in the data set. Blind source separation (BSS) is one of the many applications of PCA, where it is used to separate linearly mixed signals into their source signals. This project attempts to implement a BSS system in hardware. Due to unique characteristics of hardware implementation, the Generalized Hebbian Algorithm (GHA), a learning network model, is used. The FPGA used to compile and test the system is the Altera Cyclone III EP3C120F780I7.

  16. Sequential Projection Pursuit Principal Component Analysis – Dealing with Missing Data Associated with New -Omics Technologies

    SciTech Connect

    Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Metz, Thomas O.; McDermott, Jason E.; Walker, Julia; Rodland, Karin D.; Pounds, Joel G.; Waters, Katrina M.

    2013-03-15

    We present a new version of sequential projection pursuit Principal Component Analysis (sppPCA) that has the capability to perform PCA on large multivariate datasets that contain non-random missing values. We demonstrate that sppPCA generates more robust and informative low-dimensional representations of the data than imputation-based approaches and improved downstream statistical analyses, such as clustering or classification. A Java program to run sppPCA is freely available at https://www.biopilot.org/docs/Software/sppPCA.

  17. Using principal component analysis to monitor spatial and temporal changes in water quality.

    PubMed

    Bengraïne, Karim; Marhaba, Taha F

    2003-06-27

    Chemical, biological and physical data monitored at 12 locations along the Passaic River, New Jersey, during the year 1998 are analyzed. Principal component analysis (PCA) was used: (i) to extract the factors associated with the hydrochemistry variability; (ii) to obtain the spatial and temporal changes in the water quality. Solute content, temperature, nutrients and organics were the main patterns extracted. The spatial analysis isolated two stations showing a possible point or non-point source of pollution. This study shows the importance of environmental monitoring associated with simple but powerful statistics to better understand a complex water system. PMID:12835021

  18. Principal component analysis of bacteria using surface-enhanced Raman spectroscopy

    NASA Astrophysics Data System (ADS)

    Guicheteau, Jason; Christesen, Steven D.

    2006-05-01

    Surface-enhanced Raman scattering (SERS) provides rapid fingerprinting of biomaterial in a non-destructive manner. The problem of tissue fluorescence, which can overwhelm a normal Raman signal from biological samples, is largely overcome by treatment of biomaterials with colloidal silver. This work presents a study into the applicability of qualitative SER spectroscopy with principal component analysis (PCA) for the discrimination of four biological threat simulants; Bacillus globigii, Pantoea agglomerans, Brucella noetomae, and Yersinia rohdei. We also demonstrate differentiation of gram-negative and gram-positive species and as well as spores and vegetative cells of Bacillus globigii.

  19. Hyperspectral image compression and target detection using nonlinear principal component analysis

    NASA Astrophysics Data System (ADS)

    Du, Qian; Wei, Wei; Ma, Ben; Younan, Nicolas H.

    2013-09-01

    The widely used principal component analysis (PCA) is implemented in nonlinear by an auto-associative neural network. Compared to other nonlinear versions, such as kernel PCA, such a nonlinear PCA has explicit encoding and decoding processes, and the data can be transformed back to the original space. Its data compression performance is similar to that of PCA, but data analysis performance such as target detection is much better. To expedite its training process, graphics computing unit (GPU)-based parallel computing is applied.

  20. Fatigue among caregivers of chronic renal failure patients: a principal components analysis.

    PubMed

    Schneider, Robert A

    2003-12-01

    Quality of life for caregivers of ESRD patients has not been well addressed. The physical and psychological status of this overlooked group can be important in the recovery or adaptation of patients with chronic renal failure. One particular symptom of a reduced quality of life of such caregivers is that of fatigue. The study tested the reliability of both existing and newer fatigue measures. Measures with high reliability yielded a single construct of fatigue in a principal components analysis in this study of 99 caregivers. Implications for practice are addressed. Potential for further study is recommended. PMID:14730783

  1. Scalable multi-correlative statistics and principal component analysis with Titan.

    SciTech Connect

    Thompson, David C.; Bennett, Janine C.; Roe, Diana C.; Pebay, Philippe Pierre

    2009-02-01

    This report summarizes existing statistical engines in VTK/Titan and presents the recently parallelized multi-correlative and principal component analysis engines. It is a sequel to [PT08] which studied the parallel descriptive and correlative engines. The ease of use of these parallel engines is illustrated by the means of C++ code snippets. Furthermore, this report justifies the design of these engines with parallel scalability in mind; then, this theoretical property is verified with test runs that demonstrate optimal parallel speed-up with up to 200 processors.

  2. Visualization of learning in multilayer perceptron networks using principal component analysis.

    PubMed

    Gallagher, M; Downs, T

    2003-01-01

    This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as backpropagation and can also be used to provide insight into the learning process and the nature of the error surface. PMID:18238154

  3. Reducibility of invertible tuples to the principal component in commutative Banach algebras

    NASA Astrophysics Data System (ADS)

    Mortini, Raymond; Rupp, Rudolf

    2015-10-01

    Let A be a complex, commutative unital Banach algebra. We introduce two notions of exponential reducibility of Banach algebra tuples and present an analogue to the Corach-Suárez result on the connection between reducibility in A and in C(M(A)). Our methods are of an analytical nature. Necessary and sufficient geometric/topological conditions are given for reducibility (respectively reducibility to the principal component of Un(A)) whenever the spectrum of A is homeomorphic to a subset of {C}n.

  4. Identification of differentially expressed genes in microarray data in a principal component space.

    PubMed

    Ospina, Luis; López-Kleine, Liliana

    2013-12-01

    Microarray experiments are often conducted in order to compare gene expression between two conditions. Tests to detected mean differential expression of genes between conditions are conducted applying correction for multiple testing. Seldom, relationships between gene expression and microarray conditions are investigated in a multivariate approach. Here we propose determining the relationship between genes and conditions using a Principal Component Analysis (PCA) space and classifying genes to one of two biological conditions based on their position relative to a direction on the PC space representing each condition. PMID:23539565

  5. Principal and independent component analysis of concomitant functional near infrared spectroscopy and magnetic resonance imaging data

    NASA Astrophysics Data System (ADS)

    Schelkanova, Irina; Toronov, Vladislav

    2011-07-01

    Although near infrared spectroscopy (NIRS) is now widely used both in emerging clinical techniques and in cognitive neuroscience, the development of the apparatuses and signal processing methods for these applications is still a hot research topic. The main unresolved problem in functional NIRS is the separation of functional signals from the contaminations by systemic and local physiological fluctuations. This problem was approached by using various signal processing methods, including blind signal separation techniques. In particular, principal component analysis (PCA) and independent component analysis (ICA) were applied to the data acquired at the same wavelength and at multiple sites on the human or animal heads during functional activation. These signal processing procedures resulted in a number of principal or independent components that could be attributed to functional activity but their physiological meaning remained unknown. On the other hand, the best physiological specificity is provided by broadband NIRS. Also, a comparison with functional magnetic resonance imaging (fMRI) allows determining the spatial origin of fNIRS signals. In this study we applied PCA and ICA to broadband NIRS data to distill the components correlating with the breath hold activation paradigm and compared them with the simultaneously acquired fMRI signals. Breath holding was used because it generates blood carbon dioxide (CO2) which increases the blood-oxygen-level-dependent (BOLD) signal as CO2 acts as a cerebral vasodilator. Vasodilation causes increased cerebral blood flow which washes deoxyhaemoglobin out of the cerebral capillary bed thus increasing both the cerebral blood volume and oxygenation. Although the original signals were quite diverse, we found very few different components which corresponded to fMRI signals at different locations in the brain and to different physiological chromophores.

  6. Retest of a Principal Components Analysis of Two Household Environmental Risk Instruments.

    PubMed

    Oneal, Gail A; Postma, Julie; Odom-Maryon, Tamara; Butterfield, Patricia

    2016-08-01

    Household Risk Perception (HRP) and Self-Efficacy in Environmental Risk Reduction (SEERR) instruments were developed for a public health nurse-delivered intervention designed to reduce home-based, environmental health risks among rural, low-income families. The purpose of this study was to test both instruments in a second low-income population that differed geographically and economically from the original sample. Participants (N = 199) were recruited from the Women, Infants, and Children (WIC) program. Paper and pencil surveys were collected at WIC sites by research-trained student nurses. Exploratory principal components analysis (PCA) was conducted, and comparisons were made to the original PCA for the purpose of data reduction. Instruments showed satisfactory Cronbach alpha values for all components. HRP components were reduced from five to four, which explained 70% of variance. The components were labeled sensed risks, unseen risks, severity of risks, and knowledge. In contrast to the original testing, environmental tobacco smoke (ETS) items was not a separate component of the HRP. The SEERR analysis demonstrated four components explaining 71% of variance, with similar patterns of items as in the first study, including a component on ETS, but some differences in item location. Although low-income populations constituted both samples, differences in demographics and risk exposures may have played a role in component and item locations. Findings provided justification for changing or reducing items, and for tailoring the instruments to population-level risks and behaviors. Although analytic refinement will continue, both instruments advance the measurement of environmental health risk perception and self-efficacy. © 2016 Wiley Periodicals, Inc. PMID:27227487

  7. Analysis of the photoplethysmographic signal by means of the decomposition in principal components.

    PubMed

    Hong Enríquez, Rolando; Sautié Castellanos, Miguel; Falcón Rodríguez, Jersys; Hernández Cáceres, José Luis

    2002-08-01

    We study the plethysmographic signal using principal component analysis (PCA). By decomposing the signal using this method, we are able to regenerate it again, preserving in the process the functional relationships between the components. We have also found the relative contributions of each specific component to the signal. First return maps have been made for the series of residues of the decomposition. Further analysis using spectral methods has shown that the residues have a 1/f -like structure, which confirms the presence and conservation of this component in the signal and its relative independence with respect to the oscillating component (Hernández et al 2000 Rev. Cubana Inform. Medica 1 5). Our conclusions are that: (i) PCA is a good method to decompose the plethysmographic signal since it preserves the functional relationships in the variables, and this could be potentially useful in finding new clinically relevant indices; (ii) the 1/f process of the plethysmographic signal is preserved in the residues of the decomposed signal when PCA is used; (iii) clinically relevant parameters can potentially be obtained from photoplethysmographic signals when PCA is used. PMID:12214766

  8. Additive manufacturing method for SRF components of various geometries

    DOEpatents

    Rimmer, Robert; Frigola, Pedro E; Murokh, Alex Y

    2015-05-05

    An additive manufacturing method for forming nearly monolithic SRF niobium cavities and end group components of arbitrary shape with features such as optimized wall thickness and integral stiffeners, greatly reducing the cost and technical variability of conventional cavity construction. The additive manufacturing method for forming an SRF cavity, includes atomizing niobium to form a niobium powder, feeding the niobium powder into an electron beam melter under a vacuum, melting the niobium powder under a vacuum in the electron beam melter to form an SRF cavity; and polishing the inside surface of the SRF cavity.

  9. Fluctuations of harmonic and radial flow in heavy ion collisions with principal components

    NASA Astrophysics Data System (ADS)

    Mazeliauskas, Aleksas; Teaney, Derek

    2016-02-01

    We analyze the spectrum of harmonic flow, vn(pT) for n =0 -5 , in event-by-event hydrodynamic simulations of Pb+Pb collisions at the CERN Large Hadron Collider (√{sN N}=2.76 TeV ) with principal component analysis (PCA). The PCA procedure finds two dominant contributions to the two-particle correlation function. The leading component is identified with the event plane vn(pT) , while the subleading component is responsible for factorization breaking in hydrodynamics. For v0, v1, and v3 the subleading flow is a response to the radial excitation of the corresponding eccentricity. By contrast, for v2 the subleading flow in peripheral collisions is dominated by the nonlinear mixing between the leading elliptic flow and radial flow fluctuations. In the v2 case, the sub-sub-leading mode more closely reflects the response to the radial excitation of ɛ2. A consequence of this picture is that the elliptic flow fluctuations and factorization breaking change rapidly with centrality, and in central collisions (where the leading v2 is small and nonlinear effects can be neglected) the sub-sub-leading mode becomes important. Radial flow fluctuations and nonlinear mixing also play a significant role in the factorization breaking of v4 and v5. We construct good geometric predictors for the orientation and magnitudes of the leading and subleading flows based on a linear response to the geometry, and a quadratic mixing between the leading principal components. Finally, we suggest a set of measurements involving three point correlations which can experimentally corroborate the nonlinear mixing of radial and elliptic flow and its important contribution to factorization breaking as a function of centrality.

  10. Early forest fire detection using principal component analysis of infrared video

    NASA Astrophysics Data System (ADS)

    Saghri, John A.; Radjabi, Ryan; Jacobs, John T.

    2011-09-01

    A land-based early forest fire detection scheme which exploits the infrared (IR) temporal signature of fire plume is described. Unlike common land-based and/or satellite-based techniques which rely on measurement and discrimination of fire plume directly from its infrared and/or visible reflectance imagery, this scheme is based on exploitation of fire plume temporal signature, i.e., temperature fluctuations over the observation period. The method is simple and relatively inexpensive to implement. The false alarm rate is expected to be lower that of the existing methods. Land-based infrared (IR) cameras are installed in a step-stare-mode configuration in potential fire-prone areas. The sequence of IR video frames from each camera is digitally processed to determine if there is a fire within camera's field of view (FOV). The process involves applying a principal component transformation (PCT) to each nonoverlapping sequence of video frames from the camera to produce a corresponding sequence of temporally-uncorrelated principal component (PC) images. Since pixels that form a fire plume exhibit statistically similar temporal variation (i.e., have a unique temporal signature), PCT conveniently renders the footprint/trace of the fire plume in low-order PC images. The PC image which best reveals the trace of the fire plume is then selected and spatially filtered via simple threshold and median filter operations to remove the background clutter, such as traces of moving tree branches due to wind.

  11. Radon transform, bispectra, and principal component analysis for RTS invariant image retrieval

    NASA Astrophysics Data System (ADS)

    Shao, Yuan; Celenk, Mehmet

    1999-08-01

    An image retrieval method is presented based on shape similarity measure for multimedia and imaging database system. In the proposed algorithm, the spatial and spectral properties of images are combined using the Radon transform, bispectra, and principal components analysis. For each model image in the database, the original 2D image data are reduced to a set of 1D projections via the Radon transform, and then a feature vector is calculated from the bispectra of the resultant 1D functions. The principal component analysis is applied to further reduce the dimension of the feature vector so that it can be stored along with the original image in the database at a small cost of memory. The derived feature vector is considered as the index or key of the corresponding image, which uniquely identifies the image independent of rotation, translation, and scaling. For image retrieval, the data feature vector is computed for a query image, and matched against the feature vectors of all the model images in the database using the Tanimoto similarity measure. The closely matching images are brought out as the searching results. The proposed technique has been tested on a large image database. The experimental results show that the retrieval accuracy is very high even for query images with low signal-to-noise ratio.

  12. A Principal Component Analysis of global images of Jupiter obtained by Cassini ISS

    NASA Astrophysics Data System (ADS)

    Ordóñez Etxeberria, I.; Hueso, R.; Sánchez-Lavega, A.

    2014-04-01

    The Cassini spacecraft flybied Jupiter in December 2000. The Imaging Science Subsystem (ISS) cameras acquired a large number of images at different spatial resolution in several filters sensitive to different altitudes and to cloud color. We have used these images to build high-resolution multi-wavelength nearly full maps of the planet in cylindrical and polar projections. The images have been analyzed by means of a principal component analysis technique (PCA) which looks for spatial covariances in different filtered images and proposes a new set of images (Principal Components, PC) which contains most of the spatial variability. The goal of this research is triple since we: 1) explore correlations between the ammonia cloud layer observed in most filters and the upper hazes observed in methane band images and UV, 2) we explore the spatial distribution of chromophores similarly to previous studies using HST images [1, 2]; 3) we look for image combinations that could be useful for cloud features sharpening. Furthermore, we study a global characterization of reletive altimetry of clouds and hazes from synthetic indexes between images with different contributions from the methane absorption bands (CB1, CB2, CB3, MT1, MT2, MT3).

  13. IMPROVED SEARCH OF PRINCIPAL COMPONENT ANALYSIS DATABASES FOR SPECTRO-POLARIMETRIC INVERSION

    SciTech Connect

    Casini, R.; Lites, B. W.; Ramos, A. Asensio

    2013-08-20

    We describe a simple technique for the acceleration of spectro-polarimetric inversions based on principal component analysis (PCA) of Stokes profiles. This technique involves the indexing of the database models based on the sign of the projections (PCA coefficients) of the first few relevant orders of principal components of the four Stokes parameters. In this way, each model in the database can be attributed a distinctive binary number of 2{sup 4n} bits, where n is the number of PCA orders used for the indexing. Each of these binary numbers (indices) identifies a group of ''compatible'' models for the inversion of a given set of observed Stokes profiles sharing the same index. The complete set of the binary numbers so constructed evidently determines a partition of the database. The search of the database for the PCA inversion of spectro-polarimetric data can profit greatly from this indexing. In practical cases it becomes possible to approach the ideal acceleration factor of 2{sup 4n} as compared to the systematic search of a non-indexed database for a traditional PCA inversion. This indexing method relies on the existence of a physical meaning in the sign of the PCA coefficients of a model. For this reason, the presence of model ambiguities and of spectro-polarimetric noise in the observations limits in practice the number n of relevant PCA orders that can be used for the indexing.

  14. Registration of dynamic dopamine D2 receptor images using principal component analysis.

    PubMed

    Acton, P D; Pilowsky, L S; Suckling, J; Brammer, M J; Ell, P J

    1997-11-01

    This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D2 receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D2 receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P<0.001) improvements in image registration, leading to alignment errors in x and y of about 25% of the alternative methods, with reductions in autocorrelations over time. It could also be applied to align image sequences which the other methods failed completely to register, particularly 123I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the chi2 of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences. PMID:9371874

  15. Cardiac autonomic changes in middle-aged women: identification based on principal component analysis.

    PubMed

    Trevizani, Gabriela A; Nasario-Junior, Olivassé; Benchimol-Barbosa, Paulo R; Silva, Lilian P; Nadal, Jurandir

    2016-07-01

    The purpose of this study was to investigate the application of the principal component analysis (PCA) technique on power spectral density function (PSD) of consecutive normal RR intervals (iRR) aiming at assessing its ability to discriminate healthy women according to age groups: young group (20-25 year-old) and middle-aged group (40-60 year-old). Thirty healthy and non-smoking female volunteers were investigated (13 young [mean ± SD (median): 22·8 ± 0·9 years (23·0)] and 17 Middle-aged [51·7 ± 5·3 years (50·0)]). The iRR sequence was collected during ten minutes, breathing spontaneously, in supine position and in the morning, using a heart rate monitor. After selecting an iRR segment (5 min) with the smallest variance, an auto regressive model was used to estimate the PSD. Five principal component coefficients, extracted from PSD signals, were retained for analysis according to the Mahalanobis distance classifier. A threshold established by logistic regression allowed the separation of the groups with 100% specificity, 83·2% sensitivity and 93·3% total accuracy. The PCA appropriately classified two groups of women in relation to age (young and Middle-aged) based on PSD analysis of consecutive normal RR intervals. PMID:25532598

  16. Complex-valued neural networks for nonlinear complex principal component analysis.

    PubMed

    Rattan, Sanjay S P; Hsieh, William W

    2005-01-01

    Principal component analysis (PCA) has been generalized to complex principal component analysis (CPCA), which has been widely applied to complex-valued data, two-dimensional vector fields, and complexified real data through the Hilbert transform. Nonlinear PCA (NLPCA) can also be performed using auto-associative feed-forward neural network (NN) models, which allows the extraction of nonlinear features in the data set. This paper introduces a nonlinear complex PCA (NLCPCA) method, which allows nonlinear feature extraction and dimension reduction in complex-valued data sets. The NLCPCA uses the architecture of the NLPCA network, but with complex variables (including complex weight and bias parameters). The application of NLCPCA on test problems confirms its ability to extract nonlinear features missed by the CPCA. For similar number of model parameters, the NLCPCA captures more variance of a data set than the alternative real approach (i.e. replacing each complex variable by two real variables and applying NLPCA). The NLCPCA is also used to perform nonlinear Hilbert PCA (NLHPCA) on complexified real data. The NLHPCA applied to the tropical Pacific sea surface temperatures extracts the El Niño-Southern Oscillation signal better than the linear Hilbert PCA. PMID:15649662

  17. Forecasting of Air Quality Index in Delhi Using Neural Network Based on Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Kumar, Anikender; Goyal, P.

    2013-04-01

    Forecasting of the air quality index (AQI) is one of the topics of air quality research today as it is useful to assess the effects of air pollutants on human health in urban areas. It has been learned in the last decade that airborne pollution has been a serious and will be a major problem in Delhi in the next few years. The air quality index is a number, based on the comprehensive effect of concentrations of major air pollutants, used by Government agencies to characterize the quality of the air at different locations, which is also used for local and regional air quality management in many metro cities of the world. Thus, the main objective of the present study is to forecast the daily AQI through a neural network based on principal component analysis (PCA). The AQI of criteria air pollutants has been forecasted using the previous day's AQI and meteorological variables, which have been found to be nearly same for weekends and weekdays. The principal components of a neural network based on PCA (PCA-neural network) have been computed using a correlation matrix of input data. The evaluation of the PCA-neural network model has been made by comparing its results with the results of the neural network and observed values during 2000-2006 in four different seasons through statistical parameters, which reveal that the PCA-neural network is performing better than the neural network in all of the four seasons.

  18. Sparse principal component analysis for identifying ancestry-informative markers in genome-wide association studies.

    PubMed

    Lee, Seokho; Epstein, Michael P; Duncan, Richard; Lin, Xihong

    2012-05-01

    Genome-wide association studies (GWAS) routinely apply principal component analysis (PCA) to infer population structure within a sample to correct for confounding due to ancestry. GWAS implementation of PCA uses tens of thousands of single-nucleotide polymorphisms (SNPs) to infer structure, despite the fact that only a small fraction of such SNPs provides useful information on ancestry. The identification of this reduced set of ancestry-informative markers (AIMs) from a GWAS has practical value; for example, researchers can genotype the AIM set to correct for potential confounding due to ancestry in follow-up studies that utilize custom SNP or sequencing technology. We propose a novel technique to identify AIMs from genome-wide SNP data using sparse PCA. The procedure uses penalized regression methods to identify those SNPs in a genome-wide panel that significantly contribute to the principal components while encouraging SNPs that provide negligible loadings to vanish from the analysis. We found that sparse PCA leads to negligible loss of ancestry information compared to traditional PCA analysis of genome-wide SNP data. We further demonstrate the value of sparse PCA for AIM selection using real data from the International HapMap Project and a genomewide study of inflammatory bowel disease. We have implemented our approach in open-source R software for public use. PMID:22508067

  19. Impact-acoustics-based health monitoring of tile-wall bonding integrity using principal component analysis

    NASA Astrophysics Data System (ADS)

    Tong, F.; Tso, S. K.; Hung, M. Y. Y.

    2006-06-01

    The use of the acoustic features extracted from the impact sounds for bonding integrity assessment has been extensively investigated. Nonetheless, considering the practical implementation of tile-wall non-destructive evaluation (NDE), the traditional defects classification method based directly on frequency-domain features has been of limited application because of the overlapping feature patterns corresponding to different classes whenever there is physical surface irregularity. The purpose of this paper is to explore the clustering and classification ability of principal component analysis (PCA) as applied to the impact-acoustics signature in tile-wall inspection with a view to mitigating the adverse influence of surface non-uniformity. A clustering analysis with signature acquired on sample slabs shows that impact-acoustics signatures of different bonding quality and different surface roughness are well separated into different clusters when using the first two principal components obtained. By adopting as inputs the feature vectors extracted with PCA applied, a multilayer back-propagation artificial neural network (ANN) classifier is developed for automatic health monitoring and defects classification of tile-walls. The inspection results obtained experimentally on the prepared sample slabs are presented and discussed, confirming the utility of the proposed method, particularly in dealing with tile surface irregularity.

  20. Improved Search of Principal Component Analysis Databases for Spectro-polarimetric Inversion

    NASA Astrophysics Data System (ADS)

    Casini, R.; Asensio Ramos, A.; Lites, B. W.; López Ariste, A.

    2013-08-01

    We describe a simple technique for the acceleration of spectro-polarimetric inversions based on principal component analysis (PCA) of Stokes profiles. This technique involves the indexing of the database models based on the sign of the projections (PCA coefficients) of the first few relevant orders of principal components of the four Stokes parameters. In this way, each model in the database can be attributed a distinctive binary number of 24n bits, where n is the number of PCA orders used for the indexing. Each of these binary numbers (indices) identifies a group of "compatible" models for the inversion of a given set of observed Stokes profiles sharing the same index. The complete set of the binary numbers so constructed evidently determines a partition of the database. The search of the database for the PCA inversion of spectro-polarimetric data can profit greatly from this indexing. In practical cases it becomes possible to approach the ideal acceleration factor of 24n as compared to the systematic search of a non-indexed database for a traditional PCA inversion. This indexing method relies on the existence of a physical meaning in the sign of the PCA coefficients of a model. For this reason, the presence of model ambiguities and of spectro-polarimetric noise in the observations limits in practice the number n of relevant PCA orders that can be used for the indexing.

  1. Selection of cowpea progenies with enhanced drought-tolerance traits using principal component analysis.

    PubMed

    Sousa, C C; Damasceno-Silva, K J; Bastos, E A; Rocha, M M

    2015-01-01

    Vigna unguiculata (L.) Walp (cowpea) is a food crop with high nutritional value that is cultivated throughout tropical and subtropical regions of the world. The main constraint on high productivity of cowpea is water deficit, caused by the long periods of drought that occur in these regions. The aim of the present study was to select elite cowpea genotypes with enhanced drought tolerance, by applying principal component analysis to 219 first-cycle progenies obtained in a recurrent selection program. The experimental design comprised a simple 15 x 15 lattice with 450 plots, each of two rows of 10 plants. Plants were grown under water-deficit conditions by applying a water depth of 205 mm representing one-half of that required by cowpea. Variables assessed were flowering, maturation, pod length, number and mass of beans/pod, mass of 100 beans, and productivity/plot. Ten elite cowpea genotypes were selected, in which principal components 1 and 2 encompassed variables related to yield (pod length, beans/pod, and productivity/plot) and life precocity (flowering and maturation), respectively. PMID:26662390

  2. Power Calculation of Multi-step Combined Principal Components with Applications to Genetic Association Studies

    PubMed Central

    Li, Zhengbang; Zhang, Wei; Pan, Dongdong; Li, Qizhai

    2016-01-01

    Principal component analysis (PCA) is a useful tool to identify important linear combination of correlated variables in multivariate analysis and has been applied to detect association between genetic variants and human complex diseases of interest. How to choose adequate number of principal components (PCs) to represent the original system in an optimal way is a key issue for PCA. Note that the traditional PCA, only using a few top PCs while discarding the other PCs, might significantly lose power in genetic association studies if all the PCs contain non-ignorable signals. In order to make full use of information from all PCs, Aschard and his colleagues have proposed a multi-step combined PCs method (named mCPC) recently, which performs well especially when several traits are highly correlated. However, the power superiority of mCPC has just been illustrated by simulation, while the theoretical power performance of mCPC has not been studied yet. In this work, we attempt to investigate theoretical properties of mCPC and further propose a novel and efficient strategy to combine PCs. Extensive simulation results confirm that the proposed method is more robust than existing procedures. A real data application to detect the association between gene TRAF1-C5 and rheumatoid arthritis further shows good performance of the proposed procedure. PMID:27189724

  3. Modeling and Individualization of Head-Related Transfer Functions Using Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Fink, Kimberly J.

    Spatial localization of sound depends on interaural time delay (ITD), level difference, and on the interaction of the source with the head, torso, and pinna. Head-related transfer functions (HRTFs) model the dynamics that are used by listeners to derive spatial information from binaural signals and are used to create virtual auditory displays (VADs). This thesis presents methods to model and customize HRTFs for the creation of a tunable VAD. Prior research has investigated development of virtual auditory displays (VADs) using models of HRTFs as a function of a finite number of principal components (PCs) and associated weights (PCWs). This thesis investigates the effect of PCWs on horizontal plane HRTFs derived from a database of head-related impulse responses (HRIRs). Tuning is evaluated from a numerical perspective to determine how variation of PCWs from an average PC model affects HRTF spectral characteristics. A PC model of an average subject at 50 azimuths in the horizontal plane is developed from HRIRs of 34 subjects from a public database. An additional nine subjects are used to test the PC model and conduct three optimization experiments, in which a cost function of spectral distortion is minimized by sequentially tuning PCWs. These experiments show that error deriving from PC model truncation can be reduced and average HRTFs can be tuned to match individual HRTFs. Model order reduction is used to reduce the dimensionality of a VAD from 200 point FIR filters for each ear to 15th order IIR filters per ear, without introducing audible differences. Subject testing evaluated the performance of the tuning. First the subject listens to a known azimuth and tunes sliders related to the PCWs and ITD so that what they are hearing sound like the given azimuth. On a later day, azimuths are presented in a random order unknown to the subject and he or she is asked to make judgments about where they perceive the sound. The tuned PCWs are interpolated to obtain customized

  4. Principal component analysis of the CT density histogram to generate parametric response maps of COPD

    NASA Astrophysics Data System (ADS)

    Zha, N.; Capaldi, D. P. I.; Pike, D.; McCormack, D. G.; Cunningham, I. A.; Parraga, G.

    2015-03-01

    Pulmonary x-ray computed tomography (CT) may be used to characterize emphysema and airways disease in patients with chronic obstructive pulmonary disease (COPD). One analysis approach - parametric response mapping (PMR) utilizes registered inspiratory and expiratory CT image volumes and CT-density-histogram thresholds, but there is no consensus regarding the threshold values used, or their clinical meaning. Principal-component-analysis (PCA) of the CT density histogram can be exploited to quantify emphysema using data-driven CT-density-histogram thresholds. Thus, the objective of this proof-of-concept demonstration was to develop a PRM approach using PCA-derived thresholds in COPD patients and ex-smokers without airflow limitation. Methods: Fifteen COPD ex-smokers and 5 normal ex-smokers were evaluated. Thoracic CT images were also acquired at full inspiration and full expiration and these images were non-rigidly co-registered. PCA was performed for the CT density histograms, from which the components with the highest eigenvalues greater than one were summed. Since the values of the principal component curve correlate directly with the variability in the sample, the maximum and minimum points on the curve were used as threshold values for the PCA-adjusted PRM technique. Results: A significant correlation was determined between conventional and PCA-adjusted PRM with 3He MRI apparent diffusion coefficient (p<0.001), with CT RA950 (p<0.0001), as well as with 3He MRI ventilation defect percent, a measurement of both small airways disease (p=0.049 and p=0.06, respectively) and emphysema (p=0.02). Conclusions: PRM generated using PCA thresholds of the CT density histogram showed significant correlations with CT and 3He MRI measurements of emphysema, but not airways disease.

  5. Principal components of quiet time temporal variability of equatorial and low-latitude geomagnetic fields

    NASA Astrophysics Data System (ADS)

    Bhattacharyya, Archana; Okpala, Kingsley C.

    2015-10-01

    Diurnal variations of the horizontal component of the geomagnetic field ΔH on International Quiet days of 1999-2012, measured hourly at two stations in the same longitude zone in the Northern Hemisphere, near and away from the dip equator, have been subjected to principal component analysis. This technique is also applied to the difference ΔHEEJ of ΔH at these two stations, which is attributed to the equatorial electrojet (EEJ). The first three principal components, PC1-PC3, account for 91-96% of the variances in the data. Maximum contribution to the quiet day variations in ΔH around its peak in the morning hours at both the stations, and in the EEJ, comes from the day-to-day variation of the amplitude of PC1. Patterns of day-to-day variations of PC1 amplitudes for the equatorial station and the EEJ are essentially semiannual modulated by solar EUV flux, superimposed on a longer timescale solar EUV flux-dependent trend. Contributions from PC2 and to a lesser extent from PC3 are seen to be responsible for the absence of semiannual variations in ΔH in the afternoon hours at the equatorial station. Distribution of amplitudes of PC2 and PC3 for ΔHEEJ for weak electrojet days shows seasonal features in accordance with greater occurrence of afternoon (morning) counter electrojet during June (December) solstice. During the extended solar minimum, PC3 amplitudes for ΔH at the equatorial station and for the EEJ display annual variation. Possible sources for these seasonal features in the variations of equatorial ΔH are discussed.

  6. Revealing the X-ray variability of AGN with principal component analysis

    NASA Astrophysics Data System (ADS)

    Parker, M. L.; Fabian, A. C.; Matt, G.; Koljonen, K. I. I.; Kara, E.; Alston, W.; Walton, D. J.; Marinucci, A.; Brenneman, L.; Risaliti, G.

    2015-02-01

    We analyse a sample of 26 active galactic nuclei (AGN) with deep XMM-Newton observations, using principal component analysis (PCA) to find model-independent spectra of the different variable components. In total, we identify at least 12 qualitatively different patterns of spectral variability, involving several different mechanisms, including five sources which show evidence of variable relativistic reflection (MCG-6-30-15, NGC 4051, 1H 0707-495, NGC 3516 and Mrk 766) and three which show evidence of varying partial covering neutral absorption (NGC 4395, NGC 1365 and NGC 4151). In over half of the sources studied, the variability is dominated by changes in a power-law continuum, both in terms of changes in flux and power-law index, which could be produced by propagating fluctuations within the corona. Simulations are used to find unique predictions for different physical models, and we then attempt to qualitatively match the results from the simulations to the behaviour observed in the real data. We are able to explain a large proportion of the variability in these sources using simple models of spectral variability, but more complex models may be needed for the remainder. We have begun the process of building up a library of different principal components, so that spectral variability in AGN can quickly be matched to physical processes. We show that PCA can be an extremely powerful tool for distinguishing different patterns of variability in AGN, and that it can be used effectively on the large amounts of high-quality archival data available from the current generation of X-ray telescopes. We will make our PCA code available upon request to the lead author.

  7. Discrimination of a chestnut-oak forest unit for geologic mapping by means of a principal component enhancement of Landsat multispectral scanner data.

    USGS Publications Warehouse

    Krohn, M.D.; Milton, N.M.; Segal, D.; Enland, A.

    1981-01-01

    A principal component image enhancement has been effective in applying Landsat data to geologic mapping in a heavily forested area of E Virginia. The image enhancement procedure consists of a principal component transformation, a histogram normalization, and the inverse principal componnet transformation. The enhancement preserves the independence of the principal components, yet produces a more readily interpretable image than does a single principal component transformation. -from Authors

  8. Magnetic unmixing of first-order reversal curve diagrams using principal component analysis

    NASA Astrophysics Data System (ADS)

    Lascu, Ioan; Harrison, Richard J.; Li, Yuting; Muraszko, Joy R.; Channell, James E. T.; Piotrowski, Alexander M.; Hodell, David A.

    2015-09-01

    We describe a quantitative magnetic unmixing method based on principal component analysis (PCA) of first-order reversal curve (FORC) diagrams. For PCA, we resample FORC distributions on grids that capture diagnostic signatures of single-domain (SD), pseudosingle-domain (PSD), and multidomain (MD) magnetite, as well as of minerals such as hematite. Individual FORC diagrams are recast as linear combinations of end-member (EM) FORC diagrams, located at user-defined positions in PCA space. The EM selection is guided by constraints derived from physical modeling and imposed by data scatter. We investigate temporal variations of two EMs in bulk North Atlantic sediment cores collected from the Rockall Trough and the Iberian Continental Margin. Sediments from each site contain a mixture of magnetosomes and granulometrically distinct detrital magnetite. We also quantify the spatial variation of three EM components (a coarse silt-sized MD component, a fine silt-sized PSD component, and a mixed clay-sized component containing both SD magnetite and hematite) in surficial sediments along the flow path of the North Atlantic Deep Water (NADW). These samples were separated into granulometric fractions, which helped constrain EM definition. PCA-based unmixing reveals systematic variations in EM relative abundance as a function of distance along NADW flow. Finally, we apply PCA to the combined data set of Rockall Trough and NADW sediments, which can be recast as a four-EM mixture, providing enhanced discrimination between components. Our method forms the foundation of a general solution to the problem of unmixing multicomponent magnetic mixtures, a fundamental task of rock magnetic studies.

  9. Early detection of dental fluorosis using Raman spectroscopy and principal component analysis.

    PubMed

    González-Solís, José Luis; Martínez-Cano, Evelia; Magaña-López, Yolanda

    2015-08-01

    Raman spectroscopic technique has the potential to provide vibrational spectra of minerals by analyzing scattered light caused by monochromatic laser excitation. In this paper, recent applications of Raman spectroscopy in the study of dental hard tissues are reported. Special attention is given to mineral components in enamel and to calcium fluoride formed in/on enamel. The criteria used to classify the dental hard samples were according to the Dean Index (DI), which consists into healthy or control, mild, moderate, and severe, indicating the amount of dental fluorosis observed on enamel. A total of 39 dental samples (9 control, 9 mild, 10 moderate, and 11 severe) were analyzed in the study. Dental samples were positioned under an Olympus microscope, and around 10 points were chosen for Raman measurement. All spectra were collected by a Horiba Jobin-Yvon LabRAM HR800 Raman Spectrometer with a laser of 830-nm and 17-mW power irradiation. Raw spectra were processed by carrying out baseline correction, smoothing, and normalization to remove noise, florescence, and shot noise and then analyzed using principal component analysis (PCA). In the spectra of dental samples, we observed the main bands as the broad band due to CO[Formula: see text] (240-300 cm (-1)), CaF 2 (322 cm (-1)), PO[Formula: see text] vibrations (437 and 450 cm (-1)), PO[Formula: see text] vibrations (582, 598, and 609 cm (-1)), PO[Formula: see text] vibrations (960 cm (-1)), PO[Formula: see text] vibrations (1,045 cm (-1)), and CO[Formula: see text] vibration (1,073 cm (-1)). Nevertheless, the intensity of the band at 960 cm (-1) associated to symmetric stretch of phosphate, PO[Formula: see text], decreases as the amount of dental fluorosis increases, suggesting that the intensity of this band could be used to quantitatively measure the level of fluorosis on a dental sample. On the other hand, PCA allowed to identify two large clusters discriminating between control, and severe and moderate samples

  10. SU-E-CAMPUS-T-06: Radiochromic Film Analysis Based On Principal Components

    SciTech Connect

    Wendt, R

    2014-06-15

    Purpose: An algorithm to convert the color image of scanned EBT2 radiochromic film [Ashland, Covington KY] into a dose map was developed based upon a principal component analysis. The sensitive layer of the EBT2 film is colored so that the background streaks arising from variations in thickness and scanning imperfections may be distinguished by color from the dose in the exposed film. Methods: Doses of 0, 0.94, 1.9, 3.8, 7.8, 16, 32 and 64 Gy were delivered to radiochromic films by contact with a calibrated Sr-90/Y-90 source. They were digitized by a transparency scanner. Optical density images were calculated and analyzed by the method of principal components. The eigenimages of the 0.94 Gy film contained predominantly noise, predominantly background streaking, and background streaking plus the source, respectively, in order from the smallest to the largest eigenvalue. Weighting the second and third eigenimages by −0.574 and 0.819 respectively and summing them plus the constant 0.012 yielded a processed optical density image with negligible background streaking. This same weighted sum was transformed to the red, green and blue space of the scanned images and applied to all of the doses. The curve of processed density in the middle of the source versus applied dose was fit by a twophase association curve. A film was sandwiched between two polystyrene blocks and exposed edge-on to a different Y-90 source. This measurement was modeled with the GATE simulation toolkit [Version 6.2, OpenGATE Collaboration], and the on-axis depth-dose curves were compared. Results: The transformation defined using the principal component analysis of the 0.94 Gy film minimized streaking in the backgrounds of all of the films. The depth-dose curves from the film measurement and simulation are indistinguishable. Conclusion: This algorithm accurately converts EBT2 film images to dose images while reducing noise and minimizing background streaking. Supported by a sponsored research

  11. Estimating soil water retention using soil component additivity model

    NASA Astrophysics Data System (ADS)

    Zeiliger, A.; Ermolaeva, O.; Semenov, V.

    2009-04-01

    Soil water retention is a major soil hydraulic property that governs soil functioning in ecosystems and greatly affects soil management. Data on soil water retention are used in research and applications in hydrology, agronomy, meteorology, ecology, environmental protection, and many other soil-related fields. Soil organic matter content and composition affect both soil structure and adsorption properties; therefore water retention may be affected by changes in soil organic matter that occur because of both climate change and modifications of management practices. Thus, effects of organic matter on soil water retention should be understood and quantified. Measurement of soil water retention is relatively time-consuming, and become impractical when soil hydrologic estimates are needed for large areas. One approach to soil water retention estimation from readily available data is based on the hypothesis that soil water retention may be estimated as an additive function obtained by summing up water retention of pore subspaces associated with soil textural and/or structural components and organic matter. The additivity model and was tested with 550 soil samples from the international database UNSODA and 2667 soil samples from the European database HYPRES containing all textural soil classes after USDA soil texture classification. The root mean square errors (RMSEs) of the volumetric water content estimates for UNSODA vary from 0.021 m3m-3 for coarse sandy loam to 0.075 m3m-3 for sandy clay. Obtained RMSEs are at the lower end of the RMSE range for regression-based water retention estimates found in literature. Including retention estimates of organic matter significantly improved RMSEs. The attained accuracy warrants testing the 'additivity' model with additional soil data and improving this model to accommodate various types of soil structure. Keywords: soil water retention, soil components, additive model, soil texture, organic matter.

  12. Influence of wedges on lower limbs' kinematics and net joint moments during healthy elderly gait using principal component analysis.

    PubMed

    Soares, Denise Paschoal; de Castro, Marcelo Peduzzi; Mendes, Emília; Machado, Leandro

    2014-12-01

    The elderly are susceptible to many disorders that alter the gait pattern and could lead to falls and reduction of mobility. One of the most applied therapeutical approaches to correct altered gait patterns is the insertion of insoles. Principal Component Analysis (PCA) is a powerful method used to reduce redundant information and it allows the comparison of the complete waveform. The purpose of this study was to verify the influence of wedges on lower limbs' net joint moment and range of motion (ROM) during the gait of healthy elderly participants using PCA. In addition, discrete values of lower limbs' peak net moment and ROM were also evaluated. 20 subjects walked with no wedges (control condition) and wearing six different wedges. The variables analyzed were the Principal Components from joint net moments and ROM in the sagittal plane in the ankle and knee and joint net moments in frontal plane in the knee. The discrete variables were peak joint net moments and ROM in sagittal plane in knee and ankle. The results showed the influence of the wedges to be clearer by analyzing through PCA methods than to use discrete parameters of gait curves, where the differences between conditions could be hidden. PMID:25457428

  13. Cistanches identification based on fluorescent spectral imaging technology combined with principal component analysis and artificial neural network

    NASA Astrophysics Data System (ADS)

    Dong, Jia; Huang, Furong; Li, Yuanpeng; Xiao, Chi; Xian, Ruiyi; Ma, Zhiguo

    2015-03-01

    In this study, fluorescent spectral imaging technology combined with principal component analysis (PCA) and artificial neural networks (ANNs) was used to identify Cistanche deserticola, Cistanche tubulosa and Cistanche sinensis, which are traditional Chinese medicinal herbs. The fluorescence spectroscopy imaging system acquired the spectral images of 40 cistanche samples, and through image denoising, binarization processing to make sure the effective pixels. Furthermore, drew the spectral curves whose data in the wavelength range of 450-680 nm for the study. Then preprocessed the data by first-order derivative, analyzed the data through principal component analysis and artificial neural network. The results shows: Principal component analysis can generally distinguish cistanches, through further identification by neural networks makes the results more accurate, the correct rate of the testing and training sets is as high as 100%. Based on the fluorescence spectral imaging technique and combined with principal component analysis and artificial neural network to identify cistanches is feasible.

  14. A near infrared spectroscopic discrimination of noodle flours using a principal-component analysis coupled with chemical information.

    PubMed

    Kumagai, Masanori; Karube, Kikuko; Sato, Tomoaki; Ohisa, Naganori; Amano, Toshio; Kikuchi, Ryoei; Ogawa, Nobuaki

    2002-10-01

    Using a portable near infrared (NIR) spectrometer, we discriminated flours for making Japanese noodles (Soba), not only relying on a statistical and mathematical approach, but also on a chemical interpretation of the NIR spectra. In original NIR spectra, the particle-size difference, which results in an undesired systematic variation, was extracted and interpreted as the first-principal component factor by a principal-component analysis. The discrimination of flour materials cannot be satisfied by this factor. However, after a standardized treatment for the original spectra, the particle-size effects were eliminated; alternatively, differences in the chemical contents were extracted as principal-component factors. Using these factors, flour material discrimination was achieved much better. This study suggests a novel idea of utilizing the wavelength contribution ratio spectra for interpreting the factors extracted from the principal-component analysis for the NIR spectra. This report also describes the relationship between the NIR spectra and the chemical-analysis data. PMID:12400663

  15. Fatigue detection in strength training using three-dimensional accelerometry and principal component analysis.

    PubMed

    Brown, Niklas; Bichler, Sebastian; Fiedler, Meike; Alt, Wilfried

    2016-06-01

    Detection of neuro-muscular fatigue in strength training is difficult, due to missing criterion measures and the complexity of fatigue. Thus, a variety of methods are used to determine fatigue. The aim of this study was to use a principal component analysis (PCA) on a multifactorial data-set based on kinematic measurements to determine fatigue. Twenty participants (strength training experienced, 60% male) executed 3 sets of 3 exercises with 50 (12 repetitions), 75 (12 repetitions) and 100%-12 RM (RM). Data were collected with a 3D accelerometer and analysed by a newly developed algorithm to evaluate parameters for each repetition. A PCA with six variables was carried out on the results. A fatigue factor was computed based on the loadings on the first component. One-way ANOVA with Bonferroni post hoc analysis was calculated to test for differences between the intensity levels. All six input variables had high loadings on the first component. The ANOVA showed a significant difference between intensities (p < 0.001). Post-hoc analysis revealed a difference between 100% and the lower intensities (p < 0.05) and no difference between 50 and 75%-12RM. Based on these results, it is possible to distinguish between fatigued and non-fatigued sets of strength training. PMID:27111008

  16. Compressing movement information via principal components analysis (PCA): contrasting outcomes from the time and frequency domains.

    PubMed

    Molenaar, Peter C M; Wang, Zheng; Newell, Karl M

    2013-12-01

    PCA has become an increasingly used analysis technique in the movement domain to reveal patterns in data of various kinds (e.g., kinematics, kinetics, EEG, EMG) and to compress the dimension of the multivariate data set recorded. It appears that virtually all movement related PCA analyses have, however, been conducted in the time domain (PCAt). This standard approach can be biased when there are lead-lag (phase-related) properties to the multivariate time series data. Here we show through theoretical derivation and analysis of simulated and experimental postural kinematics data sets that PCAt and, PCA in the frequency domain (PCAf), can lead to contrasting determinations of the dimension of a data set, with the tendency of PCAt to overestimate the number of components. PCAf also provides the possibility of obtaining amplitude and phase-difference spectra for each principal component that are uniquely suitable to reveal control mechanisms of the system. The bias in the PCAt estimate of the number of components can have significant implications for the veracity of the interpretations drawn in regard to the dynamical degrees of freedom of the perceptual-motor system. PMID:24231287

  17. Progress Towards Improved Analysis of TES X-ray Data Using Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Busch, S. E.; Adams, J. S.; Bandler, S. R.; Chervenak, J. A.; Eckart, M. E.; Finkbeiner, F. M.; Fixsen, D. J.; Kelley, R. L.; Kilbourne, C. A.; Lee, S.-J.; Moseley, S. H.; Porst, J.-P.; Porter, F. S.; Sadleir, J. E.; Smith, S. J.

    2016-07-01

    The traditional method of applying a digital optimal filter to measure X-ray pulses from transition-edge sensor (TES) devices does not achieve the best energy resolution when the signals have a highly non-linear response to energy, or the noise is non-stationary during the pulse. We present an implementation of a method to analyze X-ray data from TESs, which is based upon principal component analysis (PCA). Our method separates the X-ray signal pulse into orthogonal components that have the largest variance. We typically recover pulse height, arrival time, differences in pulse shape, and the variation of pulse height with detector temperature. These components can then be combined to form a representation of pulse energy. An added value of this method is that by reporting information on more descriptive parameters (as opposed to a single number representing energy), we generate a much more complete picture of the pulse received. Here we report on progress in developing this technique for future implementation on X-ray telescopes. We used an ^{55}Fe source to characterize Mo/Au TESs. On the same dataset, the PCA method recovers a spectral resolution that is better by a factor of two than achievable with digital optimal filters.

  18. Degradation of malathion by Pseudomonas during activated sludge treatment system using principal component analysis (PCA).

    PubMed

    Imran, Hashmi; Altaf, Khan M; Jong-Guk, Kim

    2006-01-01

    Popular descriptive multivariate statistical method currently employed is the principal component analyses (PCA) method. PCA is used to develop linear combinations that successively maximize the total variance of a sample where there is no known group structure. This study aimed at demonstrating the performance evaluation of pilot activated sludge treatment system by inoculating a strain of Pseudomonas capable of degrading malathion which was isolated by enrichment technique. An intensive analytical program was followed for evaluating the efficiency of biosimulator by maintaining the dissolved oxygen (DO) concentration at 4.0 mg/L. Analyses by high performance liquid chromatographic technique revealed that 90% of malathion removal was achieved within 29 h of treatment whereas COD got reduced considerably during the treatment process and mean removal efficiency was found to be 78%. The mean pH values increased gradually during the treatment process ranging from 7.36-8.54. Similarly the mean ammonia-nitrogen (NH3-N) values were found to be fluctuating between 19.425-28.488 mg/L, mean nitrite-nitrogen (NO3-N) ranging between 1.301-2.940 mg/L and mean nitrate-nitrogen (NO3-N) ranging between 0.0071-0.0711 mg/L. The study revealed that inoculation of bacterial culture under laboratory conditions could be used in bioremediation of environmental pollution caused by xenobiotics. The PCA analyses showed that pH, COD, organic load and total malathion concentration were highly correlated and emerged as the variables controlling the first component, whereas dissolved oxygen, NO3-N and NH3-N governed the second component. The third component repeated the trend exhibited by the first two components. PMID:17078564

  19. Principal Components Analysis of Martian NIR Image Cubes to Retrieve Surface Spectral Endmembers

    NASA Astrophysics Data System (ADS)

    Klassen, David R.

    2016-07-01

    Presented here is a discussion of the complete principal components analysis (PCA) performed on all photometric NASA Infrared Telescope Facility (IRTF) NSFCAM spectral image sets from 1995–2001 and Mars Reconnaissance Orbiter (MRO) Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) spectral image sets from 2006–2008, detailing the similarities and differences and overall interpretation of the PC dimensional spaces. The purpose of the analysis is to use the PCA to recover surface spectral endmembers to be used in a full radiative transfer modeling program to recover ice cloud optical depths (and thus water content) over diurnal, seasonal, and interannual timescales. The PCA results show considerable consistency across all seasons, and can be optimized to increase the consistency through both spectral and geographic restrictions on the data.

  20. Incremental Principal Component Analysis Based Outlier Detection Methods for Spatiotemporal Data Streams

    NASA Astrophysics Data System (ADS)

    Bhushan, A.; Sharker, M. H.; Karimi, H. A.

    2015-07-01

    In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

  1. A high-performance computing toolset for relatedness and principal component analysis of SNP data.

    PubMed

    Zheng, Xiuwen; Levine, David; Shen, Jess; Gogarten, Stephanie M; Laurie, Cathy; Weir, Bruce S

    2012-12-15

    Genome-wide association studies are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed gdsfmt and SNPRelate (R packages for multi-core symmetric multiprocessing computer architectures) to accelerate two key computations on SNP data: principal component analysis (PCA) and relatedness analysis using identity-by-descent measures. The kernels of our algorithms are written in C/C++ and highly optimized. Benchmarks show the uniprocessor implementations of PCA and identity-by-descent are ∼8-50 times faster than the implementations provided in the popular EIGENSTRAT (v3.0) and PLINK (v1.07) programs, respectively, and can be sped up to 30-300-fold by using eight cores. SNPRelate can analyse tens of thousands of samples with millions of SNPs. For example, our package was used to perform PCA on 55 324 subjects from the 'Gene-Environment Association Studies' consortium studies. PMID:23060615

  2. Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis

    PubMed Central

    Iranmanesh, Vahab; Ahmad, Sharifah Mumtazah Syed; Adnan, Wan Azizun Wan; Arigbabu, Olasimbo Ayodeji; Malallah, Fahad Layth

    2014-01-01

    One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%. PMID:25133227

  3. Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

    SciTech Connect

    Clegg, Samuel M; Barefield, James E; Wiens, Roger C; Sklute, Elizabeth; Dyare, Melinda D

    2008-01-01

    Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.

  4. Application of Euclidean distance measurement and principal component analysis for gene identification.

    PubMed

    Ghosh, Antara; Barman, Soma

    2016-06-01

    Gene systems are extremely complex, heterogeneous, and noisy in nature. Many statistical tools which are used to extract relevant feature from genes provide fuzzy and ambiguous information. High-dimensional gene expression database available in public domain usually contains thousands of genes. Efficient prediction method is demanding nowadays for accurate identification of such database. Euclidean distance measurement and principal component analysis methods are applied on such databases to identify the genes. In both methods, prediction algorithm is based on homology search approach. Digital Signal Processing technique along with statistical method is used for analysis of genes in both cases. A two-level decision logic is used for gene classification as healthy or cancerous. This binary logic minimizes the prediction error and improves prediction accuracy. Superiority of the method is judged by receiver operating characteristic curve. PMID:26877227

  5. Principal component analysis and neural networks for detection of amino acid biosignatures

    NASA Astrophysics Data System (ADS)

    Dorn, Evan D.; McDonald, Gene D.; Storrie-Lombardi, Michael C.; Nealson, Kenneth H.

    2003-12-01

    We examine the applicability of Principal Component Analysis (PCA) and Artificial Neural Network (ANN) methods of data analysis to biosignature detection. These techniques show promise in classifying and simplifying the representation of patterns of amino acids resulting from biological and non-biological syntheses. PCA correctly identifies glycine and alanine as the amino acids contributing the most information to the task of discriminating biotic and abiotic samples. Trained ANNs correctly classify between 86.1 and 99.5% of a large set of amino acid samples as biotic or abiotic. These and similar techniques are important in the design of automated data analysis systems for robotic missions to distant planetary bodies. Both techniques are robust with respect to noisy and incomplete data. Analysis of the performance of PCA and ANNs also lends insight into the localization of useful information within a particular data set, a feature that may be exploited in the selection of experiments for efficient mission design.

  6. Protein–RNA specificity by high-throughput principal component analysis of NMR spectra

    PubMed Central

    Collins, Katherine M.; Oregioni, Alain; Robertson, Laura E.; Kelly, Geoff; Ramos, Andres

    2015-01-01

    Defining the RNA target selectivity of the proteins regulating mRNA metabolism is a key issue in RNA biology. Here we present a novel use of principal component analysis (PCA) to extract the RNA sequence preference of RNA binding proteins. We show that PCA can be used to compare the changes in the nuclear magnetic resonance (NMR) spectrum of a protein upon binding a set of quasi-degenerate RNAs and define the nucleobase specificity. We couple this application of PCA to an automated NMR spectra recording and processing protocol and obtain an unbiased and high-throughput NMR method for the analysis of nucleobase preference in protein–RNA interactions. We test the method on the RNA binding domains of three important regulators of RNA metabolism. PMID:25586222

  7. Picosecond spectral coherent anti-Stokes Raman scattering imaging with principal component analysis of meibomian glands

    NASA Astrophysics Data System (ADS)

    Lin, Chia-Yu; Suhalim, Jeffrey L.; Nien, Chyong Ly; Miljković, Miloš D.; Diem, Max; Jester, James V.; Potma, Eric. O.

    2011-02-01

    The lipid distribution in the mouse meibomian gland was examined with picosecond spectral anti-Stokes Raman scattering (CARS) imaging. Spectral CARS data sets were generated by imaging specific localized regions of the gland within tissue sections at consecutive Raman shifts in the CH2 stretching vibrational range. Spectral differences between the location specific CARS spectra obtained in the lipid-rich regions of the acinus and the central duct were observed, which were confirmed with a Raman microspectroscopic analysis, and attributed to meibum lipid modifications within the gland. A principal component analysis of the spectral data set reveals changes in the CARS spectrum when transitioning from the acini to the central duct. These results demonstrate the utility of picosecond spectral CARS imaging combined with multivariate analysis for assessing differences in the distribution and composition of lipids in tissues.

  8. Principal component analysis of modified gravity using weak lensing and peculiar velocity measurements

    SciTech Connect

    Asaba, Shinsuke; Hikage, Chiaki; Koyama, Kazuya; Zhao, Gong-Bo; Hojjati, Alireza; Pogosian, Levon E-mail: hikage@kmi.nagoya-u.ac.jp E-mail: gong-bo.zhao@port.ac.uk E-mail: levon@sfu.ca

    2013-08-01

    We perform a principal component analysis to assess ability of future observations to measure departures from General Relativity in predictions of the Poisson and anisotropy equations on linear scales. In particular, we focus on how the measurements of redshift-space distortions (RSD) observed from spectroscopic galaxy redshift surveys will improve the constraints when combined with lensing tomographic surveys. Assuming a Euclid-like galaxy imaging and redshift survey, we find that adding the 3D information decreases the statistical uncertainty by a factor between 3 and 7 compared to the case when only observables from lensing tomographic surveys are used. We also find that the number of well-constrained modes increases by a factor between 3 and 6. Our study indicates the importance of joint galaxy imaging and redshift surveys such as SuMIRe and Euclid to give more stringent tests of the ΛCDM model and to distinguish between various modified gravity and dark energy models.

  9. FPGA implementation of principal component regression (PCR) for real-time differentiation of dopamine from interferents.

    PubMed

    Bozorgzadeh, Bardia; Covey, Daniel P; Garris, Paul A; Mohseni, Pedram

    2015-01-01

    This paper reports on field-programmable gate array (FPGA) implementation of a digital signal processing (DSP) unit for real-time processing of neurochemical data obtained by fast-scan cyclic voltammetry (FSCV) at a carbonfiber microelectrode (CFM). The DSP unit comprises a decimation filter and two embedded processors to process the FSCV data obtained by an oversampling recording front-end and differentiate the target analyte from interferents in real time with a chemometrics algorithm using principal component regression (PCR). Interfaced with an integrated, FSCV-sensing front-end, the DSP unit successfully resolves the dopamine response from that of pH change and background-current drift, two common dopamine interferents, in flow injection analysis involving bolus injection of mixed solutions, as well as in biological tests involving electrically evoked, transient dopamine release in the forebrain of an anesthetized rat. PMID:26737451

  10. Online handwritten signature verification using neural network classifier based on principal component analysis.

    PubMed

    Iranmanesh, Vahab; Ahmad, Sharifah Mumtazah Syed; Adnan, Wan Azizun Wan; Yussof, Salman; Arigbabu, Olasimbo Ayodeji; Malallah, Fahad Layth

    2014-01-01

    One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%. PMID:25133227

  11. Ranking the airports in Turkey with data envelopment analysis and principal component analysis

    NASA Astrophysics Data System (ADS)

    Bal, Hasan; Öztürk, Esra

    2016-04-01

    Data envelopment analysis (DEA) is a linear programming (LP) technique for measuring the relative efficiency of peer decision making units(DMUs) when multiple inputs and outputs are present. This objective method was originated by Charnes et al. (1978). DEA can be used, not only for estimating the performance of units, but also for solving other problems of management such as aggregating several preference rankings into single ranking. Data Envelopment Analysis (DEA) model selection is an important step and problematic. Efficiency values for decision making units are connected to input and output data. It also depends on the number of outputs plus inputs. A new method for model selection is proposed in this study. Efficiencies are calculated for all possible DEA model specifications. It is shown that model equivalence or dissimilarity can be easily assessed using this approach. The results are analysed using Principal Component Analysis.

  12. Simultaneous determination of aniline and cyclohexylamine by principal component artificial neural networks.

    PubMed

    Absalan, Ghodratollah; Soleimani, Mohammad

    2004-05-01

    A specterophotometric method for simultaneous determination of aniline and cyclohexylamine using principal component artificial neural networks is proposed. This method is based on the reactions involving aniline and/or cyclohexylamine, with bis(acetylacetoneethylendiamine)tributylphosphine cobalt(III) perchlorate as a complexing reagent. A nonionic surfactant, Triton X-100, was used for dissolving the complexes and intensifying the signals. The absorption data were based on the spectra registered in the range of 350 - 550 nm. An artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Sigmoid transfer functions were used in the hidden and output layers to facilitate nonlinear calibration. The predictive ability of artificial neural networks was examined for the determination of aniline and cyclohexylamine in synthetic mixtures. PMID:15171298

  13. Principal components analysis and the reported low intrinsic dimensionality of gene expression microarray data

    PubMed Central

    Lenz, Michael; Müller, Franz-Josef; Zenke, Martin; Schuppert, Andreas

    2016-01-01

    Principal components analysis (PCA) is a common unsupervised method for the analysis of gene expression microarray data, providing information on the overall structure of the analyzed dataset. In the recent years, it has been applied to very large datasets involving many different tissues and cell types, in order to create a low dimensional global map of human gene expression. Here, we reevaluate this approach and show that the linear intrinsic dimensionality of this global map is higher than previously reported. Furthermore, we analyze in which cases PCA fails to detect biologically relevant information and point the reader to methods that overcome these limitations. Our results refine the current understanding of the overall structure of gene expression spaces and show that PCA critically depends on the effect size of the biological signal as well as on the fraction of samples containing this signal. PMID:27254731

  14. Principal-components analysis of fluorescence cross-section spectra from pathogenic and simulant bacteria

    NASA Astrophysics Data System (ADS)

    Heaton, Harold I.

    2005-10-01

    Principal-components analysis of a new set of highly resolved (<1 nm) fluorescence cross-section spectra excited at 354.7 nm over the 370 646 nm band has been used to demonstrate the potential ability of UV standoff lidars to discriminate among particular biological warfare agents and simulants over short ranges. The remapped spectra produced by this technique from Bacillus globigii (Bg) and Bacillus anthracis (Ba) spores were sufficiently different to allow them to be cleanly separated, and the Ba spectra obtained from Sterne and Ames strain spores were distinguishable. These patterns persisted as the spectral resolution was subsequently degraded in processing from ˜1 to 34 nm. This is to the author's knowledge the first time that resolved fluorescence spectra from biological warfare agents have been speciated or shown to be distinguishably different from those normally used surrogates by optical spectroscopy.

  15. Portable XRF and principal component analysis for bill characterization in forensic science.

    PubMed

    Appoloni, C R; Melquiades, F L

    2014-02-01

    Several modern techniques have been applied to prevent counterfeiting of money bills. The objective of this study was to demonstrate the potential of Portable X-ray Fluorescence (PXRF) technique and the multivariate analysis method of Principal Component Analysis (PCA) for classification of bills in order to use it in forensic science. Bills of Dollar, Euro and Real (Brazilian currency) were measured directly at different colored regions, without any previous preparation. Spectra interpretation allowed the identification of Ca, Ti, Fe, Cu, Sr, Y, Zr and Pb. PCA analysis separated the bills in three groups and subgroups among Brazilian currency. In conclusion, the samples were classified according to its origin identifying the elements responsible for differentiation and basic pigment composition. PXRF allied to multivariate discriminate methods is a promising technique for rapid and no destructive identification of false bills in forensic science. PMID:24393811

  16. Learning representative features for facial images based on a modified principal component analysis

    NASA Astrophysics Data System (ADS)

    Averkin, Anton; Potapov, Alexey

    2013-05-01

    The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

  17. Application of principal component analysis to long-term reservoir management

    NASA Astrophysics Data System (ADS)

    Saad, Maarouf; Turgeon, André

    1988-07-01

    Determining the optimal long-term operating policy of a multireservoir power system requires solution of a stochastic nonlinear programing problem. For small systems the solution can be found by dynamic programing, but for large systems, no direct solution method exists yet, so that one must resort to mathematical manipulations to solve the problem. This paper presents a very efficient procedure for the case where high correlation exists between the reservoirs' trajectories and hence between the state variables. The procedure consists of performing principal component analysis (PCA) on the trajectories to find a reduced model of the system. The reduced model is then substituted into the operating problem, and the resulting problem is solved by stochastic dynamic programing. The reservoir trajectories on which the PCA is performed can be obtained by solving the operating problem deterministically for a large number of equally likely flow sequences. The results of applying the manipulation to Quebec's La Grande river, which has five reservoirs, are reported.

  18. Principal components analysis and the reported low intrinsic dimensionality of gene expression microarray data.

    PubMed

    Lenz, Michael; Müller, Franz-Josef; Zenke, Martin; Schuppert, Andreas

    2016-01-01

    Principal components analysis (PCA) is a common unsupervised method for the analysis of gene expression microarray data, providing information on the overall structure of the analyzed dataset. In the recent years, it has been applied to very large datasets involving many different tissues and cell types, in order to create a low dimensional global map of human gene expression. Here, we reevaluate this approach and show that the linear intrinsic dimensionality of this global map is higher than previously reported. Furthermore, we analyze in which cases PCA fails to detect biologically relevant information and point the reader to methods that overcome these limitations. Our results refine the current understanding of the overall structure of gene expression spaces and show that PCA critically depends on the effect size of the biological signal as well as on the fraction of samples containing this signal. PMID:27254731

  19. A Principal Component Regression Approach for Estimating Ventricular Repolarization Duration Variability

    NASA Astrophysics Data System (ADS)

    Tarvainen, Mika P.; Laitinen, Tomi; Lyyra-Laitinen, Tiina; Niskanen, Juha-Pekka; Karjalainen, Pasi A.

    2007-12-01

    Ventricular repolarization duration (VRD) is affected by heart rate and autonomic control, and thus VRD varies in time in a similar way as heart rate. VRD variability is commonly assessed by determining the time differences between successive R- and T-waves, that is, RT intervals. Traditional methods for RT interval detection necessitate the detection of either T-wave apexes or offsets. In this paper, we propose a principal-component-regression- (PCR-) based method for estimating RT variability. The main benefit of the method is that it does not necessitate T-wave detection. The proposed method is compared with traditional RT interval measures, and as a result, it is observed to estimate RT variability accurately and to be less sensitive to noise than the traditional methods. As a specific application, the method is applied to exercise electrocardiogram (ECG) recordings.

  20. Determination principal component content of seed oils by THz-TDS

    NASA Astrophysics Data System (ADS)

    Li, Jiu-sheng; Li, Xiang-jun

    2009-07-01

    The terahertz transmission spectra of seed oils are measured in the frequency range extending from 0.2 to 1.4 THz using terahertz time-domain spectroscopy (THz-TDS). The absorption spectra of three acid compounds (octadecanoic acid, octadecenoic acid and octadecadienoic acid) in seed oils are recorded and simulated using both THz-TDS and density functional theory (DFT) methods. Support vector regression (SVR) model using the raw measured terahertz spectral data directly as input of the principal component is established and is employed to determinate three acid compounds content for the terahertz time-domain spectroscopy. Comparison of the experimental data using liquid chromatography with predictions based on support vector regression, respectively, exhibits excellent agreement.

  1. Efficient uncertainty quantification in stochastic finite element analysis based on functional principal components

    NASA Astrophysics Data System (ADS)

    Bianchini, Ilaria; Argiento, Raffaele; Auricchio, Ferdinando; Lanzarone, Ettore

    2015-09-01

    The great influence of uncertainties on the behavior of physical systems has always drawn attention to the importance of a stochastic approach to engineering problems. Accordingly, in this paper, we address the problem of solving a Finite Element analysis in the presence of uncertain parameters. We consider an approach in which several solutions of the problem are obtained in correspondence of parameters samples, and propose a novel non-intrusive method, which exploits the functional principal component analysis, to get acceptable computational efforts. Indeed, the proposed approach allows constructing an optimal basis of the solutions space and projecting the full Finite Element problem into a smaller space spanned by this basis. Even if solving the problem in this reduced space is computationally convenient, very good approximations are obtained by upper bounding the error between the full Finite Element solution and the reduced one. Finally, we assess the applicability of the proposed approach through different test cases, obtaining satisfactory results.

  2. Data processing method applying principal component analysis and spectral angle mapper for imaging spectroscopic sensors

    NASA Astrophysics Data System (ADS)

    García-Allende, P. B.; Conde, O. M.; Mirapeix, J.; Cubillas, A. M.; López-Higuera, J. M.

    2007-07-01

    A data processing method for hyperspectral images is presented. Each image contains the whole diffuse reflectance spectra of the analyzed material for all the spatial positions along a specific line of vision. This data processing method is composed of two blocks: data compression and classification unit. Data compression is performed by means of Principal Component Analysis (PCA) and the spectral interpretation algorithm for classification is the Spectral Angle Mapper (SAM). This strategy of classification applying PCA and SAM has been successfully tested on the raw material on-line characterization in the tobacco industry. In this application case the desired raw material (tobacco leaves) should be discriminated from other unwanted spurious materials, such as plastic, cardboard, leather, candy paper, etc. Hyperspectral images are recorded by a spectroscopic sensor consisting of a monochromatic camera and a passive Prism- Grating-Prism device. Performance results are compared with a spectral interpretation algorithm based on Artificial Neural Networks (ANN).

  3. Principal components of hand kinematics and neurophysiological signals in motor cortex during reach to grasp movements

    PubMed Central

    Aggarwal, Vikram; Thakor, Nitish V.; Schieber, Marc H.

    2014-01-01

    A few kinematic synergies identified by principal component analysis (PCA) account for most of the variance in the coordinated joint rotations of the fingers and wrist used for a wide variety of hand movements. To examine the possibility that motor cortex might control the hand through such synergies, we collected simultaneous kinematic and neurophysiological data from monkeys performing a reach-to-grasp task. We used PCA, jPCA and isomap to extract kinematic synergies from 18 joint angles in the fingers and wrist and analyzed the relationships of both single-unit and multiunit spike recordings, as well as local field potentials (LFPs), to these synergies. For most spike recordings, the maximal absolute cross-correlations of firing rates were somewhat stronger with an individual joint angle than with any principal component (PC), any jPC or any isomap dimension. In decoding analyses, where spikes and LFP power in the 100- to 170-Hz band each provided better decoding than other LFP-based signals, the first PC was decoded as well as the best decoded joint angle. But the remaining PCs and jPCs were predicted with lower accuracy than individual joint angles. Although PCs, jPCs or isomap dimensions might provide a more parsimonious description of kinematics, our findings indicate that the kinematic synergies identified with these techniques are not represented in motor cortex more strongly than the original joint angles. We suggest that the motor cortex might act to sculpt the synergies generated by subcortical centers, superimposing an ability to individuate finger movements and adapt the hand to grasp a wide variety of objects. PMID:24990564

  4. Principal components analysis based control of a multi-dof underactuated prosthetic hand

    PubMed Central

    2010-01-01

    Background Functionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG). Driving a multi degrees of freedom (DoF) hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user. Methods A Principal Components Analysis (PCA) based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand) with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs). Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control. Results Trials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture) may be achieved. Conclusions This work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis. PMID:20416036

  5. A hybrid least squares and principal component analysis algorithm for Raman spectroscopy.

    PubMed

    Van de Sompel, Dominique; Garai, Ellis; Zavaleta, Cristina; Gambhir, Sanjiv Sam

    2012-01-01

    Raman spectroscopy is a powerful technique for detecting and quantifying analytes in chemical mixtures. A critical part of Raman spectroscopy is the use of a computer algorithm to analyze the measured Raman spectra. The most commonly used algorithm is the classical least squares method, which is popular due to its speed and ease of implementation. However, it is sensitive to inaccuracies or variations in the reference spectra of the analytes (compounds of interest) and the background. Many algorithms, primarily multivariate calibration methods, have been proposed that increase robustness to such variations. In this study, we propose a novel method that improves robustness even further by explicitly modeling variations in both the background and analyte signals. More specifically, it extends the classical least squares model by allowing the declared reference spectra to vary in accordance with the principal components obtained from training sets of spectra measured in prior characterization experiments. The amount of variation allowed is constrained by the eigenvalues of this principal component analysis. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, as well as a state-of-the-art hybrid linear analysis method. The latter is a multivariate calibration method designed specifically to improve robustness to background variability in cases where training spectra of the background, as well as the mean spectrum of the analyte, are available. We demonstrate the novel algorithm's superior performance by comparing quantitative error metrics generated by each method. The experiments consider both simulated data and experimental data acquired from in vitro solutions of Raman-enhanced gold-silica nanoparticles. PMID:22723895

  6. Sequential Principal Component Analysis -An Optimal and Hardware-Implementable Transform for Image Compression

    NASA Technical Reports Server (NTRS)

    Duong, Tuan A.; Duong, Vu A.

    2009-01-01

    This paper presents the JPL-developed Sequential Principal Component Analysis (SPCA) algorithm for feature extraction / image compression, based on "dominant-term selection" unsupervised learning technique that requires an order-of-magnitude lesser computation and has simpler architecture compared to the state of the art gradient-descent techniques. This algorithm is inherently amenable to a compact, low power and high speed VLSI hardware embodiment. The paper compares the lossless image compression performance of the JPL's SPCA algorithm with the state of the art JPEG2000, widely used due to its simplified hardware implementability. JPEG2000 is not an optimal data compression technique because of its fixed transform characteristics, regardless of its data structure. On the other hand, conventional Principal Component Analysis based transform (PCA-transform) is a data-dependent-structure transform. However, it is not easy to implement the PCA in compact VLSI hardware, due to its highly computational and architectural complexity. In contrast, the JPL's "dominant-term selection" SPCA algorithm allows, for the first time, a compact, low-power hardware implementation of the powerful PCA algorithm. This paper presents a direct comparison of the JPL's SPCA versus JPEG2000, incorporating the Huffman and arithmetic coding for completeness of the data compression operation. The simulation results show that JPL's SPCA algorithm is superior as an optimal data-dependent-transform over the state of the art JPEG2000. When implemented in hardware, this technique is projected to be ideally suited to future NASA missions for autonomous on-board image data processing to improve the bandwidth of communication.

  7. Principal components analysis to identify influences on research communication and engagement during an environmental disaster

    PubMed Central

    Winters, Charlene A; Moore, Colleen F; Kuntz, Sandra W; Weinert, Clarann; Hernandez, Tanis; Black, Brad

    2016-01-01

    Objectives To discern community attitudes towards research engagement in Libby, Montana, the only Superfund site for which a public health emergency has been declared. Study design Survey study of convenience samples of residents near the Libby, Montana Superfund site. Participants Residents of the Libby, Montana area were recruited from a local retail establishment (N=120, survey 1) or a community event (N=127, survey 2). Measures Two surveys were developed in consultation with a Community Advisory Panel. Results Principal components of survey 1 showed four dimensions of community members' attitudes towards research engagement: (1) researcher communication and contributions to the community, (2) identity and affiliation of the researchers requesting participation, (3) potential personal barriers, including data confidentiality, painful or invasive procedures and effects on health insurance and (4) research benefits for the community, oneself or family. The score on the first factor was positively related to desire to participate in research (r=0.31, p=0.01). Scores on factors 2 and 3 were higher for those with diagnosis of asbestos-related disease (ARD) in the family (Cohen's d=0.41, 0.57). Survey 2 also found more positive attitudes towards research when a family member had ARD (Cohen's d=0.48). Conclusions Principal components analysis shows different dimensions of attitudes towards research engagement. The different dimensions are related to community members' desire to be invited to participate in research, awareness of past research in the community and having been screened or diagnosed with a health condition related to the Superfund contaminant. PMID:27507235

  8. Muscles data compression in body sensor network using the principal component analysis in wavelet domain

    PubMed Central

    Yekani Khoei, Elmira; Hassannejad, Reza; Mozaffari Tazehkand, Behzad

    2015-01-01

    Introduction: Body sensor network is a key technology that is used for supervising the physiological information from a long distance that enables physicians to predict and diagnose effectively the different conditions. These networks include small sensors with the ability of sensing where there are some limitations in calculating and energy. Methods: In the present research, a new compression method based on the analysis of principal components and wavelet transform is used to increase the coherence. In the present method, the first analysis of the main principles is to find the principal components of the data in order to increase the coherence for increasing the similarity between the data and compression rate. Then, according to the ability of wavelet transform, data are decomposed to different scales. In restoration process of data only special parts are restored and some parts of the data that include noise are omitted. By noise omission, the quality of the sent data increases and good compression could be obtained. Results: Pilates practices were executed among twelve patients with various dysfunctions. The results showed 0.7210, 0.8898, 0.6548, 0.6765, 0.6009, 0.7435, 0.7651, 0.7623, 0.7736, 0.8596, 0.8856 and 0.7102 compression ratios in proposed method and 0.8256, 0.9315, 0.9340, 0.9509, 0.8998, 0.9556, 0.9732, 0.9580, 0.8046, 0.9448, 0.9573 and 0.9440 compression ratios in previous method (Tseng algorithm). Conclusion: Comparing compression rates and prediction errors with the available results show the exactness of the proposed method. PMID:25901292

  9. Web document ranking via active learning and kernel principal component analysis

    NASA Astrophysics Data System (ADS)

    Cai, Fei; Chen, Honghui; Shu, Zhen

    2015-09-01

    Web document ranking arises in many information retrieval (IR) applications, such as the search engine, recommendation system and online advertising. A challenging issue is how to select the representative query-document pairs and informative features as well for better learning and exploring new ranking models to produce an acceptable ranking list of candidate documents of each query. In this study, we propose an active sampling (AS) plus kernel principal component analysis (KPCA) based ranking model, viz. AS-KPCA Regression, to study the document ranking for a retrieval system, i.e. how to choose the representative query-document pairs and features for learning. More precisely, we fill those documents gradually into the training set by AS such that each of which will incur the highest expected DCG loss if unselected. Then, the KPCA is performed via projecting the selected query-document pairs onto p-principal components in the feature space to complete the regression. Hence, we can cut down the computational overhead and depress the impact incurred by noise simultaneously. To the best of our knowledge, we are the first to perform the document ranking via dimension reductions in two dimensions, namely, the number of documents and features simultaneously. Our experiments demonstrate that the performance of our approach is better than that of the baseline methods on the public LETOR 4.0 datasets. Our approach brings an improvement against RankBoost as well as other baselines near 20% in terms of MAP metric and less improvements using P@K and NDCG@K, respectively. Moreover, our approach is particularly suitable for document ranking on the noisy dataset in practice.

  10. Non-invasive prediction of hemoglobin levels by principal component and back propagation artificial neural network

    PubMed Central

    Ding, Haiquan; Lu, Qipeng; Gao, Hongzhi; Peng, Zhongqi

    2014-01-01

    To facilitate non-invasive diagnosis of anemia, specific equipment was developed, and non-invasive hemoglobin (HB) detection method based on back propagation artificial neural network (BP-ANN) was studied. In this paper, we combined a broadband light source composed of 9 LEDs with grating spectrograph and Si photodiode array, and then developed a high-performance spectrophotometric system. By using this equipment, fingertip spectra of 109 volunteers were measured. In order to deduct the interference of redundant data, principal component analysis (PCA) was applied to reduce the dimensionality of collected spectra. Then the principal components of the spectra were taken as input of BP-ANN model. On this basis we obtained the optimal network structure, in which node numbers of input layer, hidden layer, and output layer was 9, 11, and 1. Calibration and correction sample sets were used for analyzing the accuracy of non-invasive hemoglobin measurement, and prediction sample set was used for testing the adaptability of the model. The correlation coefficient of network model established by this method is 0.94, standard error of calibration, correction, and prediction are 11.29g/L, 11.47g/L, and 11.01g/L respectively. The result proves that there exist good correlations between spectra of three sample sets and actual hemoglobin level, and the model has a good robustness. It is indicated that the developed spectrophotometric system has potential for the non-invasive detection of HB levels with the method of BP-ANN combined with PCA. PMID:24761296

  11. Use of a Principal Components Analysis for the Generation of Daily Time Series.

    NASA Astrophysics Data System (ADS)

    Dreveton, Christine; Guillou, Yann

    2004-07-01

    A new approach for generating daily time series is considered in response to the weather-derivatives market. This approach consists of performing a principal components analysis to create independent variables, the values of which are then generated separately with a random process. Weather derivatives are financial or insurance products that give companies the opportunity to cover themselves against adverse climate conditions. The aim of a generator is to provide a wider range of feasible situations to be used in an assessment of risk. Generation of a temperature time series is required by insurers or bankers for pricing weather options. The provision of conditional probabilities and a good representation of the interannual variance are the main challenges of a generator when used for weather derivatives. The generator was developed according to this new approach using a principal components analysis and was applied to the daily average temperature time series of the Paris-Montsouris station in France. The observed dataset was homogenized and the trend was removed to represent correctly the present climate. The results obtained with the generator show that it represents correctly the interannual variance of the observed climate; this is the main result of the work, because one of the main discrepancies of other generators is their inability to represent accurately the observed interannual climate variance—this discrepancy is not acceptable for an application to weather derivatives. The generator was also tested to calculate conditional probabilities: for example, the knowledge of the aggregated value of heating degree-days in the middle of the heating season allows one to estimate the probability if reaching a threshold at the end of the heating season. This represents the main application of a climate generator for use with weather derivatives.


  12. Applying stability selection to consistently estimate sparse principal components in high-dimensional molecular data

    PubMed Central

    Sill, Martin; Saadati, Maral; Benner, Axel

    2015-01-01

    Motivation: Principal component analysis (PCA) is a basic tool often used in bioinformatics for visualization and dimension reduction. However, it is known that PCA may not consistently estimate the true direction of maximal variability in high-dimensional, low sample size settings, which are typical for molecular data. Assuming that the underlying signal is sparse, i.e. that only a fraction of features contribute to a principal component (PC), this estimation consistency can be retained. Most existing sparse PCA methods use L1-penalization, i.e. the lasso, to perform feature selection. But, the lasso is known to lack variable selection consistency in high dimensions and therefore a subsequent interpretation of selected features can give misleading results. Results: We present S4VDPCA, a sparse PCA method that incorporates a subsampling approach, namely stability selection. S4VDPCA can consistently select the truly relevant variables contributing to a sparse PC while also consistently estimate the direction of maximal variability. The performance of the S4VDPCA is assessed in a simulation study and compared to other PCA approaches, as well as to a hypothetical oracle PCA that ‘knows’ the truly relevant features in advance and thus finds optimal, unbiased sparse PCs. S4VDPCA is computationally efficient and performs best in simulations regarding parameter estimation consistency and feature selection consistency. Furthermore, S4VDPCA is applied to a publicly available gene expression data set of medulloblastoma brain tumors. Features contributing to the first two estimated sparse PCs represent genes significantly over-represented in pathways typically deregulated between molecular subgroups of medulloblastoma. Availability and implementation: Software is available at https://github.com/mwsill/s4vdpca. Contact: m.sill@dkfz.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25861969

  13. Characterization of deep aquifer dynamics using principal component analysis of sequential multilevel data

    NASA Astrophysics Data System (ADS)

    Kurtzman, D.; Netzer, L.; Weisbrod, N.; Nasser, A.; Graber, E. R.; Ronen, D.

    2012-03-01

    Two sequential multilevel profiles were obtained in an observation well opened to a 130-m thick, unconfined, contaminated aquifer in Tel Aviv, Israel. While the general profile characteristics of major ions, trace elements, and volatile organic compounds were maintained in the two sampling campaigns conducted 295 days apart, the vertical locations of high concentration gradients were shifted between the two profiles. Principal component analysis (PCA) of the chemical variables resulted in a first principal component which was responsible for ∼60% of the variability, and was highly correlated with depth. PCA revealed three distinct depth-dependent water bodies in both multilevel profiles, which were found to have shifted vertically between the sampling events. This shift cut across a clayey bed which separated the top and intermediate water bodies in the first profile, and was located entirely within the intermediate water body in the second profile. Continuous electrical conductivity monitoring in a packed-off section of the observation well revealed an event in which a distinct water body flowed through the monitored section (v ∼ 150 m yr-1). It was concluded that the observed changes in the profiles result from dominantly lateral flow of water bodies in the aquifer rather than vertical flow. The significance of this study is twofold: (a) it demonstrates the utility of sequential multilevel observations from deep wells and the efficacy of PCA for evaluating the data; (b) the fact that distinct water bodies of 10 to 100 m vertical and horizontal dimensions flow under contaminated sites, which has implications for monitoring and remediation.

  14. Structural Damage Detection Using Artificial Neural Networks and Measured Frf Data Reduced via Principal Component Projection

    NASA Astrophysics Data System (ADS)

    ZANG, C.; IMREGUN, M.

    2001-05-01

    This paper deals with structural damage detection using measured frequency response functions (FRFs) as input data to artificial neural networks (ANNs). A major obstacle, the impracticality of using full-size FRF data with ANNs, was circumvented by applying a principal component analysis (PCA)-based data reduction technique to the measured FRFs. The compressed FRFs, represented by their projection onto the most significant principal components, were then used as the ANN input variables instead of the raw FRF data. The output is a prediction for the actual state of the specimen, i.e., healthy or damaged. A further advantage of this particular approach was found to be the ability to deal with relatively high measurement noise, which is of common occurrence when dealing with industrial structures. The methodology was applied to the measured FRFs of a railway wheel, each response function having 4096 spectral lines. The available FRF data were grouped into x, y and z direction FRFs and a compression ratio of about 400 was achieved for each direction. Three different networks, each corresponding to a co-ordinate direction, were trained and verified using 80 PCA-compressed FRFs. Twenty compressed FRFs, obtained from further measurements, were used for the actual damage detection tests. Half of the test FRFs were polluted further by adding 5% random noise in order to assess the robustness of the method in the presence of significant experimental noise. The results showed that, in all cases considered, it was possible to distinguish between the healthy and damaged states with very good accuracy and repeatability.

  15. Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

    NASA Astrophysics Data System (ADS)

    Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

    2013-06-01

    This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

  16. RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis.

    PubMed

    Dang, Chinh; Radha, Hayder

    2015-11-01

    Key frame extraction algorithms consider the problem of selecting a subset of the most informative frames from a video to summarize its content. Several applications, such as video summarization, search, indexing, and prints from video, can benefit from extracted key frames of the video under consideration. Most approaches in this class of algorithms work directly with the input video data set, without considering the underlying low-rank structure of the data set. Other algorithms exploit the low-rank component only, ignoring the other key information in the video. In this paper, a novel key frame extraction framework based on robust principal component analysis (RPCA) is proposed. Furthermore, we target the challenging application of extracting key frames from unstructured consumer videos. The proposed framework is motivated by the observation that the RPCA decomposes an input data into: 1) a low-rank component that reveals the systematic information across the elements of the data set and 2) a set of sparse components each of which containing distinct information about each element in the same data set. The two information types are combined into a single l1-norm-based non-convex optimization problem to extract the desired number of key frames. Moreover, we develop a novel iterative algorithm to solve this optimization problem. The proposed RPCA-based framework does not require shot(s) detection, segmentation, or semantic understanding of the underlying video. Finally, experiments are performed on a variety of consumer and other types of videos. A comparison of the results obtained by our method with the ground truth and with related state-of-the-art algorithms clearly illustrates the viability of the proposed RPCA-based framework. PMID:26087486

  17. Additive Manufacturing of Low Cost Upper Stage Propulsion Components

    NASA Technical Reports Server (NTRS)

    Protz, Christopher; Bowman, Randy; Cooper, Ken; Fikes, John; Taminger, Karen; Wright, Belinda

    2014-01-01

    NASA is currently developing Additive Manufacturing (AM) technologies and design tools aimed at reducing the costs and manufacturing time of regeneratively cooled rocket engine components. These Low Cost Upper Stage Propulsion (LCUSP) tasks are funded through NASA's Game Changing Development Program in the Space Technology Mission Directorate. The LCUSP project will develop a copper alloy additive manufacturing design process and develop and optimize the Electron Beam Freeform Fabrication (EBF3) manufacturing process to direct deposit a nickel alloy structural jacket and manifolds onto an SLM manufactured GRCop chamber and Ni-alloy nozzle. In order to develop these processes, the project will characterize both the microstructural and mechanical properties of the SLMproduced GRCop-84, and will explore and document novel design techniques specific to AM combustion devices components. These manufacturing technologies will be used to build a 25K-class regenerative chamber and nozzle (to be used with tested DMLS injectors) that will be tested individually and as a system in hot fire tests to demonstrate the applicability of the technologies. These tasks are expected to bring costs and manufacturing time down as spacecraft propulsion systems typically comprise more than 70% of the total vehicle cost and account for a significant portion of the development schedule. Additionally, high pressure/high temperature combustion chambers and nozzles must be regeneratively cooled to survive their operating environment, causing their design to be time consuming and costly to build. LCUSP presents an opportunity to develop and demonstrate a process that can infuse these technologies into industry, build competition, and drive down costs of future engines.

  18. Fluoride characterization by principal component analysis in the hydrochemical facies of Serra Geral Aquifer System in Southern Brazil.

    PubMed

    Nanni, Arthur; Roisenberg, Ari; Fachel, Jandyra M G; Mesquita, Gilberto; Danieli, Cristiano

    2008-12-01

    Principal component analysis is applied to 309 groundwater chemical data information from wells in the Serra Geral Aquifer System. Correlations among seven hydrochemical parameters are statistically examined. A four-component model is suggested and explains 81% of total variance. Component 1 represents calcium-magnesium bicarbonated groundwaters with long time of residence. Component 2 represents sulfated and chlorinated calcium and sodium groundwaters; Component 3 represents sodium bicarbonated groundwaters; and Component 4 is characterized by sodium sulfated with high fluoride facies. The components' spatial distribution shows high fluoride concentration along analyzed tectonic fault system and aligned on northeast direction in other areas, suggesting other hydrogeological fault systems. High fluoride concentration increases according to groundwater pumping depth. The Principal Component Analysis reveals features of the groundwater mixture and individualizes water facies. In this scenery, it can be determined hydrogeological blocks associated with tectonic fault system here introduced. PMID:19039492

  19. Principal components analysis: an innovative approach to establish interferences in ochratoxin A detection.

    PubMed

    Kupski, L; Badiale-Furlong, E

    2015-06-15

    This work aimed to establish an innovative approach to evaluate the effect of cereals composition on ochratoxin A extraction by multivariate analysis. Principal components analysis was applied to identify the effect of major matrix components on the recovery of ochratoxin A by QuEChERS method using HPTLC and HPLC, and to validate the method for ochratoxin A determination in wheat flour by HPLC. The matrices rice bran, wheat bran and wheat flour were characterized for their physical and chemical attributes. The ochratoxin A recovery in these matrices was highly influenced (R=0.99) by the sugar content of the matrix, while the lipids content showed a minor interference (R=0.29). From these data, the QuEChERS method was standardized for extracting ochratoxin A from flour using 1% ACN:water (2:1) as extraction solvent and dried magnesium sulfate and sodium chloride as salts. The recovery values ranged from 97.6% to 105%. The validated method was applied to evaluate natural occurrence of ochratoxin A in 20 wheat flour samples, which were contaminated with ochratoxin A levels in the range of 0.22-0.85 μg kg(-1). PMID:25660897

  20. Comparison of Fourier, principal component and wavelet analyses for high speed flame measurements

    NASA Astrophysics Data System (ADS)

    Wickersham, Andrew J.; Li, Xuesong; Ma, Lin

    2014-04-01

    The continuing advancement of high speed, combustion diagnostics calls for mathematical techniques that can extract key information from large datasets. This paper therefore describes a case study to compare the characterization of combustion dynamics behind a V-gutter flame holder using three mathematical methods: Fourier analysis, principal component analysis, (PCA), and wavelet analysis (WA). The comparison focuses on the analysis of the characteristic frequencies of flow-flame interactions, with a particular emphasis on the analysis of transient and unsteady combustion procedures, such as lean blow off. Experimental data obtained under a range of conditions were analyzed using all three methods, and several observations were made. When applied to the analysis of stable combustion processes, all three methods reported frequency characteristics that were similar both quantitatively and qualitatively. Under unstable and transient combustion conditions, the WA method is capable of revealing the dynamics of the frequency components in the measurements, while traditional Fourier and PCA methods encounter application restrictions. Lastly, these applications also demonstrated WA's suitability for practical combustion measurements beyond chemiluminescence, such as its applicability to discrete signals, insensitivity to the choice of wavelet basis, and insensitivity to the target signal extracted from the raw measurements.

  1. Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis

    USGS Publications Warehouse

    Chavez, P.S., Jr.; Kwarteng, A.Y.

    1989-01-01

    A challenge encountered with Landsat Thematic Mapper (TM) data, which includes data from size reflective spectral bands, is displaying as much information as possible in a three-image set for color compositing or digital analysis. Principal component analysis (PCA) applied to the six TM bands simultaneously is often used to address this problem. However, two problems that can be encountered using the PCA method are that information of interest might be mathematically mapped to one of the unused components and that a color composite can be difficult to interpret. "Selective' PCA can be used to minimize both of these problems. The spectral contrast among several spectral regions was mapped for a northern Arizona site using Landsat TM data. Field investigations determined that most of the spectral contrast seen in this area was due to one of the following: the amount of iron and hematite in the soils and rocks, vegetation differences, standing and running water, or the presence of gypsum, which has a higher moisture retention capability than do the surrounding soils and rocks. -from Authors

  2. Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison.

    PubMed

    Chia, Kim-seng; Abdul Rahim, Herlina; Abdul Rahim, Ruzairi

    2012-02-01

    Visible and near infrared spectroscopy is a non-destructive, green, and rapid technology that can be utilized to estimate the components of interest without conditioning it, as compared with classical analytical methods. The objective of this paper is to compare the performance of artificial neural network (ANN) (a nonlinear model) and principal component regression (PCR) (a linear model) based on visible and shortwave near infrared (VIS-SWNIR) (400-1000 nm) spectra in the non-destructive soluble solids content measurement of an apple. First, we used multiplicative scattering correction to pre-process the spectral data. Second, PCR was applied to estimate the optimal number of input variables. Third, the input variables with an optimal amount were used as the inputs of both multiple linear regression and ANN models. The initial weights and the number of hidden neurons were adjusted to optimize the performance of ANN. Findings suggest that the predictive performance of ANN with two hidden neurons outperforms that of PCR. PMID:22302428

  3. Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison*

    PubMed Central

    Chia, Kim-seng; Abdul Rahim, Herlina; Abdul Rahim, Ruzairi

    2012-01-01

    Visible and near infrared spectroscopy is a non-destructive, green, and rapid technology that can be utilized to estimate the components of interest without conditioning it, as compared with classical analytical methods. The objective of this paper is to compare the performance of artificial neural network (ANN) (a nonlinear model) and principal component regression (PCR) (a linear model) based on visible and shortwave near infrared (VIS-SWNIR) (400–1000 nm) spectra in the non-destructive soluble solids content measurement of an apple. First, we used multiplicative scattering correction to pre-process the spectral data. Second, PCR was applied to estimate the optimal number of input variables. Third, the input variables with an optimal amount were used as the inputs of both multiple linear regression and ANN models. The initial weights and the number of hidden neurons were adjusted to optimize the performance of ANN. Findings suggest that the predictive performance of ANN with two hidden neurons outperforms that of PCR. PMID:22302428

  4. Principal Component Analysis of Chinese Porcelains from the Five Dynasties to the Qing Dynasty

    NASA Astrophysics Data System (ADS)

    Yap, C. T.; Hua, Younan

    1992-10-01

    This is a study of the possibility of identifying antique Chinese porcelains according to the period or dynasty, using major and minor chemical components (SiO2 , Al2O3 , Fe2O3 , K2O, Na2O, CaO and MgO) from the body of the porcelain. Principal component analysis is applied to published data on 66 pieces of Chinese procelains made in Jingdezhen during the Five Dynasties and the Song, Yuan, Ming and Qing Dynasties. It is shown that porcelains made during the Five Dynasties and the Yuan (or Ming) and Qing Dynasties can be segregated completely without any overlap. However, there is appreciable overlap between the Five Dynasties and the Song Dynasty, some overlap between the Song and Ming Dynasties and also between the Yuan and Ming Dynasties. Interestingly, Qing procelains are well separated from all the others. The percentage of silica in the porcelain body decreases and that of alumina increases with recentness with the exception of the Yuan and Ming Dynasties, where this trend is reversed.

  5. Separating cognitive processes with principal components analysis of EEG time-frequency distributions

    NASA Astrophysics Data System (ADS)

    Bernat, Edward M.; Nelson, Lindsay D.; Holroyd, Clay B.; Gehring, William J.; Patrick, Christopher J.

    2008-08-01

    Measurement of EEG event-related potential (ERP) data has been most commonly undertaken in the time-domain, which can be complicated to interpret when separable activity overlaps in time. When the overlapping activity has distinct frequency characteristics, however, time-frequency (TF) signal processing techniques can be useful. The current report utilized ERP data from a cognitive task producing typical feedback-related negativity (FRN) and P300 ERP components which overlap in time. TF transforms were computed using the binomial reduced interference distribution (RID), and the resulting TF activity was then characterized using principal components analysis (PCA). Consistent with previous work, results indicate that the FRN was more related to theta activity (3-7 Hz) and P300 more to delta activity (below 3 Hz). At the same time, both time-domain measures were shown to be mixtures of TF theta and delta activity, highlighting the difficulties with overlapping activity. The TF theta and delta measures, on the other hand, were largely independent from each other, but also independently indexed the feedback stimulus parameters investigated. Results support the view that TF decomposition can greatly improve separation of overlapping EEG/ERP activity relevant to cognitive models of performance monitoring.

  6. Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis

    PubMed Central

    Dordek, Yedidyah; Soudry, Daniel; Meir, Ron; Derdikman, Dori

    2016-01-01

    Many recent models study the downstream projection from grid cells to place cells, while recent data have pointed out the importance of the feedback projection. We thus asked how grid cells are affected by the nature of the input from the place cells. We propose a single-layer neural network with feedforward weights connecting place-like input cells to grid cell outputs. Place-to-grid weights are learned via a generalized Hebbian rule. The architecture of this network highly resembles neural networks used to perform Principal Component Analysis (PCA). Both numerical results and analytic considerations indicate that if the components of the feedforward neural network are non-negative, the output converges to a hexagonal lattice. Without the non-negativity constraint, the output converges to a square lattice. Consistent with experiments, grid spacing ratio between the first two consecutive modules is −1.4. Our results express a possible linkage between place cell to grid cell interactions and PCA. DOI: http://dx.doi.org/10.7554/eLife.10094.001 PMID:26952211

  7. Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data.

    PubMed

    Medina, José M; Díaz, José A; Vukusic, Pete

    2015-04-20

    Iridescent structural colors in biology exhibit sophisticated spatially-varying reflectance properties that depend on both the illumination and viewing angles. The classification of such spectral and spatial information in iridescent structurally colored surfaces is important to elucidate the functional role of irregularity and to improve understanding of color pattern formation at different length scales. In this study, we propose a non-invasive method for the spectral classification of spatial reflectance patterns at the micron scale based on the multispectral imaging technique and the principal component analysis similarity factor (PCASF). We demonstrate the effectiveness of this approach and its component methods by detailing its use in the study of the angle-dependent reflectance properties of Pavo cristatus (the common peacock) feathers, a species of peafowl very well known to exhibit bright and saturated iridescent colors. We show that multispectral reflectance imaging and PCASF approaches can be used as effective tools for spectral recognition of iridescent patterns in the visible spectrum and provide meaningful information for spectral classification of the irregularity of the microstructure in iridescent plumage. PMID:25969062

  8. Using principal component analysis and general path seeker regression for investigation of air pollution and CO modeling

    NASA Astrophysics Data System (ADS)

    Ivanov, A.; Voynikova, D.; Gocheva-Ilieva, S.; Kulina, H.; Iliev, I.

    2015-10-01

    The monitoring and control of air quality in urban areas is important problem in many European countries. The main air pollutants are observed and a huge amount of data is collected during the last years. In Bulgaria, the air quality is surveyed by the official environmental agency and in many towns exceedances of harmful pollutants are detected. The aim of this study is to investigate the pollution from 9 air pollutants in the town of Dimitrovgrad, Bulgaria in the period of 5 years based on hourly data. Principal Component Analysis (PCA) is used to discover the patterns in the overall pollution and the contribution of the 9 pollutants. In addition the Generalized Path Seeker (GPS) regularized regression method is applied to find dependence of CO (carbon monoxide) with respect to other pollutants and 8 meteorological parameters. It is reported that the CO concentrations are in continuously repeated low level quantities very harmful for human health.

  9. Modal Characterization using Principal Component Analysis: application to Bessel, higher-order Gaussian beams and their superposition

    PubMed Central

    Mourka, A.; Mazilu, M.; Wright, E. M.; Dholakia, K.

    2013-01-01

    The modal characterization of various families of beams is a topic of current interest. We recently reported a new method for the simultaneous determination of both the azimuthal and radial mode indices for light fields possessing orbital angular momentum. The method is based upon probing the far-field diffraction pattern from a random aperture and using the recorded data as a ‘training set'. We then transform the observed data into uncorrelated variables using the principal component analysis (PCA) algorithm. Here, we show the generic nature of this approach for the simultaneous determination of the modal parameters of Hermite-Gaussian and Bessel beams. This reinforces the widespread applicability of this method for applications including information processing, spectroscopy and manipulation. Additionally, preliminary results demonstrate reliable decomposition of superpositions of Laguerre-Gaussians, yielding the intensities and relative phases of each constituent mode. Thus, this approach represents a powerful method for characterizing the optical multi-dimensional Hilbert space. PMID:23478330

  10. Multispectral Imaging System for the Mapping of Pigments in Works of Art by use of Principal-Component Analysis.

    PubMed

    Baronti, S; Casini, A; Lotti, F; Porcinai, S

    1998-03-10

    Image spectroscopy (IS) is an important tool for the noninvasive analysis of works of art. It generates a wide sequence of multispectral images from which a reflectance spectrum for each imaged point can be recovered. In addition, digital processing techniques can be employed to divide the images into areas of similar spectral behavior. An IS system designed and developed in our laboratory is described. The methodology used to process the acquired data integrates spectral analysis with statistical image processing: in particular, the potential of principal-component analysis applied in this area is investigated. A selection of the results obtained from a sixteenth-century oil-painted panel by Luca Signorelli is also reported. PMID:18268717

  11. A New Approach for Heparin Standardization: Combination of Scanning UV Spectroscopy, Nuclear Magnetic Resonance and Principal Component Analysis

    PubMed Central

    Lima, Marcelo A.; Rudd, Timothy R.; de Farias, Eduardo H. C.; Ebner, Lyvia F.; Gesteira, Tarsis F.; de Souza, Lauro M.; Mendes, Aline; Córdula, Carolina R.; Martins, João R. M.; Hoppensteadt, Debra; Fareed, Jawed; Sassaki, Guilherme L.; Yates, Edwin A.; Tersariol, Ivarne L. S.; Nader, Helena B.

    2011-01-01

    The year 2007 was marked by widespread adverse clinical responses to heparin use, leading to a global recall of potentially affected heparin batches in 2008. Several analytical methods have since been developed to detect impurities in heparin preparations; however, many are costly and dependent on instrumentation with only limited accessibility. A method based on a simple UV-scanning assay, combined with principal component analysis (PCA), was developed to detect impurities, such as glycosaminoglycans, other complex polysaccharides and aromatic compounds, in heparin preparations. Results were confirmed by NMR spectroscopy. This approach provides an additional, sensitive tool to determine heparin purity and safety, even when NMR spectroscopy failed, requiring only standard laboratory equipment and computing facilities. PMID:21267460

  12. Use of principal component analysis for identification of temporal and spatial patterns in the dynamics of ionospheric equatorial anomaly

    NASA Astrophysics Data System (ADS)

    Maslennikova, Yu S.; Bochkarev, V. V.; Voloskov, D. S.

    2015-01-01

    In this paper we describe results of the principal components analysis of the dynamics of Total Electronic Content (TEC) data with the use of global maps presented by the Jet Propulsion Laboratory (NASA, USA) for the period of 2007-2011. We show that the result of the decomposition in principal components essentially depends on the method used for preprocessing the data, their representation (the used coordinate system), and the data centering technique (e.g., daily and seasonal components extracting). The use of momentarily co-moving frame of reference and other special techniques provide opportunity for the detailed analysis of the ionospheric equatorial anomaly. The covariance matrix of decomposition was calculated using Spearman's rank correlation coefficient that allows reducing statistical relationship between principal components.

  13. Multi-Objective Optimization of Vehicle Sound Package in Middle Frequency Using Gray Relational Analysis Coupled with Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Chen, Shuming; Wang, Dengfeng; Shi, Tianze; Chen, Jing

    2015-12-01

    This research studies optimization design of the thickness of sound packages for a passenger car. The major characteristics indexes for performance determined to evaluate the process are sound pressure level of the interior middle frequency noise and weight of the sound package. Three kinds of materials of sound packages are selected for the optimization process. The corresponding parameters of the sound packages are the thickness of the insulation plate for outer side of the firewall, thickness of the sound absorbing wool for inner side of the firewall, thickness of PU foam for the front floor, and thickness of PU foam for the rear floor, respectively. In this paper, the optimization procedure is a multi-objective optimization. Therefore, gray relational analysis (GRA) is applied to decide the optimal combination of sound package parameters. Furthermore, the principal component analysis (PCA) is used to calculate the weighting values which are corresponding to multiple performance characteristics. Then, the results of the confirmation tests uncover that GRA coupled with principal analysis methods can effectively be applied to find the optimal combination of the thickness of the sound packages at different positions for a passenger car. Thus, the proposed method can be a useful tool to improve the automotive interior middle frequency noise and lower the weight of the sound packages. Additionally, it will also be useful for automotive manufactures and designers in other fields.

  14. Real-time myoelectric control of a multi-fingered hand prosthesis using principal components analysis

    PubMed Central

    2012-01-01

    Background In spite of the advances made in the design of dexterous anthropomorphic hand prostheses, these sophisticated devices still lack adequate control interfaces which could allow amputees to operate them in an intuitive and close-to-natural way. In this study, an anthropomorphic five-fingered robotic hand, actuated by six motors, was used as a prosthetic hand emulator to assess the feasibility of a control approach based on Principal Components Analysis (PCA), specifically conceived to address this problem. Since it was demonstrated elsewhere that the first two principal components (PCs) can describe the whole hand configuration space sufficiently well, the controller here employed reverted the PCA algorithm and allowed to drive a multi-DoF hand by combining a two-differential channels EMG input with these two PCs. Hence, the novelty of this approach stood in the PCA application for solving the challenging problem of best mapping the EMG inputs into the degrees of freedom (DoFs) of the prosthesis. Methods A clinically viable two DoFs myoelectric controller, exploiting two differential channels, was developed and twelve able-bodied participants, divided in two groups, volunteered to control the hand in simple grasp trials, using forearm myoelectric signals. Task completion rates and times were measured. The first objective (assessed through one group of subjects) was to understand the effectiveness of the approach; i.e., whether it is possible to drive the hand in real-time, with reasonable performance, in different grasps, also taking advantage of the direct visual feedback of the moving hand. The second objective (assessed through a different group) was to investigate the intuitiveness, and therefore to assess statistical differences in the performance throughout three consecutive days. Results Subjects performed several grasp, transport and release trials with differently shaped objects, by operating the hand with the myoelectric PCA-based controller

  15. Neutron Characterization of Additively Manufactured Components. Workshop Report

    SciTech Connect

    Watkins, Thomas R.; Payzant, E. Andrew; Babu, Sudarsanam Suresh

    2015-09-01

    Additive manufacturing (AM) is a collection of promising manufacturing methods that industry is beginning to explore and adopt. Macroscopically complicated and near net shape components are being built using AM, but how the material behaves in service is a big question for industry. Consequently, AM components/materials need further research into exactly what is made and how it will behave in service. This one and a half day workshop included a series of invited presentations from academia, industry and national laboratories (see Appendix A for the workshop agenda and list of talks). The workshop was welcomed by Alan Tennant, Chief Scientist, Neutron Sciences Directorate, ORNL, and opened remotely by Rob Ivestor, Deputy Director, Advanced Manufacturing Office-DOE, who declared AM adoptees as titans who will be able to create customized 3-D structures with 1 million to 1 billion micro welds with locally tailored microstructures. Further he stated that characterization with neutrons is key to be able to bring critical insight/information into the AM process/property/behavior relationship. Subsequently, the presentations spanned a slice of the current state of the art AM techniques and many of the most relevant characterization techniques using neutrons. After the talks, a panel discussion was held; workshop participants (see Appendix B for a list of attendees) providing questions and the panel answers. The main purpose of the panel discussion was to build consensus regarding the critical research needs in AM that can be addressed with neutrons. These needs were placed into three categories: modes of access for neutrons, new capabilities needed, new AM material issues and neutrons. Recommendations from the workshop were determined based on the panel discussion.

  16. Principal Components of Heritability From Neurocognitive Domains Differ Between Families With Schizophrenia and Control Subjects

    PubMed Central

    Wiener, Howard; Klei, Lambertus; Calkins, Monica; Wood, Joel; Nimgaonkar, Vishwajit; Gur, Ruben; Bradford, L. DiAnne; Richard, Jan; Edwards, Neil; Savage, Robert; Kwentus, Joseph; Allen, Trina; McEvoy, Joseph; Santos, Alberto; Gur, Raquel; Devlin, Bernie; Go, Rodney

    2013-01-01

    Objective: Various measures of neurocognitive function show mean differences among individuals with schizophrenia (SZ), their relatives, and population controls. We use eigenvector transformations that maximize heritability of multiple neurocognitive measures, namely principal components of heritability (PCH), and evaluate how they distribute in SZ families and controls. Methods: African-Americans with SZ or schizoaffective disorder (SZA) (n = 514), their relatives (n = 1092), and adult controls (n = 300) completed diagnostic interviews and computerized neurocognitive tests. PCH were estimated from 9 neurocognitive domains. Three PCH, PCH1–PCH3, were modeled to determine if status (SZ, relative, and control), other psychiatric covariates, and education were significant predictors of mean values. A small-scale linkage analysis was also conducted in a subset of the sample. Results: PCH1, PCH2, and PCH3 account for 72% of the genetic variance. PCH1 represents 8 of 9 neurocognitive domains, is most highly correlated with spatial processing and emotion recognition, and has unadjusted heritability of 68%. The means for PCH1 differ significantly among SZ, their relatives, and controls. PCH2, orthogonal to PCH1, is most closely correlated with working memory and has an unadjusted heritability of 45%. Mean PCH2 is different only between SZ families and controls. PCH3 apparently represents a heritable component of neurocognition similar across the 3 diagnostic groups. No significant linkage evidence to PCH1–PCH3 or individual neurocognitive measures was discovered. Conclusions: PCH1 is highly heritable and genetically correlated with SZ. It should prove useful in future genetic analyses. Mean PCH2 differentiates SZ families and controls but not SZ and unaffected family members. PMID:22234486

  17. Robust principal component analysis-based four-dimensional computed tomography

    NASA Astrophysics Data System (ADS)

    Gao, Hao; Cai, Jian-Feng; Shen, Zuowei; Zhao, Hongkai

    2011-06-01

    The purpose of this paper for four-dimensional (4D) computed tomography (CT) is threefold. (1) A new spatiotemporal model is presented from the matrix perspective with the row dimension in space and the column dimension in time, namely the robust PCA (principal component analysis)-based 4D CT model. That is, instead of viewing the 4D object as a temporal collection of three-dimensional (3D) images and looking for local coherence in time or space independently, we perceive it as a mixture of low-rank matrix and sparse matrix to explore the maximum temporal coherence of the spatial structure among phases. Here the low-rank matrix corresponds to the 'background' or reference state, which is stationary over time or similar in structure; the sparse matrix stands for the 'motion' or time-varying component, e.g., heart motion in cardiac imaging, which is often either approximately sparse itself or can be sparsified in the proper basis. Besides 4D CT, this robust PCA-based 4D CT model should be applicable in other imaging problems for motion reduction or/and change detection with the least amount of data, such as multi-energy CT, cardiac MRI, and hyperspectral imaging. (2) A dynamic strategy for data acquisition, i.e. a temporally spiral scheme, is proposed that can potentially maintain similar reconstruction accuracy with far fewer projections of the data. The key point of this dynamic scheme is to reduce the total number of measurements, and hence the radiation dose, by acquiring complementary data in different phases while reducing redundant measurements of the common background structure. (3) An accurate, efficient, yet simple-to-implement algorithm based on the split Bregman method is developed for solving the model problem with sparse representation in tight frames.

  18. Principal component analysis: a versatile method for processing and investigation of XPS spectra.

    PubMed

    Mc Evoy, Kevin M; Genet, Michel J; Dupont-Gillain, Christine C

    2008-10-01

    Given the relevance of principal component analysis (PCA) to the treatment of spectrometric data, we have evaluated potentialities and limitations of such useful statistical approach for the harvesting of information in large sets of X-ray photoelectron spectroscopy (XPS) spectra. Examples allowed highlighting the contribution of PCA to data treatment by comparing the results of this data analysis with those obtained by the usual XPS quantification methods. PCA was shown to improve the identification of chemical shifts of interest and to reveal correlations between peak components. First attempts to use the method led to poor results, which showed mainly the distance between series of samples analyzed at different moments. To weaken the effect of variations of minor interest, a data normalization strategy was developed and tested. A second issue was encountered with spectra suffering of an even slightly inaccurate binding energy scale correction. Indeed, minor shifts of energy channels lead to the PCA being performed on incorrect variables and consequently to misleading information. In order to improve the energy scale correction and to speed up this step of data pretreatment, a data processing method based on PCA was used. Finally, the overlap of different sources of variation was studied. Since the intensity of a given energy channel consists of electrons from several origins, having suffered inelastic collisions (background) or not (peaks), the PCA approach cannot compare them separately, which may lead to confusion or loss of information. By extracting the peaks from the background and considering them as new variables, the effect of the elemental composition could be taken into account in the case of spectra with very different backgrounds. In conclusion, PCA is a very useful diagnostic tool for the interpretation of XPS spectra, but it requires a careful and appropriate data pretreatment. PMID:18759494

  19. Robust principal component analysis-based four-dimensional computed tomography

    PubMed Central

    Gao, Hao; Cai, Jian-Feng; Shen, Zuowei; Zhao, Hongkai

    2012-01-01

    The purpose of this paper for four-dimensional (4D) computed tomography (CT) is threefold. (1) A new spatiotemporal model is presented from the matrix perspective with the row dimension in space and the column dimension in time, namely the robust PCA (principal component analysis)-based 4D CT model. That is, instead of viewing the 4D object as a temporal collection of three-dimensional (3D) images and looking for local coherence in time or space independently, we perceive it as a mixture of low-rank matrix and sparse matrix to explore the maximum temporal coherence of the spatial structure among phases. Here the low-rank matrix corresponds to the ‘background’ or reference state, which is stationary over time or similar in structure; the sparse matrix stands for the ‘motion’ or time-varying component, e.g., heart motion in cardiac imaging, which is often either approximately sparse itself or can be sparsified in the proper basis. Besides 4D CT, this robust PCA-based 4D CT model should be applicable in other imaging problems for motion reduction or/and change detection with the least amount of data, such as multi-energy CT, cardiac MRI, and hyperspectral imaging. (2) A dynamic strategy for data acquisition, i.e. a temporally spiral scheme, is proposed that can potentially maintain similar reconstruction accuracy with far fewer projections of the data. The key point of this dynamic scheme is to reduce the total number of measurements, and hence the radiation dose, by acquiring complementary data in different phases while reducing redundant measurements of the common background structure. (3) An accurate, efficient, yet simple-to-implement algorithm based on the split Bregman method is developed for solving the model problem with sparse representation in tight frames. PMID:21540490

  20. Robust principal component analysis-based four-dimensional computed tomography.

    PubMed

    Gao, Hao; Cai, Jian-Feng; Shen, Zuowei; Zhao, Hongkai

    2011-06-01

    The purpose of this paper for four-dimensional (4D) computed tomography (CT) is threefold. (1) A new spatiotemporal model is presented from the matrix perspective with the row dimension in space and the column dimension in time, namely the robust PCA (principal component analysis)-based 4D CT model. That is, instead of viewing the 4D object as a temporal collection of three-dimensional (3D) images and looking for local coherence in time or space independently, we perceive it as a mixture of low-rank matrix and sparse matrix to explore the maximum temporal coherence of the spatial structure among phases. Here the low-rank matrix corresponds to the 'background' or reference state, which is stationary over time or similar in structure; the sparse matrix stands for the 'motion' or time-varying component, e.g., heart motion in cardiac imaging, which is often either approximately sparse itself or can be sparsified in the proper basis. Besides 4D CT, this robust PCA-based 4D CT model should be applicable in other imaging problems for motion reduction or/and change detection with the least amount of data, such as multi-energy CT, cardiac MRI, and hyperspectral imaging. (2) A dynamic strategy for data acquisition, i.e. a temporally spiral scheme, is proposed that can potentially maintain similar reconstruction accuracy with far fewer projections of the data. The key point of this dynamic scheme is to reduce the total number of measurements, and hence the radiation dose, by acquiring complementary data in different phases while reducing redundant measurements of the common background structure. (3) An accurate, efficient, yet simple-to-implement algorithm based on the split Bregman method is developed for solving the model problem with sparse representation in tight frames. PMID:21540490

  1. Statistical shape modeling of human cochlea: alignment and principal component analysis

    NASA Astrophysics Data System (ADS)

    Poznyakovskiy, Anton A.; Zahnert, Thomas; Fischer, Björn; Lasurashvili, Nikoloz; Kalaidzidis, Yannis; Mürbe, Dirk

    2013-02-01

    The modeling of the cochlear labyrinth in living subjects is hampered by insufficient resolution of available clinical imaging methods. These methods usually provide resolutions higher than 125 μm. This is too crude to record the position of basilar membrane and, as a result, keep apart even the scala tympani from other scalae. This problem could be avoided by the means of atlas-based segmentation. The specimens can endure higher radiation loads and, conversely, provide better-resolved images. The resulting surface can be used as the seed for atlas-based segmentation. To serve this purpose, we have developed a statistical shape model (SSM) of human scala tympani based on segmentations obtained from 10 μCT image stacks. After segmentation, we aligned the resulting surfaces using Procrustes alignment. This algorithm was slightly modified to accommodate single models with nodes which do not necessarily correspond to salient features and vary in number between models. We have established correspondence by mutual proximity between nodes. Rather than using the standard Euclidean norm, we have applied an alternative logarithmic norm to improve outlier treatment. The minimization was done using BFGS method. We have also split the surface nodes along an octree to reduce computation cost. Subsequently, we have performed the principal component analysis of the training set with Jacobi eigenvalue algorithm. We expect the resulting method to help acquiring not only better understanding in interindividual variations of cochlear anatomy, but also a step towards individual models for pre-operative diagnostics prior to cochlear implant insertions.

  2. Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising

    PubMed Central

    Ai, Danni; Yang, Jian; Fan, Jingfan; Cong, Weijian; Wang, Yongtian

    2015-01-01

    Computed tomography (CT) has a revolutionized diagnostic radiology but involves large radiation doses that directly impact image quality. In this paper, we propose adaptive tensor-based principal component analysis (AT-PCA) algorithm for low-dose CT image denoising. Pixels in the image are presented by their nearby neighbors, and are modeled as a patch. Adaptive searching windows are calculated to find similar patches as training groups for further processing. Tensor-based PCA is used to obtain transformation matrices, and coefficients are sequentially shrunk by the linear minimum mean square error. Reconstructed patches are obtained, and a denoised image is finally achieved by aggregating all of these patches. The experimental results of the standard test image show that the best results are obtained with two denoising rounds according to six quantitative measures. For the experiment on the clinical images, the proposed AT-PCA method can suppress the noise, enhance the edge, and improve the image quality more effectively than NLM and KSVD denoising methods. PMID:25993566

  3. Evaluation of soil fertility in the succession of karst rocky desertification using principal component analysis

    NASA Astrophysics Data System (ADS)

    Xie, L. W.; Zhong, J.; Chen, F. F.; Cao, F. X.; Li, J. J.; Wu, L. C.

    2015-05-01

    Expanding of karst rocky desertification (RD) area in southwestern China is strangling the sustainable development of local agricultural economy. It is important to evaluate the soil fertility at RD regions for the sustainable management of karst lands. The changes in 19 different soil fertility-related variables along a gradient of karst rocky desertification were investigated in five different counties belonging to the central Hunan province in China. We used principal component analysis method to calculate the soil data matrix and obtained a standardized integrate soil fertility (ISF) indicator to reflect RD grades. The results showed that the succession of RD had different impacts on soil fertility indicators. The changing trend of total organic carbon (TOC), total nitrogen (TN), available phosphorus, microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) was potential RD (PRD) > light RD (LRD) > moderate RD (MRD) > intensive RD (IRD), whereas the changing trend of other indicators was not entirely consistent with the succession of RD. The degradation trend of ISF was basically parallel to the aggravation of RD, and the strength of ISF mean values were in the order of PRD > LRD > MRD > IRD. The TOC, MBC, and MBN could be regarded as the key indicators to evaluate the soil fertility.

  4. A high-performance computing toolset for relatedness and principal component analysis of SNP data

    PubMed Central

    Zheng, Xiuwen; Levine, David; Shen, Jess; Gogarten, Stephanie M.; Laurie, Cathy; Weir, Bruce S.

    2012-01-01

    Summary: Genome-wide association studies are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed gdsfmt and SNPRelate (R packages for multi-core symmetric multiprocessing computer architectures) to accelerate two key computations on SNP data: principal component analysis (PCA) and relatedness analysis using identity-by-descent measures. The kernels of our algorithms are written in C/C++ and highly optimized. Benchmarks show the uniprocessor implementations of PCA and identity-by-descent are ∼8–50 times faster than the implementations provided in the popular EIGENSTRAT (v3.0) and PLINK (v1.07) programs, respectively, and can be sped up to 30–300-fold by using eight cores. SNPRelate can analyse tens of thousands of samples with millions of SNPs. For example, our package was used to perform PCA on 55 324 subjects from the ‘Gene-Environment Association Studies’ consortium studies. Availability and implementation: gdsfmt and SNPRelate are available from R CRAN (http://cran.r-project.org), including a vignette. A tutorial can be found at https://www.genevastudy.org/Accomplishments/software. Contact: zhengx@u.washington.edu PMID:23060615

  5. Detection of single unit activity from the rat vagus using cluster analysis of principal components.

    PubMed

    Horn, Charles C; Friedman, Mark I

    2003-01-30

    In vivo recordings from subdiaphragmatic vagal afferent nerves generally lack the resolution to distinguish single unit activity. Several methods for data acquisition and analysis were combined to produce a high degree of reliability in recording electrophysiological signals from gastrointestinal and hepatic afferent fibers in the rat. Recordings with low noise were achieved by paralysis of the respiratory muscles and by pinning the nerve to a recording platform. Single unit activity was isolated using principal component (PC) analysis and cluster cutting of data in multi-dimensional space (1-3 PCs). Cluster assignments were determined by a semi-automated approach using the k-means algorithm. The accuracy of single unit classification was assessed by checking inter-spike intervals (ISIs) to determine the length of the refractory period, and by cross-correlation analysis to assess whether single units were mistakenly split into more than one cluster. These analyses produced up to four isolated single units from each nerve filament (a bundle of nerve fibers), and typically it was possible to further increase yield by recording from several nerve filaments simultaneously using an array of electrodes. PMID:12573473

  6. Tongue Motion Patterns in Post-Glossectomy and Typical Speakers: A Principal Components Analysis

    PubMed Central

    Stone, Maureen; Langguth, Julie M.; Woo, Jonghye; Chen, Hegang; Prince, Jerry L.

    2015-01-01

    Purpose In this study, the authors examined changes in tongue motion caused by glossectomy surgery. A speech task that involved subtle changes in tongue-tip positioning (the motion from /i/ to /s/) was measured. The hypothesis was that patients would have limited motion on the tumor (resected) side and would compensate with greater motion on the nontumor side in order to elevate the tongue tip and blade for /s/. Method Velocity fields were extracted from tagged magnetic resonance images in the left, middle, and right tongue of 3 patients and 10 controls. Principal components (PCs) analysis quantified motion differences and distinguished between the subject groups. Results PCs 1 and 2 represented variance in (a) size and independence of the tongue tip, and (b) direction of motion of the tip, body, or both. Patients and controls were correctly separated by a small number of PCs. Conclusions Motion of the tumor slice was different between patients and controls, but the nontumor side of the patients’ tongues did not show excessive or adaptive motion. Both groups contained apical and laminal /s/ users, and 1 patient created apical /s/ in a highly unusual manner. PMID:24023377

  7. Evaluation of soil fertility in the succession of karst rocky desertification using principal component analysis

    NASA Astrophysics Data System (ADS)

    Xie, L. W.; Zhong, J.; Cao, F. X.; Li, J. J.; Wu, L. C.

    2014-12-01

    Expanding of karst rocky desertification (RD) area in southwestern China has led to destructed ecosystem and local economic development lagging behind. It is important to understand the soil fertility at RD regions for the sustainable management of karst lands. The effects of the succession of RD on soil fertility were studied by investigating the stands and analyzing the soil samples with different RD grades in the central Hunan province, China, using the principal component analysis method. The results showed that the succession of RD had different impacts on soil fertility indicators. The changing trend of total organic carbon (TOC), total nitrogen (TN), available phosphorous (AP), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) out of 19 selected indicators in different RD regions was: potential RD (PRD) > light RD (LRD) > moderate RD (MRD) > intensive RD (IRD), whereas the changing trend of other indicators was not entirely consistent with the succession of RD. The degradation trend of soil fertility was basically parallel to the aggravation of RD, and the strength of integrated soil fertility was in the order of PRD > MRD > LRD > IRD. The TOC, total phosphorus (TP), cation exchange capacity (CEC), MBC, MBN, microbial mass phosphorous (MBP), and bulk density (BD) could be regarded as the key indicators to evaluate the soil fertility due to their close correlations to the integrated fertility.

  8. Analyzing Large-Scale Structural Change in Proteins: Comparison of Principal Component Projection and Sammon Mapping

    SciTech Connect

    Mesentean, Sidonia; Fischer, S.; Smith, Jeremy C

    2006-04-01

    Effective analysis of large-scale conformational transitions in macromolecules requires transforming them into a lower dimensional representation that captures the dominant motions. Herein, we apply and compare two different dimensionality reduction techniques, namely, principal component analysis (PCA), a linear method, and Sammon mapping, which is nonlinear. The two methods are used to analyze four different protein transition pathways of varying complexity, obtained by using either the conjugate peak refinement method or constrained molecular dynamics. For the return-stroke in myosin, both Sammon mapping and PCA show that the conformational change is dominated by a simple rotation of a rigid body. Also, in the case of the T{yields}R transition in hemoglobin, both methods are able to identify the two main quaternary transition events. In contrast, in the cases of the unfolding transition of staphylococcal nuclease or the signaling switch of Ras p21, which are both more complex conformational transitions, only Sammon mapping is able to identify the distinct phases of motion.

  9. Principal component analysis in the wavelet domain: new features for underwater object recognition

    NASA Astrophysics Data System (ADS)

    Okimoto, Gordon S.; Lemonds, David W.

    1999-08-01

    Principal component analysis (PCA) in the wavelet domain provides powerful features for underwater object recognition applications. The multiresolution analysis of the Morlet wavelet transform (MWT) is used to pre-process echo returns from targets ensonified by biologically motivated broadband signal. PCA is then used to compress and denoise the resulting time-scale signal representation for presentation to a hierarchical neural network for object classification. Wavelet/PCA features combined with multi-aspect data fusion and neural networks have resulted in impressive underwater object recognition performance using backscatter data generated by simulate dolphin echolocation clicks and bat- like linear frequency modulated upsweeps. For example, wavelet/PCA features extracted from LFM echo returns have resulted in correct classification rates of 98.6 percent over a six target suite, which includes two mine simulators and four clutter objects. For the same data, ROC analysis of the two-class mine-like versus non-mine-like problem resulted in a probability of detection of 0.981 and a probability of false alarm of 0.032 at the 'optimal' operating point. The wavelet/PCA feature extraction algorithm is currently being implemented in VLSI for use in small, unmanned underwater vehicles designed for mine- hunting operations in shallow water environments.

  10. Quantitative performance evaluation of a blurring restoration algorithm based on principal component analysis

    NASA Astrophysics Data System (ADS)

    Greco, Mario; Huebner, Claudia; Marchi, Gabriele

    2008-10-01

    In the field on blind image deconvolution a new promising algorithm, based on the Principal Component Analysis (PCA), has been recently proposed in the literature. The main advantages of the algorithm are the following: computational complexity is generally lower than other deconvolution techniques (e.g., the widely used Iterative Blind Deconvolution - IBD - method); it is robust to white noise; only the blurring point spread function support is required to perform the single-observation deconvolution (i.e., a single degraded observation of a scene is available), while the multiple-observation one is completely unsupervised (i.e., multiple degraded observations of a scene are available). The effectiveness of the PCA-based restoration algorithm has been only confirmed by visual inspection and, to the best of our knowledge, no objective image quality assessment has been performed. In this paper a generalization of the original algorithm version is proposed; then the previous unexplored issue is considered and the achieved results are compared with that of the IBD method, which is used as benchmark.

  11. Magnetic Flux Leakage and Principal Component Analysis for metal loss approximation in a pipeline

    NASA Astrophysics Data System (ADS)

    Ruiz, M.; Mujica, L. E.; Quintero, M.; Florez, J.; Quintero, S.

    2015-07-01

    Safety and reliability of hydrocarbon transportation pipelines represent a critical aspect for the Oil an Gas industry. Pipeline failures caused by corrosion, external agents, among others, can develop leaks or even rupture, which can negatively impact on population, natural environment, infrastructure and economy. It is imperative to have accurate inspection tools traveling through the pipeline to diagnose the integrity. In this way, over the last few years, different techniques under the concept of structural health monitoring (SHM) have continuously been in development. This work is based on a hybrid methodology that combines the Magnetic Flux Leakage (MFL) and Principal Components Analysis (PCA) approaches. The MFL technique induces a magnetic field in the pipeline's walls. The data are recorded by sensors measuring leakage magnetic field in segments with loss of metal, such as cracking, corrosion, among others. The data provide information of a pipeline with 15 years of operation approximately, which transports gas, has a diameter of 20 inches and a total length of 110 km (with several changes in the topography). On the other hand, PCA is a well-known technique that compresses the information and extracts the most relevant information facilitating the detection of damage in several structures. At this point, the goal of this work is to detect and localize critical loss of metal of a pipeline that are currently working.

  12. A practitioner's guide for exploring water quality patterns using principal components analysis and Procrustes.

    PubMed

    Sergeant, C J; Starkey, E N; Bartz, K K; Wilson, M H; Mueter, F J

    2016-04-01

    To design sustainable water quality monitoring programs, practitioners must choose meaningful variables, justify the temporal and spatial extent of measurements, and demonstrate that program objectives are successfully achieved after implementation. Consequently, data must be analyzed across several variables and often from multiple sites and seasons. Multivariate techniques such as ordination are common throughout the water quality literature, but methods vary widely and could benefit from greater standardization. We have found little clear guidance and open source code for efficiently conducting ordination to explore water quality patterns. Practitioners unfamiliar with techniques such as principal components analysis (PCA) are faced with a steep learning curve to summarize expansive data sets in periodic reports and manuscripts. Here, we present a seven-step framework for conducting PCA and associated tests. The last step is dedicated to conducting Procrustes analysis, a valuable but rarely used test within the water quality field that describes the degree of concordance between separate multivariate data matrices and provides residual values for similar points across each matrix. We illustrate the utility of these tools using three increasingly complex water quality case studies in US parklands. The case studies demonstrate how PCA and Procrustes analysis answer common applied monitoring questions such as (1) do data from separate monitoring locations describe similar water quality regimes, and (2) what time periods exhibit the greatest water quality regime variability? We provide data sets and annotated R code for recreating case study results and as a base for crafting new code for similar monitoring applications. PMID:27021692

  13. SR-FTIR Coupled with Principal Component Analysis Shows Evidence for the Cellular Bystander Effect.

    PubMed

    Lipiec, E; Bambery, K R; Lekki, J; Tobin, M J; Vogel, C; Whelan, D R; Wood, B R; Kwiatek, W M

    2015-07-01

    Synchrotron radiation-Fourier transform infrared (SR-FTIR) microscopy coupled with multivariate data analysis was used as an independent modality to monitor the cellular bystander effect. Single, living prostate cancer PC-3 cells were irradiated with various numbers of protons, ranging from 50-2,000, with an energy of either 1 or 2 MeV using a proton microprobe. SR-FTIR spectra of cells, fixed after exposure to protons and nonirradiated neighboring cells (bystander cells), were recorded. Spectral differences were observed in both the directly targeted and bystander cells and included changes in the DNA backbone and nucleic bases, along with changes in the protein secondary structure. Principal component analysis (PCA) was used to investigate the variance in the entire data set. The percentage of bystander cells relative to the applied number of protons with two different energies was calculated. Of all the applied quantities, the dose of 400 protons at 2 MeV was found to be the most effective for causing significant macromolecular perturbation in bystander PC-3 cells. PMID:26121225

  14. Principal component analysis of solar flares in the soft X-ray flux

    NASA Technical Reports Server (NTRS)

    Teuber, D. L.; Reichmann, E. J.; Wilson, R. M.

    1979-01-01

    The paper considers principal component analysis of solar flares in the soft X-ray flux, a technique for extracting the salient features from a mass of data. The method applies particularly to the analysis of nonstationary ensembles, and its computations require the evaluation of eigenvalues of matrices. The Eispack matrix eigen system routines were used to analyze full-disk proportional-counter data from the X-ray event analyzer which was part of the Skylab experiment. Empirical orthogonal functions were derived for events in the soft X-ray spectrum between 2.5 and 20 A during different time periods, indicating that about 90% of the cumulative power of each analyzed flare is contained in the largest eigenvector. The first two largest eigenvectors are sufficient for an empirical curve fit through the raw data and a characterization of solar flares in the soft X-ray flux, and power spectra of two largest eigenvectors reveal a reported periodicity of about 5 min.

  15. Comprehensive evaluation of antioxidant activity: A chemometric approach using principal component analysis

    NASA Astrophysics Data System (ADS)

    Casoni, Dorina; Sârbu, Costel

    2014-01-01

    A novel chemometric approach is described for evaluating the radical scavenging activity of biogenic amine related compounds by using the 2,2-diphenyl-1-picrylhydrazyl (DPPHrad ) procedure and principal component analysis (PCA) tool. By a comprehensive chemometric investigation of variations in the radical scavenging profiles provided by the full-range UV-Vis spectra for different test duration and different relative concentrations (different molar ratio - [AH]/[DPPHrad ]) of the investigated compounds, new antioxidant activity evaluation parameters were proposed. The new proposed parameters (PC1, mPC1, maxPC1) are in good agreement with the reference DPPHrad results (% RSA and IC50 derived from the reference DPPHrad test), obtained for the investigated amines and reference antioxidants. Much more, the PCA profiles are better patterns for the comprehensive characterization of radical scavenging activity of compounds, allowing visualization of complex information by a simple graphical representation and underlying the (dis)similarity of compounds related both to the reaction kinetics and compounds concentration.

  16. Lamb wave feature extraction using discrete wavelet transformation and Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Ghodsi, Mojtaba; Ziaiefar, Hamidreza; Amiryan, Milad; Honarvar, Farhang; Hojjat, Yousef; Mahmoudi, Mehdi; Al-Yahmadi, Amur; Bahadur, Issam

    2016-04-01

    In this research, a new method is presented for eliciting the proper features for recognizing and classifying the kinds of the defects by guided ultrasonic waves. After applying suitable preprocessing, the suggested method extracts the base frequency band from the received signals by discrete wavelet transform and discrete Fourier transform. This frequency band can be used as a distinctive feature of ultrasonic signals in different defects. Principal Component Analysis with improving this feature and decreasing extra data managed to improve classification. In this study, ultrasonic test with A0 mode lamb wave is used and is appropriated to reduce the difficulties around the problem. The defects under analysis included corrosion, crack and local thickness reduction. The last defect is caused by electro discharge machining (EDM). The results of the classification by optimized Neural Network depicts that the presented method can differentiate different defects with 95% precision and thus, it is a strong and efficient method. Moreover, comparing the elicited features for corrosion and local thickness reduction and also the results of the two's classification clarifies that modeling the corrosion procedure by local thickness reduction which was previously common, is not an appropriate method and the signals received from the two defects are different from each other.

  17. State and group dynamics of world stock market by principal component analysis

    NASA Astrophysics Data System (ADS)

    Nobi, Ashadun; Lee, Jae Woo

    2016-05-01

    We study the dynamic interactions and structural changes by a principal component analysis (PCA) to cross-correlation coefficients of global financial indices in the years 1998-2012. The variances explained by the first PC increase with time and show a drastic change during the crisis. A sharp change in PC coefficient implies a transition of market state, a situation which occurs frequently in the American and Asian indices. However, the European indices remain stable over time. Using the first two PC coefficients, we identify indices that are similar and more strongly correlated than the others. We observe that the European indices form a robust group over the observation period. The dynamics of the individual indices within the group increase in similarity with time, and the dynamics of indices are more similar during the crises. Furthermore, the group formation of indices changes position in two-dimensional spaces due to crises. Finally, after a financial crisis, the difference of PCs between the European and American indices narrows.

  18. A Principal Component-Based Radiative Transfer Forward Model (PCRTM) for Vertically in Homogeneous Cloud

    NASA Technical Reports Server (NTRS)

    Li, Hui; Liu, Xu; Yang, Ping; Kratz, David P.

    2010-01-01

    A principal-component based radiative transfer model (PCRTM) is developed for simulating the infrared spectral radiance at the top of the atmosphere (TOA). The PCRTM approach removes the redundancy in radiative transfer calculation in high resolution infrared spectra, and saves significant amount of computational time with great accuracy. In PCRTM, both ice and water clouds are treated as effective transmissivity and reflectivity stored in a pre-calculated lookup tables. These quantities are calculated using cloud single scattering properties such as cloud optical depth, cloud particle size, cloud phase, etc. The cloud can be inserted into any pressure layer in the PCRTM model (up to 100 layers). The effective temperature of each cloud layer is treated as a function of its optical depth. To test the accuracy of this approximation, the results are compared with the more rigorous DISORT model, which treats cloud as a plane parallel layer. The root-mean-square error of PCRTM, with respect to DISORT results, is generally less than 0.5 K in brightness temperature. However, the CPU time required by PCRTM was approximately two orders of magnitude less than that required by DISORT.

  19. Principal component analysis of the reionization history from Planck 2015 data

    NASA Astrophysics Data System (ADS)

    Dai, Wei-Ming; Guo, Zong-Kuan; Cai, Rong-Gen

    2015-12-01

    The simple assumption of an instantaneous reionization of the Universe may bias estimates of cosmological parameters. In this paper a model-independent principal component method for the reionization history is applied to give constraints on the cosmological parameters from recent Planck 2015 data. We find that the Universe is not completely reionized at redshifts z ≥8.5 at 95% C.L. Both the reionization optical depth and matter fluctuation amplitude are higher than but consistent with those obtained in the standard instantaneous reionization scheme. The high estimated value of the matter fluctuation amplitude strengthens the tension between Planck cosmic microwave background observations and some astrophysical data, such as cluster counts and weak lensing. The tension can be significantly relieved if the neutrino masses are allowed to vary. Thanks to a high scalar spectral index, the low-scale spontaneously broken supersymmetry inflationary model can fit the data well, which is marginally disfavored at 95% C.L. in the Planck analysis.

  20. Measuring Electrolyte Impedance and Noise Simultaneously by Triangular Waveform Voltage and Principal Component Analysis

    PubMed Central

    Xu, Shanzhi; Wang, Peng; Dong, Yonggui

    2016-01-01

    In order to measure the impedance variation process in electrolyte solutions, a method of triangular waveform voltage excitation is investigated together with principal component analysis (PCA). Using triangular waveform voltage as the excitation signal, the response current during one duty cycle is sampled to construct a measurement vector. The measurement matrix is then constructed by the measurement vectors obtained from different measurements. After being processed by PCA, the changing information of solution impedance is contained in the loading vectors while the response current and noise information is contained in the score vectors. The measurement results of impedance variation by the proposed signal processing method are independent of the equivalent impedance model. The noise-induced problems encountered during equivalent impedance calculation are therefore avoided, and the real-time variation information of noise in the electrode-electrolyte interface can be extracted at the same time. Planar-interdigitated electrodes are experimentally tested for monitoring the KCl concentration variation process. Experimental results indicate that the measured impedance variation curve reflects the changing process of solution conductivity, and the amplitude distribution of the noise during one duty cycle can be utilized to analyze the contact conditions of the electrode and electrolyte interface. PMID:27110787

  1. Contact- and distance-based principal component analysis of protein dynamics

    NASA Astrophysics Data System (ADS)

    Ernst, Matthias; Sittel, Florian; Stock, Gerhard

    2015-12-01

    To interpret molecular dynamics simulations of complex systems, systematic dimensionality reduction methods such as principal component analysis (PCA) represent a well-established and popular approach. Apart from Cartesian coordinates, internal coordinates, e.g., backbone dihedral angles or various kinds of distances, may be used as input data in a PCA. Adopting two well-known model problems, folding of villin headpiece and the functional dynamics of BPTI, a systematic study of PCA using distance-based measures is presented which employs distances between Cα-atoms as well as distances between inter-residue contacts including side chains. While this approach seems prohibitive for larger systems due to the quadratic scaling of the number of distances with the size of the molecule, it is shown that it is sufficient (and sometimes even better) to include only relatively few selected distances in the analysis. The quality of the PCA is assessed by considering the resolution of the resulting free energy landscape (to identify metastable conformational states and barriers) and the decay behavior of the corresponding autocorrelation functions (to test the time scale separation of the PCA). By comparing results obtained with distance-based, dihedral angle, and Cartesian coordinates, the study shows that the choice of input variables may drastically influence the outcome of a PCA.

  2. Principal Component Analysis of breast DCE-MRI Adjusted with a Model Based Method

    PubMed Central

    Eyal, Erez.; Badikhi, Daria; Furman-Haran, Edna; Kelcz, Fredrick; Kirshenbaum, Kevin J.; Degani, Hadassa

    2010-01-01

    Purpose To investigate a fast, objective and standardized method for analyzing breast DCE-MRI applying principal component analysis (PCA) adjusted with a model based method. Materials and Methods 3D gradient-echo dynamic contrast-enhanced breast images of 31 malignant and 38 benign lesions, recorded on a 1.5 Tesla scanner were retrospectively analyzed by PCA and by the model based three-time-point (3TP) method. Results Intensity scaled (IS) and enhancement scaled (ES) datasets were reduced by PCA yielding a 1st IS-eigenvector that captured the signal variation between fat and fibroglandular tissue; two IS-eigenvectors and the two first ES-eigenvectors that captured contrast-enhanced changes, whereas the remaining eigenvectors captured predominantly noise changes. Rotation of the two contrast related eigenvectors led to a high congruence between the projection coefficients and the 3TP parameters. The ES-eigenvectors and the rotation angle were highly reproducible across malignant lesions enabling calculation of a general rotated eigenvector base. ROC curve analysis of the projection coefficients of the two eigenvectors indicated high sensitivity of the 1st rotated eigenvector to detect lesions (AUC>0.97) and of the 2nd rotated eigenvector to differentiate malignancy from benignancy (AUC=0.87). Conclusion PCA adjusted with a model-based method provided a fast and objective computer-aided diagnostic tool for breast DCE-MRI. PMID:19856419

  3. Evaluation of Staining-Dependent Colour Changes in Resin Composites Using Principal Component Analysis

    PubMed Central

    Manojlovic, D.; Lenhardt, L.; Milićević, B.; Antonov, M.; Miletic, V.; Dramićanin, M. D.

    2015-01-01

    Colour changes in Gradia Direct™ composite after immersion in tea, coffee, red wine, Coca-Cola, Colgate mouthwash, and distilled water were evaluated using principal component analysis (PCA) and the CIELAB colour coordinates. The reflection spectra of the composites were used as input data for the PCA. The output data (scores and loadings) provided information about the magnitude and origin of the surface reflection changes after exposure to the staining solutions. The reflection spectra of the stained samples generally exhibited lower reflection in the blue spectral range, which was manifested in the lower content of the blue shade for the samples. Both analyses demonstrated the high staining abilities of tea, coffee, and red wine, which produced total colour changes of 4.31, 6.61, and 6.22, respectively, according to the CIELAB analysis. PCA revealed subtle changes in the reflection spectra of composites immersed in Coca-Cola, demonstrating Coca-Cola’s ability to stain the composite to a small degree. PMID:26450008

  4. Contact- and distance-based principal component analysis of protein dynamics

    SciTech Connect

    Ernst, Matthias; Sittel, Florian; Stock, Gerhard

    2015-12-28

    To interpret molecular dynamics simulations of complex systems, systematic dimensionality reduction methods such as principal component analysis (PCA) represent a well-established and popular approach. Apart from Cartesian coordinates, internal coordinates, e.g., backbone dihedral angles or various kinds of distances, may be used as input data in a PCA. Adopting two well-known model problems, folding of villin headpiece and the functional dynamics of BPTI, a systematic study of PCA using distance-based measures is presented which employs distances between C{sub α}-atoms as well as distances between inter-residue contacts including side chains. While this approach seems prohibitive for larger systems due to the quadratic scaling of the number of distances with the size of the molecule, it is shown that it is sufficient (and sometimes even better) to include only relatively few selected distances in the analysis. The quality of the PCA is assessed by considering the resolution of the resulting free energy landscape (to identify metastable conformational states and barriers) and the decay behavior of the corresponding autocorrelation functions (to test the time scale separation of the PCA). By comparing results obtained with distance-based, dihedral angle, and Cartesian coordinates, the study shows that the choice of input variables may drastically influence the outcome of a PCA.

  5. High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis.

    PubMed

    Foong, Shaohui; Sun, Zhenglong

    2016-01-01

    In this paper, a novel magnetic field-based sensing system employing statistically optimized concurrent multiple sensor outputs for precise field-position association and localization is presented. This method capitalizes on the independence between simultaneous spatial field measurements at multiple locations to induce unique correspondences between field and position. This single-source-multi-sensor configuration is able to achieve accurate and precise localization and tracking of translational motion without contact over large travel distances for feedback control. Principal component analysis (PCA) is used as a pseudo-linear filter to optimally reduce the dimensions of the multi-sensor output space for computationally efficient field-position mapping with artificial neural networks (ANNs). Numerical simulations are employed to investigate the effects of geometric parameters and Gaussian noise corruption on PCA assisted ANN mapping performance. Using a 9-sensor network, the sensing accuracy and closed-loop tracking performance of the proposed optimal field-based sensing system is experimentally evaluated on a linear actuator with a significantly more expensive optical encoder as a comparison. PMID:27529253

  6. Sensory characterization of doda burfi (Indian milk cake) using Principal Component Analysis.

    PubMed

    Chawla, Rekha; Patil, Girdhari Ramdas; Singh, Ashish Kumar

    2014-03-01

    Traditional sweetmeats of various countries hold a great and promising scope in their improvement and in order to tap the potential of the same, several companies and co-operative federations have started their organized production. Doda burfi, a heat desiccated and popular sweetmeat of northern India, is one of the regional specific, unfamiliarized products of India. The typical sweetmeat is characterized by caramelized and nutty flavour and granular texture. The purpose of this study was to determine the close relationship among various sensory attributes of the product collected from renowned manufacturers located in four different cities and to characterize an overall acceptable product. Individuals from academia participated in a round table discussion to generate descriptive terms related to colour and appearance, flavour and texture. Prior to sensory evaluation, sensory panel was trained and briefed about the terminology used to judge the product involving a descriptive intensity scale of 100 points for describing major sensory attributes. Results were analyzed using ANOVA and principal component analysis. Correlation table indicated a good degree of positive association between the attributes such as glossy appearance, dark colour, caramelized and nutty flavour and cohesive and chewy texture with the overall acceptability of the product. PMID:24587532

  7. Performance evaluation of principal component analysis for dynamic fluorescence tomographic imaging in measurement space

    NASA Astrophysics Data System (ADS)

    Liu, Xin; He, Xiaowei; Yan, Zhuangzhi

    2015-05-01

    Challenges remain in resolving drug (fluorescent biomarkers) distributions within small animals by fluorescence diffuse optical tomography (FDOT). Principal component analysis (PCA) provides the capability of detecting organs (functional structures) from dynamic FDOT images. However, the resolving performance of PCA may be affected by various experimental factors, e.g., the noise levels in measurement data, the variance in optical properties, the number of acquired frames, and so on. To address the problem, based on a simulation model, we analyze and compare the performance of PCA when applied to three typical sets of experimental conditions (frames number, noise level, and optical properties). The results show that the noise is a critical factor affecting the performance of PCA. When input data containing a low noise (<5%), by a short (e.g., 6 frame) projection sequence, we can resolve the poly(DL-lactic-coglycolic acid)/indocynaine green (PLGA/ICG) distributions in heart and lungs, even though there are great variances in optical properties. In contrast, when 20% Gaussian noise is added to the input data, it hardly resolves the distributions of PLGA/ICG in heart and lungs even though accurate optical properties are used. However, with an increased number of frames, the resolving performance of PCA may gradually recover.

  8. Stratosphere/troposphere joint variability in southern South America as estimated from a principal components analysis

    NASA Astrophysics Data System (ADS)

    Yuchechen, Adrián E.; Canziani, Pablo O.; Bischoff, Susana A.

    2016-05-01

    To understand how the tropopause annual evolution relates to the troposphere and lower stratosphere over southern South America, the study analyzes the joint behavior of single and double thermal tropopauses with the 500 and 100 hPa levels in the region. Radiosonde data spanning the period 1973-2014 were used. Geopotential height time series that were filtered known cycles were used as input for an unrotated S-mode principal components analysis. The first three leading modes of variability were analyzed. The first one has a strong semi-annual behavior, linked to wind cycles, with maximum activity in the center of the study region on the lee of the Andes. It appears to be linked to the vertical propagation of planetary and gravity waves. Semi-annual and terannual cycles dominate the second mode, the associated spatial patterns having strong resemblance with the occurrence of cold fronts. The annual time series for the third mode are coupled to a blocking index over the South Atlantic, and the associated spatial structures are also similar to blocking patterns. Results are in good agreement with observations, showing that the use of thermal tropopauses is a valid tool for studying different phenomena taking place in the region.

  9. Can a linear combination of gait principal component vectors identify hip OA stages?

    PubMed

    Ardestani, Marzieh M; Wimmer, Markus A

    2016-07-01

    Hip osteoarthritis (OA) has been shown to affect gait patterns of lower extremities. However, until now, no specific identifying gait characteristics for the various disease stages of hip OA have emerged. The present study addresses the following questions: (1) does a vector-based principal component analysis (PCA) discriminate between various disease stages? And, is this analysis more robust than using discrete gait variables? (2) Does the elimination of differences in walking speed affect the discriminatory robustness of a vector-based PCA? De-identified data sets of forty-five unilateral hip OA patients with varying disease stages and twenty-three age-matched, healthy control subjects were obtained from an available repository. PCA was performed on trial matrices consisting of all external joint moments and sagittal joint angles of one full gait cycle. Group differences in sagittal angles, external moments and the linear combination of PC vectors were investigated using spatial parameter mapping (SPM), a statistical vector field test. Several individual gait variables (i.e. joint moments or angles) demonstrated differences between healthy and moderately and/or severely affected subjects. Only the hip adduction moment could discriminate between the healthy and the early-stage OA group. There was no variable that could distinguish between all OA disease stages. In contrast, the linear combination of PC vectors demonstrated significant group differences between all stages of osteoarthritis; furthermore, these group differences stayed significant when matched speeds were input to the model. PMID:27255606

  10. Measuring Electrolyte Impedance and Noise Simultaneously by Triangular Waveform Voltage and Principal Component Analysis.

    PubMed

    Xu, Shanzhi; Wang, Peng; Dong, Yonggui

    2016-01-01

    In order to measure the impedance variation process in electrolyte solutions, a method of triangular waveform voltage excitation is investigated together with principal component analysis (PCA). Using triangular waveform voltage as the excitation signal, the response current during one duty cycle is sampled to construct a measurement vector. The measurement matrix is then constructed by the measurement vectors obtained from different measurements. After being processed by PCA, the changing information of solution impedance is contained in the loading vectors while the response current and noise information is contained in the score vectors. The measurement results of impedance variation by the proposed signal processing method are independent of the equivalent impedance model. The noise-induced problems encountered during equivalent impedance calculation are therefore avoided, and the real-time variation information of noise in the electrode-electrolyte interface can be extracted at the same time. Planar-interdigitated electrodes are experimentally tested for monitoring the KCl concentration variation process. Experimental results indicate that the measured impedance variation curve reflects the changing process of solution conductivity, and the amplitude distribution of the noise during one duty cycle can be utilized to analyze the contact conditions of the electrode and electrolyte interface. PMID:27110787

  11. Multi-point accelerometric detection and principal component analysis of heart sounds.

    PubMed

    De Panfilis, S; Moroni, C; Peccianti, M; Chiru, O M; Vashkevich, V; Parisi, G; Cassone, R

    2013-03-01

    Heart sounds are a fundamental physiological variable that provide a unique insight into cardiac semiotics. However a deterministic and unambiguous association between noises in cardiac dynamics is far from being accomplished yet due to many and different overlapping events which contribute to the acoustic emission. The current computer-based capacities in terms of signal detection and processing allow one to move from the standard cardiac auscultation, even in its improved forms like electronic stethoscopes or hi-tech phonocardiography, to the extraction of information on the cardiac activity previously unexplored. In this report, we present a new equipment for the detection of heart sounds, based on a set of accelerometric sensors placed in contact with the chest skin on the precordial area, and are able to measure simultaneously the vibration induced on the chest surface by the heart's mechanical activity. By utilizing advanced algorithms for the data treatment, such as wavelet decomposition and principal component analysis, we are able to condense the spatially extended acoustic information and to provide a synthetical representation of the heart activity. We applied our approach to 30 adults, mixed per gender, age and healthiness, and correlated our results with standard echocardiographic examinations. We obtained a 93% concordance rate with echocardiography between healthy and unhealthy hearts, including minor abnormalities such as mitral valve prolapse. PMID:23400007

  12. High-throughput mouse phenotyping using non-rigid registration and robust principal component analysis

    NASA Astrophysics Data System (ADS)

    Xie, Zhongliu; Kitamoto, Asanobu; Tamura, Masaru; Shiroishi, Toshihiko; Gillies, Duncan

    2016-03-01

    Intensive international efforts are underway towards phenotyping the mouse genome, by knocking out each of its ≍25,000 genes one-by-one for comparative study. With vast amounts of data to analyze, the traditional method using time-consuming histological examination is clearly impractical, leading to an overwhelming demand for some high-throughput phenotyping framework, especially with the employment of biomedical image informatics to efficiently identify phenotypes concerning morphological abnormality. Existing work has either excessively relied on volumetric analytics which is insensitive to phenotypes associated with no severe volume variations, or tailored for specific defects and thus fails to serve a general phenotyping purpose. Furthermore, the prevailing requirement of an atlas for image segmentation in contrast to its limited availability further complicates the issue in practice. In this paper we propose a high-throughput general-purpose phenotyping framework that is able to efficiently perform batch-wise anomaly detection without prior knowledge of the phenotype and the need for atlas-based segmentation. Anomaly detection is centered on the combined use of group-wise non-rigid image registration and robust principal component analysis (RPCA) for feature extraction and decomposition.

  13. Accuracy enhancement of GPS time series using principal component analysis and block spatial filtering

    NASA Astrophysics Data System (ADS)

    He, Xiaoxing; Hua, Xianghong; Yu, Kegen; Xuan, Wei; Lu, Tieding; Zhang, W.; Chen, X.

    2015-03-01

    This paper focuses on performance analysis and accuracy enhancement of long-term position time series of a regional network of GPS stations with two near sub-blocks, one block of 8 stations in Cascadia region and another block of 14 stations in Southern California. We have analyzed the seasonal variations of the 22 IGS site positions between 2004 and 2011. The Green's function is used to calculate the station-site displacements induced by the environmental loading due to atmospheric pressure, soil moisture, snow depth and nontidal ocean. The analysis has revealed that these loading factors can result in position shift of centimeter level, the displacement time series exhibit a periodic pattern, which can explain about 12.70-21.78% of the seasonal amplitude on vertical GPS time series, and the loading effect is significantly different among the two nearby geographical regions. After the loading effect is corrected, the principal component analysis (PCA)-based block spatial filtering is proposed to filter out the remaining common mode error (CME) of the GPS time series. The results show that the PCA-based block spatial filtering can extract the CME more accurately and effectively than the conventional overall filtering method, reducing more of the uncertainty. With the loading correction and block spatial filtering, about 68.34-73.20% of the vertical GPS seasonal power can be separated and removed, improving the reliability of the GPS time series and hence enabling better deformation analysis and higher precision geodetic applications.

  14. Principal components technique analysis for vegetation and land use discrimination. [Brazilian cerrados

    NASA Technical Reports Server (NTRS)

    Parada, N. D. J. (Principal Investigator); Formaggio, A. R.; Dossantos, J. R.; Dias, L. A. V.

    1984-01-01

    Automatic pre-processing technique called Principal Components (PRINCO) in analyzing LANDSAT digitized data, for land use and vegetation cover, on the Brazilian cerrados was evaluated. The chosen pilot area, 223/67 of MSS/LANDSAT 3, was classified on a GE Image-100 System, through a maximum-likehood algorithm (MAXVER). The same procedure was applied to the PRINCO treated image. PRINCO consists of a linear transformation performed on the original bands, in order to eliminate the information redundancy of the LANDSAT channels. After PRINCO only two channels were used thus reducing computer effort. The original channels and the PRINCO channels grey levels for the five identified classes (grassland, "cerrado", burned areas, anthropic areas, and gallery forest) were obtained through the MAXVER algorithm. This algorithm also presented the average performance for both cases. In order to evaluate the results, the Jeffreys-Matusita distance (JM-distance) between classes was computed. The classification matrix, obtained through MAXVER, after a PRINCO pre-processing, showed approximately the same average performance in the classes separability.

  15. Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.

    PubMed

    Keithley, Richard B; Wightman, R Mark

    2011-06-01

    Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586

  16. Outlier analysis and principal component analysis to detect fatigue cracks in waveguides

    NASA Astrophysics Data System (ADS)

    Rizzo, Piervincenzo; Cammarata, Marcello; Dutta, Debaditya; Sohn, Hoon

    2009-03-01

    Ultrasonic Guided Waves (UGWs) are a useful tool in structural health monitoring (SHM) applications that can benefit from built-in transduction, moderately large inspection ranges and high sensitivity to small flaws. This paper describes a SHM method based on UGWs, discrete wavelet transform (DWT), outlier analysis and principal component analysis (PCA) able to detect and quantify the onset and propagation of fatigue cracks in structural waveguides. The method combines the advantages of guided wave signals processed through the DWT with the outcomes of selecting defectsensitive features to perform a multivariate diagnosis of damage. The framework presented in this paper is applied to the detection of fatigue cracks in a steel beam. The probing hardware consists of a PXI platform that controls the generation and measurement of the ultrasonic signals by means of piezoelectric transducers made of Lead Zirconate Titanate. Although the approach is demonstrated in a beam test, it is argued that the proposed method is general and applicable to any structure that can sustain the propagation of UGWs.

  17. Evaluation of Staining-Dependent Colour Changes in Resin Composites Using Principal Component Analysis.

    PubMed

    Manojlovic, D; Lenhardt, L; Milićević, B; Antonov, M; Miletic, V; Dramićanin, M D

    2015-01-01

    Colour changes in Gradia Direct™ composite after immersion in tea, coffee, red wine, Coca-Cola, Colgate mouthwash, and distilled water were evaluated using principal component analysis (PCA) and the CIELAB colour coordinates. The reflection spectra of the composites were used as input data for the PCA. The output data (scores and loadings) provided information about the magnitude and origin of the surface reflection changes after exposure to the staining solutions. The reflection spectra of the stained samples generally exhibited lower reflection in the blue spectral range, which was manifested in the lower content of the blue shade for the samples. Both analyses demonstrated the high staining abilities of tea, coffee, and red wine, which produced total colour changes of 4.31, 6.61, and 6.22, respectively, according to the CIELAB analysis. PCA revealed subtle changes in the reflection spectra of composites immersed in Coca-Cola, demonstrating Coca-Cola's ability to stain the composite to a small degree. PMID:26450008

  18. Cardiorespiratory Coordination after Training and Detraining. A Principal Component Analysis Approach

    PubMed Central

    Balagué, Natàlia; González, Jacob; Javierre, Casimiro; Hristovski, Robert; Aragonés, Daniel; Álamo, Juan; Niño, Oscar; Ventura, Josep L.

    2016-01-01

    Our purpose was to study the effects of different training modalities and detraining on cardiorespiratory coordination (CRC). Thirty-two young males were randomly assigned to four training groups: aerobic (AT), resistance (RT), aerobic plus resistance (AT + RT), and control (C). They were assessed before training, after training (6 weeks) and after detraining (3 weeks) by means of a graded maximal test. A principal component (PC) analysis of selected cardiovascular and cardiorespiratory variables was performed to evaluate CRC. The first PC (PC1) coefficient of congruence in the three conditions (before training, after training and after detraining) was compared between groups. Two PCs were identified in 81% of participants before the training period. After this period the number of PCs and the projection of the selected variables onto them changed only in the groups subject to a training programme. The PC1 coefficient of congruence was significantly lower in the training groups compared with the C group [H(3, N=32) = 11.28; p = 0.01]. In conclusion, training produced changes in CRC, reflected by the change in the number of PCs and the congruence values of PC1. These changes may be more sensitive than the usually explored cardiorespiratory reserve, and they probably precede it. PMID:26903884

  19. Study of T-wave morphology parameters based on Principal Components Analysis during acute myocardial ischemia

    NASA Astrophysics Data System (ADS)

    Baglivo, Fabricio Hugo; Arini, Pedro David

    2011-12-01

    Electrocardiographic repolarization abnormalities can be detected by Principal Components Analysis of the T-wave. In this work we studied the efect of signal averaging on the mean value and reproducibility of the ratio of the 2nd to the 1st eigenvalue of T-wave (T21W) and the absolute and relative T-wave residuum (TrelWR and TabsWR) in the ECG during ischemia induced by Percutaneous Coronary Intervention. Also, the intra-subject and inter-subject variability of T-wave parameters have been analyzed. Results showed that TrelWR and TabsWR evaluated from the average of 10 complexes had lower values and higher reproducibility than those obtained from 1 complex. On the other hand T21W calculated from 10 complexes did not show statistical diferences versus the T21W calculated on single beats. The results of this study corroborate that, with a signal averaging technique, the 2nd and the 1st eigenvalue are not afected by noise while the 4th to 8th eigenvalues are so much afected by this, suggesting the use of the signal averaged technique before calculation of absolute and relative T-wave residuum. Finally, we have shown that T-wave morphology parameters present high intra-subject stability.

  20. Monitoring of an industrial process by multivariate control charts based on principal component analysis.

    PubMed

    Marengo, Emilio; Gennaro, Maria Carla; Gianotti, Valentina; Robotti, Elisa

    2003-01-01

    The control and monitoring of an industrial process is performed in this paper by the multivariate control charts. The process analysed consists of the bottling of the entire production of 1999 of the sparkling wine "Asti Spumante". This process is characterised by a great number of variables that can be treated with multivariate techniques. The monitoring of the process performed with classical Shewhart charts is very dangerous because they do not take into account the presence of functional relationships between the variables. The industrial process was firstly analysed by multivariate control charts based on Principal Component Analysis. This approach allowed the identification of problems in the process and of their causes. Successively, the SMART Charts (Simultaneous Scores Monitoring And Residual Tracking) were built in order to study the process in its whole. In spite of the successful identification of the presence of problems in the monitored process, the Smart chart did not allow an easy identification of the special causes of variation which casued the problems themselves. PMID:12911145

  1. Retrieve sea surface salinity using principal component regression model based on SMOS satellite data

    NASA Astrophysics Data System (ADS)

    Zhao, Hong; Li, Changjun; Li, Hongping; Lv, Kebo; Zhao, Qinghui

    2016-06-01

    The sea surface salinity (SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity (SMOS) satellite data. Based on the principal component regression (PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea (in the area of 4°-25°N, 105°-125°E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu (practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.

  2. Spatial and temporal variation of total electron content as revealed by principal component analysis

    NASA Astrophysics Data System (ADS)

    Zhu, X.; Talaat, E. R.

    2010-12-01

    Eleven years of global total electron content (TEC) data are analyzed using empirical orthogonal function (EOF) decomposition and the corresponding principal component analysis (PCA) technique. For the daily averaged TEC field, the first EOF explains more than 89% and the first four EOFs explain more than 98% of the total variance of the TEC field, indicating an effective data compression and clear separation of different physical processes. The effectiveness of the PCA technique to TEC is nearly insensitive to the horizontal resolution and the length of the data records. When the PCA is applied to global TEC including local time variations, the rich spatial and temporal variations of field can be represented by the first three EOFs that explain 88% of the total variance. The spectral analysis of the time series of reveals how different mechanisms such as solar flux variation, change of the orbital declination, nonlinear mode coupling and geomagnetic activity are separated and expressed in different EOFs. This work demonstrates the usefulness of using PCA technique to assimilate and monitor the global TEC field.

  3. Principal Component Analysis of Cerebellar Shape on MRI Separates SCA Types 2 and 6 into Two Archetypal Modes of Degeneration

    PubMed Central

    Jung, Brian C.; Choi, Soo I.; Du, Annie X.; Cuzzocreo, Jennifer L.; Geng, Zhuo Z.; Ying, Howard S.; Perlman, Susan L.; Toga, Arthur W.; Prince, Jerry L.

    2014-01-01

    Although “cerebellar ataxia” is often used in reference to a disease process, presumably there are different underlying pathogenetic mechanisms for different subtypes. Indeed, spinocerebellar ataxia (SCA) types 2 and 6 demonstrate complementary phenotypes, thus predicting a different anatomic pattern of degeneration. Here, we show that an unsupervised classification method, based on principal component analysis (PCA) of cerebellar shape characteristics, can be used to separate SCA2 and SCA6 into two classes, which may represent disease-specific archetypes. Patients with SCA2 (n=11) and SCA6 (n=7) were compared against controls (n=15) using PCA to classify cerebellar anatomic shape characteristics. Within the first three principal components, SCA2 and SCA6 differed from controls and from each other. In a secondary analysis, we studied five additional subjects and found that these patients were consistent with the previously defined archetypal clusters of clinical and anatomical characteristics. Secondary analysis of five subjects with related diagnoses showed that disease groups that were clinically and pathophysiologically similar also shared similar anatomic characteristics. Specifically, Archetype #1 consisted of SCA3 (n=1) and SCA2, suggesting that cerebellar syndromes accompanied by atrophy of the pons may be associated with a characteristic pattern of cerebellar neurodegeneration. In comparison, Archetype #2 was comprised of disease groups with pure cerebellar atrophy (episodic ataxia type 2 (n=1), idiopathic late-onset cerebellar ataxias (n=3), and SCA6). This suggests that cerebellar shape analysis could aid in discriminating between different pathologies. Our findings further suggest that magnetic resonance imaging is a promising imaging biomarker that could aid in the diagnosis and therapeutic management in patients with cerebellar syndromes. PMID:22258915

  4. Principal component analysis of cerebellar shape on MRI separates SCA types 2 and 6 into two archetypal modes of degeneration.

    PubMed

    Jung, Brian C; Choi, Soo I; Du, Annie X; Cuzzocreo, Jennifer L; Geng, Zhuo Z; Ying, Howard S; Perlman, Susan L; Toga, Arthur W; Prince, Jerry L; Ying, Sarah H

    2012-12-01

    Although "cerebellar ataxia" is often used in reference to a disease process, presumably there are different underlying pathogenetic mechanisms for different subtypes. Indeed, spinocerebellar ataxia (SCA) types 2 and 6 demonstrate complementary phenotypes, thus predicting a different anatomic pattern of degeneration. Here, we show that an unsupervised classification method, based on principal component analysis (PCA) of cerebellar shape characteristics, can be used to separate SCA2 and SCA6 into two classes, which may represent disease-specific archetypes. Patients with SCA2 (n=11) and SCA6 (n=7) were compared against controls (n=15) using PCA to classify cerebellar anatomic shape characteristics. Within the first three principal components, SCA2 and SCA6 differed from controls and from each other. In a secondary analysis, we studied five additional subjects and found that these patients were consistent with the previously defined archetypal clusters of clinical and anatomical characteristics. Secondary analysis of five subjects with related diagnoses showed that disease groups that were clinically and pathophysiologically similar also shared similar anatomic characteristics. Specifically, Archetype #1 consisted of SCA3 (n=1) and SCA2, suggesting that cerebellar syndromes accompanied by atrophy of the pons may be associated with a characteristic pattern of cerebellar neurodegeneration. In comparison, Archetype #2 was comprised of disease groups with pure cerebellar atrophy (episodic ataxia type 2 (n=1), idiopathic late-onset cerebellar ataxias (n=3), and SCA6). This suggests that cerebellar shape analysis could aid in discriminating between different pathologies. Our findings further suggest that magnetic resonance imaging is a promising imaging biomarker that could aid in the diagnosis and therapeutic management in patients with cerebellar syndromes. PMID:22258915

  5. Additively Manufactured Combustion Devices Components for LOX/Methane Applications

    NASA Technical Reports Server (NTRS)

    Greene, Sandra Elam; Protz, Christopher; Garcia, Chance; Goodman, Dwight; Baker, Kevin

    2016-01-01

    Marshall Space Flight Center (MSFC) has designed, fabricated, and hot-fire tested a variety of successful injectors, chambers, and igniters for potential liquid oxygen (LOX) and methane (CH4) systems since 2005. The most recent efforts have focused on components with additive manufacturing (AM) to include unique design features, minimize joints, and reduce final machining efforts. Inconel and copper alloys have been used with AM processes to produce a swirl coaxial injector and multiple methane cooled thrust chambers. The initial chambers included unique thermocouple ports for measuring local coolant channel temperatures along the length of the chamber. Results from hot-fire testing were used to anchor thermal models and generate a regeneratively cooled thruster for a 4,000 lbf LOX/CH4 engine. The completed thruster will be hot-fire tested in the summer of 2016 at MSFC. The thruster design can also be easily scaled and used on a 25,000 lbf engine. To further support the larger engine design, an AM gas generator injector has been designed. Hot-fire testing on this injector is planned for the summer of 2016 at MSFC.

  6. Principal Component Analysis and Target Transformation end-member recovery : application to last PFS MEX data.

    NASA Astrophysics Data System (ADS)

    D'Amore, M.; Palomba, E.; Zinzi, A.; Maturilli, A.; Helbert, J.

    2008-09-01

    no distinctive surface features (high albedo regions). When this model does not accurately fit for the data, it usually needs an additional set of component to accurately reproduce the observation. This set is a suite (library) of pure mineral, intended to model the surface spectrum. Then, once the contribution of the atmospheric components is fixed, it is possible to extract the contribution due only to the soils from the observed radiation. To accomplish this procedure the exact shape of atmospherics components is needed. They are obtained by mean of FA technique from the PSF data, selected on a wide range of observational scenarios with varying atmospheric dust and water ice clouds opacities. The independently variable components in the dataset are extracted (Fig. 1), obtaining the spectral shape of those components and allowing the occasional monitoring of local and seasonal aerosol composition, morphology and temporal evolution. Our results show that derived atmospheric components are in agreement with previous TES results, showing a high degree of temporal uniformity in the mineral suspended haze (or at least of only one component of the dust), and allow to monitor the annual variation of the these atmospheric components, that is in again in good agreement with previous works [3]. References [1] Bandfield, J.L., Christensen, P.R., Smith, M.D. (2000) JGR, 105, 9573-9588. [2] Smith, M.D., Bandfield, J.L., Christensen, P.R. (2000) JGR, 105, 9589-9607. [3] Smith, M.D. (2004) Icarus ,167,148-165

  7. T2 Mapping from highly undersampled data by REconstruction of Principal COmponent coefficient Maps (REPCOM) using Compressed Sensing

    PubMed Central

    Huang, Chuan; Graff, Christian G.; Clarkson, Eric W.; Bilgin, Ali; Altbach, Maria I.

    2011-01-01

    Recently, there has been an increased interest in quantitative MR parameters to improve diagnosis and treatment. Parameter mapping requires multiple images acquired with different timings usually resulting in long acquisition times. While acquisition time can be reduced by acquiring undersampled data, obtaining accurate estimates of parameters from undersampled data is a challenging problem, in particular for structures with high spatial frequency content. In this work, Principal Component Analysis (PCA) is combined with a model-based algorithm to reconstruct maps of selected principal component coefficients from highly undersampled radial MRI data. This novel approach linearizes the cost function of the optimization problem yielding a more accurate and reliable estimation of MR parameter maps. The proposed algorithm - REconstruction of Principal COmponent coefficient Maps (REPCOM) using Compressed Sensing - is demonstrated in phantoms and in vivo and compared to two other algorithms previously developed for undersampled data. PMID:22190358

  8. An Analysis of Principals' Ethical Decision Making Using Rest's Four Component Model of Moral Behavior.

    ERIC Educational Resources Information Center

    Klinker, JoAnn Franklin; Hackmann, Donald G.

    High school principals confront ethical dilemmas daily. This report describes a study that examined how MetLife/NASSP secondary principals of the year made ethical decisions conforming to three dispositions from Standard 5 of the ISLLC standards and whether they could identify processes used to reach those decisions through Rest's Four Component…

  9. Source apportionment of gaseous atmospheric pollutants by means of an absolute principal component scores (APCS) receptor model.

    PubMed

    Bruno, P; Caselli, M; de Gennaro, G; Traini, A

    2001-12-01

    A multivariate statistical method has been applied to apportion the atmospheric pollutant concentrations measured by automatic gas analyzers placed on a mobile laboratory for air quality monitoring in Taranto (Italy). In particular, Principal Component Analysis (PCA) followed by Absolute Principal Component Scores (APCS) technique was performed to identify the number of emission sources and their contribution to measured concentrations of CO, NOx, benzene toluene m+p-Xylene (BTX). This procedure singled out two different sources that explain about 85% of collected data variance. PMID:11798109

  10. Constructing bootstrap confidence intervals for principal component loadings in the presence of missing data: a multiple-imputation approach.

    PubMed

    van Ginkel, Joost R; Kiers, Henk A L

    2011-11-01

    Earlier research has shown that bootstrap confidence intervals from principal component loadings give a good coverage of the population loadings. However, this only applies to complete data. When data are incomplete, missing data have to be handled before analysing the data. Multiple imputation may be used for this purpose. The question is how bootstrap confidence intervals for principal component loadings should be corrected for multiply imputed data. In this paper, several solutions are proposed. Simulations show that the proposed corrections for multiply imputed data give a good coverage of the population loadings in various situations. PMID:21973098

  11. Combined probabilistic and principal component analysis approach for multivariate sensitivity evaluation and application to implanted patellofemoral mechanics.

    PubMed

    Fitzpatrick, Clare K; Baldwin, Mark A; Rullkoetter, Paul J; Laz, Peter J

    2011-01-01

    Many aspects of biomechanics are variable in nature, including patient geometry, joint mechanics, implant alignment and clinical outcomes. Probabilistic methods have been applied in computational models to predict distributions of performance given uncertain or variable parameters. Sensitivity analysis is commonly used in conjunction with probabilistic methods to identify the parameters that most significantly affect the performance outcome; however, it does not consider coupled relationships for multiple output measures. Principal component analysis (PCA) has been applied to characterize common modes of variation in shape and kinematics. In this study, a novel, combined probabilistic and PCA approach was developed to characterize relationships between multiple input parameters and output measures. To demonstrate the benefits of the approach, it was applied to implanted patellofemoral (PF) mechanics to characterize relationships between femoral and patellar component alignment and loading and the resulting joint mechanics. Prior studies assessing PF sensitivity have performed individual perturbation of alignment parameters. However, the probabilistic and PCA approach enabled a more holistic evaluation of sensitivity, including identification of combinations of alignment parameters that most significantly contributed to kinematic and contact mechanics outcomes throughout the flexion cycle, and the predictive capability to estimate joint mechanics based on alignment conditions without requiring additional analysis. The approach showed comparable results for Monte Carlo sampling with 500 trials and the more efficient Latin Hypercube sampling with 50 trials. The probabilistic and PCA approach has broad applicability to biomechanical analysis and can provide insight into the interdependencies between implant design, alignment and the resulting mechanics. PMID:20825941

  12. A principal component analysis approach to correcting the knee flexion axis during gait.

    PubMed

    Jensen, Elisabeth; Lugade, Vipul; Crenshaw, Jeremy; Miller, Emily; Kaufman, Kenton

    2016-06-14

    Accurate and precise knee flexion axis identification is critical for prescribing and assessing tibial and femoral derotation osteotomies, but is highly prone to marker misplacement-induced error. The purpose of this study was to develop an efficient algorithm for post-hoc correction of the knee flexion axis and test its efficacy relative to other established algorithms. Gait data were collected on twelve healthy subjects using standard marker placement as well as intentionally misplaced lateral knee markers. The efficacy of the algorithm was assessed by quantifying the reduction in knee angle errors. Crosstalk error was quantified from the coefficient of determination (r(2)) between knee flexion and adduction angles. Mean rotation offset error (αo) was quantified from the knee and hip rotation kinematics across the gait cycle. The principal component analysis (PCA)-based algorithm significantly reduced r(2) (p<0.001) and caused αo,knee to converge toward 11.9±8.0° of external rotation, demonstrating improved certainty of the knee kinematics. The within-subject standard deviation of αo,hip between marker placements was reduced from 13.5±1.5° to 0.7±0.2° (p<0.001), demonstrating improved precision of the knee kinematics. The PCA-based algorithm performed at levels comparable to a knee abduction-adduction minimization algorithm (Baker et al., 1999) and better than a null space algorithm (Schwartz and Rozumalski, 2005) for this healthy subject population. PMID:27079622

  13. Principal Component Analysis and Molecular Characterization of Reniform Nematode Populations in Alabama

    PubMed Central

    Nyaku, Seloame T.; Kantety, Ramesh V.; Cebert, Ernst; Lawrence, Kathy S.; Honger, Joseph O.; Sharma, Govind C.

    2016-01-01

    U.S. cotton production is suffering from the yield loss caused by the reniform nematode (RN), Rotylenchulus reniformis. Management of this devastating pest is of utmost importance because, no upland cotton cultivar exhibits adequate resistance to RN. Nine populations of RN from distinct regions in Alabama and one population from Mississippi were studied and thirteen morphometric features were measured on 20 male and 20 female nematodes from each population. Highly correlated variables (positive) in female and male RN morphometric parameters were observed for body length (L) and distance of vulva from the lip region (V) (r = 0.7) and tail length (TL) and c′ (r = 0.8), respectively. The first and second principal components for the female and male populations showed distinct clustering into three groups. These results show pattern of sub-groups within the RN populations in Alabama. A one-way ANOVA on female and male RN populations showed significant differences (p ≤ 0.05) among the variables. Multiple sequence alignment (MSA) of 18S rRNA sequences (421) showed lengths of 653 bp. Sites within the aligned sequences were conserved (53%), parsimony-informative (17%), singletons (28%), and indels (2%), respectively. Neighbor-Joining analysis showed intra and inter-nematodal variations within the populations as clone sequences from different nematodes irrespective of the sex of nematode isolate clustered together. Morphologically, the three groups (I, II and III) could not be distinctly associated with the molecular data from the 18S rRNA sequences. The three groups may be identified as being non-geographically contiguous. PMID:27147932

  14. Detection of Important Atmospheric and Surface Features by Employing Principal Component Image Transformation of GOES Imagery.

    NASA Astrophysics Data System (ADS)

    Hillger, Donald W.; Ellrod, Gary P.

    2003-05-01

    The detection of dust, fire hot spots, and smoke from the Geostationary Operational Environmental Satellite (GOES) is made easier by employing the principal component image (PCI) technique. PCIs are created by an eigenvector transformation of spectral band images from the five-band GOES Imager. The transformation is a powerful tool that provides a new set of images that are linear combinations of the original spectral band images. This facilitates viewing the explained variance or signal in the available imagery, allowing both gross and more subtle features in the imagery to be seen. Whereas this multispectral technique is normally applied to high-spatial-resolution land remote sensing imagery, the application is herein made to lower-spatial-resolution weather satellite imagery for the purpose of feature detection and enhancement. Features used as examples include atmospheric dust as well as forest and range fire hot spots and their resulting smoke plumes. The applications of PCIs to GOES utilized the three infrared window images (bands 2, 4, and 5) in dust situations as well as the visible image (band 1) in smoke situations. Two conclusions of this study are 1) atmospheric and surface features are more easily identified in multiband PCIs than in the enhanced single-band images or even in some two-band difference images and 2) the elimination of certain bands can be made either directly by inspection of the PCIs, discarding bands that do not to contribute to the PCIs showing the desired features, or by including all available bands and letting the transformation process indicate the bands that are useful for detecting the desired features. This technique will be increasingly useful with the introduction of new and increased numbers of spectral bands with current and future satellite instrumentation.

  15. Optimization of Extraction of Cycloalliin from Garlic (Allium sativum L.) by Using Principal Components Analysis

    PubMed Central

    Lee, Hyun Jung; Suh, Hyung Joo; Han, Sung Hee; Hong, Jungil; Choi, Hyeon-Son

    2016-01-01

    In this study, we report the optimal extraction conditions for obtaining organosulfur compounds, such as cycloalliin, from garlic by using principal component analysis (PCA). Extraction variables including temperature (40~80°C), time (0.5~12 h), and pH (4~12) were investigated for the highest cycloalliin yields. The cycloalliin yield (5.5 mmol/mL) at pH 10 was enhanced by ~40% relative to those (~3.9 mmol/mL) at pH 4 and pH 6. The cycloalliin level at 80°C showed the highest yield among the tested temperatures (5.05 mmol/mL). Prolonged extraction times also increased cycloalliin yield; the yield after 12 h was enhanced ~2-fold (4 mmol/mL) compared to the control. Isoalliin and cycloalliin levels were inversely correlated, whereas a direct correlation between polyphenol and cycloalliin levels was observed. In storage for 30 days, garlic stored at 60°C (11 mmol/mL) showed higher levels of cycloalliin and polyphenols than those at 40°C, with the maximum cycloalliin level (13 mmol/mL) on day 15. Based on the PCA analysis, the isoalliin level depended on the extraction time, while cycloalliin amounts were influenced not only by extraction time, but also by pH and temperature. Taken together, extraction of garlic at 80°C, with an incubation time of 12 h, at pH 10 afforded the maximum yield of cycloalliin. PMID:27390731

  16. Modeling of gas absorption cross sections by use of principal-component-analysis model parameters.

    PubMed

    Bak, Jimmy

    2002-05-20

    Monitoring the amount of gaseous species in the atmosphere and exhaust gases by remote infrared spectroscopic methods calls for the use of a compilation of spectral data, which can be used to match spectra measured in a practical application. Model spectra are based on time-consuming line-by-line calculations of absorption cross sections in databases by use of temperature as input combined with path length and partial and total pressure. It is demonstrated that principal component analysis (PCA) can be used to compress the spectrum of absorption cross sections, which depend strongly on temperature, into a reduced representation of score values and loading vectors. The temperature range from 300 to 1000 K is studied. This range is divided into two subranges (300-650 K and 650-1000K), and separate PCA models are constructed for each. The relationship between the scores and the temperature values is highly nonlinear. It is shown, however, that because the score-temperature relationships are smooth and continuous, they can be modeled by polynomials of varying degrees. The accuracy of the data compression method is validated with line-by-line-calculated absorption data of carbon monoxide and water vapor. Relative deviations between the absorption cross sections reconstructed from the PCA model parameters and the line-by-line-calculated values are found to be smaller than 0.15% for cross sections exceeding 1.27 x 10(-21) cm(-1) atm(-1) (CO) and 0.20% for cross sections exceeding 4.03 x 10(-21) cm(-1) atm(-1) (H2O). The computing time is reduced by a factor of 10(4). PMID:12027171

  17. Optimization of Extraction of Cycloalliin from Garlic (Allium sativum L.) by Using Principal Components Analysis.

    PubMed

    Lee, Hyun Jung; Suh, Hyung Joo; Han, Sung Hee; Hong, Jungil; Choi, Hyeon-Son

    2016-06-01

    In this study, we report the optimal extraction conditions for obtaining organosulfur compounds, such as cycloalliin, from garlic by using principal component analysis (PCA). Extraction variables including temperature (40~80°C), time (0.5~12 h), and pH (4~12) were investigated for the highest cycloalliin yields. The cycloalliin yield (5.5 mmol/mL) at pH 10 was enhanced by ~40% relative to those (~3.9 mmol/mL) at pH 4 and pH 6. The cycloalliin level at 80°C showed the highest yield among the tested temperatures (5.05 mmol/mL). Prolonged extraction times also increased cycloalliin yield; the yield after 12 h was enhanced ~2-fold (4 mmol/mL) compared to the control. Isoalliin and cycloalliin levels were inversely correlated, whereas a direct correlation between polyphenol and cycloalliin levels was observed. In storage for 30 days, garlic stored at 60°C (11 mmol/mL) showed higher levels of cycloalliin and polyphenols than those at 40°C, with the maximum cycloalliin level (13 mmol/mL) on day 15. Based on the PCA analysis, the isoalliin level depended on the extraction time, while cycloalliin amounts were influenced not only by extraction time, but also by pH and temperature. Taken together, extraction of garlic at 80°C, with an incubation time of 12 h, at pH 10 afforded the maximum yield of cycloalliin. PMID:27390731

  18. Food patterns measured by principal component analysis and obesity in the Nepalese adult

    PubMed Central

    Shrestha, Archana; Koju, Rajendra Prasad; Beresford, Shirley A A; Gary Chan, Kwun Chuen; Karmacharya, Biraj Man; Fitzpatrick, Annette L

    2016-01-01

    Objective About one-fourth of Nepalese adults are overweight or obese but no studies have examined their risk factors, especially pertaining to diet. The present study aimed to identify dietary patterns in a suburban Nepalese community and assess their associations with overweight and obesity prevalence. Methods This cross-sectional study used data from 1073 adults (18 years or older) participating in the baseline survey of the Dhulikhel Heart Study. We derived major dietary patterns from a principal component analysis of reported intake from a Food Frequency Questionnaire. Overweight was defined as Body Mass Index (BMI) of 25 kg/m2 or higher and obesity was defined as BMI of 30 kg/m2 or higher. Statistical analysis was conducted using generalised estimating equations with multivariate logistic regression (with household as cluster) adjusting for age, sex, ethnicity, religion, marital status, income, education, alcohol consumption, smoking, physical activity and systolic blood pressure. Results Four dietary patterns were derived: mixed, fast food, refined grain–meat–alcohol and solid fats–dairy. The refined grain–rice–alcohol pattern was significantly associated with overweight (adjusted OR 1.19, 95% CI 1.03 to 1.39; p=0.02) after adjusting for demographic and traditional cardiovascular risk factors. In adults of 40 years or older, the fast food pattern was associated with obesity controlling for demographic and traditional risk factors (adjusted OR 1.69, 95% CI 1.19 to 2.39; p value=0.003). Conclusions Our results suggest that refined grains–meat–alcohol intake is associated with higher prevalence of overweight, and fast food intake is associated with higher prevalence of obesity in older adults (40 years or above) in suburban Nepalese adults. PMID:27326232

  19. Investigation of Inversion Polymorphisms in the Human Genome Using Principal Components Analysis

    PubMed Central

    Ma, Jianzhong; Amos, Christopher I.

    2012-01-01

    Despite the significant advances made over the last few years in mapping inversions with the advent of paired-end sequencing approaches, our understanding of the prevalence and spectrum of inversions in the human genome has lagged behind other types of structural variants, mainly due to the lack of a cost-efficient method applicable to large-scale samples. We propose a novel method based on principal components analysis (PCA) to characterize inversion polymorphisms using high-density SNP genotype data. Our method applies to non-recurrent inversions for which recombination between the inverted and non-inverted segments in inversion heterozygotes is suppressed due to the loss of unbalanced gametes. Inside such an inversion region, an effect similar to population substructure is thus created: two distinct “populations” of inversion homozygotes of different orientations and their 1∶1 admixture, namely the inversion heterozygotes. This kind of substructure can be readily detected by performing PCA locally in the inversion regions. Using simulations, we demonstrated that the proposed method can be used to detect and genotype inversion polymorphisms using unphased genotype data. We applied our method to the phase III HapMap data and inferred the inversion genotypes of known inversion polymorphisms at 8p23.1 and 17q21.31. These inversion genotypes were validated by comparing with literature results and by checking Mendelian consistency using the family data whenever available. Based on the PCA-approach, we also performed a preliminary genome-wide scan for inversions using the HapMap data, which resulted in 2040 candidate inversions, 169 of which overlapped with previously reported inversions. Our method can be readily applied to the abundant SNP data, and is expected to play an important role in developing human genome maps of inversions and exploring associations between inversions and susceptibility of diseases. PMID:22808122

  20. [Principal component analysis of mineral elements and fatty acids composition in flaxseed from ten different regions].

    PubMed

    Xing, Li; Zhao, Feng-Min; Cao, You-Fu; Wang, Mei; Mei, Shuai; Li, Shao-Ping; Cai, Zhi-Yong

    2014-09-01

    Flaxseed is a kind of biomass with high edible and medical value. It is rich in many kinds of nutrients and mineral elements. China is one of the important producing places of flaxseed. In order to explore the main characteristic constituents of mineral elements and fatty acids in flaxseed, the study of analyzing the mineral elements and fatty acid composition from 10 different regions was carried out. The contents of seventeen kinds of mineral elements in flaxseed were determined by inductively coupled plasma mass spectrometry (ICP-MS). The contents of fatty acids of the flaxseed oil obtained under the same conditions were determined by gas chromatography-mass spectrometer (GC-MS). The principal component analysis (PCA) method was applied to the study of analyzing the mineral elements and fatty acid compositions in flaxseeds. The difference in mineral elements and fatty acids of flaxseed from different regions were discussed. The main characteristic constituents of mineral elements and fatty acids were analyzed. The results showed that K, Sr, Mg, Ni, Co, Cr, Cd, Se, Zn and Cu were the main characteristic constituents of the mineral elements. At the same time, C16:0, C18:0, C18: 2, C18:3, C20:0 and C20:1 were the main characteristic constituents of the fatty acids. The combination of ICP-MS, GS-MS and PCA can reveal the characteristics and difference of mineral elements and fatty acids from different regions. The results would provide important theoretical basis for the reasonable and effective utilization of flaxseed. PMID:25532360

  1. Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD.

    PubMed

    Sidhu, Gagan S; Asgarian, Nasimeh; Greiner, Russell; Brown, Matthew R G

    2012-01-01

    This study explored various feature extraction methods for use in automated diagnosis of Attention-Deficit Hyperactivity Disorder (ADHD) from functional Magnetic Resonance Image (fMRI) data. Each participant's data consisted of a resting state fMRI scan as well as phenotypic data (age, gender, handedness, IQ, and site of scanning) from the ADHD-200 dataset. We used machine learning techniques to produce support vector machine (SVM) classifiers that attempted to differentiate between (1) all ADHD patients vs. healthy controls and (2) ADHD combined (ADHD-c) type vs. ADHD inattentive (ADHD-i) type vs. controls. In different tests, we used only the phenotypic data, only the imaging data, or else both the phenotypic and imaging data. For feature extraction on fMRI data, we tested the Fast Fourier Transform (FFT), different variants of Principal Component Analysis (PCA), and combinations of FFT and PCA. PCA variants included PCA over time (PCA-t), PCA over space and time (PCA-st), and kernelized PCA (kPCA-st). Baseline chance accuracy was 64.2% produced by guessing healthy control (the majority class) for all participants. Using only phenotypic data produced 72.9% accuracy on two class diagnosis and 66.8% on three class diagnosis. Diagnosis using only imaging data did not perform as well as phenotypic-only approaches. Using both phenotypic and imaging data with combined FFT and kPCA-st feature extraction yielded accuracies of 76.0% on two class diagnosis and 68.6% on three class diagnosis-better than phenotypic-only approaches. Our results demonstrate the potential of using FFT and kPCA-st with resting-state fMRI data as well as phenotypic data for automated diagnosis of ADHD. These results are encouraging given known challenges of learning ADHD diagnostic classifiers using the ADHD-200 dataset (see Brown et al., 2012). PMID:23162439

  2. Principal Component Analysis and Molecular Characterization of Reniform Nematode Populations in Alabama.

    PubMed

    Nyaku, Seloame T; Kantety, Ramesh V; Cebert, Ernst; Lawrence, Kathy S; Honger, Joseph O; Sharma, Govind C

    2016-04-01

    U.S. cotton production is suffering from the yield loss caused by the reniform nematode (RN), Rotylenchulus reniformis. Management of this devastating pest is of utmost importance because, no upland cotton cultivar exhibits adequate resistance to RN. Nine populations of RN from distinct regions in Alabama and one population from Mississippi were studied and thirteen morphometric features were measured on 20 male and 20 female nematodes from each population. Highly correlated variables (positive) in female and male RN morphometric parameters were observed for body length (L) and distance of vulva from the lip region (V) (r = 0.7) and tail length (TL) and c' (r = 0.8), respectively. The first and second principal components for the female and male populations showed distinct clustering into three groups. These results show pattern of sub-groups within the RN populations in Alabama. A one-way ANOVA on female and male RN populations showed significant differences (p ≤ 0.05) among the variables. Multiple sequence alignment (MSA) of 18S rRNA sequences (421) showed lengths of 653 bp. Sites within the aligned sequences were conserved (53%), parsimony-informative (17%), singletons (28%), and indels (2%), respectively. Neighbor-Joining analysis showed intra and inter-nematodal variations within the populations as clone sequences from different nematodes irrespective of the sex of nematode isolate clustered together. Morphologically, the three groups (I, II and III) could not be distinctly associated with the molecular data from the 18S rRNA sequences. The three groups may be identified as being non-geographically contiguous. PMID:27147932

  3. Detection of abnormal cardiac activity using principal component analysis--a theoretical study.

    PubMed

    Greisas, Ariel; Zafrir, Zohar; Zlochiver, Sharon

    2015-01-01

    Electrogram-guided ablation has been recently developed for allowing better detection and localization of abnormal atrial activity that may be the source of arrhythmogeneity. Nevertheless, no clear indication for the benefit of using electrograms guided ablation over empirical ablation was established thus far, and there is a clear need of improving the localization of cardiac arrhythmogenic targets for ablation. In this paper, we propose a new approach for detection and localization of irregular cardiac activity during ablation procedures that is based on dimension reduction algorithms and principal component analysis (PCA). Using an 8×8 electrode array, our method produces manifolds that allow easy visualization and detection of possible arrhythmogenic ablation targets characterized by irregular conduction. We employ mathematical modeling and computer simulations to demonstrate the feasibility of the new approach for two well established arrhythmogenic sources for irregular conduction--spiral waves and patchy fibrosis. Our results show that the PCA method can differentiate between focal ectopic activity and spiral wave activity, as these two types of activity produce substantially different manifold shapes. Moreover, the technique allows the detection of spiral wave cores and their general meandering and drifting pattern. Fibrotic patches larger than 2 mm(2) could also be visualized using the PCA method, both for quiescent atrial tissue and for tissue exhibiting spiral wave activity. We envision that this method, contingent to further numerical and experimental validation studies in more complex, realistic geometrical configurations and with clinical data, can improve existing atrial ablation mapping capabilities, thus increasing success rates and optimizing arrhythmia management. PMID:25073163

  4. Simultaneous multi-wavelength phase-shifting interferometry based on principal component analysis with a color CMOS

    NASA Astrophysics Data System (ADS)

    Fan, Jingping; Lu, Xiaoxu; Xu, Xiaofei; Zhong, Liyun

    2016-05-01

    From a sequence of simultaneous multi-wavelength phase-shifting interferograms (SMWPSIs) recorded by a color CMOS, a principal component analysis (PCA) based multi-wavelength interferometry (MWI) is proposed. First, a sequence of SMWPSIs with unknown phase shifts are recorded with a single-chip color CMOS camera. Subsequently, the wrapped phases of single-wavelength are retrieved with the PCA algorithm. Finally, the unambiguous phase of the extended synthetic wavelength is achieved by the subtraction between the wrapped phases of single-wavelength. In addition, to eliminate the additional phase introduced by the microscope and intensity crosstalk among three-color channels, a two-step phase compensation method with and without the measured object in the experimental system is employed. Compared with conventional single-wavelength phase-shifting interferometry, due to no requirements for phase shifts calibration and the phase unwrapping operation, the actual unambiguous phase of the measured object can be achieved with the proposed PCA-based MWI method conveniently. Both numerical simulations and experimental results demonstrate that the proposed PCA-based MWI method can enlarge not only the measuring range, but also no amplification of noise level.

  5. Discriminating between cultivars and treatments of broccoli using mass spectral fingerprinting and analysis of variance-principal component analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Metabolite fingerprints, obtained with direct injection mass spectrometry (MS) with both positive and negative ionization, were used with analysis of variance-principal components analysis (ANOVA-PCA) to discriminate between cultivars and growing treatments of broccoli. The sample set consisted of ...

  6. QSAR Analysis of Some Antagonists for p38 map kinase Using Combination of Principal Component Analysis and Artificial Intelligence.

    PubMed

    Doosti, Elham; Shahlaei, Mohsen

    2015-01-01

    Quantitative relationships between structures of a set of p38 map kinase inhibitors and their activities were investigated by principal component regression (PCR) and principal componentartificial neural network (PC-ANN). Latent variables (called components) generated by principal component analysis procedure were applied as the input of developed Quantitative structure- activity relationships (QSAR) models. An exact study of predictability of PCR and PC-ANN showed that the later model has much higher ability to calculate the biological activity of the investigated molecules. Also, experimental and estimated biological activities of compounds used in model development step have indicated a good correlation. Obtained results show that a non-linear model explaining the relationship between the pIC50s and the calculated principal components (that extract from structural descriptors of the studied molecules) is superior than linear model. Some typical figures of merit for QSAR studies explaining the accuracy and predictability of the suggested models were calculated. Therefore, to design novel inhibitors of p38 map kinase with high potency and low undesired effects the developed QSAR models were used to estimate biological pIC50 of the studied compounds. PMID:26234506

  7. Wavelet based de-noising of breath air absorption spectra profiles for improved classification by principal component analysis

    NASA Astrophysics Data System (ADS)

    Kistenev, Yu. V.; Shapovalov, A. V.; Borisov, A. V.; Vrazhnov, D. A.; Nikolaev, V. V.; Nikiforova, O. Yu.

    2015-11-01

    The comparison results of different mother wavelets used for de-noising of model and experimental data which were presented by profiles of absorption spectra of exhaled air are presented. The impact of wavelets de-noising on classification quality made by principal component analysis are also discussed.

  8. SPATIAL AND TEMPORAL ANALYSIS OF NON-URBAN OZONE CONCENTRATIONS OVER THE EASTERN UNITED STATES USING ROTATED PRINCIPAL COMPONENT ANALYSIS

    EPA Science Inventory

    The spatial and temporal variability of 03 concentrations over the eastern United States during the period of 1985 through 1990 was examined through the use of a multivariate statistical technique called Principal Component Analysis. he original data set, which contained 77 corre...

  9. Low-complexity optical phase noise suppression in CO-OFDM system using recursive principal components elimination.

    PubMed

    Hong, Xiaojian; Hong, Xuezhi; He, Sailing

    2015-09-01

    A low-complexity optical phase noise suppression approach based on recursive principal components elimination, R-PCE, is proposed and theoretically derived for CO-OFDM systems. Through frequency domain principal components estimation and elimination, signal distortion caused by optical phase noise is mitigated by R-PCE. Since matrix inversion and domain transformation are completely avoided, compared with the case of the orthogonal basis expansion algorithm (L = 3) that offers a similar laser linewidth tolerance, the computational complexities of multiple principal components estimation are drastically reduced in the R-PCE by factors of about 7 and 5 for q = 3 and 4, respectively. The feasibility of optical phase noise suppression with the R-PCE and its decision-aided version (DA-R-PCE) in the QPSK/16QAM CO-OFDM system are demonstrated by Monte-Carlo simulations, which verify that R-PCE with only a few number of principal components q ( = 3) provides a significantly larger laser linewidth tolerance than conventional algorithms, including the common phase error compensation algorithm and linear interpolation algorithm. Numerical results show that the optimal performance of R-PCE and DA-R-PCE can be achieved with a moderate q, which is beneficial for low-complexity hardware implementation. PMID:26368499

  10. Burst and Principal Components Analyses of MEA Data for 16 Chemicals Describe at Least Three Effects Classes.

    EPA Science Inventory

    Microelectrode arrays (MEAs) detect drug and chemical induced changes in neuronal network function and have been used for neurotoxicity screening. As a proof-•of-concept, the current study assessed the utility of analytical "fingerprinting" using Principal Components Analysis (P...

  11. Optimizing principal component models for representing interfraction variation in lung cancer radiotherapy

    SciTech Connect

    Badawi, Ahmed M.; Weiss, Elisabeth; Sleeman, William C. IV; Yan Chenyu; Hugo, Geoffrey D.

    2010-09-15

    Purpose: To optimize modeling of interfractional anatomical variation during active breath-hold radiotherapy in lung cancer using principal component analysis (PCA). Methods: In 12 patients analyzed, weekly CT sessions consisting of three repeat intrafraction scans were acquired with active breathing control at the end of normal inspiration. The gross tumor volume (GTV) and lungs were delineated and reviewed on the first week image by physicians and propagated to all other images using deformable image registration. PCA was used to model the target and lung variability during treatment. Four PCA models were generated for each specific patient: (1) Individual models for the GTV and each lung from one image per week (week to week, W2W); (2) a W2W composite model of all structures; (3) individual models using all images (weekly plus repeat intrafraction images, allscans); and (4) composite model with all images. Models were reconstructed retrospectively (using all available images acquired) and prospectively (using only data acquired up to a time point during treatment). Dominant modes representing at least 95% of the total variability were used to reconstruct the observed anatomy. Residual reconstruction error between the model-reconstructed and observed anatomy was calculated to compare the accuracy of the models. Results: An average of 3.4 and 4.9 modes was required for the allscans models, for the GTV and composite models, respectively. The W2W model required one less mode in 40% of the patients. For the retrospective composite W2W model, the average reconstruction error was 0.7{+-}0.2 mm, which increased to 1.1{+-}0.5 mm when the allscans model was used. Individual and composite models did not have significantly different errors (p=0.15, paired t-test). The average reconstruction error for the prospective models of the GTV stabilized after four measurements at 1.2{+-}0.5 mm and for the composite model after five measurements at 0.8{+-}0.4 mm. Conclusions

  12. The spectral relationships between NEA and the meteorites: An overview using principal components analysis

    NASA Technical Reports Server (NTRS)

    Britt, D. T.; Tholen, D. J.; Bell, J. F.; Pieters, C. M.

    1991-01-01

    One of the primary reservoirs for meteorites is probably the planet-crossing Aten, Apollo, and Amor asteroids. Comparing the spectral characteristics of these two populations with each other and with the spectra of the main belt asteroids would provide insight into the dynamical processes that deliver meteorites to Earth. One method for obtaining an overview of general relationships in a large spectral data set is the statistical technique of principal components analysis. This technique quantifies general spectral similarities and reprojects the data in a plot of major axes of variation where distance is a measure of relative similarity. A major caveat should be kept in mind, however, spectra are sensitive to particle size and viewing geometry effects, and near Earth asteroids (NEA's) are probably significantly different from main belt asteroids in both these factors. The analysis was restricted to the spectral range of ECAS filters and included 116 meteorite spectra from the Gaffey (1976) survey and 417 asteroids from the Zellner et. al. (1985) survey of which 13 are planet-crossers. Although thirteen asteroids are not much of a sample on which to base conclusions, a few inferences can be drawn from this exercise. First, the NEA spectral characteristics are, on average, more consistent with the spectra of meteorites than are the main belt asteroids. Second, the S-type NEA's tend to be spectrally more similar to the ordinary chondrite meteorites than the main belt S-types. This suggests that the planet-crossing S-types do not represent the spectral range of the main belt S-type population and that the planet-crossing S-types are on average more like the ordinary chondrites than the main belt S-types. Third, the only direct asteroidal ordinary chondrite analog, the Q-type asteroid, 1862 Apollo, plots well within the field of the ordinary chondrite meteorites and represents the most common meteorite fall type. Finally, it is interesting that the planet

  13. Analysis of environmental variation in a Great Plains reservoir using principal components analysis and geographic information systems

    USGS Publications Warehouse

    Long, J.M.; Fisher, W.L.

    2006-01-01

    We present a method for spatial interpretation of environmental variation in a reservoir that integrates principal components analysis (PCA) of environmental data with geographic information systems (GIS). To illustrate our method, we used data from a Great Plains reservoir (Skiatook Lake, Oklahoma) with longitudinal variation in physicochemical conditions. We measured 18 physicochemical features, mapped them using GIS, and then calculated and interpreted four principal components. Principal component 1 (PC1) was readily interpreted as longitudinal variation in water chemistry, but the other principal components (PC2-4) were difficult to interpret. Site scores for PC1-4 were calculated in GIS by summing weighted overlays of the 18 measured environmental variables, with the factor loadings from the PCA as the weights. PC1-4 were then ordered into a landscape hierarchy, an emergent property of this technique, which enabled their interpretation. PC1 was interpreted as a reservoir scale change in water chemistry, PC2 was a microhabitat variable of rip-rap substrate, PC3 identified coves/embayments and PC4 consisted of shoreline microhabitats related to slope. The use of GIS improved our ability to interpret the more obscure principal components (PC2-4), which made the spatial variability of the reservoir environment more apparent. This method is applicable to a variety of aquatic systems, can be accomplished using commercially available software programs, and allows for improved interpretation of the geographic environmental variability of a system compared to using typical PCA plots. ?? Copyright by the North American Lake Management Society 2006.

  14. Principal component analysis and neurocomputing-based models for total ozone concentration over different urban regions of India

    NASA Astrophysics Data System (ADS)

    Chattopadhyay, Goutami; Chattopadhyay, Surajit; Chakraborthy, Parthasarathi

    2012-07-01

    The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser-Meyer-Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott's indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots.

  15. Dynamics and spatio-temporal variability of environmental factors in Eastern Australia using functional principal component analysis

    USGS Publications Warehouse

    Szabo, J.K.; Fedriani, E.M.; Segovia-Gonzalez, M. M.; Astheimer, L.B.; Hooper, M.J.

    2010-01-01

    This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 19982004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types. ?? 2010 World Scientific Publishing Company.

  16. Short prokaryotic DNA fragment binning using a hierarchical classifier based on linear discriminant analysis and principal component analysis.

    PubMed

    Zheng, Hao; Wu, Hongwei

    2010-12-01

    Metagenomics is an emerging field in which the power of genomic analysis is applied to an entire microbial community, bypassing the need to isolate and culture individual microbial species. Assembling of metagenomic DNA fragments is very much like the overlap-layout-consensus procedure for assembling isolated genomes, but is augmented by an additional binning step to differentiate scaffolds, contigs and unassembled reads into various taxonomic groups. In this paper, we employed n-mer oligonucleotide frequencies as the features and developed a hierarchical classifier (PCAHIER) for binning short (≤ 1,000 bps) metagenomic fragments. The principal component analysis was used to reduce the high dimensionality of the feature space. The hierarchical classifier consists of four layers of local classifiers that are implemented based on the linear discriminant analysis. These local classifiers are responsible for binning prokaryotic DNA fragments into superkingdoms, of the same superkingdom into phyla, of the same phylum into genera, and of the same genus into species, respectively. We evaluated the performance of the PCAHIER by using our own simulated data sets as well as the widely used simHC synthetic metagenome data set from the IMG/M system. The effectiveness of the PCAHIER was demonstrated through comparisons against a non-hierarchical classifier, and two existing binning algorithms (TETRA and Phylopythia). PMID:21121023

  17. High-speed, sparse-sampling three-dimensional photoacoustic computed tomography in vivo based on principal component analysis

    NASA Astrophysics Data System (ADS)

    Meng, Jing; Jiang, Zibo; Wang, Lihong V.; Park, Jongin; Kim, Chulhong; Sun, Mingjian; Zhang, Yuanke; Song, Liang

    2016-07-01

    Photoacoustic computed tomography (PACT) has emerged as a unique and promising technology for multiscale biomedical imaging. To fully realize its potential for various preclinical and clinical applications, development of systems with high imaging speed, reasonable cost, and manageable data flow are needed. Sparse-sampling PACT with advanced reconstruction algorithms, such as compressed-sensing reconstruction, has shown potential as a solution to this challenge. However, most such algorithms require iterative reconstruction and thus intense computation, which may lead to excessively long image reconstruction times. Here, we developed a principal component analysis (PCA)-based PACT (PCA-PACT) that can rapidly reconstruct high-quality, three-dimensional (3-D) PACT images with sparsely sampled data without requiring an iterative process. In vivo images of the vasculature of a human hand were obtained, thus validating the PCA-PACT method. The results showed that, compared with the back-projection (BP) method, PCA-PACT required ˜50% fewer measurements and ˜40% less time for image reconstruction, and the imaging quality was almost the same as that for BP with full sampling. In addition, compared with compressed sensing-based PACT, PCA-PACT had approximately sevenfold faster imaging speed with higher imaging accuracy. This work suggests a promising approach for low-cost, 3-D, rapid PACT for various biomedical applications.

  18. Reliability Checks on the Indo-US Stellar Spectral Library Using Artificial Neural Networks and Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Singh, Harinder P.; Yuasa, Manabu; Yamamoto, Nawo; Gupta, Ranjan

    2006-02-01

    The Indo-US coudé feed stellar spectral library (CFLIB) made available to the astronomical community recently by Valdes et al. (2004, ApJS, 152, 251) contains spectra of 1273 stars in the spectral region 3460 to 9464Å at a high resolution of 1Å (FWHM) and a wide range of spectral types. Cross-checking the reliability of this database is an important and desirable exercise since a number of stars in this database have no known spectral types and a considerable fraction of stars has not so complete coverage in the full wavelength region of 3460-9464Å resulting in gaps ranging from a few Å to several tens of Å. We use an automated classification scheme based on Artificial Neural Networks (ANN) to classify all 1273 stars in the database. In addition, principal component analysis (PCA) is carried out to reduce the dimensionality of the data set before the spectra are classified by the ANN. Most importantly, we have successfully demonstrated employment of a variation of the PCA technique to restore the missing data in a sample of 300 stars out of the CFLIB.

  19. Comparative Analysis of a Principal Component Analysis-Based and an Artificial Neural Network-Based Method for Baseline Removal.

    PubMed

    Carvajal, Roberto C; Arias, Luis E; Garces, Hugo O; Sbarbaro, Daniel G

    2016-04-01

    This work presents a non-parametric method based on a principal component analysis (PCA) and a parametric one based on artificial neural networks (ANN) to remove continuous baseline features from spectra. The non-parametric method estimates the baseline based on a set of sampled basis vectors obtained from PCA applied over a previously composed continuous spectra learning matrix. The parametric method, however, uses an ANN to filter out the baseline. Previous studies have demonstrated that this method is one of the most effective for baseline removal. The evaluation of both methods was carried out by using a synthetic database designed for benchmarking baseline removal algorithms, containing 100 synthetic composed spectra at different signal-to-baseline ratio (SBR), signal-to-noise ratio (SNR), and baseline slopes. In addition to deomonstrating the utility of the proposed methods and to compare them in a real application, a spectral data set measured from a flame radiation process was used. Several performance metrics such as correlation coefficient, chi-square value, and goodness-of-fit coefficient were calculated to quantify and compare both algorithms. Results demonstrate that the PCA-based method outperforms the one based on ANN both in terms of performance and simplicity. PMID:26917856

  20. The Relationships of Diesel Fuel Properties, Chemistry, and HCCI Engine Performance as Determined by Principal Component Analysis

    SciTech Connect

    Bunting, Bruce G; Crawford, Robert W

    2007-01-01

    In order to meet common fuel specifications such as cetane number and volatility, a refinery must blend a number of refinery stocks derived from various process units in the refinery. Fuel chemistry can be significantly altered in meeting fuel specifications. Additionally, fuel specifications are seldom changed in isolation, and the drive to meet one specification may significantly alter other specifications or fuel chemistry. Homogeneous charge compression ignition (HCCI) engines depend on the kinetic behavior of a fuel to achieve reliable ignition and are expected to be more dependent on fuel specifications and chemistry than today's conventional engines. Regression analysis can help in determining the underlying relationships between fuel specifications, chemistry, and engine performance. Principal component analysis (PCA) was used in this work, because of its ability to deal with co-linear variables and to uncover 'hidden' relationships in the data. In this paper, a set of 11 diesel fuels with widely varying properties were run in a simple HCCI engine. Fuel properties and engine performance are examined to identify underlying fuel relationships and to determine the interplay between engine behavior and fuels. Results indicate that fuel efficiency is mainly controlled by a collection of specifications related to density and energy content and ignition characteristics are controlled mainly by cetane number.

  1. Role of Principal Component Analysis in Predicting Toxicity in Prostate Cancer Patients Treated With Hypofractionated Intensity-Modulated Radiation Therapy

    SciTech Connect

    Vesprini, Danny; Sia, Michael; Lockwood, Gina; Moseley, Douglas; Rosewall, Tara; Bayley, Andrew; Bristow, Robert; Chung, Peter; Menard, Cynthia; Milosevic, Michael; Warde, Padraig; Catton, Charles

    2011-11-15

    Purpose: To determine if principal component analysis (PCA) and standard parameters of rectal and bladder wall dose-volume histograms (DVHs) of prostate cancer patients treated with hypofractionated image-guided intensity-modulated radiotherapy (hypo-IMRT) can predict acute and late gastrointestinal (GI) toxicity. Methods and Materials: One hundred twenty-one patients underwent hypo-IMRT at 3 Gy/fraction, 5 days/week to either 60 Gy or 66 Gy, with daily online image guidance. Acute and late GI and genitourinary (GU) toxicity were recorded weekly during treatment and at each follow-up. All Radiation Therapy Oncology Group (RTOG) criteria toxicity scores were dichotomized as <2 and {>=}2. Standard dosimetric parameters and the first five to six principal components (PCs) of bladder and rectal wall DVHs were tested for association with the dichotomized toxicity outcomes, using logistic regression. Results: Median follow-up of all patients was 47 months (60 Gy cohort= 52 months; 66 Gy cohort= 31 months). The incidence rates of {>=}2 acute GI and GU toxicity were 14% and 29%, respectively, with no Grade {>=}3 acute GU toxicity. Late GI and GU toxicity scores {>=}2 were 16% and 15%, respectively. There was a significant difference in late GI toxicity {>=}2 when comparing the 66 Gy to the 60 Gy cohort (38% vs. 8%, respectively, p = 0.0003). The first PC of the rectal DVH was associated with late GI toxicity (odds ratio [OR], 6.91; p < 0.001), though it was not significantly stronger than standard DVH parameters such as Dmax (OR, 6.9; p < 0.001) or percentage of the organ receiving a 50% dose (V50) (OR, 5.95; p = 0 .001). Conclusions: Hypofractionated treatment with 60 Gy in 3 Gy fractions is well tolerated. There is a steep dose response curve between 60 Gy and 66 Gy for RTOG Grade {>=}2 GI effects with the dose constraints employed. Although PCA can predict late GI toxicity for patients treated with hypo-IMRT for prostate cancer, it provides no additional information

  2. [Discrimination of varieties of borneol using terahertz spectra based on principal component analysis and support vector machine].

    PubMed

    Li, Wu; Hu, Bing; Wang, Ming-wei

    2014-12-01

    In the present paper, the terahertz time-domain spectroscopy (THz-TDS) identification model of borneol based on principal component analysis (PCA) and support vector machine (SVM) was established. As one Chinese common agent, borneol needs a rapid, simple and accurate detection and identification method for its different source and being easily confused in the pharmaceutical and trade links. In order to assure the quality of borneol product and guard the consumer's right, quickly, efficiently and correctly identifying borneol has significant meaning to the production and transaction of borneol. Terahertz time-domain spectroscopy is a new spectroscopy approach to characterize material using terahertz pulse. The absorption terahertz spectra of blumea camphor, borneol camphor and synthetic borneol were measured in the range of 0.2 to 2 THz with the transmission THz-TDS. The PCA scores of 2D plots (PC1 X PC2) and 3D plots (PC1 X PC2 X PC3) of three kinds of borneol samples were obtained through PCA analysis, and both of them have good clustering effect on the 3 different kinds of borneol. The value matrix of the first 10 principal components (PCs) was used to replace the original spectrum data, and the 60 samples of the three kinds of borneol were trained and then the unknown 60 samples were identified. Four kinds of support vector machine model of different kernel functions were set up in this way. Results show that the accuracy of identification and classification of SVM RBF kernel function for three kinds of borneol is 100%, and we selected the SVM with the radial basis kernel function to establish the borneol identification model, in addition, in the noisy case, the classification accuracy rates of four SVM kernel function are above 85%, and this indicates that SVM has strong generalization ability. This study shows that PCA with SVM method of borneol terahertz spectroscopy has good classification and identification effects, and provides a new method for species

  3. Toxicity of the components of poly(vinylchloride) polymers additives.

    PubMed

    Fishbein, L

    1984-01-01

    The salient features of the toxicity of a number of additives used in polyvinyl chloride polymers were reviewed with primary emphasis on the toxicity of plasticizers (e.g., diethylhexyl phthalate and its metabolites, butylbenzylphthalate and di(2-ethylhexyl)adipate), heat stabilizers (e.g., organotin and lead stabilizers), blowing agents (e.g., azodicarbonamide), free-radical initiators (e.g., benzoylperoxide, lauroyl peroxide, ter.butylhydroperoxide and di-tert.butylperoxide, and flame retardants (e.g., decabromodiphenyl oxide). The paucity of toxicity data on the vast majority of PVC additives should be stressed. PMID:6371824

  4. Application of Principal Component Analysis to Large-Scale Spectral Line Imaging Studies of the Interstellar Medium

    NASA Astrophysics Data System (ADS)

    Heyer, Mark H.; Peter Schloerb, F.

    1997-01-01

    The multivariate statistical technique of principal component analysis (PCA) is described and demonstrated to be a valuable tool to consolidate the large amount of information obtained with spectroscopic imaging observations of the interstellar medium. Simple interstellar cloud models with varying degrees of complexity and Gaussian noise are constructed and analyzed to demonstrate the ability of PCA to statistically extract physical features and phenomena from the data and to gauge the effects of random noise upon the analysis. Principal components are calculated for high spatial dynamic range 12CO and 13CO data cubes of the Sh 155 (Cep OB3) cloud complex. These identify the three major emission components within the cloud and the spatial differences between 12CO and 13CO emissions. Higher order eigenimages identify small velocity fluctuations and therefore provide spatial information to the turbulent velocity field within the cloud. A size line width relationship δv ~ Rα is derived from spatial and kinematic characterizations of the principal components of 12CO emission from the Sh 155, Sh 235, Sh 140, and Gem OB1 cloud complexes. The power-law indices for these clouds range from 0.42 to 0.55 and are similar to those derived from an ensemble of clouds within the Galaxy found by Larson (1981) and Solomon et al. (1987). The size-line width relationship within a given cloud provides an important diagnostic to the variation of kinetic energy with size scale within turbulent flows of the interstellar medium.

  5. Two-directional two-dimensional modified Fisher principal component analysis: an efficient approach for thermal face verification

    NASA Astrophysics Data System (ADS)

    Wang, Ning; Li, Qiong; El-Latif, Ahmed A. Abd; Peng, Jialiang; Niu, Xiamu

    2013-04-01

    In recent years, verification based on thermal face images has been extensively studied because of its invariance to illumination and immunity to forgery. However, most of them have not given full consideration to high-verification performance and singular within-class scatter matrix problems. We propose a novel thermal face verification algorithm, which is named two-directional two-dimensional modified Fisher principal component analysis. First, two-dimensional principal component analysis (2-DPCA) is utilized to extract the optimal projective vector in the row direction. Then, 2-D modified Fisher linear discriminant analysis is implemented to overcome the singular within-class scatter matrix problem of the 2-DPCA space in the column direction. Comparative experiments on the natural visible and infrared facial expression thermal face subdatabase demonstrate that the proposed approach outperforms state-of-the-art methods in terms of verification performance.

  6. Mapping Quantitative Trait Loci Underlying Function-Valued Traits Using Functional Principal Component Analysis and Multi-Trait Mapping

    PubMed Central

    Kwak, Il-Youp; Moore, Candace R.; Spalding, Edgar P.; Broman, Karl W.

    2015-01-01

    We previously proposed a simple regression-based method to map quantitative trait loci underlying function-valued phenotypes. In order to better handle the case of noisy phenotype measurements and accommodate the correlation structure among time points, we propose an alternative approach that maintains much of the simplicity and speed of the regression-based method. We overcome noisy measurements by replacing the observed data with a smooth approximation. We then apply functional principal component analysis, replacing the smoothed phenotype data with a small number of principal components. Quantitative trait locus mapping is applied to these dimension-reduced data, either with a multi-trait method or by considering the traits individually and then taking the average or maximum LOD score across traits. We apply these approaches to root gravitropism data on Arabidopsis recombinant inbred lines and further investigate their performance in computer simulations. Our methods have been implemented in the R package, funqtl. PMID:26530421

  7. The use of principal component and cluster analysis to differentiate banana peel flours based on their starch and dietary fibre components.

    PubMed

    Ramli, Saifullah; Ismail, Noryati; Alkarkhi, Abbas Fadhl Mubarek; Easa, Azhar Mat

    2010-08-01

    Banana peel flour (BPF) prepared from green or ripe Cavendish and Dream banana fruits were assessed for their total starch (TS), digestible starch (DS), resistant starch (RS), total dietary fibre (TDF), soluble dietary fibre (SDF) and insoluble dietary fibre (IDF). Principal component analysis (PCA) identified that only 1 component was responsible for 93.74% of the total variance in the starch and dietary fibre components that differentiated ripe and green banana flours. Cluster analysis (CA) applied to similar data obtained two statistically significant clusters (green and ripe bananas) to indicate difference in behaviours according to the stages of ripeness based on starch and dietary fibre components. We concluded that the starch and dietary fibre components could be used to discriminate between flours prepared from peels obtained from fruits of different ripeness. The results were also suggestive of the potential of green and ripe BPF as functional ingredients in food. PMID:24575193

  8. Principal component analysis of electron beams generated in K-shell aluminum X-pinch plasma produced by a compact LC-generator

    NASA Astrophysics Data System (ADS)

    Yilmaz, M. F.; Danisman, Y.; Larour, J.; Aranchuk, L.

    2015-06-01

    Principal component analysis (PCA) method is applied and compared with the line ratios of H-like and He-like transitions, in order to investigate the effects of electron beam on the K-shell Aluminum synthetic spectra. It is also used as a diagnostics to estimate the plasma parameters of K-shell Al X-pinch plasma spectrum. This spectrum is produced by the explosion of two 25-μm Al wires on a compact LC (40 kV, 200 kA) generator. The database for the principal component extraction is created over a previously developed, non-LTE, collisional radiative K-shell Aluminum model. As a result, PCA shows an agreement with the line ratios which are sensitive to plasma electron temperatures, densities and beam fractions. Principal component analysis also illustrates that the addition to the non-LTE model of a fraction f of electrons in an energetic beam, generates the clusters in a three dimensional vector space which are translations of each other and follows reverse v-shaped cascade trajectories, except for the f = 0.0 case. Modeling of a typical shot by PCA gives the plasma electron temperature of Te = 100 eV, density of Ne = 1 × 1020 cm-3 and hot electron fraction of f = 0.2 (with a beam energy centered at 10 keV).

  9. Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression.

    PubMed

    Liu, Zhan-yu; Huang, Jing-feng; Shi, Jing-jing; Tao, Rong-xiang; Zhou, Wan; Zhang, Li-Li

    2007-10-01

    Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2,500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level. PMID:17910117

  10. Evaluation of a comprehensive Eulerian air quality model with multiple chemical species measurements using principal component analysis

    NASA Astrophysics Data System (ADS)

    Li, Shao-Meng; Anlauf, K. G.; Wiebe, H. A.; Bottenheim, J. W.; Puckett, K. J.

    Using a principal component analysis technique and data on atmospheric gases and aerosols at a rural site in Ontario, Canada from the Eulerian model evaluation field study (EMEFS), the Eulerian acid deposition and oxidant model (ADOM) is evaluated. Seventy-nine and 76% of the variances in the data and model output, respectively, are explained by three principal components. They are a chemically aged/ transported component, a diurnal cycle component, and an area emission component, all characterized by their ratios of gases and temporal variation patterns. The ADOM component contributions to sulphur species are in general agreement with the EMEFS components, but with notable differences for key photochemical species including O 3. The temporal variations of the ADOM components are close to those of the EMEFS components. The EMEFS chemically aged/transported component shows a high degree of photochemical processing, with the ratios [NO x]/[TNO y]=0.3 and [O 3]/([TNO y]-[NO x])=9±1. The corresponding ADOM component predicts lower G[NO x]/[TNO y] and [NO 3]/([TNO y]-[NO x]) ratios, probably caused by a chemical mechanism in the model that is too fast, and lower contributions to O 3, NO 2, TNO 3, PAN, TNO y, and HCHO, probably caused by model grid dilution or lower model emissions. The EMEFS diurnal component owes its variance to the daily photochemistry and nighttime dry deposition of the chemical species. In comparison, the matching ADOM component underpredicts the ratio [O 3]/([TNO y]-[NO x]) and the NO 2 consumption and O 3 production but overpredicts the contributions to the other species. The EMEFS emission component represents emissions from local/regional area sources. The corresponding ADOM component underpredicts TNO y by 44% and the fraction of TNO y as NO x compared to the EMEFS component, suggesting that the model has lower emissions of NO x and a photochemical mechanism that converts NO x faster than indicated by the EMEFS results.

  11. Motor stator using corner scraps for additional electrical components

    DOEpatents

    Hsu, John S.; Su, Gui-Jia; Adams, Donald J.; Nagashima, James M.; Stancu, Constantin; Carlson, Douglas S.; Smith, Gregory S.

    2004-03-16

    A method for making a motor and auxiliary devices with a unified stator body comprises providing a piece of material (10) having an area larger than a cross section of the stator (11), removing material from the piece of material (10) to form a pattern for a cross section of a core (11) for the stator, and removing material from the piece of material (10) outside the cross section of the core of the stator (11) to allow positioning of cores (22, 23, 24) for supporting windings (25, 26, 27) of least one additional electromagnetic device, such as a transformer (62) in a dc-to-dc converter (61, 62) that provides a low. voltage dc output. An article of manufacture made according to the invention is also disclosed and apparatus made with the method and article of manufacture are also disclosed.

  12. Quantitative descriptive analysis and principal component analysis for sensory characterization of Indian milk product cham-cham.

    PubMed

    Puri, Ritika; Khamrui, Kaushik; Khetra, Yogesh; Malhotra, Ravinder; Devraja, H C

    2016-02-01

    Promising development and expansion in the market of cham-cham, a traditional Indian dairy product is expected in the coming future with the organized production of this milk product by some large dairies. The objective of this study was to document the extent of variation in sensory properties of market samples of cham-cham collected from four different locations known for their excellence in cham-cham production and to find out the attributes that govern much of variation in sensory scores of this product using quantitative descriptive analysis (QDA) and principal component analysis (PCA). QDA revealed significant (p < 0.05) difference in sensory attributes of cham-cham among the market samples. PCA identified four significant principal components that accounted for 72.4 % of the variation in the sensory data. Factor scores of each of the four principal components which primarily correspond to sweetness/shape/dryness of interior, surface appearance/surface dryness, rancid and firmness attributes specify the location of each market sample along each of the axes in 3-D graphs. These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring attributes of cham-cham that contribute most to its sensory acceptability. PMID:27162404

  13. Edge Principal Components and Squash Clustering: Using the Special Structure of Phylogenetic Placement Data for Sample Comparison

    PubMed Central

    Matsen IV, Frederick A.; Evans, Steven N.

    2013-01-01

    Principal components analysis (PCA) and hierarchical clustering are two of the most heavily used techniques for analyzing the differences between nucleic acid sequence samples taken from a given environment. They have led to many insights regarding the structure of microbial communities. We have developed two new complementary methods that leverage how this microbial community data sits on a phylogenetic tree. Edge principal components analysis enables the detection of important differences between samples that contain closely related taxa. Each principal component axis is a collection of signed weights on the edges of the phylogenetic tree, and these weights are easily visualized by a suitable thickening and coloring of the edges. Squash clustering outputs a (rooted) clustering tree in which each internal node corresponds to an appropriate “average” of the original samples at the leaves below the node. Moreover, the length of an edge is a suitably defined distance between the averaged samples associated with the two incident nodes, rather than the less interpretable average of distances produced by UPGMA, the most widely used hierarchical clustering method in this context. We present these methods and illustrate their use with data from the human microbiome. PMID:23505415

  14. Principal component analysis of palaeomagnetic directions: converting a Maximum Angular Deviation (MAD) into an α95 angle

    NASA Astrophysics Data System (ADS)

    Khokhlov, A.; Hulot, G.

    2016-01-01

    Directions recovered from palaeomagnetic samples are usually archived with some quantitative information about their precision, most often in the form of a so-called α95 angle. Such angles are classically co-estimated with the recovered palaeomagnetic direction from a collection of samples providing individual estimates of this direction. In some instances, however, palaeomagnetic directions have to be inferred from a single sample in which case no α95 angle can be recovered in this way. Fortunately, the progressive demagnetization techniques and principal component analysis universally used to recover directional information from single samples provide alternative measures of the error affecting the recovered direction, known as Maximum Angular Deviation (MAD) angles. These have so far only been considered as rough quality indicators. Here, however, we show that directions recovered in this way can be assumed to satisfy a Fisher distribution, and that the corresponding MAD angles can be rescaled into α95 estimates by multiplying it by an appropriate factor, which only depends on the number of demagnetization steps used in the principal component analysis and on whether one relies on a standard or a so-called `anchored' principal component analysis. These coefficients have been tabulated and practical recommendations for taking advantage of them outlined in the final section of the text. They provide simple means for users to produce much needed error bars on declination and inclination time series recovered from sedimentary long sequences.

  15. The perceptions of teachers and principals toward providing additional compensation to teachers in high-need subject areas

    NASA Astrophysics Data System (ADS)

    Longing, Jeffrey Lucian

    The purpose of this study was to determine possible differences in the perceptions of teachers teaching in high-need areas (i.e., math, science, special education, etc.) and teachers not teaching in high-need areas, (i.e., business education, physical education, etc.) as defined by the states of Arkansas and Louisiana, regarding higher compensation for high-need teachers. In addition, possible perception differences among principals and teachers were determined. The independent variables consisted of gender, position held, years of certified experience, and certification areas. The dependent variable was the perceptions of the participants on providing higher compensation for high-need teachers in order to attract and retain them. The data for all variables were collected using the Teacher Compensation Survey. The sample for this study was limited to teachers, grades 9 through 12, and principals of public high schools in south Arkansas and north Louisiana. Forty-four school districts in south Arkansas (Arkansas Department of Education, 2008a) and north Louisiana (Louisiana Department of Education, 2008a) met the criteria for this study. Twenty-two superintendents gave permission for their districts to participate in the research. A sample of 849 teachers and 38 principals were identified in these districts. Surveys were returned from 350 teachers, creating a 41% response rate. When the 31 principals that returned surveys were added to the total population, the response rate increased to 43% with 381 of the 887 surveyed responding. However, 42 of the teachers and two of the principals skipped some of the questions on the survey and were not included in the study. The researcher used a One-Way ANOVA and independent t-tests to determine the presence of statistical differences at the .05 level. The data showed that most math and science teachers agreed that high-need teachers should be compensated at a higher rate than teachers not teaching in high-need areas. The data

  16. Personality disorders in substance abusers: Validation of the DIP-Q through principal components factor analysis and canonical correlation analysis

    PubMed Central

    Hesse, Morten

    2005-01-01

    Background Personality disorders are common in substance abusers. Self-report questionnaires that can aid in the assessment of personality disorders are commonly used in assessment, but are rarely validated. Methods The Danish DIP-Q as a measure of co-morbid personality disorders in substance abusers was validated through principal components factor analysis and canonical correlation analysis. A 4 components structure was constructed based on 238 protocols, representing antagonism, neuroticism, introversion and conscientiousness. The structure was compared with (a) a 4-factor solution from the DIP-Q in a sample of Swedish drug and alcohol abusers (N = 133), and (b) a consensus 4-components solution based on a meta-analysis of published correlation matrices of dimensional personality disorder scales. Results It was found that the 4-factor model of personality was congruent across the Danish and Swedish samples, and showed good congruence with the consensus model. A canonical correlation analysis was conducted on a subset of the Danish sample with staff ratings of pathology. Three factors that correlated highly between the two variable sets were found. These variables were highly similar to the three first factors from the principal components analysis, antagonism, neuroticism and introversion. Conclusion The findings support the validity of the DIP-Q as a measure of DSM-IV personality disorders in substance abusers. PMID:15910688

  17. Oil spill source identification by principal component analysis of electrospray ionization Fourier transform ion cyclotron resonance mass spectra.

    PubMed

    Corilo, Yuri E; Podgorski, David C; McKenna, Amy M; Lemkau, Karin L; Reddy, Christopher M; Marshall, Alan G; Rodgers, Ryan P

    2013-10-01

    One fundamental challenge with either acute or chronic oil spills is to identify the source, especially in highly polluted areas, near natural oil seeps, when the source contains more than one petroleum product or when extensive weathering has occurred. Here we focus on heavy fuel oil that spilled (~200,000 L) from two suspected fuel tanks that were ruptured on the motor vessel (M/V) Cosco Busan when it struck the San Francisco-Oakland Bay Bridge in November 2007. We highlight the utility of principal component analysis (PCA) of elemental composition data obtained by high resolution FT-ICR mass spectrometry to correctly identify the source of environmental contamination caused by the unintended release of heavy fuel oil (HFO). Using ultrahigh resolution electrospray ionization (ESI) Fourier transform ion cyclotron resonance mass spectrometry, we uniquely assigned thousands of elemental compositions of heteroatom-containing species in neat samples from both tanks and then applied principal component analysis. The components were based on double bond equivalents for constituents of elemental composition, CcHhN1S1. To determine if the fidelity of our source identification was affected by weathering, field samples were collected at various intervals up to two years after the spill. We are able to identify a suite of polar petroleum markers that are environmentally persistent, enabling us to confidently identify that only one tank was the source of the spilled oil: in fact, a single principal component could account for 98% of the variance. Although identification is unaffected by the presence of higher polarity, petrogenic oxidation (weathering) products, future studies may require removal of such species by anion exchange chromatography prior to mass spectral analysis due to their preferential ionization by ESI. PMID:24033143

  18. Quantitative evaluation of grain shapes by utilizing elliptic Fourier and principal component analyses: Implications for sedimentary environment discrimination

    NASA Astrophysics Data System (ADS)

    Suzuki, K.; Fujiwara, H.; Ohta, T.

    2013-12-01

    Fourier analysis has allowed new advancements in determining the shape of sand grains. However, the full quantification of grain shapes has not as yet been accomplished, because Fourier expansion produces numerous descriptors, making it difficult to give a comprehensive interpretation to the results of Fourier analysis. In order to overcome this difficulty, this study focuses on the combined application of elliptic Fourier and principal component analyses (EF-PCA). The EF-PCA method allows to reduce the number of extracted Fourier variables and enables a visual inspection of the results of Fourier analysis. Thus, this approach would facilitate the understanding of the sedimentological significances of the results obtained using Fourier expansion. 0.250-0.355 mm sized quartz grains collected from glacial, foreshore, fluvial and aeolian environments were scanned by digitalizing microscope in 200 magnification ratio. Then the elliptic Fourier coefficients of grain outlines were analyzed using a program package SHAPE (Iwata and Ukai, 2002). In order to examine the degree of roundness and surface smoothness of grains, principal component analysis was then performed on both unstandardized and standardized data matrices obtained by elliptic Fourier analysis. The result of EF-PCA based on unstandardized data matrix extracted descriptors describing overall form and shape of grains because unstandardized data matrix would enhance the contribution of large amplitude and low frequency trigonometric functions. The shape descriptors extracted by this method can be interpreted as elongation index (REF1) and multiple bump indices (REF2, REF3, and REF2 + REF3). These descriptors indicate that aeolian, foreshore, and fluvial sediments contain grains with shapes similar to circles, ellipses, and cylinders, respectively. Meanwhile, the result of EF-PCA based on standardized data matrix enhanced the contribution of low amplitude and high frequency trigonometric functions, meaning that

  19. Principal component analysis for surface reflection components and structure in the facial image and synthesis of the facial image in various ages

    NASA Astrophysics Data System (ADS)

    Hirose, Misa; Toyota, Saori; Ojima, Nobutoshi; Ogawa-Ochiai, Keiko; Tsumura, Norimichi

    2015-03-01

    In this paper, principal component analysis is applied to pigmentation distributions, surface reflectance components and facial landmarks in the whole facial images to obtain feature values. Furthermore, the relationship between the obtained feature vectors and age is estimated by multiple regression analysis to modulate facial images in woman of ages 10 to 70. In our previous work, we analyzed only pigmentation distributions and the reproduced images looked younger than the reproduced age by the subjective evaluation. We considered that this happened because we did not modulate the facial structures and detailed surfaces such as wrinkles. By analyzing landmarks represented facial structures and surface reflectance components, we analyzed the variation of facial structures and fine asperity distributions as well as pigmentation distributions in the whole face. As a result, our method modulate the appearance of a face by changing age more appropriately.

  20. Household Food Waste: Multivariate Regression and Principal Components Analyses of Awareness and Attitudes among U.S. Consumers

    PubMed Central

    2016-01-01

    We estimate models of consumer food waste awareness and attitudes using responses from a national survey of U.S. residents. Our models are interpreted through the lens of several theories that describe how pro-social behaviors relate to awareness, attitudes and opinions. Our analysis of patterns among respondents’ food waste attitudes yields a model with three principal components: one that represents perceived practical benefits households may lose if food waste were reduced, one that represents the guilt associated with food waste, and one that represents whether households feel they could be doing more to reduce food waste. We find our respondents express significant agreement that some perceived practical benefits are ascribed to throwing away uneaten food, e.g., nearly 70% of respondents agree that throwing away food after the package date has passed reduces the odds of foodborne illness, while nearly 60% agree that some food waste is necessary to ensure meals taste fresh. We identify that these attitudinal responses significantly load onto a single principal component that may represent a key attitudinal construct useful for policy guidance. Further, multivariate regression analysis reveals a significant positive association between the strength of this component and household income, suggesting that higher income households most strongly agree with statements that link throwing away uneaten food to perceived private benefits. PMID:27441687

  1. Household Food Waste: Multivariate Regression and Principal Components Analyses of Awareness and Attitudes among U.S. Consumers.

    PubMed

    Qi, Danyi; Roe, Brian E

    2016-01-01

    We estimate models of consumer food waste awareness and attitudes using responses from a national survey of U.S. residents. Our models are interpreted through the lens of several theories that describe how pro-social behaviors relate to awareness, attitudes and opinions. Our analysis of patterns among respondents' food waste attitudes yields a model with three principal components: one that represents perceived practical benefits households may lose if food waste were reduced, one that represents the guilt associated with food waste, and one that represents whether households feel they could be doing more to reduce food waste. We find our respondents express significant agreement that some perceived practical benefits are ascribed to throwing away uneaten food, e.g., nearly 70% of respondents agree that throwing away food after the package date has passed reduces the odds of foodborne illness, while nearly 60% agree that some food waste is necessary to ensure meals taste fresh. We identify that these attitudinal responses significantly load onto a single principal component that may represent a key attitudinal construct useful for policy guidance. Further, multivariate regression analysis reveals a significant positive association between the strength of this component and household income, suggesting that higher income households most strongly agree with statements that link throwing away uneaten food to perceived private benefits. PMID:27441687

  2. Patient-Assessed Late Toxicity Rates and Principal Component Analysis After Image-Guided Radiation Therapy for Prostate Cancer

    SciTech Connect

    Skala, Marketa; Rosewall, Tara; Dawson, Laura; Divanbeigi, Lorella; Lockwood, Gina; Thomas, Christopher; Crook, Juanita; Chung, Peter; Warde, Padraig; Catton, Charles . E-mail: charles.catton@rmp.uhn.on.ca

    2007-07-01

    Purpose: The aims of this study were to determine the incidence of patient-assessed late toxicity after high-dose, image-guided radiation therapy in a cohort of men with prostate cancer; and to correlate toxicity with conventional dosimetric parameters and rectal and bladder dose-volume histograms (DVH) reduced using principal component analysis. Methods and Materials: Toxicity questionnaires were sent to 690 men treated for localized prostate cancer to 75.6 Gy or 79.8 Gy using three-dimensional conformal radiation therapy (3DCRT) or intensity-modulated radiation therapy (IMRT) between 1997 and 2003 at the Princess Margaret Hospital. Toxicity was graded according to the modified Radiation Therapy Oncology Group (RTOG)-late effects normal tissue (LENT) scoring system. Late rectal and bladder toxicity scores were dichotomized as < Grade 2 and {>=} Grade 2, and correlated with dosimetric parameters and with the first three principal components of rectal and bladder DVHs. Results: In all, 63% of the patients completed the questionnaire. At a median follow-up of 37 months, the incidence of late rectal toxicity RTOG Grades 1, 2, and 3 was 25.2%, 2.5%, and 0.7% respectively. The incidence of late urinary toxicity RTOG Grade 1, 2, and 3 was 16.5%, 8.8%, and 0.9% respectively. Maintenance of erectile function sufficient for intercourse was reported in 68%. No dosimetric parameter analyzed, including principal component analysis reduction of DVHs, correlated with late toxicity. Conclusions: Postal questionnaire was effective for collection of patient-assessed late toxicity data. The incidence of late toxicity was low, with a lack of correlation to dosimetric parameters. We attribute this to the use of conformal techniques and daily image guidance.

  3. Automated identification of novel amphetamines using a pure neural network and neural networks coupled with principal component analysis

    NASA Astrophysics Data System (ADS)

    Gosav, S.; Praisler, M.; Dorohoi, D. O.; Popa, G.

    2005-06-01

    A pure neural network (NN) and several neural networks coupled with principal component analysis (PC-NN) have been developed in order to identify illicit amphetamines necessary in the investigation of drugs of abuse for epidemiological, clinical, and forensic purposes. The NN system has as input variables 260 spectral data, representing absorption intensities measured for each normalized infrared spectrum at 260 wavenumbers 10 cm -1 apart. In the case of PC-NN systems, the original spectral data (absorption intensities) have been compressed with the principal component analysis method (PCA), the scores of the principal components (PCs) being the inputs of these systems. We have built nine PC-NN systems, which have a different number of input variables: 3PCs, 4PCs, 5PCs, 6PCs, 7PCs, 8PCs, 9PCs, 10PCs and 15PCs. All systems are specialized to distinguish between stimulant amphetamines (class code M), hallucinogenic amphetamines (class code T) and nonamphetamines (class code N). We are now presenting a comparison of the validation results obtained for the NN system and for the best PC-NN system based on the scores of the first nine PCs (9PC-NN). The NN system correctly classifies all the positive samples, as opposed to the 9PC-NN system, which is characterized by a true positive rate (TP) of 90.91%. The true negative rate (TN) obtained for the first system (83.33%) is slightly higher than in the case of the later system (82.71%). Thus, the NN system is more sensitive and selective than the 9PC-NN system. We are also presenting a spectroscopic analysis of the false negative samples obtained in the case of 9PC-NN system.

  4. Screening of patients with bronchopulmonary diseases using methods of infrared laser photoacoustic spectroscopy and principal component analysis

    NASA Astrophysics Data System (ADS)

    Kistenev, Yury V.; Karapuzikov, Alexander I.; Kostyukova, Nadezhda Yu.; Starikova, Marina K.; Boyko, Andrey A.; Bukreeva, Ekaterina B.; Bulanova, Anna A.; Kolker, Dmitry B.; Kuzmin, Dmitry A.; Zenov, Konstantin G.; Karapuzikov, Alexey A.

    2015-06-01

    A human exhaled air analysis by means of infrared (IR) laser photoacoustic spectroscopy is presented. Eleven healthy nonsmoking volunteers (control group) and seven patients with chronic obstructive pulmonary disease (COPD, target group) were involved in the study. The principal component analysis method was used to select the most informative ranges of the absorption spectra of patients' exhaled air in terms of the separation of the studied groups. It is shown that the data of the profiles of exhaled air absorption spectrum in the informative ranges allow identifying COPD patients in comparison to the control group.

  5. Identification of neural networks that contribute to motion sickness through principal components analysis of fos labeling induced by galvanic vestibular stimulation.

    PubMed

    Balaban, Carey D; Ogburn, Sarah W; Warshafsky, Susan G; Ahmed, Abdul; Yates, Bill J

    2014-01-01

    Motion sickness is a complex condition that includes both overt signs (e.g., vomiting) and more covert symptoms (e.g., anxiety and foreboding). The neural pathways that mediate these signs and symptoms are yet to identified. This study mapped the distribution of c-fos protein (Fos)-like immunoreactivity elicited during a galvanic vestibular stimulation paradigm that is known to induce motion sickness in felines. A principal components analysis was used to identify networks of neurons activated during this stimulus paradigm from functional correlations between Fos labeling in different nuclei. This analysis identified five principal components (neural networks) that accounted for greater than 95% of the variance in Fos labeling. Two of the components were correlated with the severity of motion sickness symptoms, and likely participated in generating the overt signs of the condition. One of these networks included neurons in locus coeruleus, medial, inferior and lateral vestibular nuclei, lateral nucleus tractus solitarius, medial parabrachial nucleus and periaqueductal gray. The second included neurons in the superior vestibular nucleus, precerebellar nuclei, periaqueductal gray, and parabrachial nuclei, with weaker associations of raphe nuclei. Three additional components (networks) were also identified that were not correlated with the severity of motion sickness symptoms. These networks likely mediated the covert aspects of motion sickness, such as affective components. The identification of five statistically independent component networks associated with the development of motion sickness provides an opportunity to consider, in network activation dimensions, the complex progression of signs and symptoms that are precipitated in provocative environments. Similar methodology can be used to parse the neural networks that mediate other complex responses to environmental stimuli. PMID:24466215

  6. Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis

    PubMed Central

    2011-01-01

    Wheat is one of the most important crops in Australia, and the identification of young plants is an important step towards developing an automated system for monitoring crop establishment and also for differentiating crop from weeds. In this paper, a framework to differentiate early narrow-leaf wheat from two common weeds from their digital images is developed. A combination of colour, texture and shape features is used. These features are reduced to three descriptors using Principal Component Analysis. The three components provide an effective and significant means for distinguishing the three grasses. Further analysis enables threshold levels to be set for the discrimination of the plant species. The PCA model was evaluated on an independent data set of plants and the results show accuracy of 88% and 85% in the differentiation of ryegrass and brome grass from wheat, respectively. The outcomes of this study can be integrated into new knowledge in developing computer vision systems used in automated weed management. PMID:21943349

  7. Chemical fingerprinting by RP-RRLC-DAD and principal component analysis of Ziziphora clinopodioides from different locations.

    PubMed

    Tiana, Shuge; Yu, Qian; Xin, Lude; Zhou, Zhaohui Sunny; Upur, Halmuart

    2012-09-01

    An efficient and accurate fingerprinting method using reversed-phase rapid-resolution liquid-chromatography coupled with photodiode array detection has been developed and optimized to examine the variance in active compounds among Ziziphora clinopodioides Lam from different locations. Three active components, diosmin, linarin and pulegone, were identified by matching their retention times and UV spectra with the corresponding reference compounds. Our results indicated that chromatographic fingerprints, in combination with principal component analysis (PCA) and hierarchical clustering analysis (HCA), could efficiently identify and distinguish Z. clinopodioides from different sources. Our fingerprinting methods and data will be useful for quality control, and thus, more effective dosing in clinical application of Z. clinopodioides. PMID:23074902

  8. Differentiating parts of Cinnamomum cassia using LC-qTOF-MS in conjunction with principal component analysis.

    PubMed

    Chen, Pei-Yi; Yu, Jhe-Wei; Lu, Fen-Ling; Lin, Mei-Chih; Cheng, Hwei-Fang

    2016-09-01

    Cinnamon bark (Rou Gui in Chinese), cinnamon twig (Gui Zhi) and shaved cinnamon bark (Gui Sin) have been widely used as spices and in traditional Chinese medicine since ancient times. On-going issues related to quality and authenticity necessitate the development of analytical methods capable of providing an objective evaluation of samples. In this study, chemical fingerprints of cinnamon bark, cinnamon twigs and shaved cinnamon bark were established using liquid chromatography quadruple time-of-flight mass spectrometry in conjunction with principal component analysis (PCA). From 125 samples of cinnamon, we identified the following eight compounds and their the detection ratios: coumarin, cinnamaldehyde, cinnamyl alcohol, cinnamic acid, 2-hydroxycinnamaldehyde, 2-hydroxycinnamic acid, 2-methoxycinnamaldehyde and 4-methoxycinnamaldehyde. Of these, 4-methoxycinnamaldehyde presented the largest variations in detection ratio, making up 64.0, 97.4 and 50.0% in cinnamon bark, cinnamon twig, and shaved cinnamon bark, respectively. The quantities of cinnamyl alcohol, coumarin and cinnamaldehyde also varied between the three parts of the plant. Chemical fingerprints of the three cinnamon samples were established using principal component analysis, the results of which indicate that cinnamon bark and shaved cinnamon bark could be easily differentiated, despite a marked similarity in outward appearance. Cinnamon twig was also shown to depart from the other clusters. The proposed method provides a fast and efficient means of identifying cinnamon herbs for quality control purposes. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26873449

  9. An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks

    PubMed Central

    Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong

    2015-01-01

    Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms. PMID:26262622

  10. Classification of a target analyte in solid mixtures using principal component analysis, support vector machines, and Raman spectroscopy

    NASA Astrophysics Data System (ADS)

    O'Connell, Marie-Louise; Howley, Tom; Ryder, Alan G.; Leger, Marc N.; Madden, Michael G.

    2005-06-01

    The quantitative analysis of illicit materials using Raman spectroscopy is of widespread interest for law enforcement and healthcare applications. One of the difficulties faced when analysing illicit mixtures is the fact that the narcotic can be mixed with many different cutting agents. This obviously complicates the development of quantitative analytical methods. In this work we demonstrate some preliminary efforts to try and account for the wide variety of potential cutting agents, by discrimination between the target substance and a wide range of excipients. Near-infrared Raman spectroscopy (785 nm excitation) was employed to analyse 217 samples, a number of them consisting of a target analyte (acetaminophen) mixed with excipients of different concentrations by weight. The excipients used were sugars (maltose, glucose, lactose, sorbitol), inorganic materials (talcum powder, sodium bicarbonate, magnesium sulphate), and food products (caffeine, flour). The spectral data collected was subjected to a number of pre-treatment statistical methods including first derivative and normalisation transformations, to make the data more suitable for analysis. Various methods were then used to discriminate the target analytes, these included Principal Component Analysis (PCA), Principal Component Regression (PCR) and Support Vector Machines.

  11. Principal Component Analysis of the Time- and Position-dependent Point-Spread Function of the Advanced Camera for Surveys

    NASA Astrophysics Data System (ADS)

    Jee, M. J.; Blakeslee, J. P.; Sirianni, M.; Martel, A. R.; White, R. L.; Ford, H. C.

    2007-12-01

    We describe the time- and position-dependent point-spread function (PSF) variation of the wide-field channel (WFC) of the Advanced Camera for Surveys (ACS) with the principal component analysis (PCA) technique. The time-dependent change is caused by the temporal variation of the HST focus, whereas the position-dependent PSF variation in ACS WFC at a given focus is mainly the result of changes in aberrations and charge diffusion across the detector, which appear as position-dependent changes in the elongation of the astigmatic core and blurring of the PSF, respectively. Using ˜ 20 ) of principal components or "eigen-PSFs" per exposure can robustly reproduce the observed variation of the ellipticity and size of the PSF. Our primary interest in this investigation is the application of this PSF library to precision weak-lensing analyses, where accurate knowledge of the instrument's PSF is crucial. However, the high fidelity of the model judged from the nice agreement with observed PSFs suggests that the model is potentially also useful in other applications, such as crowded field stellar photometry, galaxy profile fitting, AGN studies, etc., which similarly demand a fair knowledge of the PSFs at objects' locations. Our PSF models, applicable to any WFC image rectified with the Lanczos3 kernel, are publicly available.

  12. An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks.

    PubMed

    Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong

    2015-01-01

    Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms. PMID:26262622

  13. Development of a cell formation heuristic by considering realistic data using principal component analysis and Taguchi's method

    NASA Astrophysics Data System (ADS)

    Kumar, Shailendra; Sharma, Rajiv Kumar

    2015-12-01

    Over the last four decades of research, numerous cell formation algorithms have been developed and tested, still this research remains of interest to this day. Appropriate manufacturing cells formation is the first step in designing a cellular manufacturing system. In cellular manufacturing, consideration to manufacturing flexibility and production-related data is vital for cell formation. The consideration to this realistic data makes cell formation problem very complex and tedious. It leads to the invention and implementation of highly advanced and complex cell formation methods. In this paper an effort has been made to develop a simple and easy to understand/implement manufacturing cell formation heuristic procedure with considerations to the number of production and manufacturing flexibility-related parameters. The heuristic minimizes inter-cellular movement cost/time. Further, the proposed heuristic is modified for the application of principal component analysis and Taguchi's method. Numerical example is explained to illustrate the approach. A refinement in the results is observed with adoption of principal component analysis and Taguchi's method.

  14. Parsimonious classification of binary lacunarity data computed from food surface images using kernel principal component analysis and artificial neural networks.

    PubMed

    Iqbal, Abdullah; Valous, Nektarios A; Sun, Da-Wen; Allen, Paul

    2011-02-01

    Lacunarity is about quantifying the degree of spatial heterogeneity in the visual texture of imagery through the identification of the relationships between patterns and their spatial configurations in a two-dimensional setting. The computed lacunarity data can designate a mathematical index of spatial heterogeneity, therefore the corresponding feature vectors should possess the necessary inter-class statistical properties that would enable them to be used for pattern recognition purposes. The objectives of this study is to construct a supervised parsimonious classification model of binary lacunarity data-computed by Valous et al. (2009)-from pork ham slice surface images, with the aid of kernel principal component analysis (KPCA) and artificial neural networks (ANNs), using a portion of informative salient features. At first, the dimension of the initial space (510 features) was reduced by 90% in order to avoid any noise effects in the subsequent classification. Then, using KPCA, the first nineteen kernel principal components (99.04% of total variance) were extracted from the reduced feature space, and were used as input in the ANN. An adaptive feedforward multilayer perceptron (MLP) classifier was employed to obtain a suitable mapping from the input dataset. The correct classification percentages for the training, test and validation sets were 86.7%, 86.7%, and 85.0%, respectively. The results confirm that the classification performance was satisfactory. The binary lacunarity spatial metric captured relevant information that provided a good level of differentiation among pork ham slice images. PMID:21062668

  15. Raman spectroscopy combined with principal component analysis and k nearest neighbour analysis for non-invasive detection of colon cancer

    NASA Astrophysics Data System (ADS)

    Li, Xiaozhou; Yang, Tianyue; Li, Siqi; Wang, Deli; Song, Youtao; Zhang, Su

    2016-03-01

    This paper attempts to investigate the feasibility of using Raman spectroscopy for the diagnosis of colon cancer. Serum taken from 75 healthy volunteers, 65 colon cancer patients and 60 post-operation colon cancer patients was measured in this experiment. In the Raman spectra of all three groups, the Raman peaks at 750, 1083, 1165, 1321, 1629 and 1779 cm-1 assigned to nucleic acids, amino acids and chromophores were consistently observed. All of these six Raman peaks were observed to have statistically significant differences between groups. For quantitative analysis, the multivariate statistical techniques of principal component analysis (PCA) and k nearest neighbour analysis (KNN) were utilized to develop diagnostic algorithms for classification. In PCA, several peaks in the principal component (PC) loadings spectra were identified as the major contributors to the PC scores. Some of the peaks in the PC loadings spectra were also reported as characteristic peaks for colon tissues, which implies correlation between peaks in PC loadings spectra and those in the original Raman spectra. KNN was also performed on the obtained PCs, and a diagnostic accuracy of 91.0% and a specificity of 92.6% were achieved.

  16. Construction of Training Sets for Valid Calibration of in Vivo Cyclic Voltammetric Data by Principal Component Analysis.

    PubMed

    Rodeberg, Nathan T; Johnson, Justin A; Cameron, Courtney M; Saddoris, Michael P; Carelli, Regina M; Wightman, R Mark

    2015-11-17

    Principal component regression, a multivariate calibration technique, is an invaluable tool for the analysis of voltammetric data collected in vivo with acutely implanted microelectrodes. This method utilizes training sets to separate cyclic voltammograms into contributions from multiple electroactive species. The introduction of chronically implanted microelectrodes permits longitudinal measurements at the same electrode and brain location over multiple recordings. The reliability of these measurements depends on a consistent calibration methodology. One published approach has been the use of training sets built with data from separate electrodes and animals to evaluate neurochemical signals in multiple subjects. Alternatively, responses to unpredicted rewards have been used to generate calibration data. This study addresses these approaches using voltammetric data from three different experiments in freely moving rats obtained with acutely implanted microelectrodes. The findings demonstrate critical issues arising from the misuse of principal component regression that result in significant underestimates of concentrations and improper statistical model validation that, in turn, can lead to inaccurate data interpretation. Therefore, the calibration methodology for chronically implanted microelectrodes needs to be revisited and improved before measurements can be considered reliable. PMID:26477708

  17. Concentration profiles of collagen and proteoglycan in articular cartilage by Fourier transform infrared imaging and principal component regression

    NASA Astrophysics Data System (ADS)

    Yin, Jianhua; Xia, Yang; Lu, Mei

    2012-03-01

    Fourier-transform infrared imaging (FT-IRI) technique with the principal component regression (PCR) method was used to quantitatively determine the 2D images and the depth-dependent concentration profiles of two principal macromolecular components (collagen and proteoglycan) in articular cartilage. Ten 6 μm thick sections of canine humeral cartilage were imaged at a pixel size of 6.25 μm in FT-IRI. The infrared spectra extracted from FT-IRI experiments were imported into a PCR program to calculate the quantitative distributions of both collagen and proteoglycan in dry cartilage, which were subsequently converted into the wet-weight based concentration profiles. The proteoglycan profiles by FT-IRI and PCR significantly correlated in linear regression with the proteoglycan profiles by the non-destructive μMRI (the goodness-of-fit 0.96 and the Pearson coefficient 0.98). Based on these concentration relationships, the concentration images of collagen and proteoglycan in both healthy and lesioned articular cartilage were successfully constructed two dimensionally. The simultaneous construction of both collagen and proteoglycan concentration images demonstrates that this combined imaging and chemometrics approach could be used as a sensitive tool to accurately resolve and visualize the concentration distributions of macromolecules in biological tissues.

  18. The Havemann-Taylor Fast Radiative Transfer Code: Exact fast radiative transfer for scattering atmospheres using Principal Components (PCs)

    NASA Astrophysics Data System (ADS)

    Havemann, Stephan; Thelen, Jean-Claude; Taylor, Jonathan P.; Keil, Andreas

    2009-03-01

    The Havemann-Taylor Fast Radiative Transfer Code (HT-FRTC) has been developed for the simulation of highly spectrally resolved measurements from satellite based (i.e. Infrared Atmospheric Sounding Interferometer (IASI), Atmospheric Infrared Sounder (AIRS)) and airborne (i.e. Atmospheric Research Interferometer Evaluation System (ARIES)) instruments. The use of principle components enables the calculation of a complete spectrum in less than a second. The principal compoents are derived from a diverse training set of atmospheres and surfaces and contain their spectral characteristics in a highly compressed form. For any given atmosphere/surface, the HT-FRTC calculates the weightings (also called scores) of a few hundred principal components based on selected monochromatic radiative transfer calculations, which is far cheaper than thousands of channel radiance calculations. By intercomparison with line-by-line and other fast models the HT-FRTC has been shown to be accurate. The HT-FRTC has been successfully applied to simultaneous variational retrievals of atmospheric temperature and humidity profiles, surface temperature and surface emissivity over land. This is the subject of another presentation at this conference. The HT-FRTC has now also been extended to include an exact treatment of scattering by aerosols/clouds. The radiative transfer problem is solved using a discrete ordinate method (DISORT). Modelling results at high-spectral resolution for non-clear sky atmospheres obtained with the HT-FRTC are presented.

  19. Towards the generation of a parametric foot model using principal component analysis: A pilot study.

    PubMed

    Scarton, Alessandra; Sawacha, Zimi; Cobelli, Claudio; Li, Xinshan

    2016-06-01

    There have been many recent developments in patient-specific models with their potential to provide more information on the human pathophysiology and the increase in computational power. However they are not yet successfully applied in a clinical setting. One of the main challenges is the time required for mesh creation, which is difficult to automate. The development of parametric models by means of the Principle Component Analysis (PCA) represents an appealing solution. In this study PCA has been applied to the feet of a small cohort of diabetic and healthy subjects, in order to evaluate the possibility of developing parametric foot models, and to use them to identify variations and similarities between the two populations. Both the skin and the first metatarsal bones have been examined. Besides the reduced sample of subjects considered in the analysis, results demonstrated that the method adopted herein constitutes a first step towards the realization of a parametric foot models for biomechanical analysis. Furthermore the study showed that the methodology can successfully describe features in the foot, and evaluate differences in the shape of healthy and diabetic subjects. PMID:27068864

  20. Principal Components Analysis of Reflectance Spectra from the Mars Exploration Rover Opportunity

    NASA Technical Reports Server (NTRS)

    Mercer, C. M.; Cohen, B. A.

    2010-01-01

    In the summer of 2007 a global dust storm on Mars effectively disabled Opportunity's Miniature Thermal Emission Spectrometer (Mini-TES), the primary instrument used by the Athena Science Team to identify locally unique rocks on the Martian surface. The science team needs another way to distinguish interesting rocks from their surroundings on a tactical timescale. This study was designed to develop the ability to identify locally unique rocks on the Martian surface remotely using the Mars Exploration Rovers' Panoramica Camera (PanCam) instrument. Meridiani bedrock observed by Opportunity is largely characterized by sulfate-rich sandstones and hematite spherules. Additionally, loose fragments of bedrock and "cobbles" of foreign origin collet on the surface, some of which are interpreted as meteorites.

  1. A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

    PubMed Central

    2015-01-01

    Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications. PMID:26267377

  2. A hybrid color space for skin detection using genetic algorithm heuristic search and principal component analysis technique.

    PubMed

    Maktabdar Oghaz, Mahdi; Maarof, Mohd Aizaini; Zainal, Anazida; Rohani, Mohd Foad; Yaghoubyan, S Hadi

    2015-01-01

    Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications. PMID:26267377

  3. PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

    PubMed Central

    2014-01-01

    Background The chemical composition of aerosols and particle size distributions are the most significant factors affecting air quality. In particular, the exposure to finer particles can cause short and long-term effects on human health. In the present paper PM10 (particulate matter with aerodynamic diameter lower than 10 μm), CO, NOx (NO and NO2), Benzene and Toluene trends monitored in six monitoring stations of Bari province are shown. The data set used was composed by bi-hourly means for all parameters (12 bi-hourly means per day for each parameter) and it’s referred to the period of time from January 2005 and May 2007. The main aim of the paper is to provide a clear illustration of how large data sets from monitoring stations can give information about the number and nature of the pollutant sources, and mainly to assess the contribution of the traffic source to PM10 concentration level by using multivariate statistical techniques such as Principal Component Analysis (PCA) and Absolute Principal Component Scores (APCS). Results Comparing the night and day mean concentrations (per day) for each parameter it has been pointed out that there is a different night and day behavior for some parameters such as CO, Benzene and Toluene than PM10. This suggests that CO, Benzene and Toluene concentrations are mainly connected with transport systems, whereas PM10 is mostly influenced by different factors. The statistical techniques identified three recurrent sources, associated with vehicular traffic and particulate transport, covering over 90% of variance. The contemporaneous analysis of gas and PM10 has allowed underlining the differences between the sources of these pollutants. Conclusions The analysis of the pollutant trends from large data set and the application of multivariate statistical techniques such as PCA and APCS can give useful information about air quality and pollutant’s sources. These knowledge can provide useful advices to environmental policies in

  4. [Simultaneous separation and detection of principal component isomer and related substances of raw material drug of ammonium glycyrrhizinate by RP-HPLC and structure confirmation].

    PubMed

    Zhao, Yan-Yan; Liu, Li-Yan; Han, Yuan-Yuan; Li, Yue-Qiu; Wang, Yan; Shi, Min-Jian

    2013-08-01

    A simple, fast and sensitive analytical method for the simultaneous separation and detection of 18alpha-glycyrrhizinic acid, 18beta-glycyrrhizinic acid, related substance A and related substance B by RP-HPLC and drug quality standard was established. The structures of principal component isomer and related substances of raw material drug of ammonium glycyrrhizinate have been confirmed. Reference European Pharmacopoeia EP7.0 version, British Pharmacopoeia 2012 version, National Drug Standards of China (WS 1-XG-2002), domestic and international interrelated literature were referred to select the composition of mobile phase. The experimental parameters including salt concentration, pH, addition quantities of organic solvent, column temperature and flow rate were optimized. Finally, the assay was conducted on a Durashell-C18 column (250 mm x 4.6 mm, 5 microm) with 0.01 mol x mL(-1) ammonium perchlorate (add ammonia to adjust the pH value to 8.2) -methanol (48 : 52) as mobile phase at the flow rate of 0.8 mL x min(-1), and the detection wavelength was set at 254 nm. The column temperature was 50 degrees C and the injection volume was 10 microL. The MS, NMR, UV and RP-HPLC were used to confirm the structures of principal component isomer and related substances of raw material drug of ammonium glycyrrhizinate. Under the optimized separation conditions, the calibration curves of 18 alpha-glycyrrhizinic acid, 18beta-glycyrrhizinic acid, related substance A and related substance B showed good linearity within the concentration of 0.50-100 microg x mL(-1) (r = 0.999 9). The detection limits for 18alpha-glycyrrhizinic acid, 18beta-glycyrrhizinic acid, related substance A and related substance B were 0.15, 0.10, 0.10, 0.15 microg x mL(-1) respectively. The method is sensitive, reproducible and the results are accurate and reliable. It can be used for chiral resolution of 18alpha-glycyrrhizinic acid, 18Pbeta-glycyrrhizinic acid, and detection content of principal component and

  5. Large-scale hydraulic tomography and joint inversion of head and tracer data using the Principal Component Geostatistical Approach (PCGA)

    NASA Astrophysics Data System (ADS)

    Lee, J.; Kitanidis, P. K.

    2014-07-01

    The stochastic geostatistical inversion approach is widely used in subsurface inverse problems to estimate unknown parameter fields and corresponding uncertainty from noisy observations. However, the approach requires a large number of forward model runs to determine the Jacobian or sensitivity matrix, thus the computational and storage costs become prohibitive when the number of unknowns, m, and the number of observations, n increase. To overcome this challenge in large-scale geostatistical inversion, the Principal Component Geostatistical Approach (PCGA) has recently been developed as a "matrix-free" geostatistical inversion strategy that avoids the direct evaluation of the Jacobian matrix through the principal components (low-rank approximation) of the prior covariance and the drift matrix with a finite difference approximation. As a result, the proposed method requires about K runs of the forward problem in each iteration independently of m and n, where K is the number of principal components and can be much less than m and n for large-scale inverse problems. Furthermore, the PCGA is easily adaptable to different forward simulation models and various data types for which the adjoint-state method may not be implemented suitably. In this paper, we apply the PCGA to representative subsurface inverse problems to illustrate its efficiency and scalability. The low-rank approximation of the large-dimensional dense prior covariance matrix is computed through a randomized eigen decomposition. A hydraulic tomography problem in which the number of observations is typically large is investigated first to validate the accuracy of the PCGA compared with the conventional geostatistical approach. Then the method is applied to a large-scale hydraulic tomography with 3 million unknowns and it is shown that underlying subsurface structures are characterized successfully through an inversion that involves an affordable number of forward simulation runs. Lastly, we present a joint

  6. Assessment of the differences in the physical, chemical and phytochemical properties of four strawberry cultivars using principal component analysis.

    PubMed

    Šamec, Dunja; Maretić, Marina; Lugarić, Ivana; Mešić, Aleksandar; Salopek-Sondi, Branka; Duralija, Boris

    2016-03-01

    The worldwide established strawberry cultivar 'Albion' and three recently introduced cultivars in Europe: 'Monterey', 'Capri', and 'Murano', grown hydroponically, were studied to ascertain the influence of cultivar and harvesting date on the physical, chemical, antioxidant and phytochemical properties of their fruits. Interrelationships of investigated parameters and these cultivars were investigated by the statistical approach of principal component analysis (PCA). Results indicated that cultivar had a more significant effect on the analyzed parameters than harvesting date. Thus grouping of the variables in a PCA plot indicated that each cultivar has specific characteristics important for consumer or industrial use. Cultivar 'Monterey' was the richest in phytochemical contents and consequently in antioxidant activity, 'Albion' showed the highest contents of total soluble solids, titratable acidity content and ascorbic acid, 'Capri' had the highest value of firmness, while 'Murano' had lighter color in comparison to others. Potential use of these cultivars has been assessed according to these important measured attributes. PMID:26471624

  7. Discrimination of selected species of pathogenic bacteria using near-infrared Raman spectroscopy and principal components analysis

    NASA Astrophysics Data System (ADS)

    de Siqueira e Oliveira, Fernanda S.; Giana, Hector E.; Silveira, Landulfo, Jr.

    2012-03-01

    It has been proposed a method based on Raman spectroscopy for identification of different microorganisms involved in bacterial urinary tract infections. Spectra were collected from different bacterial colonies (Gram negative: E. coli, K. pneumoniae, P. mirabilis, P. aeruginosa, E. cloacae and Gram positive: S. aureus and Enterococcus sp.), grown in culture medium (Agar), using a Raman spectrometer with a fiber Raman probe (830 nm). Colonies were scraped from Agar surface placed in an aluminum foil for Raman measurements. After pre-processing, spectra were submitted to a Principal Component Analysis and Mahalanobis distance (PCA/MD) discrimination algorithm. It has been found that the mean Raman spectra of different bacterial species show similar bands, being the S. aureus well characterized by strong bands related to carotenoids. PCA/MD could discriminate Gram positive bacteria with sensitivity and specificity of 100% and Gram negative bacteria with good sensitivity and high specificity.

  8. Discrimination of selected species of pathogenic bacteria using near-infrared Raman spectroscopy and principal components analysis

    NASA Astrophysics Data System (ADS)

    de Siqueira e Oliveira, Fernanda SantAna; Giana, Hector Enrique; Silveira, Landulfo

    2012-10-01

    A method, based on Raman spectroscopy, for identification of different microorganisms involved in bacterial urinary tract infections has been proposed. Spectra were collected from different bacterial colonies (Gram-negative: Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Pseudomonas aeruginosa and Enterobacter cloacae, and Gram-positive: Staphylococcus aureus and Enterococcus spp.), grown on culture medium (agar), using a Raman spectrometer with a fiber Raman probe (830 nm). Colonies were scraped from the agar surface and placed on an aluminum foil for Raman measurements. After preprocessing, spectra were submitted to a principal component analysis and Mahalanobis distance (PCA/MD) discrimination algorithm. We found that the mean Raman spectra of different bacterial species show similar bands, and S. aureus was well characterized by strong bands related to carotenoids. PCA/MD could discriminate Gram-positive bacteria with sensitivity and specificity of 100% and Gram-negative bacteria with sensitivity ranging from 58 to 88% and specificity ranging from 87% to 99%.

  9. Diagnosing basal cell carcinoma in vivo by near-infrared Raman spectroscopy: a Principal Components Analysis discrimination algorithm

    NASA Astrophysics Data System (ADS)

    Silveira, Landulfo, Jr.; Silveira, Fabrício L.; Bodanese, Benito; Pacheco, Marcos Tadeu T.; Zângaro, Renato A.

    2012-02-01

    This work demonstrated the discrimination among basal cell carcinoma (BCC) and normal human skin in vivo using near-infrared Raman spectroscopy. Spectra were obtained in the suspected lesion prior resectional surgery. After tissue withdrawn, biopsy fragments were submitted to histopathology. Spectra were also obtained in the adjacent, clinically normal skin. Raman spectra were measured using a Raman spectrometer (830 nm) with a fiber Raman probe. By comparing the mean spectra of BCC with the normal skin, it has been found important differences in the 800-1000 cm-1 and 1250-1350 cm-1 (vibrations of C-C and amide III, respectively, from lipids and proteins). A discrimination algorithm based on Principal Components Analysis and Mahalanobis distance (PCA/MD) could discriminate the spectra of both tissues with high sensitivity and specificity.

  10. A principal-component and least-squares method for allocating polycyclic aromatic hydrocarbons in sediment to multiple sources

    SciTech Connect

    Burns, W.A.; Mankiewicz, P.J.; Bence, A.E.; Page, D.S.; Parker, K.R.

    1997-06-01

    A method was developed to allocate polycyclic aromatic hydrocarbons (PAHs) in sediment samples to the PAH sources from which they came. The method uses principal-component analysis to identify possible sources and a least-squares model to find the source mix that gives the best fit of 36 PAH analytes in each sample. The method identified 18 possible PAH sources in a large set of field data collected in Prince William Sound, Alaska, USA, after the 1989 Exxon Valdez oil spill, including diesel oil, diesel soot, spilled crude oil in various weathering states, natural background, creosote, and combustion products from human activities and forest fires. Spill oil was generally found to be a small increment of the natural background in subtidal sediments, whereas combustion products were often the predominant sources for subtidal PAHs near sites of past or present human activity. The method appears to be applicable to other situations, including other spills.

  11. Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

    NASA Astrophysics Data System (ADS)

    Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

    2016-01-01

    Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

  12. Use of principal components analysis and three-dimensional atmospheric-transport models for reactor-consequence evaluation

    SciTech Connect

    Gudiksen, P.H.; Walton, J.J.; Alpert, D.J.; Johnson, J.D.

    1982-01-01

    This work explores the use of principal components analysis coupled to three-dimensional atmospheric transport and dispersion models for evaluating the environmental consequences of reactor accidents. This permits the inclusion of meteorological data from multiple sites and the effects of topography in the consequence evaluation; features not normally included in such analyses. The technique identifies prevailing regional wind patterns and their frequencies for use in the transport and dispersion calculations. Analysis of a hypothetical accident scenario involving a release of radioactivity from a reactor situated in a river valley indicated the technique is quite useful whenever recurring wind patterns exist, as is often the case in complex terrain situations. Considerable differences were revealed in a comparison with results obtained from a more conventional Gaussian plume model using only the reactor site meteorology and no topographic effects.

  13. Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

    NASA Astrophysics Data System (ADS)

    dos Santos, T. S.; Mendes, D.; Torres, R. R.

    2015-08-01

    Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANN) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon, Northeastern Brazil and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model out- put and observed monthly precipitation. We used GCMs experiments for the 20th century (RCP Historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANN significantly outperforms the MLR downscaling of monthly precipitation variability.

  14. Proteoglycan concentrations in healthy and diseased articular cartilage by Fourier transform infrared imaging and principal component regression

    NASA Astrophysics Data System (ADS)

    Yin, Jianhua; Xia, Yang

    2014-12-01

    Fourier transform infrared imaging (FTIRI) combining with principal component regression (PCR) analysis were used to determine the reduction of proteoglycan (PG) in articular cartilage after the transection of the anterior cruciate ligament (ACL). A number of canine knee cartilage sections were harvested from the meniscus-covered and meniscus-uncovered medial tibial locations from the control joints, the ACL joints at three time points after the surgery, and their contralateral joints. The PG loss in the ACL cartilage was related positively to the durations after the surgery. The PG loss in the contralateral knees was less than that of the ACL knees. The PG loss in the meniscus-covered cartilage was less than that of the meniscus-uncovered tissue in both ACL and contralateral knees. The quantitative mapping of PG loss could monitor the disease progression and repair processes in arthritis.

  15. Effective use of principal component analysis with high resolution remote sensing data to delineate hydrothermal alteration and carbonate rocks

    NASA Technical Reports Server (NTRS)

    Feldman, Sandra C.

    1987-01-01

    Methods of applying principal component (PC) analysis to high resolution remote sensing imagery were examined. Using Airborne Imaging Spectrometer (AIS) data, PC analysis was found to be useful for removing the effects of albedo and noise and for isolating the significant information on argillic alteration, zeolite, and carbonate minerals. An effective technique for using PC analysis using an input the first 16 AIS bands, 7 intermediate bands, and the last 16 AIS bands from the 32 flat field corrected bands between 2048 and 2337 nm. Most of the significant mineralogical information resided in the second PC. PC color composites and density sliced images provided a good mineralogical separation when applied to a AIS data set. Although computer intensive, the advantage of PC analysis is that it employs algorithms which already exist on most image processing systems.

  16. An application of principal component analysis and logistic regression to facilitate production scheduling decision support system: an automotive industry case

    NASA Astrophysics Data System (ADS)

    Mehrjoo, Saeed; Bashiri, Mahdi

    2013-05-01

    Production planning and control (PPC) systems have to deal with rising complexity and dynamics. The complexity of planning tasks is due to some existing multiple variables and dynamic factors derived from uncertainties surrounding the PPC. Although literatures on exact scheduling algorithms, simulation approaches, and heuristic methods are extensive in production planning, they seem to be inefficient because of daily fluctuations in real factories. Decision support systems can provide productive tools for production planners to offer a feasible and prompt decision in effective and robust production planning. In this paper, we propose a robust decision support tool for detailed production planning based on statistical multivariate method including principal component analysis and logistic regression. The proposed approach has been used in a real case in Iranian automotive industry. In the presence of existing multisource uncertainties, the results of applying the proposed method in the selected case show that the accuracy of daily production planning increases in comparison with the existing method.

  17. Principal components analysis of distal humeral shape in Pliocene to recent African hominids: the contribution of geometric morphometrics.

    PubMed

    Bacon, A M

    2000-04-01

    The shape of the distal humerus in Homo, Pan (P. paniscus and P. troglodytes), Gorilla, and six australopithecines is compared using a geometric approach (Procrustes superimposition of landmarks). Fourteen landmarks are defined on the humerus in a two-dimensional space. Principal components analysis (PCA) is performed on all superimposed coordinates. I have chosen to discuss the precise place of KNM-KP 271 variously assigned to Australopithecus anamensis, Homo sp., or Praeanthropus africanus, in comparison with a sample of australopithecines. AL 288-1, AL 137-48 (Hadar), STW 431 (Sterkfontein), and TM 1517 (Kromdraai) are commonly attributed to Australopithecus afarensis (the two former), Australopithecus africanus, and Paranthropus robustus, respectively, while the taxonomic place of KNM-ER 739 (Homo or Paranthropus?) is not yet clearly defined. The analysis does not emphasize a particular affinity between KNM-KP 271 and modern Homo, nor with A. afarensis, as previously demonstrated (Lague and Jungers [1996] PMID:10727967

  18. 3-Way characterization of soils by Procrustes rotation, matrix-augmented principal components analysis and parallel factor analysis.

    PubMed

    Andrade, J M; Kubista, M; Carlosena, A; Prada, D

    2007-11-01

    Three different approaches for 3-way analyses, namely, Procrustes rotation, parallel factor analysis (PARAFAC) and matrix-augmented principal component analysis, have been compared considering a four-seasons study on soil pollution. Each sampling season comprised 92 roadsoil samples and 12 analytical variables (heavy metals, loss on ignition, pH and humidity). Results show that the three chemometric techniques lead to essentially the same conclusions. Hence, Procrustes rotation, a mathematical technique scarcely applied in analytical chemistry, revealed as a useful tool for 3-way data analysis with potential advantages, including its conceptual simplicity and straightforward interpretation of the results. A novel application of the consensus vectors allowed definition of "consensus scores" so that visualization of the samples and temporal patterns can be made. Results also suggested that the trilinearity assumption imbedded in PARAFAC is essentially fulfilled when studying the temporal evolution of an environmental system where no new pollution sources appear during the course of the study. PMID:17950053

  19. Beyond spheroids and discs: classifications of CANDELS galaxy structure at 1.4 < z < 2 via principal component analysis

    NASA Astrophysics Data System (ADS)

    Peth, Michael A.; Lotz, Jennifer M.; Freeman, Peter E.; McPartland, Conor; Mortazavi, S. Alireza; Snyder, Gregory F.; Barro, Guillermo; Grogin, Norman A.; Guo, Yicheng; Hemmati, Shoubaneh; Kartaltepe, Jeyhan S.; Kocevski, Dale D.; Koekemoer, Anton M.; McIntosh, Daniel H.; Nayyeri, Hooshang; Papovich, Casey; Primack, Joel R.; Simons, Raymond C.

    2016-05-01

    Important but rare and subtle processes driving galaxy morphology and star formation may be missed by traditional spiral, elliptical, irregular or Sérsic bulge/disc classifications. To overcome this limitation, we use a principal component analysis (PCA) of non-parametric morphological indicators (concentration, asymmetry, Gini coefficient, M20, multimode, intensity and deviation) measured at rest-frame B band (corresponding to HST/WFC3 F125W at 1.4 1010 M⊙) galaxy morphologies. PCA quantifies the correlations between these morphological indicators and determines the relative importance of each. The first three principal components (PCs) capture ˜75 per cent of the variance inherent to our sample. We interpret the first PC as bulge strength, the second PC as dominated by concentration and the third PC as dominated by asymmetry. Both PC1 and PC2 correlate with the visual appearance of a central bulge and predict galaxy quiescence. PC1 is a better predictor of quenching than stellar mass, as good as other structural indicators (Sérsic-n or compactness). We divide the PCA results into groups using an agglomerative hierarchical clustering method. Unlike Sérsic, this classification scheme separates compact galaxies from larger, smooth protoelliptical systems, and star-forming disc-dominated clumpy galaxies from star-forming bulge-dominated asymmetric galaxies. Distinguishing between these galaxy structural types in a quantitative manner is an important step towards understanding the connections between morphology, galaxy assembly and star formation.

  20. Global Observations of SO2 and HCHO Using an Innovative Algorithm based on Principal Component Analysis of Satellite Radiance Data

    NASA Astrophysics Data System (ADS)

    Li, Can; Joiner, Joanna; Krotkov, Nickolay; Fioletov, Vitali; McLinden, Chris

    2015-04-01

    We report on the latest progress in the development and application of a new trace gas retrieval algorithm for spaceborne UV-VIS spectrometers. Developed at NASA Goddard Space Flight Center, this algorithm utilizes the principal component analysis (PCA) technique to extract a series of spectral features (principal components or PCs) explaining the variance of measured reflectance spectra. For a species of interests that has no or very small background signals such as SO2 or HCHO, the leading PCs (that explain the most variance) obtained over the clean areas are generally associated with various physical processes (e.g., ozone absorption, rotational Raman scattering) and measurement details (e.g., wavelength shift) other than the signals of interests. By fitting these PCs and pre-computed Jacobians for the target species to a measured radiance spectrum, we can then estimate its atmospheric loading. The PCA algorithm has been operationally implemented to produce the new generation NASA Aura/OMI standard planetary boundary layer (PBL) SO2 product. Comparison with the previous OMI PBL SO2 product indicates that the PCA algorithm reduces the retrieval noise by a factor of two and greatly improves the data quality, allowing detection of smaller point SO2 pollution sources that have not been previously measured from space. We have also demonstrated the algorithm for SO2 retrievals using the new NASA/NOAA S-NPP/OMPS UV spectrometer. For HCHO, the new algorithm shows great promise as evidenced by results obtained from both OMI and OMPS. Finally, we discuss the most recent progress in the algorithm development, including the implementation of a new Jacobians lookup table to more appropriately account for the sensitivity of satellite sensors to various measurement conditions (e.g., viewing geometry, surface reflectance and cloudiness).

  1. Two principal components of solar magnetic field variations and prediction of solar activity on multi-millennium timescale

    NASA Astrophysics Data System (ADS)

    Zharkova, Valentina; Popova, Helen; Zharkov, Sergei; Shepherd, Simon

    2016-07-01

    We present principal components analysis (PCA) of temporal magnetic field variations over the solar cycles 21-24 and their classification with symbolic regression analysis using Hamiltonian method. PCA reveals 4 pairs of magnetic waves with a significant variance and the two principal components with the highest eigen values covering about 40% of this variance. The PC waves are found to have close frequencies while travelling from the opposite hemispheres with an increasing phase shift. Extrapolation of these PCs through their summary curve backward for 5000 years reveals a repeated number of ~350-400 year grand cycles superimposed on 22 year-cycles with the features showing a remarkable resemblance to sunspot activity reported in the past including Maunder, Dalton and Wolf minima, as well as the modern, medieval and roman warmth periods. The summary curve calculated forward for the next millennium predicts further three grand cycles with the closest grand minimum (Maunder minimum) occurring in the forthcoming cycles 25-27 when the two magnetic field waves approach the phase shift of 11 years. We also note a super-grand cycle of about 2000 years which reveal the 5 repeated grand cycles of 350 years with the similar patterns. We discuss a role of other 3 pairs of magnetic waves in shaping the solar activity and compare our predicted curve with the previous predictions of the solar activity on a long timescale based on the terrestrial proxies. These grand cycle variations are probed by Parker's two layer dynamo model with meridional circulation revealing two dynamo waves generated with close frequencies. Their interaction leads to beating effects responsible for the grand cycles (300-350 years) and super-grand cycles of 2000 years superimposed on standard 22 year cycles. This approach opens a new era in investigation and prediction of solar activity on long-term timescales.

  2. Volcanic ash fall events identified using principal component analysis of a high-resolution speleothem trace element dataset

    NASA Astrophysics Data System (ADS)

    Jamieson, Robert A.; Baldini, James U. L.; Frappier, Amy B.; Müller, Wolfgang

    2015-09-01

    Large multivariate trace element datasets produced by LA-ICP-MS speleothem analysis can pose difficulties for analysis and interpretation. Processes acting on various timescales and magnitudes affect trace element concentrations, and deconvolving the most important controls is often complex. Here Principal Component Analysis (PCA) is applied to identify the modes and timings of variation which best explain the overall variability in an exceptionally high-resolution (10 μm vertical resolution) multivariate trace element record produced by LA-ICP-MS from a modern (1979-2001) Belizean stalagmite with excellent age control. Principal Component 1 (PC1) in this dataset is defined by a weak correlation between multiple elements, and may reflect non-carbonate material incorporated within the speleothem. Elevated PC1 scores in ATM-7 occur following regional volcanic eruptions with ash clouds extending over the cave site, as demonstrated using NASA remote sensing data from the Total Ozone Mapping Spectrometer and HYSPLIT trajectory modelling. Spikes in PC1 occur at the beginning of the wet season, and this may reflect a seasonal flushing event that transports volcanogenic material through the karst and incorporates it within the speleothem. Our results suggest that PCA can simplify exploration of large laser ablation datasets, and that PCA is a valuable tool for identifying the dominant controls on stalagmite trace element chemistry. Future studies should evaluate how transferable this technique is to other sites with different environmental conditions where volcanic ashfall has occurred. This research potentially adds tephrochronology to the stalagmite dating toolkit or, conversely, opens the door to using stalagmites to identify previously unknown or uncertainly dated eruptions.

  3. Structure and Principal Components Analyses Reveal an Intervarietal Fusion in Malaysian Mistletoe Fig (Ficus deltoidea Jack) Populations

    PubMed Central

    Zimisuhara, Birifdzi; Valdiani, Alireza; Shaharuddin, Noor Azmi; Qamaruzzaman, Faridah; Maziah, Mahmood

    2015-01-01

    Genetic structure and biodiversity of the medicinal plant Ficus deltoidea have rarely been scrutinized. To fill these lacunae, five varieties, consisting of 30 F. deltoidea accessions were collected across the country and studied on the basis of molecular and morphological data. Molecular analysis of the accessions was performed using nine Inter Simple Sequence Repeat (ISSR) markers, seven of which were detected as polymorphic markers. ISSR-based clustering generated four clusters supporting the geographical distribution of the accessions to some extent. The Jaccard’s similarity coefficient implied the existence of low diversity (0.50–0.75) in the studied population. STRUCTURE analysis showed a low differentiation among the sampling sites, while a moderate varietal differentiation was unveiled with two main populations of F. deltoidea. Our observations confirmed the occurrence of gene flow among the accessions; however, the highest degree of this genetic interference was related to the three accessions of FDDJ10, FDTT16 and FDKT25. These three accessions may be the genetic intervarietal fusion points of the plant’s population. Principal Components Analysis (PCA) relying on quantitative morphological characteristics resulted in two principal components with Eigenvalue >1 which made up 89.96% of the total variation. The cluster analysis performed by the eight quantitative characteristics led to grouping the accessions into four clusters with a Euclidean distance ranged between 0.06 and 1.10. Similarly, a four-cluster dendrogram was generated using qualitative traits. The qualitative characteristics were found to be more discriminating in the cluster and PCA analyses, while ISSRs were more informative on the evolution and genetic structure of the population. PMID:26114389

  4. Quantitative Profiling of Polar Metabolites in Herbal Medicine Injections for Multivariate Statistical Evaluation Based on Independence Principal Component Analysis

    PubMed Central

    Wang, Yuefei; Xu, Lei; Wang, Meng; Zhao, Buchang; Jia, Lifu; Pan, Hao; Zhu, Yan; Gao, Xiumei

    2014-01-01

    Botanical primary metabolites extensively exist in herbal medicine injections (HMIs), but often were ignored to control. With the limitation of bias towards hydrophilic substances, the primary metabolites with strong polarity, such as saccharides, amino acids and organic acids, are usually difficult to detect by the routinely applied reversed-phase chromatographic fingerprint technology. In this study, a proton nuclear magnetic resonance (1H NMR) profiling method was developed for efficient identification and quantification of small polar molecules, mostly primary metabolites in HMIs. A commonly used medicine, Danhong injection (DHI), was employed as a model. With the developed method, 23 primary metabolites together with 7 polyphenolic acids were simultaneously identified, of which 13 metabolites with fully separated proton signals were quantified and employed for further multivariate quality control assay. The quantitative 1H NMR method was validated with good linearity, precision, repeatability, stability and accuracy. Based on independence principal component analysis (IPCA), the contents of 13 metabolites were characterized and dimensionally reduced into the first two independence principal components (IPCs). IPC1 and IPC2 were then used to calculate the upper control limits (with 99% confidence ellipsoids) of χ2 and Hotelling T2 control charts. Through the constructed upper control limits, the proposed method was successfully applied to 36 batches of DHI to examine the out-of control sample with the perturbed levels of succinate, malonate, glucose, fructose, salvianic acid and protocatechuic aldehyde. The integrated strategy has provided a reliable approach to identify and quantify multiple polar metabolites of DHI in one fingerprinting spectrum, and it has also assisted in the establishment of IPCA models for the multivariate statistical evaluation of HMIs. PMID:25157567

  5. Detection of 'archaeological features' among reflectance spectra of natural soils and archaeological soils using principal component analysis (PCA)

    NASA Astrophysics Data System (ADS)

    Choi, Yoon Jung; Lampel, Johannes; Jordan, David; Fiedler, Sabine; Wagner, Thomas

    2016-04-01

    Archaeological terminology 'soil-mark' refers to buried archaeological features being visible on the ground surface. Soil-marks have been identified by archaeologists based on their personal experience and knowledge. This study suggests a quantitative spectral analysis method to detect such archaeological features. This study identifies 'archaeological spectra' (reflectance spectra from surfaces containing archaeological materials) among various soil spectra using PCA (principal component analysis). Based on the results of the PCA, a difference (D) between the original spectrum and modified spectrum, which represents the principal component (PC) values of natural soils, can be determined. If the difference D between the two spectra is small, then the spectrum is similar to the spectral features of natural soils. If not, it identifies that the spectrum is more likely to be non-natural soil, probably an archaeological material. The method is applied on soil spectra from a prehistoric settlement site in Calabria, Italy. For the spectral range between 400 to 700nm, the difference value D for archaeological material ranges from 0.11 to 0.73 (the value varies depending on the number of PCs used). For natural soil, D ranges only from 0.04 to 0.09. The results shows D value is significantly larger for archaeological spectra, which indicates that the method can be applied to identify archaeological material among an unknown group of soil spectra, if a set of samples of natural soils exists. The study will present results of applying this method to various wavelength ranges and spectra from different sites. The major aim is to find optimised settings of the PCA method which can be applied in a universal way for identifying archaeological spectra.

  6. Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra

    NASA Astrophysics Data System (ADS)

    Unglert, K.; Radić, V.; Jellinek, A. M.

    2016-06-01

    Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as self-organizing maps (SOM) and principal component analysis (PCA) can help to quickly and automatically identify important patterns related to impending eruptions. For the first time, we evaluate the performance of SOM and PCA on synthetic volcano seismic spectra constructed from observations during two well-studied eruptions at Klauea Volcano, Hawai'i, that include features observed in many volcanic settings. In particular, our objective is to test which of the techniques can best retrieve a set of three spectral patterns that we used to compose a synthetic spectrogram. We find that, without a priori knowledge of the given set of patterns, neither SOM nor PCA can directly recover the spectra. We thus test hierarchical clustering, a commonly used method, to investigate whether clustering in the space of the principal components and on the SOM, respectively, can retrieve the known patterns. Our clustering method applied to the SOM fails to detect the correct number and shape of the known input spectra. In contrast, clustering of the data reconstructed by the first three PCA modes reproduces these patterns and their occurrence in time more consistently. This result suggests that PCA in combination with hierarchical clustering is a powerful practical tool for automated identification of characteristic patterns in volcano seismic spectra. Our results indicate that, in contrast to PCA, common clustering algorithms may not be ideal to group patterns on the SOM and that it is crucial to evaluate the performance of these tools on a control dataset prior to their application to real data.

  7. Comparison of five Lonicera flowers by simultaneous determination of multi-components with single reference standard method and principal component analysis.

    PubMed

    Gao, Wen; Wang, Rui; Li, Dan; Liu, Ke; Chen, Jun; Li, Hui-Jun; Xu, Xiaojun; Li, Ping; Yang, Hua

    2016-01-01

    The flowers of Lonicera japonica Thunb. were extensively used to treat many diseases. As the demands for L. japonica increased, some related Lonicera plants were often confused or misused. Caffeoylquinic acids were always regarded as chemical markers in the quality control of L. japonica, but they could be found in all Lonicera species. Thus, a simple and reliable method for the evaluation of different Lonicera flowers is necessary to be established. In this work a method based on single standard to determine multi-components (SSDMC) combined with principal component analysis (PCA) for control and distinguish of Lonicera species flowers have been developed. Six components including three caffeoylquinic acids and three iridoid glycosides were assayed simultaneously using chlorogenic acid as the reference standard. The credibility and feasibility of the SSDMC method were carefully validated and the results demonstrated that there were no remarkable differences compared with external standard method. Finally, a total of fifty-one batches covering five Lonicera species were analyzed and PCA was successfully applied to distinguish the Lonicera species. This strategy simplifies the processes in the quality control of multiple-componential herbal medicine which effectively adapted for improving the quality control of those herbs belonging to closely related species. PMID:26432385

  8. Integral field unit spectroscopy of 10 early-type galactic nuclei - I. Principal component analysis Tomography and nuclear activity

    NASA Astrophysics Data System (ADS)

    Ricci, T. V.; Steiner, J. E.; Menezes, R. B.

    2014-05-01

    Most massive galaxies show emission lines that can be characterized as LINERs. To what extent this emission is related to AGNs or to stellar processes is still an open question. In this paper, we analysed a sample of such galaxies to study the central region in terms of nuclear and circumnuclear emission lines, as well as the stellar component properties. For this reason, we selected 10 massive (σ > 200 km s-1) nearby (d < 31 Mpc) galaxies and observed them with the IFU/GMOS (integral field unit/Gemini Multi-Object Spectrograph) spectrograph on the Gemini South Telescope. The data were analysed with principal component analysis (PCA) Tomography to assess the main properties of the objects. Two spectral regions were analysed: a yellow region (5100-5800 Å), adequate to show the properties of the stellar component, and a red region (6250-6800 Å), adequate to analyse the gaseous component. We found that all objects previously known to present emission lines have a central AGN-type emitting source. They also show gaseous and stellar kinematics typical of discs. Such discs may be co-aligned (NGC 1380 and ESO 208 G-21), in counter-rotation (IC 1459 and NGC 7097) or misaligned (IC 5181 and NGC 4546). We also found one object with a gaseous disc but no stellar disc (NGC 2663), one with a stellar disc but no gaseous disc (NGC 1404), one with neither stellar nor gaseous disc (NGC 1399) and one with probably ionization cones (NGC 3136). PCA Tomography is an efficient method for detecting both the central AGN and gaseous and stellar discs. In the two cases (NGC 1399 and NGC 1404) in which no lines were previously reported, we found no evidence of either nuclear or circumnuclear emission, using PCA Tomography only.

  9. Principal Component Analysis of Doppler Radar Data. Part I: Geometric Connections between Eigenvectors and the Core Region of Atmospheric Vortices.

    NASA Astrophysics Data System (ADS)

    Harasti, Paul R.; List, Roland

    2005-11-01

    This is the first in a three-part series of papers that present the first applications of principal component analysis (PCA) to Doppler radar data. Although this novel approach has potential applications to many types of atmospheric phenomena, the specific goal of this series is to describe and verify a methodology that establishes the position and radial extent of the core region of atmospheric vortices. The underlying assumption in the current application is that the streamlines of the nondivergent component of the horizontal wind are predominantly circular, which is a characteristic often observed in intense vortices such as tropical cyclones.The method employs an S2-mode PCA on the Doppler velocity data taken from a single surveillance scan and arranged sequentially in a matrix according to the range and azimuth coordinates. Part I begins the series by examining the eigenvectors obtained from such a PCA applied to a Doppler velocity model for a modified, Rankine-combined vortex, where the ratio of the radius of maximum wind to the range from the radar to the circulation center is varied over a wide range of values typically encountered in the field. Results show that the first two eigenvectors within the eigenspace of range coordinates represent over 99% of the total variance in the data. It is also demonstrated that the coordinates of particular cusps in the curves of the eigenvector coefficients plotted against their indices are geometrically related to both the position of circulation center and the radius of maximum wind.

  10. Chemical and principal-component analyses of the essential oils of Apioideae taxa (Apiaceae) from central Balkan.

    PubMed

    Kapetanos, Chrysostomos; Karioti, Anastasia; Bojović, Srdjan; Marin, Petar; Veljić, Milan; Skaltsa, Helen

    2008-01-01

    The volatile constituents of the essential oils of 23 taxa belonging to the Apioideae subfamily were studied in detail. The investigated taxa were Pimpinella serbica (Vis.) Bentham & Hooker, Libanotis montana Cr., Cnidium silaifolium (Jacq.) Simk. ssp. orientale (Boiss.) Tutin, Bupleurum praealtum L., B. sibthorpianum S. S. var. diversifolium (Roch.) Hay, Aegopodium podagraria L., Torilis anthriscus (L.) Gmel., Orlaya grandiflora (L.) Hoffm., Laserpitium siler L., Laser trilobum (L.) Brokh., Chaerophyllum aureum L., C. hirsutum L., C. temulum L., Pastinaca sativa L., P. hirsuta Pancic., Tordylium maximum L., Physospermum cornubiense (L.) DC., Peucedanum alsaticum L., P. oreoselinum (L.) Moench, P. cervaria (L.) Cuss., P. austriacum (Jacq.) Koch, P. longifolium W. et K., and P. officinale L. All of these species grow wild in the central part of the Balkan Peninsula. The essential oils were found to be complex mixtures of various compounds, more than 100 constituents being in each taxon, with contributions of main products never exceeding 25% of the total content. Sesquiterpene hydrocarbons were found to be the main group of constituents of all taxa, except for Peucedanum species, where monoterpene hydrocarbons were identified as the main components. The chemotaxonomic value of the essential-oil composition is discussed according to results of principal-component analysis (PCA). The essential-oil composition mainly reflects current taxonomic relationships between the investigated taxa. PMID:18205131

  11. A Neuro-Fuzzy Inference System Combining Wavelet Denoising, Principal Component Analysis, and Sequential Probability Ratio Test for Sensor Monitoring

    SciTech Connect

    Na, Man Gyun; Oh, Seungrohk

    2002-11-15

    A neuro-fuzzy inference system combined with the wavelet denoising, principal component analysis (PCA), and sequential probability ratio test (SPRT) methods has been developed to monitor the relevant sensor using the information of other sensors. The parameters of the neuro-fuzzy inference system that estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system, and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors.

  12. ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap

    PubMed Central

    Metsalu, Tauno; Vilo, Jaak

    2015-01-01

    The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of high-dimensional data, often followed by visualizing two of the components on the scatterplot. Although widely used, the method is lacking an easy-to-use web interface that scientists with little programming skills could use to make plots of their own data. The same applies to creating heatmaps: it is possible to add conditional formatting for Excel cells to show colored heatmaps, but for more advanced features such as clustering and experimental annotations, more sophisticated analysis tools have to be used. We present a web tool called ClustVis that aims to have an intuitive user interface. Users can upload data from a simple delimited text file that can be created in a spreadsheet program. It is possible to modify data processing methods and the final appearance of the PCA and heatmap plots by using drop-down menus, text boxes, sliders etc. Appropriate defaults are given to reduce the time needed by the user to specify input parameters. As an output, users can download PCA plot and heatmap in one of the preferred file formats. This web server is freely available at http://biit.cs.ut.ee/clustvis/. PMID:25969447

  13. Spatiotemporal Filtering Using Principal Component Analysis and Karhunen-Loeve Expansion Approaches for Regional GPS Network Analysis

    NASA Technical Reports Server (NTRS)

    Dong, D.; Fang, P.; Bock, F.; Webb, F.; Prawirondirdjo, L.; Kedar, S.; Jamason, P.

    2006-01-01

    Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering.We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

  14. Additive and non-additive genetic components of the jack male life history in Chinook salmon (Oncorhynchus tshawytscha).

    PubMed

    Forest, Adriana R; Semeniuk, Christina A D; Heath, Daniel D; Pitcher, Trevor E

    2016-08-01

    Chinook salmon, Oncorhynchus tshawytscha, exhibit alternative reproductive tactics (ARTs) where males exist in two phenotypes: large "hooknose" males and smaller "jacks" that reach sexual maturity after only 1 year in seawater. The mechanisms that determine "jacking rate"-the rate at which males precociously sexually mature-are known to involve both genetics and differential growth rates, where individuals that become jacks exhibit higher growth earlier in life. The additive genetic components have been studied and it is known that jack sires produce significantly more jack offspring than hooknose sires, and vice versa. The current study was the first to investigate both additive and non-additive genetic components underlying jacking through the use of a full-factorial breeding design using all hooknose sires. The effect of dams and sires descendant from a marker-assisted broodstock program that identified "high performance" and "low performance" lines using growth- and survival-related gene markers was also studied. Finally, the relative growth of jack, hooknose, and female offspring was examined. No significant dam, sire, or interaction effects were observed in this study, and the maternal, additive, and non-additive components underlying jacking were small. Differences in jacking rates in this study were determined by dam performance line, where dams that originated from the low performance line produced significantly more jacks. Jack offspring in this study had a significantly larger body size than both hooknose males and females starting 1 year post-fertilization. This study provides novel information regarding the genetic architecture underlying ARTs in Chinook salmon that could have implications for the aquaculture industry, where jacks are not favoured due to their small body size and poor flesh quality. PMID:27450674

  15. Hybrid Application of Model Reconstruction, Principal Components, and Multifractal Analyses for Detection of Phenomena in Radar Backscatter

    NASA Astrophysics Data System (ADS)

    Borissova, M. N.; Shirer, H. N.

    2001-05-01

    Remotely sensing cirrus clouds with a 35 GHz radar is made difficult by the fact that insects flying through the radar beam backscatter signals that are statistically and geometrically similar to those from ice crystals. The distinction between ice crystals in cirrus clouds and insects flying in the boundary layer is achieved by creating a hybrid procedure, which synergistically combines the power of the Model Reconstruction Algorithm (MRA), Principal Components Analysis (PCA) and MultiFractal Analysis (MFA). This procedure isolates and identifies objectively the typical fractal structures of ice crystals and insects that are embedded in the radar backscattering signals. MRA is used to determine the length of correlated subsets in the data series and to construct the multivariate data arrays containing the principal variance structures. PCA then is employed to separate the typical structures of the ice-crystal signals from those of the insects that are captured in the multivariate arrays. MFA is applied for final separation of the two types of signals. To demonstrate this separation, the results are presented on the traditional bifractal plane of Davis and collaborators and in a new three-dimensional separation space, in which a third parameter related to the variance of the power of the signals is added to the two multifractal parameters. The new procedure is tested on 50 ice-crystal and 50 insect series that were collected in June 1998 using a single-channel, narrow-beam radar located at the Atmospheric Radiation Measurement Facility in Lemont, Oklahoma. A 98% data separation is achieved using the three-parameter depiction, in contrast to the 87% separation obtained using the two traditional multifractal parameters enhanced by PCA filtering, and the 50% separation resulting from the unenhanced bifractal analysis of the original signals.

  16. Liquid Chromatography-diode Array Detector-electrospray Mass Spectrometry and Principal Components Analyses of Raw and Processed Moutan Cortex

    PubMed Central

    Deng, Xian-Mei; Yu, Jiang-Yong; Ding, Meng-Jin; Zhao, Ming; Xue, Xing-Yang; Che, Chun-Tao; Wang, Shu-Mei; Zhao, Bin; Meng, Jiang

    2016-01-01

    Background: Raw Moutan Cortex (RMC), derived from the root bark of Paeonia suffruticosa, and Processed Moutan Cortex (PMC) is obtained from RMC by undergoing a stir-frying process. Both of them are indicated for different pharmacodynamic action in traditional Chinese medicine, and they have been used in China and other Asian countries for thousands of years. Objective: To establish a method to study the RMC and PMC, revealing their different chemical composition by fingerprint, qualitative, and quantitative ways. Materials and Methods: High-performance liquid chromatography coupled with diode array detector and electrospray mass spectrometry (HPLC-DAD-ESIMS) were used for the analysis. Therefore, the analytes were separated on an Ultimate TM XB-C18 analytical column (250 mm × 4.6 mm, 5.0 μm) with a gradient elution program by a mobile phase consisting of acetonitrile and 0.1% (v/v) formic acid water solution. The flow rate, injection volume, detection wavelength, and column temperature were set at 1.0 mL/min, 10 μL, 254 nm, and 30°C, respectively. Besides, principal components analysis and the test of significance were applied in data analysis. Results: The results clearly showed a significant difference among RMC and PMC, indicating the significant changes in their chemical compositions before and after the stir-frying process. Conclusion: The HPLC-DAD-ESIMS coupled with chemometrics analysis could be used for comprehensive quality evaluation of raw and processed Moutan Cortex. SUMMARY The experiment study the RMC and PMC by HPLC-DAD-ESIMS couple with chemometrics analysis. The results of their fingerprints, qualitative, and quantitative all clearly showed significant changes in their chemical compositions before and after stir-frying processed. Abbreviation used: HPLC-DAD-ESIMS: High-performance Liquid Chromatography-Diode Array Detector-Electrospray Mass Spectrometry, RMC: Raw moutan cortex, PMC: Processed moutan cortex, TCM: Traditional Chinese medicine

  17. Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis

    PubMed Central

    2011-01-01

    Background The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA). Method Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence. Results and Discussion The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD. Conclusion This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and

  18. Automated principal component-based orthogonal signal correction applied to fused near infrared-mid-infrared spectra of French olive oils.

    PubMed

    Harrington, Peter de B; Kister, Jacky; Artaud, Jacques; Dupuy, Nathalie

    2009-09-01

    An approach for automating the determination of the number of components in orthogonal signal correction (OSC) has been devised. In addition, a novel principal component OSC (PC-OSC) is reported that builds softer models for removing background from signals and is much faster than the partial least-squares (PLS) based OSC algorithm. These signal correction methods were evaluated by classifying fused near- and mid-infrared spectra of French olive oils by geographic origin. Two classification methods, partial least-squares-discriminant analysis (PLS-DA) and a fuzzy rule-building expert system (FuRES), were used to evaluate the signal correction of the fused vibrational spectra from the olive oils. The number of components was determined by using bootstrap Latin partitions (BLPs) in the signal correction routine and maximizing the average projected difference resolution (PDR). The same approach was used to select the number of latent variables in the PLS-DA evaluation and perfect classification was obtained. Biased PLS-DA models were also evaluated that optimized the number of latent variables to yield the minimum prediction error. Fuzzy or soft classification systems benefit from background removal. The FuRES prediction results did not differ significantly from the results that were obtained using either the unbiased or biased PLS-DA methods, but was an order of magnitude faster in the evaluations when a sufficient number of PC-OSC components were selected. The importance of bootstrapping was demonstrated for the automated OSC and PC-OSC methods. In addition, the PLS-DA algorithms were also automated using BLPs and proved effective. PMID:19655711

  19. Rear shape in 3 dimensions summarized by principal component analysis is a good predictor of body condition score in Holstein dairy cows.

    PubMed

    Fischer, A; Luginbühl, T; Delattre, L; Delouard, J M; Faverdin, P

    2015-07-01

    Body condition is an indirect estimation of the level of body reserves, and its variation reflects cumulative variation in energy balance. It interacts with reproductive and health performance, which are important to consider in dairy production but not easy to monitor. The commonly used body condition score (BCS) is time consuming, subjective, and not very sensitive. The aim was therefore to develop and validate a method assessing BCS with 3-dimensional (3D) surfaces of the cow's rear. A camera captured 3D shapes 2 m from the floor in a weigh station at the milking parlor exit. The BCS was scored by 3 experts on the same day as 3D imaging. Four anatomical landmarks had to be identified manually on each 3D surface to define a space centered on the cow's rear. A set of 57 3D surfaces from 56 Holstein dairy cows was selected to cover a large BCS range (from 0.5 to 4.75 on a 0 to 5 scale) to calibrate 3D surfaces on BCS. After performing a principal component analysis on this data set, multiple linear regression was fitted on the coordinates of these surfaces in the principal components' space to assess BCS. The validation was performed on 2 external data sets: one with cows used for calibration, but at a different lactation stage, and one with cows not used for calibration. Additionally, 6 cows were scanned once and their surfaces processed 8 times each for repeatability and then these cows were scanned 8 times each the same day for reproducibility. The selected model showed perfect calibration and a good but weaker validation (root mean square error=0.31 for the data set with cows used for calibration; 0.32 for the data set with cows not used for calibration). Assessing BCS with 3D surfaces was 3 times more repeatable (standard error=0.075 versus 0.210 for BCS) and 2.8 times more reproducible than manually scored BCS (standard error=0.103 versus 0.280 for BCS). The prediction error was similar for both validation data sets, indicating that the method is not less

  20. Clustering of immunological, metabolic and genetic features in latent autoimmune diabetes in adults: evidence from principal component analysis.

    PubMed

    Pes, Giovanni Mario; Delitala, Alessandro Palmerio; Errigo, Alessandra; Delitala, Giuseppe; Dore, Maria Pina

    2016-06-01

    Latent autoimmune diabetes in adults (LADA) which accounts for more than 10 % of all cases of diabetes is characterized by onset after age 30, absence of ketoacidosis, insulin independence for at least 6 months, and presence of circulating islet-cell antibodies. Its marked heterogeneity in clinical features and immunological markers suggests the existence of multiple mechanisms underlying its pathogenesis. The principal component (PC) analysis is a statistical approach used for finding patterns in data of high dimension. In this study the PC analysis was applied to a set of variables from a cohort of Sardinian LADA patients to identify a smaller number of latent patterns. A list of 11 variables including clinical (gender, BMI, lipid profile, systolic and diastolic blood pressure and insulin-free time period), immunological (anti-GAD65, anti-IA-2 and anti-TPO antibody titers) and genetic features (predisposing gene variants previously identified as risk factors for autoimmune diabetes) retrieved from clinical records of 238 LADA patients referred to the Internal Medicine Unit of University of Sassari, Italy, were analyzed by PC analysis. The predictive value of each PC on the further development of insulin dependence was evaluated using Kaplan-Meier curves. Overall 4 clusters were identified by PC analysis. In component PC-1, the dominant variables were: BMI, triglycerides, systolic and diastolic blood pressure and duration of insulin-free time period; in PC-2: genetic variables such as Class II HLA, CTLA-4 as well as anti-GAD65, anti-IA-2 and anti-TPO antibody titers, and the insulin-free time period predominated; in PC-3: gender and triglycerides; and in PC-4: total cholesterol. These components explained 18, 15, 12, and 12 %, respectively, of the total variance in the LADA cohort. The predictive power of insulin dependence of the four components was different. PC-2 (characterized mostly by high antibody titers and presence of predisposing genetic markers