NASA Astrophysics Data System (ADS)
Xiang, M.-S.; Liu, X.-W.; Shi, J.-R.; Yuan, H.-B.; Huang, Y.; Luo, A.-L.; Zhang, H.-W.; Zhao, Y.-H.; Zhang, J.-N.; Ren, J.-J.; Chen, B.-Q.; Wang, C.; Li, J.; Huo, Z.-Y.; Zhang, W.; Wang, J.-L.; Zhang, Y.; Hou, Y.-H.; Wang, Y.-F.
2017-01-01
Accurate determination of stellar atmospheric parameters and elemental abundances is crucial for Galactic archaeology via large-scale spectroscopic surveys. In this paper, we estimate stellar atmospheric parameters - effective temperature Teff, surface gravity log g and metallicity [Fe/H], absolute magnitudes MV and MKs, α-element to metal (and iron) abundance ratio [α/M] (and [α/Fe]), as well as carbon and nitrogen abundances [C/H] and [N/H] from the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) spectra with a multivariate regression method based on kernel-based principal component analysis, using stars in common with other surveys (Hipparcos, Kepler, Apache Point Observatory Galactic Evolution Experiment) as training data sets. Both internal and external examinations indicate that given a spectral signal-to-noise ratio (SNR) better than 50, our method is capable of delivering stellar parameters with a precision of ˜100 K for Teff, ˜0.1 dex for log g, 0.3-0.4 mag for MV and MKs, 0.1 dex for [Fe/H], [C/H] and [N/H], and better than 0.05 dex for [α/M] ([α/Fe]). The results are satisfactory even for a spectral SNR of 20. The work presents first determinations of [C/H] and [N/H] abundances from a vast data set of LAMOST, and, to our knowledge, the first reported implementation of absolute magnitude estimation directly based on a vast data set of observed spectra. The derived stellar parameters for millions of stars from the LAMOST surveys will be publicly available in the form of value-added catalogues.
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
NASA Astrophysics Data System (ADS)
Larson, Timothy; Gould, Timothy; Riley, Erin A.; Austin, Elena; Fintzi, Jonathan; Sheppard, Lianne; Yost, Michael; Simpson, Christopher
2017-03-01
We have applied the absolute principal component scores (APCS) receptor model to on-road, background-adjusted measurements of NOx, CO, CO2, black carbon (BC), and particle number (PN) obtained from a continuously moving platform deployed over nine afternoon sampling periods in Seattle, WA. Two Varimax-rotated principal component features described 75% of the overall variance of the observations. A heavy-duty vehicle feature was correlated with black carbon and particle number, whereas a light-duty feature was correlated with CO and CO2. NOx had moderate correlation with both features. The bootstrapped APCS model predictions were used to estimate area-wide, average fuel-based emission factors and their respective 95% confidence limits. The average emission factors for NOx, CO, BC and PN (14.8, 18.9, 0.40 g/kg, and 4.3 × 1015 particles/kg for heavy duty vehicles, and 3.2, 22.4, 0.016 g/kg, and 0.19 × 1015 particles/kg for light-duty vehicles, respectively) are consistent with previous estimates based on remote sensing, vehicle chase studies, and recent dynamometer tests. Information on the spatial distribution of the concentrations contributed by these two vehicle categories relative to background during the sampling period was also obtained.
Recursive principal components analysis.
Voegtlin, Thomas
2005-10-01
A recurrent linear network can be trained with Oja's constrained Hebbian learning rule. As a result, the network learns to represent the temporal context associated to its input sequence. The operation performed by the network is a generalization of Principal Components Analysis (PCA) to time-series, called Recursive PCA. The representations learned by the network are adapted to the temporal statistics of the input. Moreover, sequences stored in the network may be retrieved explicitly, in the reverse order of presentation, thus providing a straight-forward neural implementation of a logical stack.
Inductive robust principal component analysis.
Bao, Bing-Kun; Liu, Guangcan; Xu, Changsheng; Yan, Shuicheng
2012-08-01
In this paper we address the error correction problem that is to uncover the low-dimensional subspace structure from high-dimensional observations, which are possibly corrupted by errors. When the errors are of Gaussian distribution, Principal Component Analysis (PCA) can find the optimal (in terms of least-square-error) low-rank approximation to highdimensional data. However, the canonical PCA method is known to be extremely fragile to the presence of gross corruptions. Recently, Wright et al. established a so-called Robust Principal Component Analysis (RPCA) method, which can well handle grossly corrupted data [14]. However, RPCA is a transductive method and does not handle well the new samples which are not involved in the training procedure. Given a new datum, RPCA essentially needs to recalculate over all the data, resulting in high computational cost. So, RPCA is inappropriate for the applications that require fast online computation. To overcome this limitation, in this paper we propose an Inductive Robust Principal Component Analysis (IRPCA) method. Given a set of training data, unlike RPCA that targets on recovering the original data matrix, IRPCA aims at learning the underlying projection matrix, which can be used to efficiently remove the possible corruptions in any datum. The learning is done by solving a nuclear norm regularized minimization problem, which is convex and can be solved in polynomial time. Extensive experiments on a benchmark human face dataset and two video surveillance datasets show that IRPCA can not only be robust to gross corruptions, but also handle well the new data in an efficient way.
Principal components analysis competitive learning.
López-Rubio, Ezequiel; Ortiz-de-Lazcano-Lobato, Juan Miguel; Muñoz-Pérez, José; Gómez-Ruiz, José Antonio
2004-11-01
We present a new neural model that extends the classical competitive learning by performing a principal components analysis (PCA) at each neuron. This model represents an improvement with respect to known local PCA methods, because it is not needed to present the entire data set to the network on each computing step. This allows a fast execution while retaining the dimensionality-reduction properties of the PCA. Furthermore, every neuron is able to modify its behavior to adapt to the local dimensionality of the input distribution. Hence, our model has a dimensionality estimation capability. The experimental results we present show the dimensionality-reduction capabilities of the model with multisensor images.
Fast Steerable Principal Component Analysis
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
Fast Steerable Principal Component Analysis.
Zhao, Zhizhen; Shkolnisky, Yoel; Singer, Amit
2016-03-01
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(nL(3) + L(4)), while existing algorithms take O(nL(4)). 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.
Nonlinear principal component analysis of climate data
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.
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…
Bayesian robust principal component analysis.
Ding, Xinghao; He, Lihan; Carin, Lawrence
2011-12-01
A hierarchical Bayesian model is considered for decomposing a matrix into low-rank and sparse components, assuming the observed matrix is a superposition of the two. The matrix is assumed noisy, with unknown and possibly non-stationary noise statistics. The Bayesian framework infers an approximate representation for the noise statistics while simultaneously inferring the low-rank and sparse-outlier contributions; the model is robust to a broad range of noise levels, without having to change model hyperparameter settings. In addition, the Bayesian framework allows exploitation of additional structure in the matrix. For example, in video applications each row (or column) corresponds to a video frame, and we introduce a Markov dependency between consecutive rows in the matrix (corresponding to consecutive frames in the video). The properties of this Markov process are also inferred based on the observed matrix, while simultaneously denoising and recovering the low-rank and sparse components. We compare the Bayesian model to a state-of-the-art optimization-based implementation of robust PCA; considering several examples, we demonstrate competitive performance of the proposed model.
Dynamic competitive probabilistic principal components analysis.
López-Rubio, Ezequiel; Ortiz-DE-Lazcano-Lobato, Juan Miguel
2009-04-01
We present a new neural model which extends the classical competitive learning (CL) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. The model also has the ability to learn the number of basis vectors required to represent the principal directions of each cluster, so it overcomes a drawback of most local PCA models, where the dimensionality of a cluster must be fixed a priori. Experimental results are presented to show the performance of the network with multispectral image data.
Principal Component Analysis of Brown Dwarfs
NASA Astrophysics Data System (ADS)
Cleary, Colleen; Rodriguez, David
2017-01-01
Principal component analysis is a technique for reducing variables and emphasizing patterns in a data set. In this study, the data set consisted of the attributes of 174 brown dwarfs. The PCA was performed on several photometric measurements in near-infrared wavelengths and colors in order to determine if these variables showed a correlation with the physical parameters. This research resulted in two separate models that predict luminosity and temperature. The application of principal component analysis on the near-infrared photometric measurements and colors of brown dwarfs, along with models, provides alternate methods for predicting the luminosity and temperature of brown dwarfs using only photometric measurements.
Stochastic convex sparse principal component analysis.
Baytas, Inci M; Lin, Kaixiang; Wang, Fei; Jain, Anil K; Zhou, Jiayu
2016-12-01
Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present in the data. However, there is an obvious disadvantage of traditional PCA when it is applied to analyze data where interpretability is important. In applications, where the features have some physical meanings, we lose the ability to interpret the principal components extracted by conventional PCA because each principal component is a linear combination of all the original features. For this reason, sparse PCA has been proposed to improve the interpretability of traditional PCA by introducing sparsity to the loading vectors of principal components. The sparse PCA can be formulated as an ℓ1 regularized optimization problem, which can be solved by proximal gradient methods. However, these methods do not scale well because computation of the exact gradient is generally required at each iteration. Stochastic gradient framework addresses this challenge by computing an expected gradient at each iteration. Nevertheless, stochastic approaches typically have low convergence rates due to the high variance. In this paper, we propose a convex sparse principal component analysis (Cvx-SPCA), which leverages a proximal variance reduced stochastic scheme to achieve a geometric convergence rate. We further show that the convergence analysis can be significantly simplified by using a weak condition which allows a broader class of objectives to be applied. The efficiency and effectiveness of the proposed method are demonstrated on a large-scale electronic medical record cohort.
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...
PCA: Principal Component Analysis for spectra modeling
NASA Astrophysics Data System (ADS)
Hurley, Peter D.; Oliver, Seb; Farrah, Duncan; Wang, Lingyu; Efstathiou, Andreas
2012-07-01
The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. This code, written in IDL, classifies principal components of IRS spectra to define a new classification scheme using 5D Gaussian mixtures modelling. The five PCs and average spectra for the four classifications to classify objects are made available with the code.
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.
Complex principal components for robust motion estimation.
Mauldin, F William; Viola, Francesco; Walker, William F
2010-11-01
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
PROJECTED PRINCIPAL COMPONENT ANALYSIS IN FACTOR MODELS
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
Machine condition monitoring using principal component representations
NASA Astrophysics Data System (ADS)
He, Qingbo; Yan, Ruqiang; Kong, Fanrang; Du, Ruxu
2009-02-01
The purpose of this paper is to find the low-dimensional principal component (PC) representations from the statistical features of the measured signals to characterize and hence, monitor machine conditions. The PC representations can be automatically extracted using the principal component analysis (PCA) technique from the time- and frequency-domains statistical features of the measured signals. First, a mean correlation rule is proposed to evaluate the capability of each of the PCs in characterizing machine conditions and to select the most representative PCs to classify machine fault patterns. Then a procedure that uses the low-dimensional PC representations for machine condition monitoring is proposed. The experimental results from an internal-combustion engine sound analysis and an automobile gearbox vibration analysis show that the proposed method is effective for machine condition monitoring.
Multilevel sparse functional principal component analysis.
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.
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.
The principal components of response strength.
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
Nonlinear principal component analysis of climate data
NASA Astrophysics Data System (ADS)
Monahan, Adam Hugh
2000-11-01
A nonlinear generalisation of Principal Component Analysis (PCA), denoted Nonlinear Principal Component Analysis (NLPCA), is introduced and applied to the analysis of climate data. It is found empirically that NLPCA partitions variance in the same fashion as does PCA. An important distinction is drawn between a modal P-dimensional NLPCA analysis, in which the approximation is the sum of P nonlinear functions of one variable, and a nonmodal analysis, in which the P-dimensional NLPCA approximation is determined as a nonlinear non- additive function of P variables. Nonlinear Principal Component Analysis is first applied to a data set sampled from the Lorenz attractor. The 1D and 2D NLPCA approximations explain 76% and 99.5% of the total variance, respectively, in contrast to 60% and 95% explained by the 1D and 2D PCA approximations. When applied to a data set consisting of monthly-averaged tropical Pacific Ocean sea surface temperatures (SST), the modal 1D NLPCA approximation describes average variability associated with the El Niño/Southern Oscillation (ENSO) phenomenon, as does the 1D PCA approximation. The NLPCA approximation, however, characterises the asymmetry in spatial pattern of SST anomalies between average warm and cold events in a manner that the PCA approximation cannot. The second NLPCA mode of SST is found to characterise differences in ENSO variability between individual events, and in particular is consistent with the celebrated 1977 ``regime shift''. A 2D nonmodal NLPCA approximation is determined, the interpretation of which is complicated by the fact that a secondary feature extraction problem has to be carried out. It is found that this approximation contains much the same information as that provided by the modal analysis. A modal NLPC analysis of tropical Indo-Pacific sea level pressure (SLP) finds that the first mode describes average ENSO variability in this field, and also characterises an asymmetry in SLP fields between average warm and
The principal components of response strength.
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.
Principal components analysis of Jupiter VIMS spectra
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.
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.
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.
Extraction of principal components from biosignals by neural net.
Krajca, V; Principe, J C; Petránek, S
1999-01-01
This contribution gives the information on a useful application of principal component analysis (PCA) in the field of electroencephalogram (EEG) and laser-Doppler signal processing. The principal components are estimated by a neural network (NN) approach.
On the Fallibility of Principal Components in Research
ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.; Li, Tenglong
2017-01-01
The measurement error in principal components extracted from a set of fallible measures is discussed and evaluated. It is shown that as long as one or more measures in a given set of observed variables contains error of measurement, so also does any principal component obtained from the set. The error variance in any principal component is shown…
Incorporating principal component analysis into air quality ...
The efficacy of standard air quality model evaluation techniques is becoming compromised as the simulation periods continue to lengthen in response to ever increasing computing capacity. Accordingly, the purpose of this paper is to demonstrate a statistical approach called Principal Component Analysis (PCA) with the intent of motivating its use by the evaluation community. One of the main objectives of PCA is to identify, through data reduction, the recurring and independent modes of variations (or signals) within a very large dataset, thereby summarizing the essential information of that dataset so that meaningful and descriptive conclusions can be made. In this demonstration, PCA is applied to a simple evaluation metric – the model bias associated with EPA's Community Multi-scale Air Quality (CMAQ) model when compared to weekly observations of sulfate (SO42−) and ammonium (NH4+) ambient air concentrations measured by the Clean Air Status and Trends Network (CASTNet). The advantages of using this technique are demonstrated as it identifies strong and systematic patterns of CMAQ model bias across a myriad of spatial and temporal scales that are neither constrained to geopolitical boundaries nor monthly/seasonal time periods (a limitation of many current studies). The technique also identifies locations (station–grid cell pairs) that are used as indicators for a more thorough diagnostic evaluation thereby hastening and facilitating understanding of the prob
Compressive-projection principal component analysis.
Fowler, James E
2009-10-01
Principal component analysis (PCA) is often central to dimensionality reduction and compression in many applications, yet its data-dependent nature as a transform computed via expensive eigendecomposition often hinders its use in severely resource-constrained settings such as satellite-borne sensors. A process is presented that effectively shifts the computational burden of PCA from the resource-constrained encoder to a presumably more capable base-station decoder. The proposed approach, compressive-projection PCA (CPPCA), is driven by projections at the sensor onto lower-dimensional subspaces chosen at random, while the CPPCA decoder, given only these random projections, recovers not only the coefficients associated with the PCA transform, but also an approximation to the PCA transform basis itself. An analysis is presented that extends existing Rayleigh-Ritz theory to the special case of highly eccentric distributions; this analysis in turn motivates a reconstruction process at the CPPCA decoder that consists of a novel eigenvector reconstruction based on a convex-set optimization driven by Ritz vectors within the projected subspaces. As such, CPPCA constitutes a fundamental departure from traditional PCA in that it permits its excellent dimensionality-reduction and compression performance to be realized in an light-encoder/heavy-decoder system architecture. In experimental results, CPPCA outperforms a multiple-vector variant of compressed sensing for the reconstruction of hyperspectral data.
Quality Aware Compression of Electrocardiogram Using Principal Component Analysis.
Gupta, Rajarshi
2016-05-01
Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression.
Principal component analysis on chemical abundances spaces
NASA Astrophysics Data System (ADS)
Ting, Yuan-Sen; Freeman, Kenneth C.; Kobayashi, Chiaki; De Silva, Gayandhi M.; Bland-Hawthorn, Joss
2012-04-01
In preparation for the High Efficiency and Resolution Multi-Element Spectrograph (HERMES) chemical tagging survey of about a million Galactic FGK stars, we estimate the number of independent dimensions of the space defined by the stellar chemical element abundances [X/Fe]. This leads to a way to study the origin of elements from observed chemical abundances using principal component analysis. We explore abundances in several environments, including solar neighbourhood thin/thick disc stars, halo metal-poor stars, globular clusters, open clusters, the Large Magellanic Cloud and the Fornax dwarf spheroidal galaxy. By studying solar-neighbourhood stars, we confirm the universality of the r-process that tends to produce [neutron-capture elements/Fe] in a constant ratio. We find that, especially at low metallicity, the production of r-process elements is likely to be associated with the production of α-elements. This may support the core-collapse supernovae as the r-process site. We also verify the overabundances of light s-process elements at low metallicity, and find that the relative contribution decreases at higher metallicity, which suggests that this lighter elements primary process may be associated with massive stars. We also verify the contribution from the s-process in low-mass asymptotic giant branch (AGB) stars at high metallicity. Our analysis reveals two types of core-collapse supernovae: one produces mainly α-elements, the other produces both α-elements and Fe-peak elements with a large enhancement of heavy Fe-peak elements which may be the contribution from hypernovae. Excluding light elements that may be subject to internal mixing, K and Cu, we find that the [X/Fe] chemical abundance space in the solar neighbourhood has about six independent dimensions both at low metallicity (-3.5 ≲ [Fe/H] ≲-2) and high metallicity ([Fe/H] ≳-1). However the dimensions come from very different origins in these two cases. The extra contribution from low-mass AGB
Lo, Yu-Lung; Lai, Chun-Hau; Lin, Jing-Fung; Hsu, Ping-Feng
2004-04-01
This study demonstrates a new method for simultaneously measuring both the angle of the principal axis and the phase retardation of the linear birefringence in optical materials. We used a circular common-path interferometer (polariscope) as the basic structure modulated by an electro-optic (EO) modulator. An algorithm was developed to simultaneously measure the principal axis and the phase retardation of a lambda/4 or lambda/8 plate as a sample. In the case of a lambda/4 plate, the average absolute error of the principal axis is approximately 3.77 degrees, and that of the phase retardation is approximately 1.03 degrees (1.09%). The retardation error is within the 5% uncertainty range of a commercial wave plate. Fortunately, the nonlinear error caused by the reflection phase retardation of the beam splitter dose not appear in the new system. Therefore the error could be attributed to misalignment and defects in the EO modulator or the other optical components. As for the repeatability of this new common-path heterodyne interferometer, the average deviation for the principal axis is 0.186 degrees and the phase retardation is 0.356 degrees. For the stability, the average deviation for the principal axis is 0.405 degrees and the phase retardation is 0.635 degrees. The resolution of this new system is estimated to be approximately 0.5 degrees, and the principal axis and phase retardation could be measured up to pi and 2pi, respectively, without ambiguity.
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…
Selection of principal components based on Fisher discriminant ratio
NASA Astrophysics Data System (ADS)
Zeng, Xiangyan; Naghedolfeizi, Masoud; Arora, Sanjeev; Yousif, Nabil; Aberra, Dawit
2016-05-01
Principal component analysis transforms a set of possibly correlated variables into uncorrelated variables, and is widely used as a technique of dimensionality reduction and feature extraction. In some applications of dimensionality reduction, the objective is to use a small number of principal components to represent most variation in the data. On the other hand, the main purpose of feature extraction is to facilitate subsequent pattern recognition and machine learning tasks, such as classification. Selecting principal components for classification tasks aims for more than dimensionality reduction. The capability of distinguishing different classes is another major concern. Components that have larger eigenvalues do not necessarily have better distinguishing capabilities. In this paper, we investigate a strategy of selecting principal components based on the Fisher discriminant ratio. The ratio of between class variance to within class variance is calculated for each component, based on which the principal components are selected. The number of relevant components is determined by the classification accuracy. To alleviate overfitting which is common when there are few training data available, we use a cross-validation procedure to determine the number of principal components. The main objective is to select the components that have large Fisher discriminant ratios so that adequate class separability is obtained. The number of selected components is determined by the classification accuracy of the validation data. The selection method is evaluated by face recognition experiments.
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.
NASA Astrophysics Data System (ADS)
Galapon, Eric A.
2016-03-01
The divergent integral ∫a b f ( x ) ( x - x 0 ) - n - 1 d x , for -∞ < a < x0 < b < ∞ and n = 0, 1, 2, …, is assigned, under certain conditions, the value equal to the simple average of the contour integrals ∫C±f(z)(z - x0)-n-1dz, where C+ (C-) is a path that starts from a and ends at b and which passes above (below) the pole at x0. It is shown that this value, which we refer to as the analytic principal value, is equal to the Cauchy principal value for n = 0 and to the Hadamard finite-part of the divergent integral for positive integer n. This implies that, where the conditions apply, the Cauchy principal value and the Hadamard finite-part integral are in fact values of absolutely convergent integrals. Moreover, it leads to the replacement of the boundary values in the Sokhotski-Plemelj-Fox theorem with integrals along some arbitrary paths. The utility of the analytic principal value in the numerical, analytical, and asymptotic evaluations of the principal value and the finite-part integral is discussed and demonstrated.
Principal Components as a Data Reduction and Noise Reduction Technique
NASA Technical Reports Server (NTRS)
Imhoff, M. L.; Campbell, W. J.
1982-01-01
The potential of principal components as a pipeline data reduction technique for thematic mapper data was assessed and principal components analysis and its transformation as a noise reduction technique was examined. Two primary factors were considered: (1) how might data reduction and noise reduction using the principal components transformation affect the extraction of accurate spectral classifications; and (2) what are the real savings in terms of computer processing and storage costs of using reduced data over the full 7-band TM complement. An area in central Pennsylvania was chosen for a study area. The image data for the project were collected using the Earth Resources Laboratory's thematic mapper simulator (TMS) instrument.
Evaluation of ground water monitoring network by principal component analysis.
Gangopadhyay, S; Gupta, A; Nachabe, M H
2001-01-01
Principal component analysis is a data reduction technique used to identify the important components or factors that explain most of the variance of a system. This technique was extended to evaluating a ground water monitoring network where the variables are monitoring wells. The objective was to identify monitoring wells that are important in predicting the dynamic variation in potentiometric head at a location. The technique is demonstrated through an application to the monitoring network of the Bangkok area. Principal component analysis was carried out for all the monitoring wells of the aquifer, and a ranking scheme based on the frequency of occurrence of a particular well as principal well was developed. The decision maker with budget constraints can now opt to monitor principal wells which can adequately capture the potentiometric head variation in the aquifer. This was evaluated by comparing the observed potentiometric head distribution using data from all available wells and wells selected using the ranking scheme as a guideline.
Fast, Exact Bootstrap Principal Component Analysis for p > 1 million
Fisher, Aaron; Caffo, Brian; Schwartz, Brian; Zipunnikov, Vadim
2015-01-01
Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subject (p) is much larger than the number of subjects (n), calculating and storing the leading principal components from each bootstrap sample can be computationally infeasible. To address this, we outline methods for fast, exact calculation of bootstrap principal components, eigenvalues, and scores. Our methods leverage the fact that all bootstrap samples occupy the same n-dimensional subspace as the original sample. As a result, all bootstrap principal components are limited to the same n-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that subspace. Several uncertainty metrics can be computed solely based on the bootstrap distribution of these low dimensional coordinates, without calculating or storing the p-dimensional bootstrap components. Fast bootstrap PCA is applied to a dataset of sleep electroencephalogram recordings (p = 900, n = 392), and to a dataset of brain magnetic resonance images (MRIs) (p ≈ 3 million, n = 352). For the MRI dataset, our method allows for standard errors for the first 3 principal components based on 1000 bootstrap samples to be calculated on a standard laptop in 47 minutes, as opposed to approximately 4 days with standard methods. PMID:27616801
NASA Astrophysics Data System (ADS)
Shen, Chenhua
2017-02-01
We applied traditional principal component analysis (TPCA) and nonstationary principal component analysis (NSPCA) to determine principal components in the six daily air-pollutant concentration series (SO2, NO2, CO, O3, PM2.5 and PM10) in Nanjing from January 2013 to March 2016. The results show that using TPCA, two principal components can reflect the variance of these series: primary pollutants (SO2, NO2, CO, PM2.5 and PM10) and secondary pollutants (e.g., O3). However, using NSPCA, three principal components can be determined to reflect the detrended variance of these series: 1) a mixture of primary and secondary pollutants, 2) primary pollutants and 3) secondary pollutants. Various approaches can obtain different principal components. This phenomenon is closely related to methods for calculating the cross-correlation between each of the air pollutants. NSPCA is a more applicable, reliable method for analyzing the principal components of a series in the presence of nonstationarity and for a long-range correlation than can TPCA. Moreover, using detrended cross-correlation analysis (DCCA), the cross-correlation between O3 and NO2 is negative at a short timescale and positive at a long timescale. In hourly timescales, O3 is negatively correlated with NO2 due to a photochemical interaction, and in daily timescales, O3 is positively correlated with NO2 because of the decomposition of O3. In monthly timescales, the cross-correlation between O3 with NO2 has similar performance to those of O3 with meteorological elements. DCCA is again shown to be more appropriate for disclosing the cross-correlation between series in the presence of nonstationarity than is Pearson's method. DCCA can improve our understanding of their interactional mechanisms.
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
Principal components analysis of a JWST NIRSpec detector subsystem
NASA Astrophysics Data System (ADS)
Rauscher, Bernard J.; Arendt, Richard G.; Fixsen, D. J.; Greenhouse, Matthew A.; Lander, Matthew; Lindler, Don; Loose, Markus; Moseley, S. H.; Mott, D. Brent; Wen, Yiting; Wilson, Donna V.; Xenophontos, Christos
2013-09-01
We present principal component analysis (PCA) of a flight-representative James Webb Space Telescope Near Infrared Spectrograph (NIRSpec) Detector Subsystem. Although our results are specific to NIRSpec and its T ~ 40 K SIDECAR ASICs and 5 μm cutoff H2RG detector arrays, the underlying technical approach is more general. We describe how we measured the system's response to small environmental perturbations by modulating a set of bias voltages and temperature. We used this information to compute the system's 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 PCA to determine a set of environmental requirements (temperature stability, electrical stability, etc.) that enabled the planned instrument to meet performance requirements.
Using Kernel Principal Components for Color Image Segmentation
NASA Astrophysics Data System (ADS)
Wesolkowski, Slawo
2002-11-01
Distinguishing objects on the basis of color is fundamental to humans. In this paper, a clustering approach is used to segment color images. Clustering is usually done using a single point or vector as a cluster prototype. The data can be clustered in the input or feature space where the feature space is some nonlinear transformation of the input space. The idea of kernel principal component analysis (KPCA) was introduced to align data along principal components in the kernel or feature space. KPCA is a nonlinear transformation of the input data that finds the eigenvectors along which this data has maximum information content (or variation). The principal components resulting from KPCA are nonlinear in the input space and represent principal curves. This is a necessary step as colors in RGB are not linearly correlated especially considering illumination effects such as shading or highlights. The performance of the k-means (Euclidean distance-based) and Mixture of Principal Components (vector angle-based) algorithms are analyzed in the context of the input space and the feature space obtained using KPCA. Results are presented on a color image segmentation task. The results are discussed and further extensions are suggested.
Brandon, Scott C E; Graham, Ryan B; Almosnino, Sivan; Sadler, Erin M; Stevenson, Joan M; Deluzio, Kevin J
2013-12-01
Principal component analysis is a powerful tool in biomechanics for reducing complex multivariate datasets to a subset of important parameters. However, interpreting the biomechanical meaning of these parameters can be a subjective process. Biomechanical interpretations that are based on visual inspection of extreme 5th and 95th percentile waveforms may be confounded when extreme waveforms express more than one biomechanical feature. This study compares interpretation of principal components using representative extremes with a recently developed method, called single component reconstruction, which provides an uncontaminated visualization of each individual biomechanical feature. Example datasets from knee joint moments, lateral gastrocnemius EMG, and lumbar spine kinematics are used to demonstrate that the representative extremes method and single component reconstruction can yield equivalent interpretations of principal components. However, single component reconstruction interpretation cannot be contaminated by other components, which may enhance the use and understanding of principal component analysis within the biomechanics community.
Incremental principal component pursuit for video background modeling
Rodriquez-Valderrama, Paul A.; Wohlberg, Brendt
2017-03-14
An incremental Principal Component Pursuit (PCP) algorithm for video background modeling that is able to process one frame at a time while adapting to changes in background, with a computational complexity that allows for real-time processing, having a low memory footprint and is robust to translational and rotational jitter.
Distributed Principal Component Analysis for Wireless Sensor Networks
Le Borgne, Yann-Aël; Raybaud, Sylvain; Bontempi, Gianluca
2008-01-01
The Principal Component Analysis (PCA) is a data dimensionality reduction tech-nique well-suited for processing data from sensor networks. It can be applied to tasks like compression, event detection, and event recognition. This technique is based on a linear trans-form where the sensor measurements are projected on a set of principal components. When sensor measurements are correlated, a small set of principal components can explain most of the measurements variability. This allows to significantly decrease the amount of radio communication and of energy consumption. In this paper, we show that the power iteration method can be distributed in a sensor network in order to compute an approximation of the principal components. The proposed implementation relies on an aggregation service, which has recently been shown to provide a suitable framework for distributing the computation of a linear transform within a sensor network. We also extend this previous work by providing a detailed analysis of the computational, memory, and communication costs involved. A com-pression experiment involving real data validates the algorithm and illustrates the tradeoffs between accuracy and communication costs. PMID:27873788
A fast minimum variance beamforming method using principal component analysis.
Kim, Kyuhong; Park, Suhyun; Kim, Jungho; Park, Sung-Bae; Bae, MooHo
2014-06-01
Minimum variance (MV) beamforming has been studied for improving the performance of a diagnostic ultrasound imaging system. However, it is not easy for the MV beamforming to be implemented in a real-time ultrasound imaging system because of the enormous amount of computation time associated with the covariance matrix inversion. In this paper, to address this problem, we propose a new fast MV beamforming method that almost optimally approximates the MV beamforming while reducing the computational complexity greatly through dimensionality reduction using principal component analysis (PCA). The principal components are estimated offline from pre-calculated conventional MV weights. Thus, the proposed method does not directly calculate the MV weights but approximates them by a linear combination of a few selected dominant principal components. The combinational weights are calculated in almost the same way as in MV beamforming, but in the transformed domain of beamformer input signal by the PCA, where the dimension of the transformed covariance matrix is identical to the number of some selected principal component vectors. Both computer simulation and experiment were carried out to verify the effectiveness of the proposed method with echo signals from simulation as well as phantom and in vivo experiments. It is confirmed that our method can reduce the dimension of the covariance matrix down to as low as 2 × 2 while maintaining the good image quality of MV beamforming.
SAS program for quantitative stratigraphic correlation by principal components
Hohn, M.E.
1985-01-01
A SAS program is presented which constructs a composite section of stratigraphic events through principal components analysis. The variables in the analysis are stratigraphic sections and the observational units are range limits of taxa. The program standardizes data in each section, extracts eigenvectors, estimates missing range limits, and computes the composite section from scores of events on the first principal component. Provided is an option of several types of diagnostic plots; these help one to determine conservative range limits or unrealistic estimates of missing values. Inspection of the graphs and eigenvalues allow one to evaluate goodness of fit between the composite and measured data. The program is extended easily to the creation of a rank-order composite. ?? 1985.
Image denoising using principal component analysis in the wavelet domain
NASA Astrophysics Data System (ADS)
Bacchelli, Silvia; Papi, Serena
2006-05-01
In this work we describe a method for removing Gaussian noise from digital images, based on the combination of the wavelet packet transform and the principal component analysis. In particular, since the aim of denoising is to retain the energy of the signal while discarding the energy of the noise, our basic idea is to construct powerful tailored filters by applying the Karhunen-Loeve transform in the wavelet packet domain, thus obtaining a compaction of the signal energy into a few principal components, while the noise is spread over all the transformed coefficients. This allows us to act with a suitable shrinkage function on these new coefficients, removing the noise without blurring the edges and the important characteristics of the images. The results of a large numerical experimentation encourage us to keep going in this direction with our studies.
Selecting the Number of Principal Components in Functional Data
Li, Yehua; Wang, Naisyin; Carroll, Raymond J.
2013-01-01
Functional principal component analysis (FPCA) has become the most widely used dimension reduction tool for functional data analysis. We consider functional data measured at random, subject-specific time points, contaminated with measurement error, allowing for both sparse and dense functional data, and propose novel information criteria to select the number of principal component in such data. We propose a Bayesian information criterion based on marginal modeling that can consistently select the number of principal components for both sparse and dense functional data. For dense functional data, we also developed an Akaike information criterion (AIC) based on the expected Kullback-Leibler information under a Gaussian assumption. In connecting with factor analysis in multivariate time series data, we also consider the information criteria by Bai & Ng (2002) and show that they are still consistent for dense functional data, if a prescribed undersmoothing scheme is undertaken in the FPCA algorithm. We perform intensive simulation studies and show that the proposed information criteria vastly outperform existing methods for this type of data. Surprisingly, our empirical evidence shows that our information criteria proposed for dense functional data also perform well for sparse functional data. An empirical example using colon carcinogenesis data is also provided to illustrate the results. PMID:24376287
Selecting the Number of Principal Components in Functional Data.
Li, Yehua; Wang, Naisyin; Carroll, Raymond J
2013-12-19
Functional principal component analysis (FPCA) has become the most widely used dimension reduction tool for functional data analysis. We consider functional data measured at random, subject-specific time points, contaminated with measurement error, allowing for both sparse and dense functional data, and propose novel information criteria to select the number of principal component in such data. We propose a Bayesian information criterion based on marginal modeling that can consistently select the number of principal components for both sparse and dense functional data. For dense functional data, we also developed an Akaike information criterion (AIC) based on the expected Kullback-Leibler information under a Gaussian assumption. In connecting with factor analysis in multivariate time series data, we also consider the information criteria by Bai & Ng (2002) and show that they are still consistent for dense functional data, if a prescribed undersmoothing scheme is undertaken in the FPCA algorithm. We perform intensive simulation studies and show that the proposed information criteria vastly outperform existing methods for this type of data. Surprisingly, our empirical evidence shows that our information criteria proposed for dense functional data also perform well for sparse functional data. An empirical example using colon carcinogenesis data is also provided to illustrate the results.
Robust principal component analysis in water quality index development
NASA Astrophysics Data System (ADS)
Ali, Zalina Mohd; Ibrahim, Noor Akma; Mengersen, Kerrie; Shitan, Mahendran; Juahir, Hafizan
2014-06-01
Some statistical procedures already available in literature are employed in developing the water quality index, WQI. The nature of complexity and interdependency that occur in physical and chemical processes of water could be easier explained if statistical approaches were applied to water quality indexing. The most popular statistical method used in developing WQI is the principal component analysis (PCA). In literature, the WQI development based on the classical PCA mostly used water quality data that have been transformed and normalized. Outliers may be considered in or eliminated from the analysis. However, the classical mean and sample covariance matrix used in classical PCA methodology is not reliable if the outliers exist in the data. Since the presence of outliers may affect the computation of the principal component, robust principal component analysis, RPCA should be used. Focusing in Langat River, the RPCA-WQI was introduced for the first time in this study to re-calculate the DOE-WQI. Results show that the RPCA-WQI is capable to capture similar distribution in the existing DOE-WQI.
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.
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.
Principal component analysis of minimal excitatory postsynaptic potentials.
Astrelin, A V; Sokolov, M V; Behnisch, T; Reymann, K G; Voronin, L L
1998-02-20
'Minimal' excitatory postsynaptic potentials (EPSPs) are often recorded from central neurones, specifically for quantal analysis. However the EPSPs may emerge from activation of several fibres or transmission sites so that formal quantal analysis may give false results. Here we extended application of the principal component analysis (PCA) to minimal EPSPs. We tested a PCA algorithm and a new graphical 'alignment' procedure against both simulated data and hippocampal EPSPs. Minimal EPSPs were recorded before and up to 3.5 h following induction of long-term potentiation (LTP) in CA1 neurones. In 29 out of 45 EPSPs, two (N=22) or three (N=7) components were detected which differed in latencies, rise time (Trise) or both. The detected differences ranged from 0.6 to 7.8 ms for the latency and from 1.6-9 ms for Trise. Different components behaved differently following LTP induction. Cases were found when one component was potentiated immediately after tetanus whereas the other with a delay of 15-60 min. The immediately potentiated component could decline in 1-2 h so that the two components contributed differently into early (< 1 h) LTP1 and later (1-4 h) LTP2 phases. The noise deconvolution techniques was applied to both conventional EPSP amplitudes and scores of separate components. Cases are illustrated when quantal size (upsilon) estimated from the EPSP amplitudes increased whereas upsilon estimated from the component scores was stable during LTP1. Analysis of component scores could show apparent double-fold increases in upsilon which are interpreted as reflections of synchronized quantal releases. In general, the results demonstrate PCA applicability to separate EPSPs into different components and its usefulness for precise analysis of synaptic transmission.
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
Bioactivity of beaver castoreum constituents using principal components analysis.
Schulte, B A; Müller-Schwarze, D; Tang, R; Webster, F X
1995-07-01
North American beaver (Castor canadensis) were observed to sniff from the water and make land visits to some synthetic chemical components of castoreum placed on experimental scent mounds (ESM). In previous analysis, the elicitation (presence/absence), completeness, and/or strength (number, duration) of these key responses served as separate measures of biological activity. In this paper, we used principal components analysis (PCA) to combine linearly six related measures of observed response and one index of overnight visitation calculated over all trials. The first principal component accounted for a majority of the variation and allowed ranking of the samples based on their composite bioactivity. A second PCA, based only on response trials (excluding trials with no responses), showed that responses to the synthetic samples, once elicited, did not vary greatly in completeness or strength. None of the samples evoked responses as complete or strong as the castoreum control. Castoreum also elicited more multiple land visits (repeated visits to the ESM by the same individual or by more than one family member) than the synthetic samples, indicating that an understanding of the castoreum chemosignal requires consideration of responses by the family unit, and not just the land visit by the initial responder.
Self-aggregation in scaled principal component space
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.
APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO RELAXOGRAPHIC IMAGES
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.
Point-process principal components analysis via geometric optimization.
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.
PRINCIPAL COMPONENTS FOR NON-LOCAL MEANS IMAGE DENOISING.
Tasdizen, Tolga
2008-01-01
This paper presents an image denoising algorithm that uses principal component analysis (PCA) in conjunction with the non-local means image denoising. Image neighborhood vectors used in the non-local means algorithm are first projected onto a lower-dimensional subspace using PCA. Consequently, neighborhood similarity weights for denoising are computed using distances in this subspace rather than the full space. This modification to the non-local means algorithm results in improved accuracy and computational performance. We present an analysis of the proposed method's accuracy as a function of the dimensionality of the projection subspace and demonstrate that denoising accuracy peaks at a relatively low number of dimensions.
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.
Water reuse systems: A review of the principal components
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.
Power analysis of principal components regression in genetic association studies.
Shen, Yan-feng; Zhu, Jun
2009-10-01
Association analysis provides an opportunity to find genetic variants underlying complex traits. A principal components regression (PCR)-based approach was shown to outperform some competing approaches. However, a limitation of this method is that the principal components (PCs) selected from single nucleotide polymorphisms (SNPs) may be unrelated to the phenotype. In this article, we investigate the theoretical properties of such a method in more detail. We first derive the exact power function of the test based on PCR, and hence clarify the relationship between the test power and the degrees of freedom (DF). Next, we extend the PCR test to a general weighted PCs test, which provides a unified framework for understanding the properties of some related statistics. We then compare the performance of these tests. We also introduce several data-driven adaptive alternatives to overcome difficulties in the PCR approach. Finally, we illustrate our results using simulations based on real genotype data. Simulation study shows the risk of using the unsupervised rule to determine the number of PCs, and demonstrates that there is no single uniformly powerful method for detecting genetic variants.
Direct Numerical Simulation of Combustion Using Principal Component Analysis
NASA Astrophysics Data System (ADS)
Owoyele, Opeoluwa; Echekki, Tarek
2016-11-01
We investigate the potential of accelerating chemistry integration during the direct numerical simulation (DNS) of complex fuels based on the transport equations of representative scalars that span the desired composition space using principal component analysis (PCA). The transported principal components (PCs) offer significant potential to reduce the computational cost of DNS through a reduction in the number of transported scalars, as well as the spatial and temporal resolution requirements. The strategy is demonstrated using DNS of a premixed methane-air flame in a 2D vortical flow and is extended to the 3D geometry to further demonstrate the computational efficiency of PC transport. The PCs are derived from a priori PCA of a subset of the full thermo-chemical scalars' vector. The PCs' chemical source terms and transport properties are constructed and tabulated in terms of the PCs using artificial neural networks (ANN). Comparison of DNS based on a full thermo-chemical state and DNS based on PC transport based on 6 PCs shows excellent agreement even for species that are not included in the PCA reduction. The transported PCs reproduce some of the salient features of strongly curved and strongly strained flames. The 2D DNS results also show a significant reduction of two orders of magnitude in the computational cost of the simulations, which enables an extension of the PCA approach to 3D DNS under similar computational requirements. This work was supported by the National Science Foundation Grant DMS-1217200.
Scalable Robust Principal Component Analysis using Grassmann Averages.
Hauberg, Soren; Feragen, Aasa; Enficiaud, Raffi; Black, Michael
2015-12-23
In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average (GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average (TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.
Scalable Robust Principal Component Analysis Using Grassmann Averages.
Hauberg, Sren; Feragen, Aasa; Enficiaud, Raffi; Black, Michael J
2016-11-01
In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average ( GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average ( TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.
Using Principal Components as Auxiliary Variables in Missing Data Estimation.
Howard, Waylon J; Rhemtulla, Mijke; Little, Todd D
2015-01-01
To deal with missing data that arise due to participant nonresponse or attrition, methodologists have recommended an "inclusive" strategy where a large set of auxiliary variables are used to inform the missing data process. In practice, the set of possible auxiliary variables is often too large. We propose using principal components analysis (PCA) to reduce the number of possible auxiliary variables to a manageable number. A series of Monte Carlo simulations compared the performance of the inclusive strategy with eight auxiliary variables (inclusive approach) to the PCA strategy using just one principal component derived from the eight original variables (PCA approach). We examined the influence of four independent variables: magnitude of correlations, rate of missing data, missing data mechanism, and sample size on parameter bias, root mean squared error, and confidence interval coverage. Results indicate that the PCA approach results in unbiased parameter estimates and potentially more accuracy than the inclusive approach. We conclude that using the PCA strategy to reduce the number of auxiliary variables is an effective and practical way to reap the benefits of the inclusive strategy in the presence of many possible auxiliary variables.
Anisoplanatic Imaging Through Turbulence Using Principal Component Analysis
NASA Astrophysics Data System (ADS)
Baena-Gallé, R.; Katsaggelos, A.; Molina, R.; Mateos, J.; Gladysz, S.
The performance of optical systems is highly degraded by atmospheric turbulence when observing both vertically (e.g., astronomy, remote sensing) or horizontally (long-range surveillance). This problem can be partially alleviated using adaptive optics (AO) but only for small fields of view (FOV) described by the isoplanatic angle for which the turbulence-induced aberrations are considered constant. Additionally, this problem can also be tackled using post-processing techniques such as deconvolution algorithms which take into account the variability of the point spread function (PSF) in anisoplanatic conditions. Variability of the PSF across the FOV in anisoplanatc imagery can be described using principal component analysis (Karhunen-Loeve transform). Then, a certain number of variable PSFs can be used to create new basis functions, called principal components (PC), which can be considered constant across the FOV and, therefore, potentially be used to perform global deconvolution. Our aim is twofold: firstly, to describe the shape and statistics of the anisoplanatic PSF for single-conjugate AO systems with only a few parameters and, secondly, using this information to obtain the set of PSFs at positions in the FOV so that the associated variability is properly described. Additionally, these PSFs are to be decomposed into PCs. Finally, the entire FOV is deconvolved globally using deconvolution algorithms which account for uncertainties involved in local estimates of the PSFs. Our approach is tested on simulated, single-conjugate AO data.
Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.
Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J
2016-12-22
Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.
Principal components granulometric analysis of tidally dominated depositional environments
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.
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.
CMB constraints on principal components of the inflaton potential
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.
Principal Semantic Components of Language and the Measurement of Meaning
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
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.
Robust principal component analysis based on maximum correntropy criterion.
He, Ran; Hu, Bao-Gang; Zheng, Wei-Shi; Kong, Xiang-Wei
2011-06-01
Principal component analysis (PCA) minimizes the mean square error (MSE) and is sensitive to outliers. In this paper, we present a new rotational-invariant PCA based on maximum correntropy criterion (MCC). A half-quadratic optimization algorithm is adopted to compute the correntropy objective. At each iteration, the complex optimization problem is reduced to a quadratic problem that can be efficiently solved by a standard optimization method. The proposed method exhibits the following benefits: 1) it is robust to outliers through the mechanism of MCC which can be more theoretically solid than a heuristic rule based on MSE; 2) it requires no assumption about the zero-mean of data for processing and can estimate data mean during optimization; and 3) its optimal solution consists of principal eigenvectors of a robust covariance matrix corresponding to the largest eigenvalues. In addition, kernel techniques are further introduced in the proposed method to deal with nonlinearly distributed data. Numerical results demonstrate that the proposed method can outperform robust rotational-invariant PCAs based on L(1) norm when outliers occur.
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.
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.
Quantitative analysis of planetary reflectance spectra with principal components analysis
NASA Technical Reports Server (NTRS)
Johnson, P. E.; Smith, M. O.; Adams, J. B.
1985-01-01
A technique is presented for quantitative analysis of planetary reflectance spectra as mixtures of particles on microscopic and macroscopic scales using principal components analysis. This technique allows for determination of the endmembers being mixed, their abundance, and the scale of mixing, as well as other physical parameters. Eighteen lunar telescopic reflectance spectra of the Copernicus crater region, from 600 nm to 1800 nm in wavelength, are modeled in terms of five likely endmembers: mare basalt, mature mare soil, anorthosite, mature highland soil, and clinopyroxene. These endmembers were chosen from a similar analysis of 92 lunar soil and rock samples. The models fit the data to within 2 percent rms. It is found that the goodness of fit is marginally better for intimate mixing over macroscopic mixing.
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.
Principal Component Analysis for Normal-Distribution-Valued Symbolic Data.
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.
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.
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.
Demixed principal component analysis of neural population data
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
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.
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.
Principal components analysis of reward prediction errors in a reinforcement learning task.
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.
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)
Principal Component Analysis of Computed Emission Lines from Protostellar Jets
NASA Astrophysics Data System (ADS)
Cerqueira, A. H.; Reyes-Iturbide, J.; De Colle, F.; Vasconcelos, M. J.
2015-08-01
A very important issue concerning protostellar jets is the mechanism behind their formation. Obtaining information on the region at the base of a jet can shed light on the subject, and some years ago this was done through a search for a rotational signature in the jet line spectrum. The existence of such signatures, however, remains controversial. In order to contribute to the clarification of this issue, in this paper we show that principal component analysis (PCA) can potentially help to distinguish between rotation and precession effects in protostellar jet images. This method reduces the dimensions of the data, facilitating the efficient extraction of information from large data sets such as those arising from integral field spectroscopy. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates, the eigenvectors, ordered by principal components of decreasing variance. The projection of the data on these coordinates produces images called tomograms, while eigenvectors can be displayed as eigenspectra. The combined analysis of both can allow the identification of patterns correlated to a particular physical property that would otherwise remain hidden, and can help to separate the effects of physically uncorrelated phenomena in the data. These are, for example, rotation and precession in the kinematics of a stellar jet. In order to show the potential of PCA analysis, we apply it to synthetic spectro-imaging datacubes generated as an output of numerical simulations of protostellar jets. In this way we generate a benchmark with which a PCA diagnostics of real observations can be confronted. Using the computed emission line profiles for [O i]λ6300 and [S ii]λ6716, we recover and analyze the effects of rotation and precession in tomograms generated by PCA. We show that different combinations of the eigenvectors can be used to enhance and to identify the rotation features present in the data. Our results indicate that PCA can be
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.
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...
Construction Formula of Biological Age Using the Principal Component Analysis
Jia, Linpei; Zhang, Weiguang; Jia, Rufu
2016-01-01
The biological age (BA) equation is a prediction model that utilizes an algorithm to combine various biological markers of ageing. Different from traditional concepts, the BA equation does not emphasize the importance of a golden index but focuses on using indices of vital organs to represent the senescence of whole body. This model has been used to assess the ageing process in a more precise way and may predict possible diseases better as compared with the chronological age (CA). The principal component analysis (PCA) is applied as one of the common and frequently used methods in the construction of the BA formula. Compared with other methods, PCA has its own study procedures and features. Herein we summarize the up-to-date knowledge about the BA formula construction and discuss the influential factors, so as to give an overview of BA estimate by PCA, including composition of samples, choices of test items, and selection of ageing biomarkers. We also discussed the advantages and disadvantages of PCA with reference to the construction mechanism, accuracy, and practicability of several common methods in the construction of the BA formula. PMID:28050560
A Principal Component Analysis of the Diffuse Interstellar Bands
NASA Astrophysics Data System (ADS)
Ensor, T.; Cami, J.; Bhatt, N. H.; Soddu, A.
2017-02-01
We present a principal component (PC) analysis of 23 line-of-sight parameters (including the strengths of 16 diffuse interstellar bands, DIBs) for a well-chosen sample of single-cloud sightlines representing a broad range of environmental conditions. Our analysis indicates that the majority (∼93%) of the variations in the measurements can be captured by only four parameters The main driver (i.e., the first PC) is the amount of DIB-producing material in the line of sight, a quantity that is extremely well traced by the equivalent width of the λ5797 DIB. The second PC is the amount of UV radiation, which correlates well with the λ5797/λ5780 DIB strength ratio. The remaining two PCs are more difficult to interpret, but are likely related to the properties of dust in the line of sight (e.g., the gas-to-dust ratio). With our PCA results, the DIBs can then be used to estimate these line-of-sight parameters.
PRINCIPAL COMPONENT ANALYSIS STUDIES OF TURBULENCE IN OPTICALLY THICK GAS
Correia, C.; Medeiros, J. R. De; Lazarian, A.; Burkhart, B.; Pogosyan, D.
2016-02-20
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 and 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.
Spatially Weighted Principal Component Regression for High-dimensional Prediction
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
Mixtures of Probabilistic Principal Component Analyzers for Anomaly Detection
Fang, Yi; Ganguly, Auroop R
2007-01-01
Anomaly detection tools have been increasingly used in recent years to generate predictive insights on rare events. The typical challenges encountered in such applications include a large number of data dimensions and absence of labeled data. An anomaly detection strategy for these scenarios is dimensionality reduction followed by clustering in the reduced space, with the degree of anomaly of an event or observation quantified by statistical distance from the clusters. However, most research efforts so far are focused on single abrupt anomalies, while the correlation between observations is completely ignored. In this paper, we address the problem of detection of both abrupt and sustained anomalies with high dimensions. The task becomes more challenging than only detecting abrupt outliers because of the gradual and indiscriminant changes in sustained anomalies. We utilize a mixture model of probabilistic principal component analyzers to quantify each observation by probabilistic measures. A statistical process control method is then used to monitor both abrupt and gradual changes. On the other hand, the mixture model can be regarded as a trade-off strategy between linear and nonlinear dimensionality reductions in terms of computational efficiency. This compromise is particularly important in real-time deployment. The proposed method is evaluated on simulated and benchmark data, as well as on data from wide-area sensors at a truck weigh station test-bed.
Construction Formula of Biological Age Using the Principal Component Analysis.
Jia, Linpei; Zhang, Weiguang; Jia, Rufu; Zhang, Hongliang; Chen, Xiangmei
2016-01-01
The biological age (BA) equation is a prediction model that utilizes an algorithm to combine various biological markers of ageing. Different from traditional concepts, the BA equation does not emphasize the importance of a golden index but focuses on using indices of vital organs to represent the senescence of whole body. This model has been used to assess the ageing process in a more precise way and may predict possible diseases better as compared with the chronological age (CA). The principal component analysis (PCA) is applied as one of the common and frequently used methods in the construction of the BA formula. Compared with other methods, PCA has its own study procedures and features. Herein we summarize the up-to-date knowledge about the BA formula construction and discuss the influential factors, so as to give an overview of BA estimate by PCA, including composition of samples, choices of test items, and selection of ageing biomarkers. We also discussed the advantages and disadvantages of PCA with reference to the construction mechanism, accuracy, and practicability of several common methods in the construction of the BA formula.
Comparison of analytical eddy current models using principal components analysis
NASA Astrophysics Data System (ADS)
Contant, S.; Luloff, M.; Morelli, J.; Krause, T. W.
2017-02-01
Monitoring the gap between the pressure tube (PT) and the calandria tube (CT) in CANDU® fuel channels is essential, as contact between the two tubes can lead to delayed hydride cracking of the pressure tube. Multifrequency transmit-receive eddy current non-destructive evaluation is used to determine this gap, as this method has different depths of penetration and variable sensitivity to noise, unlike single frequency eddy current non-destructive evaluation. An Analytical model based on the Dodd and Deeds solutions, and a second model that accounts for normal and lossy self-inductances, and a non-coaxial pickup coil, are examined for representing the response of an eddy current transmit-receive probe when considering factors that affect the gap response, such as pressure tube wall thickness and pressure tube resistivity. The multifrequency model data was analyzed using principal components analysis (PCA), a statistical method used to reduce the data set into a data set of fewer variables. The results of the PCA of the analytical models were then compared to PCA performed on a previously obtained experimental data set. The models gave similar results under variable PT wall thickness conditions, but the non-coaxial coil model, which accounts for self-inductive losses, performed significantly better than the Dodd and Deeds model under variable resistivity conditions.
Incorporating principal component analysis into air quality model evaluation
NASA Astrophysics Data System (ADS)
Eder, Brian; Bash, Jesse; Foley, Kristen; Pleim, Jon
2014-01-01
The efficacy of standard air quality model evaluation techniques is becoming compromised as the simulation periods continue to lengthen in response to ever increasing computing capacity. Accordingly, the purpose of this paper is to demonstrate a statistical approach called Principal Component Analysis (PCA) with the intent of motivating its use by the evaluation community. One of the main objectives of PCA is to identify, through data reduction, the recurring and independent modes of variations (or signals) within a very large dataset, thereby summarizing the essential information of that dataset so that meaningful and descriptive conclusions can be made. In this demonstration, PCA is applied to a simple evaluation metric - the model bias associated with EPA's Community Multi-scale Air Quality (CMAQ) model when compared to weekly observations of sulfate (SO42-) and ammonium (NH4+) ambient air concentrations measured by the Clean Air Status and Trends Network (CASTNet). The advantages of using this technique are demonstrated as it identifies strong and systematic patterns of CMAQ model bias across a myriad of spatial and temporal scales that are neither constrained to geopolitical boundaries nor monthly/seasonal time periods (a limitation of many current studies). The technique also identifies locations (station-grid cell pairs) that are used as indicators for a more thorough diagnostic evaluation thereby hastening and facilitating understanding of the probable mechanisms responsible for the unique behavior among bias regimes. A sampling of results indicates that biases are still prevalent in both SO42- and NH4+ simulations that can be attributed to either: 1) cloud processes in the meteorological model utilized by CMAQ, which are found to overestimated convective clouds and precipitation, while underestimating larger-scale resolved clouds that are less likely to precipitate, and 2) biases associated with Midwest NH3 emissions which may be partially ameliorated
Tracing Cattle Breeds with Principal Components Analysis Ancestry Informative SNPs
Lewis, Jamey; Abas, Zafiris; Dadousis, Christos; Lykidis, Dimitrios; Paschou, Peristera; Drineas, Petros
2011-01-01
The recent release of the Bovine HapMap dataset represents the most detailed survey of bovine genetic diversity to date, providing an important resource for the design and development of livestock production. We studied this dataset, comprising more than 30,000 Single Nucleotide Polymorphisms (SNPs) for 19 breeds (13 taurine, three zebu, and three hybrid breeds), seeking to identify small panels of genetic markers that can be used to trace the breed of unknown cattle samples. Taking advantage of the power of Principal Components Analysis and algorithms that we have recently described for the selection of Ancestry Informative Markers from genomewide datasets, we present a decision-tree which can be used to accurately infer the origin of individual cattle. In doing so, we present a thorough examination of population genetic structure in modern bovine breeds. Performing extensive cross-validation experiments, we demonstrate that 250-500 carefully selected SNPs suffice in order to achieve close to 100% prediction accuracy of individual ancestry, when this particular set of 19 breeds is considered. Our methods, coupled with the dense genotypic data that is becoming increasingly available, have the potential to become a valuable tool and have considerable impact in worldwide livestock production. They can be used to inform the design of studies of the genetic basis of economically important traits in cattle, as well as breeding programs and efforts to conserve biodiversity. Furthermore, the SNPs that we have identified can provide a reliable solution for the traceability of breed-specific branded products. PMID:21490966
Extremum-seeking based antiskid control and functional principal components
NASA Astrophysics Data System (ADS)
Tunay, Ilker
The first part of this work extends principal component analysis (PCA) to random elements of abstract Hilbert spaces. Using only standard functional analysis, it is shown that the optimal PCA subspace is spanned by the eigenvectors of the covariance operator and the linear variety that minimizes average squared error contains the mean of the distribution. An immediate application is converting a nonlinear parametrization of a dynamical system model to an optimal linear one. This makes possible adaptive control of such systems by using established linear estimation techniques. The second part describes the modeling of an electrohydraulic pressure servo valve and brake hydraulic system, and the design of an inner-loop controller which can be used with independent antiskid or auto-brake controllers. The effects of connecting lines on stability and performance are explicitly taken into account in control design by using analytical solutions to two-dimensional viscous compressible model of fluid motion in the pipes. The modal approximation technique is used in the simulations. In order to facilitate control design, singular perturbation analysis is employed to reduce the order of the model in a systematic fashion. Combining partial feedback linearization and linear H-infinity control, stability robustness against oil parameter variations and component wear is guaranteed. The closed-loop response is almost linear, fast, sufficiently damped, consistent over the whole operating range and the asymmetry between filling and dumping is significantly reduced. The third part gives an overview of extremum-seeking control and presents an antiskid controller for transport aircraft. The controller does not assume knowledge of tire-runway friction characteristics. Information about the local slope of the friction coefficient function is obtained from phase difference measurements of an injected sine wave. The Popov criterion is used to show robust stability. A realistic model of the
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
40 CFR 60.5080 - What are the principal components of the model rule?
Code of Federal Regulations, 2011 CFR
2011-07-01
... 40 Protection of Environment 6 2011-07-01 2011-07-01 false What are the principal components of... are the principal components of the model rule? The model rule contains the nine major components listed in paragraphs (a) through (i) of this section. (a) Increments of progress toward compliance....
40 CFR 60.2570 - What are the principal components of the model rule?
Code of Federal Regulations, 2011 CFR
2011-07-01
... 40 Protection of Environment 6 2011-07-01 2011-07-01 false What are the principal components of... 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)...
40 CFR 60.2998 - What are the principal components of the model rule?
Code of Federal Regulations, 2011 CFR
2011-07-01
... 40 Protection of Environment 6 2011-07-01 2011-07-01 false What are the principal components of... 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)...
Component retention in principal component analysis with application to cDNA microarray data
Cangelosi, Richard; Goriely, Alain
2007-01-01
Shannon entropy is used to provide an estimate of the number of interpretable components in a principal component analysis. In addition, several ad hoc stopping rules for dimension determination are reviewed and a modification of the broken stick model is presented. The modification incorporates a test for the presence of an "effective degeneracy" among the subspaces spanned by the eigenvectors of the correlation matrix of the data set then allocates the total variance among subspaces. A summary of the performance of the methods applied to both published microarray data sets and to simulated data is given. This article was reviewed by Orly Alter, John Spouge (nominated by Eugene Koonin), David Horn and Roy Varshavsky (both nominated by O. Alter). PMID:17229320
Principal-Component Analysis of the Characteristics Desirable in Baker's Yeasts
Oda, Yuji; Ouchi, Kozo
1989-01-01
Twenty-seven properties considered to be required for good bakery products were examined in 56 industrial and 2 laboratory yeast strains. The data obtained were applied to principal-component analysis, one of the multivariate statistical analyses. The first and second principal components together were extracted, and these accounted for 77.7% of the variance. The first principal component was interpreted as the glycolytic activity of yeast in dough, and the second one was interpreted as the balance of leavening abilities in sweet and flour doughs from the factor loadings. The scattergram on the two principal components was effective in grouping the 58 yeast strains used. PMID:16347943
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
Mollazadeh, Mohsen; Aggarwal, Vikram; Thakor, Nitish V; Schieber, Marc H
2014-10-15
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.
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...
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...
Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis
2011-01-01
Background The electroencephalography (EEG) signals are known to involve the firings of neurons in the brain. The P300 wave is a high potential caused by an event-related stimulus. The detection of P300s included in the measured EEG signals is widely investigated. The difficulties in detecting them are that they are mixed with other signals generated over a large brain area and their amplitudes are very small due to the distance and resistivity differences in their transmittance. Methods A novel real-time feature extraction method for detecting P300 waves by combining an adaptive nonlinear principal component analysis (ANPCA) and a multilayer neural network is proposed. The measured EEG signals are first filtered using a sixth-order band-pass filter with cut-off frequencies of 1 Hz and 12 Hz. The proposed ANPCA scheme consists of four steps: pre-separation, whitening, separation, and estimation. In the experiment, four different inter-stimulus intervals (ISIs) are utilized: 325 ms, 350 ms, 375 ms, and 400 ms. Results The developed multi-stage principal component analysis method applied at the pre-separation step has reduced the external noises and artifacts significantly. The introduced adaptive law in the whitening step has made the subsequent algorithm in the separation step to converge fast. The separation performance index has varied from -20 dB to -33 dB due to randomness of source signals. The robustness of the ANPCA against background noises has been evaluated by comparing the separation performance indices of the ANPCA with four algorithms (NPCA, NSS-JD, JADE, and SOBI), in which the ANPCA algorithm demonstrated the shortest iteration time with performance index about 0.03. Upon this, it is asserted that the ANPCA algorithm successfully separates mixed source signals. Conclusions The independent components produced from the observed data using the proposed method illustrated that the extracted signals were clearly the P300 components elicited by task
A visual basic program for principal components transformation of digital images
NASA Astrophysics Data System (ADS)
Carr, James R.
1998-04-01
Principal components transformation of multispectral and hyperspectral digital imagery is useful for: (1) reducing the number of useful bands, a distinct advantage when using hyperspectral imagery; (2) obtaining image bands that are orthogonal (statistically independent); (3) improving supervised and unsupervised classification; and (4) image compression. A Visual Basic program is presented for principal components transformation of digital images using principal components analysis or correspondence analysis. Principal components analysis is well known for this application. Correspondence analysis is only recently applied for such transformation. The program can import raw digital images, with or without header records; or, the program can accept Windows bitmap (BMP) files. After transformation, output, transformed images can be exported in raw or BMP format. The program can be used as a simple file format conversion program (raw to BMP or BMP to raw) without performing principal components transformation. An application demonstrates the use of the program.
Jiang, Xiping; Jiang, Yu; Wu, Fang; Wu, Fenghuang
2014-01-01
Interpretation of mineral hyperspectral images provides large amounts of high-dimensional data, which is often complicated by mixed pixels. The quantitative interpretation of hyperspectral images is known to be extremely difficult when three types of information are unknown, namely, the number of pure pixels, the spectrum of pure pixels, and the mixing matrix. The problem is made even more complex by the disturbance of noise. The key to interpreting abstract mineral component information, i.e., pixel unmixing and abundance inversion, is how to effectively reduce noise, dimension, and redundancy. A three-step procedure is developed in this study for quantitative interpretation of hyperspectral images. First, the principal component analysis (PCA) method can be used to process the pixel spectrum matrix and keep characteristic vectors with larger eigenvalues. This can effectively reduce the noise and redundancy, which facilitates the abstraction of major component information. Second, the independent component analysis (ICA) method can be used to identify and unmix the pixels based on the linear mixed model. Third, the pure-pixel spectrums can be normalized for abundance inversion, which gives the abundance of each pure pixel. In numerical experiments, both simulation data and actual data were used to demonstrate the performance of our three-step procedure. Under simulation data, the results of our procedure were compared with theoretical values. Under the actual data measured from core hyperspectral images, the results obtained through our algorithm are compared with those of similar software (Mineral Spectral Analysis 1.0, Nanjing Institute of Geology and Mineral Resources). The comparisons show that our method is effective and can provide reference for quantitative interpretation of hyperspectral images.
Foch, Eric; Milner, Clare E
2014-01-03
Iliotibial band syndrome (ITBS) is a common knee overuse injury among female runners. Atypical discrete trunk and lower extremity biomechanics during running may be associated with the etiology of ITBS. Examining discrete data points limits the interpretation of a waveform to a single value. Characterizing entire kinematic and kinetic waveforms may provide additional insight into biomechanical factors associated with ITBS. Therefore, the purpose of this cross-sectional investigation was to determine whether female runners with previous ITBS exhibited differences in kinematics and kinetics compared to controls using a principal components analysis (PCA) approach. Forty participants comprised two groups: previous ITBS and controls. Principal component scores were retained for the first three principal components and were analyzed using independent t-tests. The retained principal components accounted for 93-99% of the total variance within each waveform. Runners with previous ITBS exhibited low principal component one scores for frontal plane hip angle. Principal component one accounted for the overall magnitude in hip adduction which indicated that runners with previous ITBS assumed less hip adduction throughout stance. No differences in the remaining retained principal component scores for the waveforms were detected among groups. A smaller hip adduction angle throughout the stance phase of running may be a compensatory strategy to limit iliotibial band strain. This running strategy may have persisted after ITBS symptoms subsided.
A robust polynomial principal component analysis for seismic noise attenuation
NASA Astrophysics Data System (ADS)
Wang, Yuchen; Lu, Wenkai; Wang, Benfeng; Liu, Lei
2016-12-01
Random and coherent noise attenuation is a significant aspect of seismic data processing, especially for pre-stack seismic data flattened by normal moveout correction or migration. Signal extraction is widely used for pre-stack seismic noise attenuation. Principle component analysis (PCA), one of the multi-channel filters, is a common tool to extract seismic signals, which can be realized by singular value decomposition (SVD). However, when applying the traditional PCA filter to seismic signal extraction, the result is unsatisfactory with some artifacts when the seismic data is contaminated by random and coherent noise. In order to directly extract the desired signal and fix those artifacts at the same time, we take into consideration the amplitude variation with offset (AVO) property and thus propose a robust polynomial PCA algorithm. In this algorithm, a polynomial constraint is used to optimize the coefficient matrix. In order to simplify this complicated problem, a series of sub-optimal problems are designed and solved iteratively. After that, the random and coherent noise can be effectively attenuated simultaneously. Applications on synthetic and real data sets note that our proposed algorithm can better suppress random and coherent noise and have a better performance on protecting the desired signals, compared with the local polynomial fitting, conventional PCA and a L1-norm based PCA method.
Gao, H; Wu, Y; Zhang, T; Wu, Y; Jiang, L; Zhan, J; Li, J; Yang, R
2014-12-01
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.
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
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.
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.
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.
Burst and Principal Components Analyses of MEA Data Separates Chemicals by Class
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...
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.
NASA Astrophysics Data System (ADS)
Richman, Michael B.; Gong, Xiaofeng
1999-06-01
When applying eigenanalysis, one decision analysts make is the determination of what magnitude an eigenvector coefficient (e.g., principal component (PC) loading) must achieve to be considered as physically important. Such coefficients can be displayed on maps or in a time series or tables to gain a fuller understanding of a large array of multivariate data. Previously, such a decision on what value of loading designates a useful signal (hereafter called the loading `cutoff') for each eigenvector has been purely subjective. The importance of selecting such a cutoff is apparent since those loading elements in the range of zero to the cutoff are ignored in the interpretation and naming of PCs since only the absolute values of loadings greater than the cutoff are physically analyzed. This research sets out to objectify the problem of best identifying the cutoff by application of matching between known correlation/covariance structures and their corresponding eigenpatterns, as this cutoff point (known as the hyperplane width) is varied.A Monte Carlo framework is used to resample at five sample sizes. Fourteen different hyperplane cutoff widths are tested, bootstrap resampled 50 times to obtain stable results. The key findings are that the location of an optimal hyperplane cutoff width (one which maximized the information content match between the eigenvector and the parent dispersion matrix from which it was derived) is a well-behaved unimodal function. On an individual eigenvector, this enables the unique determination of a hyperplane cutoff value to be used to separate those loadings that best reflect the relationships from those that do not. The effects of sample size on the matching accuracy are dramatic as the values for all solutions (i.e., unrotated, rotated) rose steadily from 25 through 250 observations and then weakly thereafter. The specific matching coefficients are useful to assess the penalties incurred when one analyzes eigenvector coefficients of a
Laclaustra, Martin; Frangi, Alejandro F; Garcia, Daniel; Boisrobert, Loïc; Frangi, Andres G; Pascual, Isaac
2007-03-01
Endothelial dysfunction is associated with cardiovascular diseases and their risk factors (CVRF), and flow-mediated dilation (FMD) is increasingly used to explore it. In this test, artery diameter changes after post-ischaemic hyperaemia are classically quantified using maximum peak vasodilation (FMDc). To obtain more detailed descriptors of FMD we applied principal component analysis (PCA) to diameter-time curves (absolute), vasodilation-time curves (relative) and blood-velocity-time curves. Furthermore, combined PCA of vessel size and blood-velocity curves allowed exploring links between flow and dilation. Vessel diameter data for PCA (post-ischaemic: 140 s) were acquired from brachial ultrasound image sequences of 173 healthy male subjects using a computerized technique previously reported by our team based on image registration (Frangi et al 2003 IEEE Trans. Med. Imaging 22 1458). PCA provides a set of axes (called eigenmodes) that captures the underlying variation present in a database of waveforms so that the first few eigenmodes retain most of the variation. These eigenmodes can be used to synthesize each waveform analysed by means of only a few parameters, as well as potentially any signal of the same type derived from tests of new patients. The eigenmodes obtained seemed related to visual features of the waveform of the FMD process. Subsequently, we used eigenmodes to parameterize our data. Most of the main parameters (13 out of 15) correlated with FMDc. Furthermore, not all parameters correlated with the same CVRF tested, that is, serum lipids (i.e., high LDL-c associated with slow vessel return to a baseline, while low HDL-c associated with a lower vasodilation in response to similar velocity stimulus), thus suggesting that this parameterization allows a more detailed and factored description of the process than FMDc.
NASA Astrophysics Data System (ADS)
Gross-Colzy, Lydwine S.; Frouin, Robert J.
2003-11-01
A methodology is proposed to retrieve marine reflectance and chlorophyll-a concentration from space by decomposing the satellite reflectance into principal components. The components sensitive to the ocean signal are combined to retrieve the principal components of marine reflectance, allowing reconstruction of marine reflectance and estimation of chlorophyll-a concentration. Multi-layered perceptrons are used to approximate the functions relating the useful principal components of satellite reflectance to the principal components of marine reflectance. The algorithm is developed and evaluated using non-noisy and noisy synthetic data sets created for a wide range of angular and geophysical conditions. In the absence of noise on satellite reflectance, the relative error on marine reflectance does not exceed 2%. Accurate retrieval of the first principal component of marine reflectance allows a global relative error of 5.4% on chlorophyll-a concentration. In the presence of 1% non-correlated and 5% spectrally correlated noise on satellite reflectance, the relative error is increased to 6% and 21%, respectively. Application to SeaWiFS imagery yields marine reflectance and chlorophyll-a concentration fields that resemble those obtained from the standard SeaWiFS processing, but are generally less contrasted. Accuracy can be improved by including bio-optical variability in the simulated marine reflectance ensembles.
Optimized principal component analysis on coronagraphic images of the fomalhaut system
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.
Lange, Oliver F; Grubmüller, Helmut
2006-11-16
The suitability of principal component analysis (PCA) to yield slow collective coordinates for use within a dimension reduced description of conformational motions in proteins is evaluated. Two proteins are considered, T4 lysozyme and crambin. We present a quantitative evaluation of the convergence of conformational coordinates obtained with principal component analysis. Detailed analyses of (>200 ns) molecular dynamics trajectories and crystallographic data suggests that simulations of a few nanoseconds should generally provide a stable and statistically reliable definition of the essential and near constraints subspaces. Moreover, a systematic assessment of the density of states of the dynamics of all principal components showed that for an optimal separation of time scales it is crucial to include also side chain atoms in the PCA.
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.
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.
NASA Astrophysics Data System (ADS)
Wang, Yue J.; Luo, Lan; Li, Haifeng; Freedman, Matthew T.
1999-05-01
As a step toward understanding the complex information from data and relationships, structural and discriminative knowledge reveals insight that may prove useful in data interpretation and exploration. This paper reports the development of an automated and intelligent procedure for generating the hierarchy of minimize entropy models and principal component visualization spaces for improved data explanation. The proposed hierarchical mimimax entropy modeling and probabilistic principal component projection are both statistically principles and visually effective at revealing all of the interesting aspects of the data set. The methods involve multiple use of standard finite normal mixture models and probabilistic principal component projections. The strategy is that the top-level model and projection should explain the entire data set, best revealing the presence of clusters and relationships, while lower-level models and projections should display internal structure within individual clusters, such as the presence of subclusters and attribute trends, which might not be apparent in the higher-level models and projections. With may complementary mixture models and visualization projections, each level will be relatively simple while the complete hierarchy maintains overall flexibility yet still conveys considerable structural information. In particular, a model identification procedure is developed to select the optimal number and kernel shapes of local clusters from a class of data, resulting in a standard finite normal mixtures with minimum conditional bias and variance, and a probabilistic principal component neural network is advanced to generate optimal projections, leading to a hierarchical visualization algorithm allowing the complete data set to be analyzed at the top level, with best separated subclusters of data points analyzed at deeper levels. Hierarchial probabilistic principal component visualization involves (1) evaluation of posterior probabilities for
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.
Common functional principal components analysis: a new approach to analyzing human movement data.
Coffey, N; Harrison, A J; Donoghue, O A; Hayes, K
2011-12-01
In many human movement studies angle-time series data on several groups of individuals are measured. Current methods to compare groups include comparisons of the mean value in each group or use multivariate techniques such as principal components analysis and perform tests on the principal component scores. Such methods have been useful, though discard a large amount of information. Functional data analysis (FDA) is an emerging statistical analysis technique in human movement research which treats the angle-time series data as a function rather than a series of discrete measurements. This approach retains all of the information in the data. Functional principal components analysis (FPCA) is an extension of multivariate principal components analysis which examines the variability of a sample of curves and has been used to examine differences in movement patterns of several groups of individuals. Currently the functional principal components (FPCs) for each group are either determined separately (yielding components that are group-specific), or by combining the data for all groups and determining the FPCs of the combined data (yielding components that summarize the entire data set). The group-specific FPCs contain both within and between group variation and issues arise when comparing FPCs across groups when the order of the FPCs alter in each group. The FPCs of the combined data may not adequately describe all groups of individuals and comparisons between groups typically use t-tests of the mean FPC scores in each group. When these differences are statistically non-significant it can be difficult to determine how a particular intervention is affecting movement patterns or how injured subjects differ from controls. In this paper we aim to perform FPCA in a manner allowing sensible comparisons between groups of curves. A statistical technique called common functional principal components analysis (CFPCA) is implemented. CFPCA identifies the common sources of variation
Enhancement of TEM Data and Noise Characterization by Principal Component Analysis
2010-05-01
lated signals: IRE Transactions on Information Theory, IT-2, 41–46. Minty, B., and J. Hovgaard, 2002, Reducing noise in gamma - ray spectrometry using...spectral component analysis: Exploration Geophysics, 33, 172–176. Minty, B., and P. McFadden, 1998, Improved NASVD smoothing of airborne gamma - ray ... Airborne Electromagnetic GCV - Generalized Cross Validation m - meter mV - millivolt PCA - Principal Component Analysis RF - Radio Frequency RGB - Red
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,…
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…
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…
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…
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)
ERIC Educational Resources Information Center
Langager, Mark
2010-01-01
This paper is an exploratory study of the childhood academic language environments (CALEs) of bilingual Japanese expatriate students. Student (n=28) and parent (n=67) surveys were adapted from the Life History Calendar (Caspi et al. 1996) to gather retrospective CALE data at a Japanese-English bilingual high school. Principal Components Analysis…
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).
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…
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…
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.
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.
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.
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.
Feature selection in gene expression data using principal component analysis and rough set theory.
Mishra, Debahuti; Dash, Rajashree; Rath, Amiya Kumar; Acharya, Milu
2011-01-01
In many fields such as data mining, machine learning, pattern recognition and signal processing, data sets containing huge number of features are often involved. Feature selection is an essential data preprocessing technique for such high-dimensional data classification tasks. Traditional dimensionality reduction approach falls into two categories: Feature Extraction (FE) and Feature Selection (FS). Principal component analysis is an unsupervised linear FE method for projecting high-dimensional data into a low-dimensional space with minimum loss of information. It discovers the directions of maximal variances in the data. The Rough set approach to feature selection is used to discover the data dependencies and reduction in the number of attributes contained in a data set using the data alone, requiring no additional information. For selecting discriminative features from principal components, the Rough set theory can be applied jointly with PCA, which guarantees that the selected principal components will be the most adequate for classification. We call this method Rough PCA. The proposed method is successfully applied for choosing the principal features and then applying the Upper and Lower Approximations to find the reduced set of features from a gene expression data.
Automatic Classification of Staphylococci by Principal-Component Analysis and a Gradient Method1
Hill, L. R.; Silvestri, L. G.; Ihm, P.; Farchi, G.; Lanciani, P.
1965-01-01
Hill, L. R. (Università Statale, Milano, Italy), L. G. Silvestri, P. Ihm, G. Farchi, and P. Lanciani. Automatic classification of staphylococci by principal-component analysis and a gradient method. J. Bacteriol. 89:1393–1401. 1965.—Forty-nine strains from the species Staphylococcus aureus, S. saprophyticus, S. lactis, S. afermentans, and S. roseus were submitted to different taxometric analyses; clustering was performed by single linkage, by the unweighted pair group method, and by principal-component analysis followed by a gradient method. Results were substantially the same with all methods. All S. aureus clustered together, sharply separated from S. roseus and S. afermentans; S. lactis and S. saprophyticus fell between, with the latter nearer to S. aureus. The main purpose of this study was to introduce a new taxometric technique, based on principal-component analysis followed by a gradient method, and to compare it with some other methods in current use. Advantages of the new method are complete automation and therefore greater objectivity, execution of the clustering in a space of reduced dimensions in which different characters have different weights, easy recognition of taxonomically important characters, and opportunity for representing clusters in three-dimensional models; the principal disadvantage is the need for large computer facilities. Images PMID:14293013
Effect of noise in principal component analysis with an application to ozone pollution
NASA Astrophysics Data System (ADS)
Tsakiri, Katerina G.
This thesis analyzes the effect of independent noise in principal components of k normally distributed random variables defined by a covariance matrix. We prove that the principal components as well as the canonical variate pairs determined from joint distribution of original sample affected by noise can be essentially different in comparison with those determined from the original sample. However when the differences between the eigenvalues of the original covariance matrix are sufficiently large compared to the level of the noise, the effect of noise in principal components and canonical variate pairs proved to be negligible. The theoretical results are supported by simulation study and examples. Moreover, we compare our results about the eigenvalues and eigenvectors in the two dimensional case with other models examined before. This theory can be applied in any field for the decomposition of the components in multivariate analysis. One application is the detection and prediction of the main atmospheric factor of ozone concentrations on the example of Albany, New York. Using daily ozone, solar radiation, temperature, wind speed and precipitation data, we determine the main atmospheric factor for the explanation and prediction of ozone concentrations. A methodology is described for the decomposition of the time series of ozone and other atmospheric variables into the global term component which describes the long term trend and the seasonal variations, and the synoptic scale component which describes the short term variations. By using the Canonical Correlation Analysis, we show that solar radiation is the only main factor between the atmospheric variables considered here for the explanation and prediction of the global and synoptic scale component of ozone. The global term components are modeled by a linear regression model, while the synoptic scale components by a vector autoregressive model and the Kalman filter. The coefficient of determination, R2, for the
Principal component analysis of MCG-06-30-15 with XMM-Newton
NASA Astrophysics Data System (ADS)
Parker, M. L.; Marinucci, A.; Brenneman, L.; Fabian, A. C.; Kara, E.; Matt, G.; Walton, D. J.
2014-01-01
We analyse the spectral variability of MCG-06-30-15 with 600 k s of XMM-Newton data, including 300 k s of new data from the joint XMM-Newton and NuSTAR 2013 observational campaign. We use principal component analysis to find high-resolution, model-independent spectra of the different variable components of the spectrum. We find that over 99 per cent of the variability can be described by just three components, which are consistent with variations in the normalization of the power-law continuum (˜97 per cent), the photon index (˜2 per cent) and the normalization of a relativistically blurred reflection spectrum (˜0.5 per cent). We also find a fourth significant component but this is heavily diluted by noise, and we can attribute all the remaining spectral variability to noise. All three components are found to be variable on time-scales from 20 down to 1 k s, which corresponds to a distance from the central black hole of less than 70 gravitational radii. We compare these results with those derived from spectral fitting, and find them to be in very good agreement with our interpretation of the principal components. We conclude that the observed relatively weak variability in the reflected component of the spectrum of MCG-06-30-15 is due to the effects of light-bending close to the event horizon of the black hole, and demonstrate that principal component analysis is an effective tool for analysing spectral variability in this regime.
NASA Technical Reports Server (NTRS)
Armstrong, J. T.; Hummel, C. A.; Quirrenbach, A.; Buscher, D. F.; Mozurkewich, D.; Vivekanand, M.; Simon, R. S.; Denison, C. S.; Johnston, K. J.; Pan, X.-P.
1992-01-01
The orbit of the double-lined spectroscopic binary Phi Cygni, the distance to the system, and the masses and absolute magnitudes of its components are presented via measurements with the Mar III Optical Interferometer. On the basis of a reexamination of the spectroscopic data of Rach & Herbig (1961), the values and uncertainties are adopted for the period and the projected semimajor axes from the present fit to the spectroscopic data and the values of the remaining elements from the present fit to the Mark III data. The elements of the true orbit are derived, and the masses and absolute magnitudes of the components, and the distance to the system are calculated.
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.
Obtaining a linear combination of the principal components of a matrix on quantum computers
NASA Astrophysics Data System (ADS)
Daskin, Ammar
2016-10-01
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.
Sulthana, Ayesha; Latha, K C; Imran, Mohammad; Rathan, Ramya; Sridhar, R; Balasubramanian, S
2014-01-01
Fuzzy principal component regression (FPCR) is proposed to model the non-linear process of sewage treatment plant (STP) data matrix. The dimension reduction of voluminous data was done by principal component analysis (PCA). The PCA score values were partitioned by fuzzy-c-means (FCM) clustering, and a Takagi-Sugeno-Kang (TSK) fuzzy model was built based on the FCM functions. The FPCR approach was used to predict the reduction in chemical oxygen demand (COD) and biological oxygen demand (BOD) of treated wastewater of Vidyaranyapuram STP with respect to the relations modeled between fuzzy partitioned PCA scores and target output. The designed FPCR model showed the ability to capture the behavior of non-linear processes of STP. The predicted values of reduction in COD and BOD were analyzed by performing the linear regression analysis. The predicted values for COD and BOD reduction showed positive correlation with the observed 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.
The analysis of mixtures: Application of principal component analysis to XAS spectra
Wasserman, S.R.
1996-10-01
Many samples which are subjected to XAS analysis contain the element of interest in more than one chemical form. The interpretation of the spectras from such samples is often not straightforward, particularly if appropriate model systems are not available. We have applied principal component analysis (PCA) to real and simulated systems which contain mixtures of several species for a given element PCA has been extensively used for the analysis of other types of spectra, including MS, IR and UV-VIS. The application of PCA to XAS is illustrated by examining the speciation of iron within coals. PCA can determine how many different species that contain a particular element are present in a series of spectra. In tandem with model compounds, principal component analysis can suggest which of the models may contribute to the observed XAS spectra.
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.
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.
NASA Astrophysics Data System (ADS)
Wang, Shouyu; Jin, Ying; Yan, Keding; Xue, Liang; Liu, Fei; Li, Zhenhua
2014-11-01
Quantitative interferometric microscopy is used in biological and medical fields and a wealth of applications are proposed in order to detect different kinds of biological samples. Here, we develop a phase detecting cytometer based on quantitative interferometric microscopy with expanded principal component analysis phase retrieval method to obtain phase distributions of red blood cells with a spatial resolution ~1.5 μm. Since expanded principal component analysis method is a time-domain phase retrieval algorithm, it could avoid disadvantages of traditional frequency-domain algorithms. Additionally, the phase retrieval method realizes high-speed phase imaging from multiple microscopic interferograms captured by CCD camera when the biological cells are scanned in the field of view. We believe this method can be a powerful tool to quantitatively measure the phase distributions of different biological samples in biological and medical fields.
Online signature recognition using principal component analysis and artificial neural network
NASA Astrophysics Data System (ADS)
Hwang, Seung-Jun; Park, Seung-Je; Baek, Joong-Hwan
2016-12-01
In this paper, we propose an algorithm for on-line signature recognition using fingertip point in the air from the depth image acquired by Kinect. We extract 10 statistical features from X, Y, Z axis, which are invariant to changes in shifting and scaling of the signature trajectories in three-dimensional space. Artificial neural network is adopted to solve the complex signature classification problem. 30 dimensional features are converted into 10 principal components using principal component analysis, which is 99.02% of total variances. We implement the proposed algorithm and test to actual on-line signatures. In experiment, we verify the proposed method is successful to classify 15 different on-line signatures. Experimental result shows 98.47% of recognition rate when using only 10 feature vectors.
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.
Bio-inspired controller for a dexterous prosthetic hand based on Principal Components Analysis.
Matrone, G; Cipriani, C; Secco, E L; Carrozza, M C; Magenes, G
2009-01-01
Controlling a dexterous myoelectric prosthetic hand with many degrees of freedom (DoFs) could be a very demanding task, which requires the amputee for high concentration and ability in modulating many different muscular contraction signals. In this work a new approach to multi-DoF control is proposed, which makes use of Principal Component Analysis (PCA) to reduce the DoFs space dimensionality and allow to drive a 15 DoFs hand by means of a 2 DoFs signal. This approach has been tested and properly adapted to work onto the underactuated robotic hand named CyberHand, using mouse cursor coordinates as input signals and a principal components (PCs) matrix taken from the literature. First trials show the feasibility of performing grasps using this method. Further tests with real EMG signals are foreseen.
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.
Principal Component Analysis and Cluster Analysis in Profile of Electrical System
NASA Astrophysics Data System (ADS)
Iswan; Garniwa, I.
2017-03-01
This paper propose to present approach for profile of electrical system, presented approach is combination algorithm, namely principal component analysis (PCA) and cluster analysis. Based on relevant data of gross domestic regional product and electric power and energy use. This profile is set up to show the condition of electrical system of the region, that will be used as a policy in the electrical system of spatial development in the future. This paper consider 24 region in South Sulawesi province as profile center points and use principal component analysis (PCA) to asses the regional profile for development. Cluster analysis is used to group these region into few cluster according to the new variable be produced PCA. The general planning of electrical system of South Sulawesi province can provide support for policy making of electrical system development. The future research can be added several variable into existing variable.
Das, Atanu; Mukhopadhyay, Chaitali
2007-10-28
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.
[Infrared spectroscopy analysis of SF6 using multiscale weighted principal component analysis].
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.
Guo, H; Wang, T; Louie, P K K
2004-06-01
Receptor-oriented source apportionment models are often used to identify sources of ambient air pollutants and to estimate source contributions to air pollutant concentrations. In this study, a PCA/APCS model was applied to the data on non-methane hydrocarbons (NMHCs) measured from January to December 2001 at two sampling sites: Tsuen Wan (TW) and Central & Western (CW) Toxic Air Pollutants Monitoring Stations in Hong Kong. This multivariate method enables the identification of major air pollution sources along with the quantitative apportionment of each source to pollutant species. The PCA analysis identified four major pollution sources at TW site and five major sources at CW site. The extracted pollution sources included vehicular internal engine combustion with unburned fuel emissions, use of solvent particularly paints, liquefied petroleum gas (LPG) or natural gas leakage, and industrial, commercial and domestic sources such as solvents, decoration, fuel combustion, chemical factories and power plants. The results of APCS receptor model indicated that 39% and 48% of the total NMHCs mass concentrations measured at CW and TW were originated from vehicle emissions, respectively. 32% and 36.4% of the total NMHCs were emitted from the use of solvent and 11% and 19.4% were apportioned to the LPG or natural gas leakage, respectively. 5.2% and 9% of the total NMHCs mass concentrations were attributed to other industrial, commercial and domestic sources, respectively. It was also found that vehicle emissions and LPG or natural gas leakage were the main sources of C(3)-C(5) alkanes and C(3)-C(5) alkenes while aromatics were predominantly released from paints. Comparison of source contributions to ambient NMHCs at the two sites indicated that the contribution of LPG or natural gas at CW site was almost twice that at TW site. High correlation coefficients (R(2) > 0.8) between the measured and predicted values suggested that the PCA/APCS model was applicable for estimation of sources of NMHCs in ambient air.
Karpuzcu, M Ekrem; Fairbairn, David; Arnold, William A; Barber, Brian L; Kaufenberg, Elizabeth; Koskinen, William C; Novak, Paige J; Rice, Pamela J; Swackhamer, Deborah L
2014-01-01
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 (PC1) was attributed to urban wastewater-derived sources, including municipal wastewater and residential septic tank effluents, while Principal Component 2 (PC2) was attributed to agricultural sources. The variances of the concentrations of cotinine, DEET and the prescription drugs carbamazepine, erythromycin and sulfamethoxazole were best explained by PC1, while the variances of the concentrations of the agricultural pesticides atrazine, metolachlor and acetochlor were best explained by PC2. Mixed use compounds carbaryl, iprodione and daidzein did not specifically group with either PC1 or PC2. Furthermore, despite the fact that caffeine and acetaminophen have been historically associated with human use, they could not be attributed to a single dominant land use category (e.g., urban/residential or agricultural). Contributions from septic systems did not clarify the source for these two compounds, suggesting that additional sources, such as runoff from biosolid-amended soils, may exist. Based on these results, PCA may be a useful way to broadly categorize the sources of new and previously uncharacterized emerging contaminants or may help to clarify transport pathways in a given area. Acetaminophen and caffeine were not ideal markers for urban/residential contamination sources in the study area and may need to be reconsidered as such in other areas as well.
Friesen, Christine Elizabeth; Seliske, Patrick; Papadopoulos, Andrew
2016-01-01
Objectives. Socioeconomic status (SES) is a comprehensive indicator of health status and is useful in area-level health research and informing public health resource allocation. Principal component analysis (PCA) is a useful tool for developing SES indices to identify area-level disparities in SES within communities. While SES research in Canada has relied on census data, the voluntary nature of the 2011 National Household Survey challenges the validity of its data, especially income variables. This study sought to determine the appropriateness of replacing census income information with tax filer data in neighbourhood SES index development. Methods. Census and taxfiler data for Guelph, Ontario were retrieved for the years 2005, 2006, and 2011. Data were extracted for eleven income and non-income SES variables. PCA was employed to identify significant principal components from each dataset and weights of each contributing variable. Variable-specific factor scores were applied to standardized census and taxfiler data values to produce SES scores. Results. The substitution of taxfiler income variables for census income variables yielded SES score distributions and neighbourhood SES classifications that were similar to SES scores calculated using entirely census variables. Combining taxfiler income variables with census non-income variables also produced clearer SES level distinctions. Internal validation procedures indicated that utilizing multiple principal components produced clearer SES level distinctions than using only the first principal component. Conclusion. Identifying socioeconomic disparities between neighbourhoods is an important step in assessing the level of disadvantage of communities. The ability to replace census income information with taxfiler data to develop SES indices expands the versatility of public health research and planning in Canada, as more data sources can be explored. The apparent usefulness of PCA also contributes to the improvement
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.
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.
APPLICATION OF PRINCIPAL COMPONENT ANALYSIS AND BAYESIAN DECOMPOSITION TO RELAXOGRAPHIC IMAGING
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.
NASA Astrophysics Data System (ADS)
Yousefi, Fakhri; Karimi, Hajir; Mohammadiyan, Somayeh
2016-11-01
This paper applies the model including back-propagation network (BPN) and principal component analysis (PCA) to estimate the effective viscosity of carbon nanotubes suspension. The effective viscosities of multiwall carbon nanotubes suspension are examined as a function of the temperature, nanoparticle volume fraction, effective length of nanoparticle and the viscosity of base fluids using artificial neural network. The obtained results by BPN-PCA model have good agreement with the experimental data.
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.
Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters.
Tao, Dapeng; Lin, Xu; Jin, Lianwen; Li, Xuelong
2016-03-01
Chinese character font recognition (CCFR) has received increasing attention as the intelligent applications based on optical character recognition becomes popular. However, traditional CCFR systems do not handle noisy data effectively. By analyzing in detail the basic strokes of Chinese characters, we propose that font recognition on a single Chinese character is a sequence classification problem, which can be effectively solved by recurrent neural networks. For robust CCFR, we integrate a principal component convolution layer with the 2-D long short-term memory (2DLSTM) and develop principal component 2DLSTM (PC-2DLSTM) algorithm. PC-2DLSTM considers two aspects: 1) the principal component layer convolution operation helps remove the noise and get a rational and complete font information and 2) simultaneously, 2DLSTM deals with the long-range contextual processing along scan directions that can contribute to capture the contrast between character trajectory and background. Experiments using the frequently used CCFR dataset suggest the effectiveness of PC-2DLSTM compared with other state-of-the-art font recognition methods.
Aberration measurement based on principal component analysis of aerial images of optimized marks
NASA Astrophysics Data System (ADS)
Yan, Guanyong; Wang, Xiangzhao; Li, Sikun; Yang, Jishuo; Xu, Dongbo
2014-10-01
We propose an aberration measurement technique based on principal component analysis of aerial images of optimized marks (AMAI-OM). Zernike aberrations are retrieved using a linear relationship between the aerial image and Zernike coefficients. The linear relationship is composed of the principal components (PCs) and regression matrix. A centering process is introduced to compensate position offsets of the measured aerial image. A new test mark is designed in order to improve the centering accuracy and theoretical accuracy of aberration measurement together. The new test marks are composed of three spaces with different widths, and their parameters are optimized by using an accuracy evaluation function. The offsets of the measured aerial image are compensated in the centering process and the adjusted PC coefficients are obtained. Then the Zernike coefficients are calculated according to these PC coefficients using a least square method. The simulations using the lithography simulators PROLITH and Dr.LiTHO validate the accuracy of our method. Compared with the previous aberration measurement technique based on principal component analysis of aerial image (AMAI-PCA), the measurement accuracy of Zernike aberrations under the real measurement condition of the aerial image is improved by about 50%.
Zou, Ling; Zhang, Yingchun; Yang, Laurence T; Zhou, Renlai
2010-02-01
The authors have developed a new approach by combining the wavelet denoising and principal component analysis methods to reduce the number of required trials for efficient extraction of brain evoked-related potentials (ERPs). Evoked-related potentials were initially extracted using wavelet denoising to enhance the signal-to-noise ratio of raw EEG measurements. Principal components of ERPs accounting for 80% of the total variance were extracted as part of the subspace of the ERPs. Finally, the ERPs were reconstructed from the selected principal components. Computer simulation results showed that the combined approach provided estimations with higher signal-to-noise ratio and lower root mean squared error than each of them alone. The authors further tested this proposed approach in single-trial ERPs extraction during an emotional process and brain responses analysis to emotional stimuli. The experimental results also demonstrated the effectiveness of this combined approach in ERPs extraction and further supported the view that emotional stimuli are processed more intensely.
Generalized multilevel function-on-scalar regression and principal component analysis.
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.
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.
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.
Hanson, Cynthia; Sieverts, Michael; Vargis, Elizabeth
2016-11-25
Raman spectroscopy has been used for decades to detect and identify biological substances as it provides specific molecular information. Spectra collected from biological samples are often complex, requiring the aid of data truncation techniques such as principal component analysis (PCA) and multivariate classification methods. Classification results depend on the proper selection of principal components (PCs) and how PCA is performed (scaling and/or centering). There are also guidelines for choosing the optimal number of PCs such as a scree plot, Kaiser criterion, or cumulative percent variance. The goal of this research is to evaluate these methods for best implementation of PCA and PC selection to classify Raman spectra of bacteria. Raman spectra of three different isolates of mycobacteria (Mycobacterium sp. JLS, Mycobacterium sp. KMS, Mycobacterium sp. MCS) were collected and then passed through PCA and linear discriminant analysis for classification. Principal component analysis implementation as well as PC selection was evaluated by comparing the highest possible classification accuracies against accuracies determined by PC selection methods for each centering and scaling option. Centered and unscaled data provided the best results when selecting PCs based on cumulative percent variance.
Principal Component Analysis for Condition Monitoring of a Network of Bridge Structures
NASA Astrophysics Data System (ADS)
Hanley, Ciarán; Kelliher, Denis; Pakrashi, Vikram
2015-07-01
The use of visual inspections as the primary data gathering tool for modern bridge management systems is widespread, and thus leads to the collection and storage of large amounts of data points. Consequently, there exists an opportunity to use multivariate techniques to analyse large scale data sets as a descriptive and predictive tool. One such technique for analysing large data sets is principal component analysis (PCA), which can reduce the dimensionality of a data set into its most important components, while retaining as much variation as possible. An example is applied to a network of bridges in order to demonstrate the utility of the technique as applied to bridge management systems.
Strale, Mathieu; Krysinska, Karolina; Overmeiren, Gaëtan Van; Andriessen, Karl
2016-04-20
This study investigated the geographic distribution of suicide and railway suicide in Belgium over 2008--2013 on local (i.e., district or arrondissement) level. There were differences in the regional distribution of suicide and railway suicides in Belgium over the study period. Principal component analysis identified three groups of correlations among population variables and socio-economic indicators, such as population density, unemployment, and age group distribution, on two components that helped explaining the variance of railway suicide at a local (arrondissement) level. This information is of particular importance to prevent suicides in high-risk areas on the Belgian railway network.
Batch process monitoring based on multiple-phase online sorting principal component analysis.
Lv, Zhaomin; Yan, Xuefeng; Jiang, Qingchao
2016-09-01
Existing phase-based batch or fed-batch process monitoring strategies generally have two problems: (1) phase number, which is difficult to determine, and (2) uneven length feature of data. In this study, a multiple-phase online sorting principal component analysis modeling strategy (MPOSPCA) is proposed to monitor multiple-phase batch processes online. Based on all batches of off-line normal data, a new multiple-phase partition algorithm is proposed, where k-means and a defined average Euclidean radius are employed to determine the multiple-phase data set and phase number. Principal component analysis is then applied to build the model in each phase, and all the components are retained. In online monitoring, the Euclidean distance is used to select the monitoring model. All the components undergo online sorting through a parameter defined by Bayesian inference (BI). The first several components are retained to calculate the T(2) statistics. Finally, the respective probability indices of [Formula: see text] is obtained using BI as the moving average strategy. The feasibility and effectiveness of MPOSPCA are demonstrated through a simple numerical example and the fed-batch penicillin fermentation process.
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.
A multi-dimensional functional principal components analysis of EEG data.
Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla
2017-01-10
The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations.
de Souza, Juliana Bottoni; Reisen, Valdério Anselmo; Santos, Jane Méri; Franco, Glaura Conceição
2014-01-01
OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q) type in the residuals in order to eliminate the autocorrelation structures present in the components. RESULTS For every 10:49 μg/m3 increase (interquartile range) in levels of the pollutant PM10 there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive – while in the usual generalized additive model, the estimate was 2.0%. CONCLUSIONS Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model − principal component analysis, showed better results in estimating relative risk and quality of fit. PMID:25119940
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.
NASA Astrophysics Data System (ADS)
Cao, Le; Wang, Chenggang; Mao, Mao; Grosshans, Holger; Cao, Nianwen
2016-12-01
The ozone depletion events (ODEs) in the springtime Arctic have been investigated since the 1980s. It is found that the depletion of ozone is highly associated with an auto-catalytic reaction cycle, which involves mostly the bromine-containing compounds. Moreover, bromide stored in various substrates in the Arctic such as the underlying surface covered by ice and snow can be also activated by the absorbed HOBr. Subsequently, this leads to an explosive increase of the bromine amount in the troposphere, which is called the "bromine explosion mechanism". In the present study, a reaction scheme representing the chemistry of ozone depletion and halogen release is processed with two different mechanism reduction approaches, namely, the concentration sensitivity analysis and the principal component analysis. In the concentration sensitivity analysis, the interdependence of the mixing ratios of ozone and principal bromine species on the rate of each reaction in the ODE mechanism is identified. Furthermore, the most influential reactions in different time periods of ODEs are also revealed. By removing 11 reactions with the maximum absolute values of sensitivities lower than 10 %, a reduced reaction mechanism of ODEs is derived. The onsets of each time period of ODEs in simulations using the original reaction mechanism and the reduced reaction mechanism are identical while the maximum deviation of the mixing ratio of principal bromine species between different mechanisms is found to be less than 1 %. By performing the principal component analysis on an array of the sensitivity matrices, the dependence of a particular species concentration on a combination of the reaction rates in the mechanism is revealed. Redundant reactions are indicated by principal components corresponding to small eigenvalues and insignificant elements in principal components with large eigenvalues. Through this investigation, aside from the 11 reactions identified as unimportant in the concentration
NASA Astrophysics Data System (ADS)
Viet Ha, Nguyen; Golinval, Jean-Claude
2010-10-01
This paper addresses the problem of damage detection and localization in linear-form structures. Principal component analysis (PCA) is a popular technique for dynamic system investigation. The aim of the paper is to present a damage diagnosis method based on sensitivities of PCA results in the frequency domain. Starting from frequency response functions (FRFs) measured at different locations on the structure; PCA is performed to determine the main features of the signals. Sensitivities of principal directions obtained from PCA to structural parameters are then computed and inspected according to the location of sensors; their variation from the healthy state to the damaged state indicates damage locations. It is worth noting that damage localization is performed without the need of modal identification. Influences of some features as noise, choice of parameter and number of sensors are discussed. The efficiency and limitations of the proposed method are illustrated using numerical and real-world examples.
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.
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.
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.
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.
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.
Biometric variability of goat populations revealed by means of principal component analysis.
Pires, Luanna Chácara; Machado, Théa M Medeiros; Araújo, Adriana Mello; Olson, Timothy A; da Silva, João Batista Lopes; Torres, Robledo Almeida; Costa, Márcio da Silva
2012-12-01
The aim was to analyze variation in 12 Brazilian and Moroccan goat populations, and, through principal component analysis (PCA), check the importance of body measures and their indices as a means of distinguishing among individuals and populations. The biometric measurements were wither height (WH), brisket height (BH) and ear length (EL). Thorax depth (WH-BH) and the three indices, TD/WH, EL/TD and EL/WH, were also calculated. Of the seven components extracted, the first three principal components were sufficient to explain 99.5% of the total variance of the data. Graphical dispersion by genetic groups revealed that European dairy breeds clustered together. The Moroccan breeds were separated into two groups, one comprising the Drâa and the other the Zagora and Rhâali breeds. Whereas, on the one side, the Anglo-Nubian and undefined breeds were the closest to one another the goats of the Azul were observed to have the highest variation of all the breeds. The Anglo-Nubian and Boer breeds were similar to each other. The Nambi-type goats remained distinct from all the other populations. In general, the use of graphical representation of PCA values allowed to distinguish genetic groups.
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
Biometric variability of goat populations revealed by means of principal component analysis
Pires, Luanna Chácara; Machado, Théa M. Medeiros; Araújo, Adriana Mello; Olson, Timothy A.; da Silva, João Batista Lopes; Torres, Robledo Almeida; Costa, Márcio da Silva
2012-01-01
The aim was to analyze variation in 12 Brazilian and Moroccan goat populations, and, through principal component analysis (PCA), check the importance of body measures and their indices as a means of distinguishing among individuals and populations. The biometric measurements were wither height (WH), brisket height (BH) and ear length (EL). Thorax depth (WH-BH) and the three indices, TD/WH, EL/TD and EL/WH, were also calculated. Of the seven components extracted, the first three principal components were sufficient to explain 99.5% of the total variance of the data. Graphical dispersion by genetic groups revealed that European dairy breeds clustered together. The Moroccan breeds were separated into two groups, one comprising the Drâa and the other the Zagora and Rhâali breeds. Whereas, on the one side, the Anglo-Nubian and undefined breeds were the closest to one another the goats of the Azul were observed to have the highest variation of all the breeds. The Anglo-Nubian and Boer breeds were similar to each other. The Nambi-type goats remained distinct from all the other populations. In general, the use of graphical representation of PCA values allowed to distinguish genetic groups. PMID:23271938
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.
The application of principal component analysis to quantify technique in sports.
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.
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.
Dissecting the molecular structure of the Orion B cloud: insight from principal component analysis
NASA Astrophysics Data System (ADS)
Gratier, Pierre; Bron, Emeric; Gerin, Maryvonne; Pety, Jérôme; Guzman, Viviana V.; Orkisz, Jan; Bardeau, Sébastien; Goicoechea, Javier R.; Le Petit, Franck; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Roueff, Evelyne; Sievers, Albrech; Tremblin, Pascal
2017-03-01
Context. The combination of wideband receivers and spectrometers currently available in (sub-)millimeter observatories deliver wide-field hyperspectral imaging of the interstellar medium. Tens of spectral lines can be observed over degree wide fields in about 50 h. This wealth of data calls for restating the physical questions about the interstellar medium in statistical terms. Aims: We aim to gain information on the physical structure of the interstellar medium from a statistical analysis of many lines from different species over a large field of view, without requiring detailed radiative transfer or astrochemical modeling. Methods: We coupled a non-linear rescaling of the data with one of the simplest multivariate analysis methods, namely the principal component analysis, to decompose the observed signal into components that we interpret first qualitatively and then quantitatively based on our deep knowledge of the observed region and of the astrochemistry at play. Results: We identify three principal components, linear compositions of line brightness temperatures, that are correlated at various levels with the column density, the volume density and the UV radiation field. Conclusions: When sampling a sufficiently diverse mixture of physical parameters, it is possible to decompose the molecular emission in order to gain physical insight on the observed interstellar medium. This opens a new avenue for future studies of the interstellar medium. Based on observations carried out at the IRAM-30 m single-dish telescope. IRAM is supported by INSU/CNRS (France), MPG (Germany) and IGN (Spain).
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.
Estimation of surface curvature from full-field shape data using principal component analysis
NASA Astrophysics Data System (ADS)
Sharma, Sameer; Vinuchakravarthy, S.; Subramanian, S. J.
2017-01-01
Three-dimensional digital image correlation (3D-DIC) is a popular image-based experimental technique for estimating surface shape, displacements and strains of deforming objects. In this technique, a calibrated stereo rig is used to obtain and stereo-match pairs of images of the object of interest from which the shapes of the imaged surface are then computed using the calibration parameters of the rig. Displacements are obtained by performing an additional temporal correlation of the shapes obtained at various stages of deformation and strains by smoothing and numerically differentiating the displacement data. Since strains are of primary importance in solid mechanics, significant efforts have been put into computation of strains from the measured displacement fields; however, much less attention has been paid to date to computation of curvature from the measured 3D surfaces. In this work, we address this gap by proposing a new method of computing curvature from full-field shape measurements using principal component analysis (PCA) along the lines of a similar work recently proposed to measure strains (Grama and Subramanian 2014 Exp. Mech. 54 913-33). PCA is a multivariate analysis tool that is widely used to reveal relationships between a large number of variables, reduce dimensionality and achieve significant denoising. This technique is applied here to identify dominant principal components in the shape fields measured by 3D-DIC and these principal components are then differentiated systematically to obtain the first and second fundamental forms used in the curvature calculation. The proposed method is first verified using synthetically generated noisy surfaces and then validated experimentally on some real world objects with known ground-truth curvatures.
Labbe, David R; de Guise, Jacques A; Mezghani, Neila; Godbout, Véronique; Grimard, Guy; Baillargeon, David; Lavigne, Patrick; Fernandes, Julio; Ranger, Pierre; Hagemeister, Nicola
2010-12-01
The pivot shift test reproduces a complex instability of the knee joint following rupture of the anterior cruciate ligament. The grade of the pivot shift test has been shown to correlate to subjective criteria of knee joint function, return to physical activity and long-term outcome. This severity is represented by a grade that is attributed by a clinician in a subjective manner, rendering the pivot shift test poorly reliable. The purpose of this study was to unveil the kinematic parameters that are evaluated by clinicians when they establish a pivot shift grade. To do so, eight orthopaedic surgeons performed a total of 127 pivot shift examinations on 70 subjects presenting various degrees of knee joint instability. The knee joint kinematics were recorded using electromagnetic sensors and principal component analysis was used to determine which features explain most of the variability between recordings. Four principal components were found to account for most of this variability (69%), with only the first showing a correlation to the pivot shift grade (r = 0.55). Acceleration and velocity of tibial translation were found to be the features that best correlate to the first principal component, meaning they are the most useful for distinguishing different recordings. The magnitudes of the tibial translation and rotation were amongst those that accounted for the least variability. These results indicate that future efforts to quantify the pivot shift should focus more on the velocity and acceleration of tibial translation and less on the traditionally accepted parameters that are the magnitudes of posterior translation and external tibial rotation.
NASA Astrophysics Data System (ADS)
Whitworth, M.; Giles, D.; Murphy, W.
The Jurassic strata of the Cotswolds escarpment of southern central United Kingdom are associated with extensive mass movement activity, including mudslide systems, rotational and translational landslides. These mass movements can pose a significant engineering risk and have been the focus of research into the use of remote sensing techniques as a tool for landslide identification and delineation on clay slopes. The study has utilised a field site on the Cotswold escarpment above the village of Broad- way, Worcestershire, UK. Geomorphological investigation was initially undertaken at the site in order to establish ground control on landslides and other landforms present at the site. Subsequent to this, Airborne Thematic Mapper (ATM) imagery and colour stereo photography were acquired by the UK Natural Environment Research Coun- cil (NERC) for further analysis and interpretation. This paper describes the textu- ral enhancement of the airborne imagery undertaken using both mean euclidean dis- tance (MEUC) and grey level co-occurrence matrix entropy (GLCM) together with a combined texture-principal component based supervised image classification that was adopted as the method for landslide identification. The study highlights the importance of image texture for discriminating mass movements within multispectral imagery and demonstrates that by adopting a combined texture-principal component image classi- fication we have been able to achieve classification accuracy of 84 % with a Kappa statistic of 0.838 for landslide classes. This paper also highlights the potential prob- lems that can be encountered when using high-resolution multispectral imagery, such as the presence of dense variable woodland present within the image, and presents a solution using principal component analysis.
An application of principal component analysis to the clavicle and clavicle fixation devices
2010-01-01
Background Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Materials and methods Twenty-one high-resolution computerized tomography scans of the clavicle were reconstructed and analyzed using a specifically developed statistical software package. After performing statistical shape analysis, PCA was applied to study the factors that account for anatomical variation. Results The first principal component representing size accounted for 70.5 percent of anatomical variation. The addition of a further three principal components accounted for almost 87 percent. Using statistical shape analysis, clavicles in males have a greater lateral depth and are longer, wider and thicker than in females. However, the sternal angle in females is larger than in males. PCA confirmed these differences between genders but also noted that men exhibit greater variance and classified clavicles into five morphological groups. Discussion And Conclusions This unique approach is the first that standardizes a clavicular orientation. It provides information that is useful to both, the biomedical engineer and clinician. Other applications include implant design with regard to modifying current or designing future clavicle fixation devices. Our findings support the need for further development of clavicle fixation devices and the questioning of whether gender-specific devices are necessary. PMID:20346123
Zhang, Yiwei; Pan, Wei
2014-01-01
Genome-wide association studies (GWAS) have been established as a major tool to identify genetic variants associated with complex traits, such as common diseases. However, GWAS may suffer from false positives and false negatives due to confounding population structures, including known or unknown relatedness. Another important issue is unmeasured environmental risk factors. Among many methods for adjusting for population structures, two approaches stand out: one is principal component regression (PCR) based on principal component analysis (PCA), which is perhaps most popular due to its early appearance, simplicity and general effectiveness; the other is based on a linear mixed model (LMM) that has emerged recently as perhaps the most flexible and effective, especially for samples with complex structures as in model organisms. As shown previously, the PCR approach can be regarded as an approximation to a LMM; such an approximation depends on the number of the top principal components (PCs) used, the choice of which is often difficult in practice. Hence, in the presence of population structure, the LMM appears to outperform the PCR method. However, due to the different treatments of fixed versus random effects in the two approaches, we show an advantage of PCR over LMM: in the presence of an unknown but spatially confined environmental confounder (e.g. environmental pollution or life style), the PCs may be able to implicitly and effectively adjust for the confounder while the LMM cannot. Accordingly, to adjust for both population structures and non-genetic confounders, we propose a hybrid method combining the use and thus strengths of PCR and LMM. We use real genotype data and simulated phenotypes to confirm the above points, and establish the superior performance of the hybrid method across all scenarios. PMID:25536929
NASA Astrophysics Data System (ADS)
Tipton, John; Hooten, Mevin; Goring, Simon
2017-01-01
Scientific records of temperature and precipitation have been kept for several hundred years, but for many areas, only a shorter record exists. To understand climate change, there is a need for rigorous statistical reconstructions of the paleoclimate using proxy data. Paleoclimate proxy data are often sparse, noisy, indirect measurements of the climate process of interest, making each proxy uniquely challenging to model statistically. We reconstruct spatially explicit temperature surfaces from sparse and noisy measurements recorded at historical United States military forts and other observer stations from 1820 to 1894. One common method for reconstructing the paleoclimate from proxy data is principal component regression (PCR). With PCR, one learns a statistical relationship between the paleoclimate proxy data and a set of climate observations that are used as patterns for potential reconstruction scenarios. We explore PCR in a Bayesian hierarchical framework, extending classical PCR in a variety of ways. First, we model the latent principal components probabilistically, accounting for measurement error in the observational data. Next, we extend our method to better accommodate outliers that occur in the proxy data. Finally, we explore alternatives to the truncation of lower-order principal components using different regularization techniques. One fundamental challenge in paleoclimate reconstruction efforts is the lack of out-of-sample data for predictive validation. Cross-validation is of potential value, but is computationally expensive and potentially sensitive to outliers in sparse data scenarios. To overcome the limitations that a lack of out-of-sample records presents, we test our methods using a simulation study, applying proper scoring rules including a computationally efficient approximation to leave-one-out cross-validation using the log score to validate model performance. The result of our analysis is a spatially explicit reconstruction of spatio
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
Principal components analysis corrects for stratification in genome-wide association studies.
Price, Alkes L; Patterson, Nick J; Plenge, Robert M; Weinblatt, Michael E; Shadick, Nancy A; Reich, David
2006-08-01
Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
Pattern of polynuclear aromatic hydrocarbons on indoor air: Exploratory principal component analysis
Mitra, S. ) Wilson, N.K. )
1992-01-01
Principal component analysis (PCA) was used to study polynuclear aromatic hydrocarbon (PAH) profiles in indoor air. Fifteen PAHs were measured in ten different homes in Columbus (Ohio) which had different indoor emission characteristics such as gas utilities, wood-burning fireplaces, and cigarette smokers. Different PAH concentration patterns emerged depending upon the emission sources present in the different homes. Of these, cigarette smoking appeared to have the greatest impact on the indoor PAH concentrations. The PCA allowed convenient displays of the multidimensional data set from which the PAH concentration characteristics could be elucidated. The interrelationship between the different PAHs was also studied by correlation analysis.
NASA Astrophysics Data System (ADS)
Smilek, Jan; Hadas, Zdenek
2017-02-01
In this paper we propose the use of principal component analysis to process the measured acceleration data in order to determine the direction of acceleration with the highest variance on given frequency of interest. This method can be used for improving the power generated by inertial energy harvesters. Their power output is highly dependent on the excitation acceleration magnitude and frequency, but the axes of acceleration measurements might not always be perfectly aligned with the directions of movement, and therefore the generated power output might be severely underestimated in simulations, possibly leading to false conclusions about the feasibility of using the inertial energy harvester for the examined application.
Scalable multi-correlative statistics and principal component analysis with Titan.
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.
Computing Steerable Principal Components of a Large Set of Images and Their Rotations
Ponce, Colin; Singer, Amit
2013-01-01
We present here an efficient algorithm to compute the Principal Component Analysis (PCA) of a large image set consisting of images and, for each image, the set of its uniform rotations in the plane. We do this by pointing out the block circulant structure of the covariance matrix and utilizing that structure to compute its eigenvectors. We also demonstrate the advantages of this algorithm over similar ones with numerical experiments. Although it is useful in many settings, we illustrate the specific application of the algorithm to the problem of cryo-electron microscopy. PMID:21536533
Computing steerable principal components of a large set of images and their rotations.
Ponce, Colin; Singer, Amit
2011-11-01
We present here an efficient algorithm to compute the Principal Component Analysis (PCA) of a large image set consisting of images and, for each image, the set of its uniform rotations in the plane. We do this by pointing out the block circulant structure of the covariance matrix and utilizing that structure to compute its eigenvectors. We also demonstrate the advantages of this algorithm over similar ones with numerical experiments. Although it is useful in many settings, we illustrate the specific application of the algorithm to the problem of cryo-electron microscopy.
Guicheteau, J; Argue, L; Emge, D; Hyre, A; Jacobson, M; Christesen, S
2008-03-01
Surface-enhanced Raman spectroscopy (SERS) can provide rapid fingerprinting of biomaterial in a nondestructive manner. The adsorption of colloidal silver to biological material suppresses native biofluorescence while providing electromagnetic surface enhancement of the normal Raman signal. This work validates the applicability of qualitative SER spectroscopy for analysis of bacterial species by utilizing principal component analysis (PCA) to show discrimination of biological threat simulants, based upon multivariate statistical confidence limits bounding known data clusters. Gram-positive Bacillus spores (Bacillus atrophaeus, Bacillus anthracis, and Bacillus thuringiensis) are investigated along with the Gram-negative bacterium Pantoea agglomerans.
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.
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.
Vasil'eva, L L; Trut, L N
1990-03-01
In the course of long-term experiment on domestication of silver fox, it was necessary to determine the most important behavioral features, selection for which would give domestication effect. The mathematical method of principal components was used for the analysis of 20 fox behavior traits. The combination of the initial traits which reflected the structure of the integral trait, i.e. domesticated type of behavior, was determined. The practical use of this combination seems to more adequately estimate the level of domestication of a given animal, which is indispensable for subsequent effective selection.
NASA Astrophysics Data System (ADS)
Hurley, P. D.; Oliver, S.; Farrah, D.; Wang, L.; Efstathiou, A.
2012-08-01
The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. We further investigate the principal components (PCs) of ULIRGs derived in Wang et al. We quantitatively show that five PCs are optimal for describing the Infrared Spectrograph spectra. These five components (PC1-PC5) and the mean spectrum provide a template basis set that reproduces spectra of all z < 0.35 ULIRGs within the noise. For comparison, the spectra are also modelled with a combination of radiative transfer models of both starbursts and the dusty torus surrounding active galactic nuclei (AGN). The five PCs typically provide better fits than the models. We argue that the radiative transfer models require a colder dust component and have difficulty in modelling strong polycyclic aromatic hydrocarbon features. Aided by the models we also interpret the physical processes that the PCs represent. The third PC is shown to indicate the nature of the dominant power source, while PC1 is related to the inclination of the AGN torus. Finally, we use the five PCs to define a new classification scheme using 5D Gaussian mixture modelling and trained on widely used optical classifications. The five PCs, average spectra for the four classifications and the code to classify objects are made available at: .
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.
Regional assessment of trends in vegetation change dynamics using principal component analysis
NASA Astrophysics Data System (ADS)
Osunmadewa, B. A.; Csaplovics, E.; R. A., Majdaldin; Adeofun, C. O.; Aralova, D.
2016-10-01
Vegetation forms the basis for the existence of animal and human. Due to changes in climate and human perturbation, most of the natural vegetation of the world has undergone some form of transformation both in composition and structure. Increased anthropogenic activities over the last decades had pose serious threat on the natural vegetation in Nigeria, many vegetated areas are either transformed to other land use such as deforestation for agricultural purpose or completely lost due to indiscriminate removal of trees for charcoal, fuelwood and timber production. This study therefore aims at examining the rate of change in vegetation cover, the degree of change and the application of Principal Component Analysis (PCA) in the dry sub-humid region of Nigeria using Normalized Difference Vegetation Index (NDVI) data spanning from 1983-2011. The method used for the analysis is the T-mode orientation approach also known as standardized PCA, while trends are examined using ordinary least square, median trend (Theil-Sen) and monotonic trend. The result of the trend analysis shows both positive and negative trend in vegetation change dynamics over the 29 years period examined. Five components were used for the Principal Component Analysis. The results of the first component explains about 98 % of the total variance of the vegetation (NDVI) while components 2-5 have lower variance percentage (< 1%). Two ancillary land use land cover data of 2000 and 2009 from European Space Agency (ESA) were used to further explain changes observed in the Normalized Difference Vegetation Index. The result of the land use data shows changes in land use pattern which can be attributed to anthropogenic activities such as cutting of trees for charcoal production, fuelwood and agricultural practices. The result of this study shows the ability of remote sensing data for monitoring vegetation change in the dry-sub humid region of Nigeria.
Plazas-Nossa, Leonardo; Hofer, Thomas; Gruber, Günter; Torres, Andres
2017-02-01
This work proposes a methodology for the forecasting of online water quality data provided by UV-Vis spectrometry. Therefore, a combination of principal component analysis (PCA) to reduce the dimensionality of a data set and artificial neural networks (ANNs) for forecasting purposes was used. The results obtained were compared with those obtained by using discrete Fourier transform (DFT). The proposed methodology was applied to four absorbance time series data sets composed by a total number of 5705 UV-Vis spectra. Absolute percentage errors obtained by applying the proposed PCA/ANN methodology vary between 10% and 13% for all four study sites. In general terms, the results obtained were hardly generalizable, as they appeared to be highly dependent on specific dynamics of the water system; however, some trends can be outlined. PCA/ANN methodology gives better results than PCA/DFT forecasting procedure by using a specific spectra range for the following conditions: (i) for Salitre wastewater treatment plant (WWTP) (first hour) and Graz West R05 (first 18 min), from the last part of UV range to all visible range; (ii) for Gibraltar pumping station (first 6 min) for all UV-Vis absorbance spectra; and (iii) for San Fernando WWTP (first 24 min) for all of UV range to middle part of visible range.
Evaluation of river water quality monitoring stations by principal component analysis.
Ouyang, Ying
2005-07-01
The development of a surface water monitoring network is a critical element in the assessment, restoration, and protection of stream water quality. This study applied principal component analysis (PCA) and principal factor analysis (PFA) techniques to evaluate the effectiveness of the surface water quality-monitoring network in a river where the evaluated variables are monitoring stations. The objective was to identify monitoring stations that are important in assessing annual variations of river water quality. Twenty-two stations used for monitoring physical, chemical, and biological parameters, located at the main stem of the lower St. Johns River in Florida, USA, were selected for the purpose of this study. Results show that 3 monitoring stations were identified as less important in explaining the annual variance of the data set, and therefore could be the non-principal stations. In addition, the PFA technique was also employed to identify important water quality parameters. Results reveal that total organic carbon, dissolved organic carbon, total nitrogen, dissolved nitrate and nitrite, orthophosphate, alkalinity, salinity, Mg, and Ca were the parameters that are most important in assessing variations of water quality in the river. This study suggests that PCA and PFA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.
Retest of a Principal Components Analysis of Two Household Environmental Risk Instruments.
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.
NASA Astrophysics Data System (ADS)
Durigon, Angelica; Lier, Quirijn de Jong van; Metselaar, Klaas
2016-10-01
To date, measuring plant transpiration at canopy scale is laborious and its estimation by numerical modelling can be used to assess high time frequency data. When using the model by Jacobs (1994) to simulate transpiration of water stressed plants it needs to be reparametrized. We compare the importance of model variables affecting simulated transpiration of water stressed plants. A systematic literature review was performed to recover existing parameterizations to be tested in the model. Data from a field experiment with common bean under full and deficit irrigation were used to correlate estimations to forcing variables applying principal component analysis. New parameterizations resulted in a moderate reduction of prediction errors and in an increase in model performance. Ags model was sensitive to changes in the mesophyll conductance and leaf angle distribution parameterizations, allowing model improvement. Simulated transpiration could be separated in temporal components. Daily, afternoon depression and long-term components for the fully irrigated treatment were more related to atmospheric forcing variables (specific humidity deficit between stomata and air, relative air humidity and canopy temperature). Daily and afternoon depression components for the deficit-irrigated treatment were related to both atmospheric and soil dryness, and long-term component was related to soil dryness.
Analysis of the photoplethysmographic signal by means of the decomposition in principal components.
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.
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
Corriveau, H; Arsenault, A B; Dutil, E; Lepage, Y
1992-01-01
An evaluation based on the Bobath approach to treatment has previously been developed and partially validated. The purpose of the present study was to verify the content validity of this evaluation with the use of a statistical approach known as principal components analysis. Thirty-eight hemiplegic subjects participated in the study. Analysis of the scores on each of six parameters (sensorium, active movements, muscle tone, reflex activity, postural reactions, and pain) was evaluated on three occasions across a 2-month period. Each time this produced three factors that contained 70% of the variation in the data set. The first component mainly reflected variations in mobility, the second mainly variations in muscle tone, and the third mainly variations in sensorium and pain. The results of such exploratory analysis highlight the fact that some of the parameters are not only important but also interrelated. These results seem to partially support the conceptual framework substantiating the Bobath approach to treatment.
Improved gene prediction by principal component analysis based autoregressive Yule-Walker method.
Roy, Manidipa; Barman, Soma
2016-01-10
Spectral analysis using Fourier techniques is popular with gene prediction because of its simplicity. Model-based autoregressive (AR) spectral estimation gives better resolution even for small DNA segments but selection of appropriate model order is a critical issue. In this article a technique has been proposed where Yule-Walker autoregressive (YW-AR) process is combined with principal component analysis (PCA) for reduction in dimensionality. The spectral peaks of DNA signal are used to detect protein-coding regions based on the 1/3 frequency component. Here optimal model order selection is no more critical as noise is removed by PCA prior to power spectral density (PSD) estimation. Eigenvalue-ratio is used to find the threshold between signal and noise subspaces for data reduction. Superiority of proposed method over fast Fourier Transform (FFT) method and autoregressive method combined with wavelet packet transform (WPT) is established with the help of receiver operating characteristics (ROC) and discrimination measure (DM) respectively.
NASA Astrophysics Data System (ADS)
Pacholski, Michaeleen L.
2004-06-01
Principal component analysis (PCA) has been successfully applied to time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra, images and depth profiles. Although SIMS spectral data sets can be small (in comparison to datasets typically discussed in literature from other analytical techniques such as gas or liquid chromatography), each spectrum has thousands of ions resulting in what can be a difficult comparison of samples. Analysis of industrially-derived samples means the identity of most surface species are unknown a priori and samples must be analyzed rapidly to satisfy customer demands. PCA enables rapid assessment of spectral differences (or lack there of) between samples and identification of chemically different areas on sample surfaces for images. Depth profile analysis helps define interfaces and identify low-level components in the system.
NASA Astrophysics Data System (ADS)
Marchetti, A.; Granett, B. R.; Guzzo, L.; Fritz, A.; Garilli, B.; Scodeggio, M.; Abbas, U.; Adami, C.; Arnouts, S.; Bolzonella, M.; Bottini, D.; Cappi, A.; Coupon, J.; Cucciati, O.; De Lucia, G.; de la Torre, S.; Franzetti, P.; Fumana, M.; Ilbert, O.; Iovino, A.; Krywult, J.; Le Brun, V.; Le Fevre, O.; Maccagni, D.; Malek, K.; Marulli, F.; McCracken, H. J.; Meneux, B.; Paioro, L.; Polletta, M.; Pollo, A.; Schlagenhaufer, H.; Tasca, L.; Tojeiro, R.; Vergani, D.; Zanichelli, A.; Bel, J.; Bersanelli, M.; Blaizot, J.; Branchini, E.; Burden, A.; Davidzon, I.; Di Porto, C.; Guennou, L.; Marinoni, C.; Mellier, Y.; Moscardini, L.; Nichol, R. C.; Peacock, J. A.; Percival, W. J.; Phleps, S.; Schimd, C.; Wolk, M.; Zamorani, G.
2013-01-01
We develop a Principal Component Analysis aimed at classifying a subset of 27 350 spectra of galaxies in the range 0.4 < z < 1.0 collected by the VIMOS Public Extragalactic Redshift Survey (VIPERS). We apply an iterative algorithm to simultaneously repair parts of spectra affected by noise and/or sky residuals, and reconstruct gaps due to rest-frame transformation, and obtain a set of orthogonal spectral templates that span the diversity of galaxy types. By taking the three most significant components, we find that we can describe the whole sample without contamination from noise. We produce a catalogue of eigencoefficients and template spectra that will be part of future VIPERS data releases. Our templates effectively condense the spectral information into two coefficients that can be related to the age and star formation rate of the galaxies. We examine the spectrophotometric types in this space and identify early, intermediate, late and starburst galaxies.
Sousa, C C; Damasceno-Silva, K J; Bastos, E A; Rocha, M M
2015-12-07
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.
Liu, Xiao-Fang; Xue, Chang-Hu; Wang, Yu-Ming; Li, Zhao-Jie; Xue, Yong; Xu, Jie
2011-11-01
The present study is to investigate the feasibility of multi-elements analysis in determination of the geographical origin of sea cucumber Apostichopus japonicus, and to make choice of the effective tracers in sea cucumber Apostichopus japonicus geographical origin assessment. The content of the elements such as Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Hg and Pb in sea cucumber Apostichopus japonicus samples from seven places of geographical origin were determined by means of ICP-MS. The results were used for the development of elements database. Cluster analysis(CA) and principal component analysis (PCA) were applied to differentiate the sea cucumber Apostichopus japonicus geographical origin. Three principal components which accounted for over 89% of the total variance were extracted from the standardized data. The results of Q-type cluster analysis showed that the 26 samples could be clustered reasonably into five groups, the classification results were significantly associated with the marine distribution of the sea cucumber Apostichopus japonicus samples. The CA and PCA were the effective methods for elements analysis of sea cucumber Apostichopus japonicus samples. The content of the mineral elements in sea cucumber Apostichopus japonicus samples was good chemical descriptors for differentiating their geographical origins.
Belda-Lois, Juan-Manuel; Sanchez-Sanchez, M Luz
2015-08-01
Functional Principal Component Analysis (FPCA) is an increasingly used methodology for analysis of biomedical data. This methodology aims to obtain Functional Principal Components (FPCs) from Functional Data (time dependent functions). However, in biomedical data, the most common scenario of this analysis is from discrete time values. Standard procedures for FPCA require obtaining the functional data from these discrete values before extracting the FPCs. The problem appears when there are missing values in a non-negligible sample of subjects, especially at the beginning or the end of the study, because this approach can compromise the analysis due to the need to extrapolate or dismiss subjects with missing values. In this paper, we present an alternative methodology extracting the FPCs directly from the sampled data, avoiding the need to have functional data before extracting them. We demonstrate the feasibility of our approach from real data obtained from the analysis of balance recovery after stroke. Finally, we demonstrate that FPCA can obtain differences between groups when these differences are more related to the dynamics of the process than data values at given points.
Sittel, Florian; Jain, Abhinav; Stock, Gerhard
2014-07-07
Principal component analysis of molecular dynamics simulations is a popular method to account for the essential dynamics of the system on a low-dimensional free energy landscape. Using Cartesian coordinates, first the translation and overall rotation need to be removed from the trajectory. Since the rotation depends via the moment of inertia on the molecule's structure, this separation is only straightforward for relatively rigid systems. Adopting millisecond molecular dynamics simulations of the folding of villin headpiece and the functional dynamics of BPTI provided by D. E. Shaw Research, it is demonstrated via a comparison of local and global rotational fitting that the structural dynamics of flexible molecules necessarily results in a mixing of overall and internal motion. Even for the small-amplitude functional motion of BPTI, the conformational distribution obtained from a Cartesian principal component analysis therefore reflects to some extend the dominant overall motion rather than the much smaller internal motion of the protein. Internal coordinates such as backbone dihedral angles, on the other hand, are found to yield correct and well-resolved energy landscapes for both examples. The virtues and shortcomings of the choice of various fitting schemes and coordinate sets as well as the generality of these results are discussed in some detail.
Complex-valued neural networks for nonlinear complex principal component analysis.
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.
Jiang, Mingfeng; Zhu, Lingyan; Wang, Yaming; Xia, Ling; Shou, Guofa; Liu, Feng; Crozier, Stuart
2011-03-21
Non-invasively reconstructing the transmembrane potentials (TMPs) from body surface potentials (BSPs) constitutes one form of the inverse ECG problem that can be treated as a regression problem with multi-inputs and multi-outputs, and which can be solved using the support vector regression (SVR) method. In developing an effective SVR model, feature extraction is an important task for pre-processing the original input data. This paper proposes the application of principal component analysis (PCA) and kernel principal component analysis (KPCA) to the SVR method for feature extraction. Also, the genetic algorithm and simplex optimization method is invoked to determine the hyper-parameters of the SVR. Based on the realistic heart-torso model, the equivalent double-layer source method is applied to generate the data set for training and testing the SVR model. The experimental results show that the SVR method with feature extraction (PCA-SVR and KPCA-SVR) can perform better than that without the extract feature extraction (single SVR) in terms of the reconstruction of the TMPs on epi- and endocardial surfaces. Moreover, compared with the PCA-SVR, the KPCA-SVR features good approximation and generalization ability when reconstructing the TMPs.
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.
Sigirli, Deniz; Ercan, Ilker
2015-01-16
Most of the studies in medical and biological sciences are related with the examination of geometrical properties of an organ or organism. Growth and allometry studies are important in the way of investigating the effects of diseases and the environmental factors effects on the structure of the organ or organism. Thus statistical shape analysis has recently become more important in the medical and biological sciences. Shape is all geometrical information that remains when location, scale and rotational effects are removed from an object. Allometry, which is a relationship between size and shape, plays an important role in the development of statistical shape analysis. The aim of the present study was to compare two different models for allometry which includes tangent coordinates and principal component scores of tangent coordinates as dependent variables in multivariate regression analysis. The results of the simulation study showed that, the model constructed by taking tangent coordinates as dependent variables is more appropriate than the model constructed by taking principal component scores of tangent coordinates as dependent variables, for all sample sizes.
Sigirli, Deniz; Ercan, Ilker
2015-09-01
Most of the studies in medical and biological sciences are related to the examination of geometrical properties of an organ or organism. Growth and allometry studies are important in the way of investigating the effects of diseases and the environmental factors effects on the structure of the organ or organism. Thus, statistical shape analysis has recently become more important in the medical and biological sciences. Shape is all geometrical information that remains when location, scale and rotational effects are removed from an object. Allometry, which is a relationship between size and shape, plays an important role in the development of statistical shape analysis. The aim of the present study was to compare two different models for allometry which includes tangent coordinates and principal component scores of tangent coordinates as dependent variables in multivariate regression analysis. The results of the simulation study showed that the model constructed by taking tangent coordinates as dependent variables is more appropriate than the model constructed by taking principal component scores of tangent coordinates as dependent variables, for all sample sizes.
Principal Component Analysis in the Spectral Analysis of the Dynamic Laser Speckle Patterns
NASA Astrophysics Data System (ADS)
Ribeiro, K. M.; Braga, R. A., Jr.; Horgan, G. W.; Ferreira, D. D.; Safadi, T.
2014-02-01
Dynamic laser speckle is a phenomenon that interprets an optical patterns formed by illuminating a surface under changes with coherent light. Therefore, the dynamic change of the speckle patterns caused by biological material is known as biospeckle. Usually, these patterns of optical interference evolving in time are analyzed by graphical or numerical methods, and the analysis in frequency domain has also been an option, however involving large computational requirements which demands new approaches to filter the images in time. Principal component analysis (PCA) works with the statistical decorrelation of data and it can be used as a data filtering. In this context, the present work evaluated the PCA technique to filter in time the data from the biospeckle images aiming the reduction of time computer consuming and improving the robustness of the filtering. It was used 64 images of biospeckle in time observed in a maize seed. The images were arranged in a data matrix and statistically uncorrelated by PCA technique, and the reconstructed signals were analyzed using the routine graphical and numerical methods to analyze the biospeckle. Results showed the potential of the PCA tool in filtering the dynamic laser speckle data, with the definition of markers of principal components related to the biological phenomena and with the advantage of fast computational processing.
Roopwani, Rahul; Buckner, Ira S
2011-10-14
Principal component analysis (PCA) was applied to pharmaceutical powder compaction. A solid fraction parameter (SF(c/d)) and a mechanical work parameter (W(c/d)) representing irreversible compression behavior were determined as functions of applied load. Multivariate analysis of the compression data was carried out using PCA. The first principal component (PC1) showed loadings for the solid fraction and work values that agreed with changes in the relative significance of plastic deformation to consolidation at different pressures. The PC1 scores showed the same rank order as the relative plasticity ranking derived from the literature for common pharmaceutical materials. The utility of PC1 in understanding deformation was extended to binary mixtures using a subset of the original materials. Combinations of brittle and plastic materials were characterized using the PCA method. The relationships between PC1 scores and the weight fractions of the mixtures were typically linear showing ideal mixing in their deformation behaviors. The mixture consisting of two plastic materials was the only combination to show a consistent positive deviation from ideality. The application of PCA to solid fraction and mechanical work data appears to be an effective means of predicting deformation behavior during compaction of simple powder mixtures.
Zhang, Xian; Noah, Jack Adam; Hirsch, Joy
2016-01-01
Global systemic effects not specific to a task can be prominent in functional near-infrared spectroscopy (fNIRS) signals and the separation of task-specific fNIRS signals and global nonspecific effects is challenging due to waveform correlations. We describe a principal component spatial filter algorithm for separation of the global and local effects. The effectiveness of the approach is demonstrated using fNIRS signals acquired during a right finger-thumb tapping task where the response patterns are well established. Both the temporal waveforms and the spatial pattern consistencies between oxyhemoglobin and deoxyhemoglobin signals are significantly improved, consistent with the basic physiological basis of fNIRS signals and the expected pattern of activity associated with the task.
Butler, Rebecca A; Lambon Ralph, Matthew A; Woollams, Anna M
2014-12-01
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
Choi, Ji Yeh; Hwang, Heungsun; Yamamoto, Michio; Jung, Kwanghee; Woodward, Todd S
2016-02-08
Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.
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
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.
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.
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.
NASA Astrophysics Data System (ADS)
Singh, Dharmendra; Kumar, Harish
-tion mode (HH/HV or VV/VH), polarimetric (PLR) mode (HH/HV/VH/VV), and ScanSAR (WB) mode (HH/VV) [15]. These makes PALSAR imagery very attractive for spatially and temporally consistent monitoring system. The Overview of Principal Component Analysis is that the most of the information within all the bands can be compressed into a much smaller number of bands with little loss of information. It allows us to extract the low-dimensional subspaces that capture the main linear correlation among the high-dimensional image data. 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. In this paper we have explored the fusion technique for enhancing the land cover classification of low resolution satellite data espe-cially freely available satellite data. For this purpose, we have considered to fuse the PALSAR principal component data with MODIS principal component data. Initially, the MODIS band 1 and band 2 is considered, its principal component is computed. Similarly the PALSAR HH, HV and VV polarized data are considered, and there principal component is also computed. con-sequently, the PALSAR principal component image is fused with MODIS principal component image. The aim of this paper is to analyze the effect of classification accuracy on major type of land cover types like agriculture, water and urban bodies with fusion of PALSAR data to MODIS data. Curvelet transformation has been applied for fusion of these two satellite images and Minimum Distance classification technique has been applied for the resultant fused image. It is qualitatively and visually observed that the overall classification accuracy of MODIS image after fusion is enhanced. This type of fusion technique may be quite helpful in near future to use freely available satellite data to develop monitoring system for different land cover classes on the earth.
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
SU-E-CAMPUS-T-06: Radiochromic Film Analysis Based On Principal Components
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
Pavement macrotexture estimation using principal component analysis of tire/road noise
NASA Astrophysics Data System (ADS)
Zhang, Yiying; McDaniel, J. Gregory; Wang, Ming L.
2014-04-01
An investigation on the prediction of macrotexture Mean Texture Depth (MTD) of pavement from a moving vehicle is conducted. The MTD was predicted by using the tire/road noise measured from a microphone mounted underneath a moving vehicle. Principal Component Analysis (PCA) is used to filter noise from microphone data prioer to estimating its energy over an optimally selected bandwidth. Energy obtained using this method is named PCA energy, hence the developed method for MTD estimation is termed as PCA Energy Method. The acoustic energy is assumed to have positive linear correlation with MTD of pavement. Moreover, PCA was used to differentiate important information about the road surface from noisy data while vehicle is moving, yielding a set of principal component vectors representing the conditions of each road section. This principal component vector was used to compute the PCA energy that is to be used for MTD prediction. The frequency band most relative to pavement macrotexture was determined to be 140 to 700 Hz through theoretical and statistical research. Then, a MTD prediction model was built based on a Taylor series expansion with two variables, PCA energy and the driving speed of the vehicle. The model parameters were obtained from an engineered track (interstate highway) with known MTD, and then applied to urban roads for the feasibility test. The accuracy of the model is 83.61% for the engineered track, which is 10% higher than the previous energy-based methods without PCA treatment. Moreover, applicability of the model is increased by the extended MTD prediction range between 0.2 and 3 mm compared to that of the engineered track having 0.4 to 1.5 mm. In addition, the MTD could be predicted every 7.8 meters and with good repeatability in the urban road test, which proves the feasibility of the proposed approach. Therefore, the PCA Energy Method is a reliable, efficient, and cost effective way to predict MTD for engineering applications as an important
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.
Progress Towards Improved Analysis of TES X-ray Data Using Principal Component Analysis
NASA Technical Reports Server (NTRS)
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.
2015-01-01
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 55Fe 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.
Chen, Yanxian; Chang, Billy Heung Wing; Ding, Xiaohu; He, Mingguang
2016-11-22
In the present study we attempt to use hypothesis-independent analysis in investigating the patterns in refraction growth in Chinese children, and to explore the possible risk factors affecting the different components of progression, as defined by Principal Component Analysis (PCA). A total of 637 first-born twins in Guangzhou Twin Eye Study with 6-year annual visits (baseline age 7-15 years) were available in the analysis. Cluster 1 to 3 were classified after a partitioning clustering, representing stable, slow and fast progressing groups of refraction respectively. Baseline age and refraction, paternal refraction, maternal refraction and proportion of two myopic parents showed significant differences across the three groups. Three major components of progression were extracted using PCA: "Average refraction", "Acceleration" and the combination of "Myopia stabilization" and "Late onset of refraction progress". In regression models, younger children with more severe myopia were associated with larger "Acceleration". The risk factors of "Acceleration" included change of height and weight, near work, and parental myopia, while female gender, change of height and weight were associated with "Stabilization", and increased outdoor time was related to "Late onset of refraction progress". We therefore concluded that genetic and environmental risk factors have different impacts on patterns of refraction progression.
Chen, Yanxian; Chang, Billy Heung Wing; Ding, Xiaohu; He, Mingguang
2016-01-01
In the present study we attempt to use hypothesis-independent analysis in investigating the patterns in refraction growth in Chinese children, and to explore the possible risk factors affecting the different components of progression, as defined by Principal Component Analysis (PCA). A total of 637 first-born twins in Guangzhou Twin Eye Study with 6-year annual visits (baseline age 7–15 years) were available in the analysis. Cluster 1 to 3 were classified after a partitioning clustering, representing stable, slow and fast progressing groups of refraction respectively. Baseline age and refraction, paternal refraction, maternal refraction and proportion of two myopic parents showed significant differences across the three groups. Three major components of progression were extracted using PCA: “Average refraction”, “Acceleration” and the combination of “Myopia stabilization” and “Late onset of refraction progress”. In regression models, younger children with more severe myopia were associated with larger “Acceleration”. The risk factors of “Acceleration” included change of height and weight, near work, and parental myopia, while female gender, change of height and weight were associated with “Stabilization”, and increased outdoor time was related to “Late onset of refraction progress”. We therefore concluded that genetic and environmental risk factors have different impacts on patterns of refraction progression. PMID:27874105
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.
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%.
Ryder, Alan G
2002-03-01
Eighty-five solid samples consisting of illegal narcotics diluted with several different materials were analyzed by near-infrared (785 nm excitation) Raman spectroscopy. Principal Component Analysis (PCA) was employed to classify the samples according to narcotic type. The best sample discrimination was obtained by using the first derivative of the Raman spectra. Furthermore, restricting the spectral variables for PCA to 2 or 3% of the original spectral data according to the most intense peaks in the Raman spectrum of the pure narcotic resulted in a rapid discrimination method for classifying samples according to narcotic type. This method allows for the easy discrimination between cocaine, heroin, and MDMA mixtures even when the Raman spectra are complex or very similar. This approach of restricting the spectral variables also decreases the computational time by a factor of 30 (compared to the complete spectrum), making the methodology attractive for rapid automatic classification and identification of suspect materials.
Variation of fundamental parameters and dark energy: A principal component approach
NASA Astrophysics Data System (ADS)
Amendola, L.; Leite, A. C. O.; Martins, C. J. A. P.; Nunes, N. J.; Pedrosa, P. O. J.; Seganti, A.
2012-09-01
We discuss methods based on principal component analysis to constrain the dark energy equation of state using a combination of Type Ia supernovae at low redshift and spectroscopic measurements of varying fundamental couplings at higher redshifts. We discuss the performance of this method when future better-quality data sets are available, focusing on two forthcoming European southern observatory (ESO) spectrographs—Echelle spectrograph for rocky exoplanet and stable spectroscopic observations (ESPRESSO) for the very large telescope (VLT) and Cosmic dynamics explorer (CODEX) for the European extremely large telescope (E-ELT)—which include these measurements as a key part of their science cases. These can realize the prospect of a detailed characterization of dark energy properties almost all the way up to redshift 4.
Portable XRF and principal component analysis for bill characterization in forensic science.
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.
A high-performance computing toolset for relatedness and principal component analysis of SNP data.
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.
NASA Astrophysics Data System (ADS)
Hyland, David C.; Davis, Lawrence D.; Denoyer, Keith K.
1998-12-01
While significant theoretical and experimental progress has been made in the development of neural network-based systems for the autonomous identification and control of space platforms, there remain important unresolved issues associated with the reliable prediction of convergence speed and the avoidance of inordinately slow convergence. To speed convergence of neural identifiers, we introduce the preprocessing of identifier inputs using Principal Component Analysis (PCA) algorithms. Which automatically transform the neural identifier's external inputs so as to make the correlation matrix identity, resulting in enormous improvements in the convergence speed of the neural identifier. From a study of several such algorithms, we developed a new PCA approach which exhibits excellent convergence properties, insensitivity to noise and reliable accuracy.
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.
NASA Technical Reports Server (NTRS)
Smith, M. O.; Adams, J. B.; Johnson, P. E.
1985-01-01
A procedure was developed for analyzing remote reflectance spectra, including multispectral images, that quantifies parameters such as types of mineral mixtures, the abundances of mixed minerals, and particle sizes. Principal components analysis reduced the spectral dimensionality and allowed testing the uniqueness and validity of spectral mixing models. By analyzing variations in the overall spectral reflectance curves, the type of spectral mixture was identified, mineral abundances quantified and the effects of particle size identified. The results demonstrate an advantage in classification accuracy over classical forms of analysis that ignore effects of particle-size or mineral-mixture systematics on spectra. The approach is applicable to remote sensing data of planetary surfaces for quantitative determinations of mineral abundances.
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.
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.
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.
Principal component analysis of gait kinematics data in acute and chronic stroke patients.
Milovanović, Ivana; Popović, Dejan B
2012-01-01
We present the joint angles analysis by means of the principal component analysis (PCA). The data from twenty-seven acute and chronic hemiplegic patients were used and compared with data from five healthy subjects. The data were collected during walking along a 10-meter long path. The PCA was applied on a data set consisting of hip, knee, and ankle joint angles of the paretic and the nonparetic leg. The results point to significant differences in joint synergies between the acute and chronic hemiplegic patients that are not revealed when applying typical methods for gait assessment (clinical scores, gait speed, and gait symmetry). The results suggest that the PCA allows classification of the origin for the deficit in the gait when compared to healthy subjects; hence, the most appropriate treatment can be applied in the rehabilitation.
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.
NASA Astrophysics Data System (ADS)
Frausto-Reyes, C.; Medina-Gutiérrez, C.; Sato-Berrú, R.; Sahagún, L. R.
2005-09-01
Using Raman spectroscopy, with an excitation radiation source of 514.5 nm, and principal component analysis (PCA) was elaborated a method to study qualitatively the ethanol content in tequila samples. This method is based in the OH region profile (water) of the Raman spectra. Also, this method, using the fluorescence background of the Raman spectra, can be used to distinguish silver tequila from aged tequilas. The first three PCs of the Raman spectra, that provide the 99% of the total variance of the data set, were used for the samples classification. The PCA1 and PCA2 are related with the water (or ethanol) content of the sample, whereas the PCA3 is related with the fluorescence background of the Raman spectra.
Frausto-Reyes, C; Medina-Gutiérrez, C; Sato-Berrú, R; Sahagún, L R
2005-09-01
Using Raman spectroscopy, with an excitation radiation source of 514.5 nm, and principal component analysis (PCA) was elaborated a method to study qualitatively the ethanol content in tequila samples. This method is based in the OH region profile (water) of the Raman spectra. Also, this method, using the fluorescence background of the Raman spectra, can be used to distinguish silver tequila from aged tequilas. The first three PCs of the Raman spectra, that provide the 99% of the total variance of the data set, were used for the samples classification. The PCA1 and PCA2 are related with the water (or ethanol) content of the sample, whereas the PCA3 is related with the fluorescence background of the Raman spectra.
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
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.
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.
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.
A new simple /spl infin/OH neuron model as a biologically plausible principal component analyzer.
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.
NASA Astrophysics Data System (ADS)
Choi, Wook-Jin; Choi, Tae-Sun
2009-08-01
Pulmonary nodule detection is a binary classification problem. The main objective is to classify nodule from the lung computed tomography (CT) images. The intra class variability is mainly due to the grey-level variance, texture differences and shape. The purpose of this study is to develop a novel nodule detection method which is based on Two-dimensional Principal Component Analysis (2DPCA). We extract the futures using 2DPCA from nodule candidate images. Nodule candidates are classified using threshold. The proposed method reduces False Positive (FP) rate. We tested the proposed algorithm by using Lung Imaging Database Consortium (LIDC) database of National Cancer Institute (NCI). The experimental results demonstrate the effectiveness and efficiency of the proposed method. The proposed method achieved 85.11% detection rate with 1.13 FPs per scan.
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.
Nur Azira, T; Che Man, Y B; Raja Mohd Hafidz, R N; Aina, M A; Amin, I
2014-05-15
The study was aimed to differentiate between porcine and bovine gelatines in adulterated samples by utilising sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) combined with principal component analysis (PCA). The distinct polypeptide patterns of 6 porcine type A and 6 bovine type B gelatines at molecular weight ranged from 50 to 220 kDa were studied. Experimental samples of raw gelatine were prepared by adding porcine gelatine in a proportion ranging from 5% to 50% (v/v) to bovine gelatine and vice versa. The method used was able to detect 5% porcine gelatine added to the bovine gelatine. There were no differences in the electrophoretic profiles of the jelly samples when the proteins were extracted with an acetone precipitation method. The simple approach employing SDS-PAGE and PCA reported in this paper may provide a useful tool for food authenticity issues concerning gelatine.
Plant-wide process monitoring based on mutual information-multiblock principal component analysis.
Jiang, Qingchao; Yan, Xuefeng
2014-09-01
Multiblock principal component analysis (MBPCA) methods are gaining increasing attentions in monitoring plant-wide processes. Generally, MBPCA assumes that some process knowledge is incorporated for block division; however, process knowledge is not always available. A new totally data-driven MBPCA method, which employs mutual information (MI) to divide the blocks automatically, has been proposed. By constructing sub-blocks using MI, the division not only considers linear correlations between variables, but also takes into account non-linear relations thereby involving more statistical information. The PCA models in sub-blocks reflect more local behaviors of process, and the results in all blocks are combined together by support vector data description. The proposed method is implemented on a numerical process and the Tennessee Eastman process. Monitoring results demonstrate the feasibility and efficiency.
Thomas, James R; Gedeon, Patrick C; Grant, Barry J; Madura, Jeffry D
2012-07-03
Monoamine transporters (MATs) function by coupling ion gradients to the transport of dopamine, norepinephrine, or serotonin. Despite their importance in regulating neurotransmission, the exact conformational mechanism by which MATs function remains elusive. To this end, we have performed seven 250 ns accelerated molecular dynamics simulations of the leucine transporter, a model for neurotransmitter MATs. By varying the presence of binding-pocket leucine substrate and sodium ions, we have sampled plausible conformational states representative of the substrate transport cycle. The resulting trajectories were analyzed using principal component analysis of transmembrane helices 1b and 6a. This analysis revealed seven unique structures: two of the obtained conformations are similar to the currently published crystallographic structures, one conformation is similar to a proposed open inward structure, and four conformations represent novel structures of potential importance to the transport cycle. Further analysis reveals that the presence of binding-pocket sodium ions is necessary to stabilize the locked-occluded and open-inward conformations.
Principal components analysis based control of a multi-dof underactuated prosthetic hand
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
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.
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.
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.
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
Standardized principal components for vegetation variability monitoring across space and time
NASA Astrophysics Data System (ADS)
Mathew, T. R.; Vohora, V. K.
2016-08-01
Vegetation at any given location changes through time and in space. In what quantity it changes, where and when can help us in identifying sources of ecosystem stress, which is very useful for understanding changes in biodiversity and its effect on climate change. Such changes known for a region are important in prioritizing management. The present study considers the dynamics of savanna vegetation in Kruger National Park (KNP) through the use of temporal satellite remote sensing images. Spatial variability of vegetation is a key characteristic of savanna landscapes and its importance to biodiversity has been demonstrated by field-based studies. The data used for the study were sourced from the U.S. Agency for International Development where AVHRR derived Normalized Difference Vegetation Index (NDVI) images available at spatial resolutions of 8 km and at dekadal scales. The study area was extracted from these images for the time-period 1984-2002. Maximum value composites were derived for individual months resulting in an image dataset of 216 NDVI images. Vegetation dynamics across spatio-temporal domains were analyzed using standardized principal components analysis (SPCA) on the NDVI time-series. Each individual image variability in the time-series is considered. The outcome of this study demonstrated promising results - the variability of vegetation change in the area across space and time, and also indicated changes in landscape on 6 individual principal components (PCs) showing differences not only in magnitude, but also in pattern, of different selected eco-zones with constantly changing and evolving ecosystem.
Veligdan, James T.
1993-01-01
Atmospheric effects on sighting measurements are compensated for by adjusting any sighting measurements using a correction factor that does not depend on atmospheric state conditions such as temperature, pressure, density or turbulence. The correction factor is accurately determined using a precisely measured physical separation between two color components of a light beam (or beams) that has been generated using either a two-color laser or two lasers that project different colored beams. The physical separation is precisely measured by fixing the position of a short beam pulse and measuring the physical separation between the two fixed-in-position components of the beam. This precisely measured physical separation is then used in a relationship that includes the indexes of refraction for each of the two colors of the laser beam in the atmosphere through which the beam is projected, thereby to determine the absolute displacement of one wavelength component of the laser beam from a straight line of sight for that projected component of the beam. This absolute displacement is useful to correct optical measurements, such as those developed in surveying measurements that are made in a test area that includes the same dispersion effects of the atmosphere on the optical measurements. The means and method of the invention are suitable for use with either single-ended systems or a double-ended systems.
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.
RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis.
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.
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).
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.
Analysis of heavy metal sources in soil using kriging interpolation on principal components.
Ha, Hoehun; Olson, James R; Bian, Ling; Rogerson, Peter A
2014-05-06
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.
NASA Astrophysics Data System (ADS)
Aviyente, Selin; Bernat, Edward M.; Malone, Stephen M.; Iacono, William G.
2010-12-01
Joint time-frequency representations offer a rich representation of event related potentials (ERPs) that cannot be obtained through individual time or frequency domain analysis. This representation, however, comes at the expense of increased data volume and the difficulty of interpreting the resulting representations. Therefore, methods that can reduce the large amount of time-frequency data to experimentally relevant components are essential. In this paper, we present a method that reduces the large volume of ERP time-frequency data into a few significant time-frequency parameters. The proposed method is based on applying the widely used matching pursuit (MP) approach, with a Gabor dictionary, to principal components extracted from the time-frequency domain. The proposed PCA-Gabor decomposition is compared with other time-frequency data reduction methods such as the time-frequency PCA approach alone and standard matching pursuit methods using a Gabor dictionary for both simulated and biological data. The results show that the proposed PCA-Gabor approach performs better than either the PCA alone or the standard MP data reduction methods, by using the smallest amount of ERP data variance to produce the strongest statistical separation between experimental conditions.
Scullin, Michael K; Harrison, Tyler L; Factor, Stewart A; Bliwise, Donald L
2014-01-15
Sleep disturbances are common in many neurodegenerative diseases and may include altered sleep duration, fragmented sleep, nocturia, excessive daytime sleepiness, and vivid dreaming experiences, with occasional parasomnias. Although representing the "gold standard," polysomnography is not always cost-effective or available for measuring sleep disturbance, particularly for screening. Although numerous sleep-related questionnaires exist, many focus on a specific sleep disturbance (e.g., restless legs, REM Behavior Disorder) and do not capture efficiently the variety of sleep issues experienced by such patients. We administered the 12-item Neurodegenerative Disease Sleep Questionnaire (NDSQ) and the Epworth Sleepiness Scale to 145 idiopathic Parkinson's disease patients. Principal component analysis using eigenvalues greater than 1 suggested five separate components: sleep quality (e.g., sleep fragmentation), nocturia, vivid dreams/nightmares, restless legs symptoms, and sleep-disordered breathing. These results demonstrate construct validity of our sleep questionnaire and suggest that the NDSQ may be a useful screening tool for sleep disturbances in at least some types of neurodegenerative disorders.
Detecting determinism in EEG signals using principal component analysis and surrogate data testing.
Meghdadi, Amir H; Fazel-Rezai, Reza; Aghakhani, Yahya
2006-01-01
A novel method is proposed here to determine whether a time series is deterministic even in the presence of noise. The method is the extension of an existing method based on smoothness analysis of the signal in state space with surrogate data testing. While classical measures fail to detect determinism when the time series is corrupted by noise, the proposed method can clearly distinguish between pure stochastic and originally deterministic but noisy time series. A set of measures is defined here named partial smoothness indexes corresponding to principal components of the time series in state space. It is shown that when the time series is not pure stochastic, at least one of the indexes reflects determinism. The method is first successfully tested through simulation on a chaotic Lorenz time series contaminated with noise and then applied on EEG signals. Testing results on both our experimental recorded EEG signals and a benchmark EEG database verifies this hypothesis that EEG signals are deterministic in nature while contain some stochastic components as well.
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
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
Identification of redundant air quality measurements through the use of principal component analysis
NASA Astrophysics Data System (ADS)
Pires, J. C. M.; Pereira, M. C.; Alvim-Ferraz, M. C. M.; Martins, F. G.
This study aims to show how principal component analysis (PCA) can be used to identify redundant measurements in air quality monitoring networks. The minimum number of air quality monitoring sites in Oporto Metropolitan Area (Oporto-MA) was evaluated using PCA and then compared to the one settled by the legislation. Nine sites, monitoring NO 2, O 3 and PM 10, were selected and the air pollutant concentrations were analysed from January 2003 to December 2005. PCA was applied to the data corresponding to the first two years that were divided into annual quarters to verify the persistence of the PCA results. The number of principal components (PCs) was selected by applying two criteria: Kaiser (PCs with eigenvalues greater than 1) and ODV 90 (PCs representing at least 90% of the original data variance). Each pollutant was analysed separately. The two criteria led to different results. Using Kaiser criterion for the eight analysed periods, two PCs were selected in: (i) five periods for O 3 and PM 10; and (ii) six periods for NO 2. These PCs had important contributions of the same groups of monitoring sites. The percentage of the original data variance contained in the selected PCs using this criterion was always below 90%. Thus, the results obtained using ODV 90 were considered with more confidence. Using this criterion, only five monitoring sites for NO 2, three for O 3 and seven for PM 10 were needed to characterize the region. The number of monitoring sites for NO 2 and O 3 was in agreement with what was established by the legislation. However, for PM 10, Oporto-MA needed two more monitoring sites. To validate PCA results, statistical models were determined to estimate air pollutant concentrations at removed monitoring sites using the concentrations measured at the remaining monitoring sites. These models were applied to a year's data. The good performance obtained by the models showed that the monitoring sites selected by the procedure presented in this study were
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
Adam, Craig D; Sherratt, Sarah L; Zholobenko, Vladimir L
2008-01-15
The technique of principal component analysis has been applied to the UV-vis spectra of inks obtained from a wide range of black ballpoint pens available in the UK market. Both the pen ink and material extracted from the ink line on paper have been examined. Here, principal component analysis characterised each spectrum within a group through the numerical loadings attached to the first few principal components. Analysis of the spectra from multiple measurements on the same brand of pen showed excellent reproducibility and clear discrimination between inks that was supported by statistical analysis. Indeed it was possible to discriminate between the pen ink and the ink line from all brands examined in this way, suggesting that the solvent extraction process may have an influence on these results. For the complete set of 25 pens, interpretation of the loadings for the first few principal components showed that both the pen inks and the extracted ink lines may be classified in an objective manner and in agreement with the results of parallel thin layer chromatography studies. Within each class almost all inks could be individualised. Further work has shown that principal component analysis may be used to identify a particular ink from a database of reference UV-vis spectra and a strategy for developing this approach is suggested.
Part III: Principal component analysis: bridging the gap between strain, sex and drug effects.
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.
Robust principal component analysis-based four-dimensional computed tomography.
Gao, Hao; Cai, Jian-Feng; Shen, Zuowei; Zhao, Hongkai
2011-06-07
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.
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
NASA Astrophysics Data System (ADS)
Warren, J. S.; Hughes, J. P.; Badenes, C.
2005-12-01
We present results from a Principal Component Analysis (PCA) of Tycho's supernova remnant (SNR). PCA is a statistical technique we implemented to characterize X-ray spectra extracted from distinct spatial regions across the entire image of the remnant. We used the PCA to determine the location of the contact discontinuity (CD) in Tycho, which marks the boundary between shocked ejecta and shocked interstellar material, and found an azimuthal-angle-averaged radius of 241". For the average radius of the outer blast wave (BW) we found 251". Taking account of projection effects, the ratio of BW:CD is 1:0.93, which is inconsistent with adiabatic hydrodynamic models of SNR evolution. The BW:CD ratio can be explained if cosmic ray acceleration of ions is occurring at the forward shock. Such a scenario is further supported by evidence from the morphology and spectral nature of the BW emission for the acceleration of cosmic ray electrons. We also present PCA results regarding the ranges in Si and Fe composition in Tycho, and a newly uncovered spectral variation in the form of a low energy excess that has not been previously noted.
SR-FTIR Coupled with Principal Component Analysis Shows Evidence for the Cellular Bystander Effect.
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.
Multiple linear and principal component regressions for modelling ecotoxicity bioassay response.
Gomes, Ana I; Pires, José C M; Figueiredo, Sónia A; Boaventura, Rui A R
2014-01-01
The ecotoxicological response of the living organisms in an aquatic system depends on the physical, chemical and bacteriological variables, as well as the interactions between them. An important challenge to scientists is to understand the interaction and behaviour of factors involved in a multidimensional process such as the ecotoxicological response. With this aim, multiple linear regression (MLR) and principal component regression were applied to the ecotoxicity bioassay response of Chlorella vulgaris and Vibrio fischeri in water collected at seven sites of Leça river during five monitoring campaigns (February, May, June, August and September of 2006). The river water characterization included the analysis of 22 physicochemical and 3 microbiological parameters. The model that best fitted the data was MLR, which shows: (i) a negative correlation with dissolved organic carbon, zinc and manganese, and a positive one with turbidity and arsenic, regarding C. vulgaris toxic response; (ii) a negative correlation with conductivity and turbidity and a positive one with phosphorus, hardness, iron, mercury, arsenic and faecal coliforms, concerning V. fischeri toxic response. This integrated assessment may allow the evaluation of the effect of future pollution abatement measures over the water quality of Leça River.
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.
Xu, Shanzhi; Wang, Peng; Dong, Yonggui
2016-04-22
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.
Clouthier, Allison L; Bohm, Eric R; Rudan, John F; Shay, Barbara L; Rainbow, Michael J; Deluzio, Kevin J
2017-01-01
Multicentre studies are rare in three dimensional motion analyses due to challenges associated with combining waveform data from different centres. Principal component analysis (PCA) is a statistical technique that can be used to quantify variability in waveform data and identify group differences. A correction technique based on PCA is proposed that can be used in post processing to remove nuisance variation introduced by the differences between centres. Using this technique, the waveform bias that exists between the two datasets is corrected such that the means agree. No information is lost in the individual datasets, but the overall variability in the combined data is reduced. The correction is demonstrated on gait kinematics with synthesized crosstalk and on gait data from knee arthroplasty patients collected in two centres. The induced crosstalk was successfully removed from the knee joint angle data. In the second example, the removal of the nuisance variation due to the multicentre data collection allowed significant differences in implant type to be identified. This PCA-based technique can be used to correct for differences between waveform datasets in post processing and has the potential to enable multicentre motion analysis studies.
Contact- and distance-based principal component analysis of protein dynamics
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.
Principal Components of Thermography analyses of the Silk Tomb, Petra (Jordan)
NASA Astrophysics Data System (ADS)
Gomez-Heras, Miguel; Alvarez de Buergo, Monica; Fort, Rafael
2015-04-01
This communication presents the results of an active thermography survey of the Silk Tomb, which belongs to the Royal Tombs compound in the archaeological city of Petra in Jordan. The Silk Tomb is carved in the variegated Palaeozoic Umm Ishrin sandstone and it is heavily backweathered due to surface runoff from the top of the cliff where it is carved. Moreover, the name "Silk Tomb" was given because of the colourful display of the variegated sandstone due to backweathering. A series of infrared images were taken as the façade was heated by sunlight to perform a Principal Component of Thermography analyses with IR view 1.7.5 software. This was related to indirect moisture measurements (percentage of Wood Moisture Equivalent) taken across the façade, by means of a Protimeter portable moisture meter. Results show how moisture retention is deeply controlled by lithological differences across the façade. Research funded by Geomateriales 2 S2013/MIT-2914 and CEI Moncloa (UPM, UCM, CSIC) through a PICATA contract and the equipment from RedLAbPAt Network
Principal component analysis under population genetic models of range expansion and admixture.
François, Olivier; Currat, Mathias; Ray, Nicolas; Han, Eunjung; Excoffier, Laurent; Novembre, John
2010-06-01
In a series of highly influential publications, Cavalli-Sforza and colleagues used principal component (PC) analysis to produce maps depicting how human genetic diversity varies across geographic space. Within Europe, the first axis of variation (PC1) was interpreted as evidence for the demic diffusion model of agriculture, in which farmers expanded from the Near East approximately 10,000 years ago and replaced the resident hunter-gatherer populations with little or no interbreeding. These interpretations of the PC maps have been recently questioned as the original results can be reproduced under models of spatially covarying allele frequencies without any expansion. Here, we study PC maps for data simulated under models of range expansion and admixture. Our simulations include a spatially realistic model of Neolithic farmer expansion and assume various levels of interbreeding between farmer and resident hunter-gatherer populations. An important result is that under a broad range of conditions, the gradients in PC1 maps are oriented along a direction perpendicular to the axis of the expansion, rather than along the same axis as the expansion. We propose that this surprising pattern is an outcome of the "allele surfing" phenomenon, which creates sectors of high allele-frequency differentiation that align perpendicular to the direction of the expansion.
Keithley, Richard B; Wightman, R Mark
2011-06-07
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.
Foong, Shaohui; Sun, Zhenglong
2016-08-12
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.
A high-performance computing toolset for relatedness and principal component analysis of SNP data
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
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.
Tardiff, Mark F.; Runkle, Robert C.; Anderson, K. K.; Smith, L. E.
2006-01-23
The goal of primary radiation monitoring in support of routine screening and emergency response is to detect characteristics in vehicle radiation signatures that indicate the presence of potential threats. Two conceptual approaches to analyzing gamma-ray spectra for threat detection are isotope identification and anomaly detection. While isotope identification is the time-honored method, an emerging technique is anomaly detection that uses benign vehicle gamma ray signatures to define an expectation of the radiation signature for vehicles that do not pose a threat. Newly acquired spectra are then compared to this expectation using statistical criteria that reflect acceptable false alarm rates and probabilities of detection. The gamma-ray spectra analyzed here were collected at a U.S. land Port of Entry (POE) using a NaI-based radiation portal monitor (RPM). The raw data were analyzed to develop a benign vehicle expectation by decimating the original pulse-height channels to 35 energy bins, extracting composite variables via principal components analysis (PCA), and estimating statistically weighted distances from the mean vehicle spectrum with the mahalanobis distance (MD) metric. This paper reviews the methods used to establish the anomaly identification criteria and presents a systematic analysis of the response of the combined PCA and MD algorithm to modeled mono-energetic gamma-ray sources.
Zhang, Yiwei; Guan, Weihua; Pan, Wei
2013-01-01
For unrelated samples, principal component (PC) analysis has been established as a simple and effective approach to adjusting for population stratification in association analysis of common variants (CVs, with minor allele frequencies MAF > 5%). However, it is less clear how it would perform in analysis of low-frequency variants (LFVs, MAF between 1% and 5%), or of rare variants (RVs, MAF < 5%). Furthermore, with next-generation sequencing data, it is unknown whether PCs should be constructed based on CVs, LFVs, or RVs. In this study, we used the 1000 Genomes Project sequence data to explore the construction of PCs and their use in association analysis of LFVs or RVs for unrelated samples. It is shown that a few top PCs based on either CVs or LFVs could separate two continental groups, European and African samples, but those based on only RVs performed less well. When applied to several association tests in simulated data with population stratification, using PCs based on either CVs or LFVs was effective in controlling Type I error rates, while nonadjustment led to inflated Type I error rates. Perhaps the most interesting observation is that, although the PCs based on LFVs could better separate the two continental groups than those based on CVs, the use of the former could lead to overadjustment in the sense of substantial power loss in the absence of population stratification; in contrast, we did not see any problem with the use of the PCs based on CVs in all our examples.
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.
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.
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.
NASA Astrophysics Data System (ADS)
Nogueira, Grazielle V.; Silveira, Landulfo, Jr.; Martin, Airton A.; Zangaro, Renato A.; Pacheco, Marcos T.; Chavantes, Maria C.; Zampieri, Marcelo; Pasqualucci, Carlos A. G.
2004-07-01
FT- Raman Spectroscopy (FT-Raman) could allow identification and evaluation of human atherosclerotic lesions. A Raman spectrum can provide biochemical information of arteries which can help identifying the disease status and evolution. In this study, it is shown the results of FT-Raman for identification of human carotid arteries in vitro. Fragments of human carotid arteries were analyzed using a FT-Raman spectrometer with a Nd:YAG laser at 1064nm operating at an excitation power of 300mW. Spectra were obtained with 250 scans and spectral resolution of 4 cm-1. Each collection time was approximately 8 min. A total of 75 carotid fragments were spectroscopically scanned and FT-Raman results were compared with histopathology. Principal Components Analysis (PCA) was used to model an algorithm for tissue classification into three categories: normal, atherosclerotic plaque without calcification and atherosclerotic plaque with calcification. Non-atherosclerotic (normal) artery, atherosclerotic plaque and calcified plaque exhibit different spectral signatures related to biochemicals presented in each tissue type, such as, bands of collagen and elastin (proteins), cholesterol and its esters and calcium hydroxyapatite and carbonate apatite respectively. Results show that there is 96% match between classifications based on PCA algorithm and histopathology. The diagnostic applied over all 75 samples had sensitivity and specificity of about 89% and 100%, respectively, for atherosclerotic plaque and 100% and 98% for calcified plaque.
NASA Astrophysics Data System (ADS)
Toupin, S.; de Senneville, B. Denis; Ozenne, V.; Bour, P.; Lepetit-Coiffe, M.; Boissenin, M.; Jais, P.; Quesson, B.
2017-02-01
The use of magnetic resonance (MR) thermometry for the monitoring of thermal ablation is rapidly expanding. However, this technique remains challenging for the monitoring of the treatment of cardiac arrhythmia by radiofrequency ablation due to the heart displacement with respiration and contraction. Recent studies have addressed this problem by compensating in-plane motion in real-time with optical-flow based tracking technique. However, these algorithms are sensitive to local variation of signal intensity on magnitude images associated with tissue heating. In this study, an optical-flow algorithm was combined with a principal component analysis method to reduce the impact of such effects. The proposed method was integrated to a fully automatic cardiac MR thermometry pipeline, compatible with a future clinical workflow. It was evaluated on nine healthy volunteers under free breathing conditions, on a phantom and in vivo on the left ventricle of a sheep. The results showed that local intensity changes in magnitude images had lower impact on motion estimation with the proposed method. Using this strategy, the temperature mapping accuracy was significantly improved.
Manojlovic, D; Lenhardt, L; Milićević, B; Antonov, M; Miletic, V; Dramićanin, M D
2015-10-09
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.
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.
Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising.
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.
Ciucci, Sara; Ge, Yan; Durán, Claudio; Palladini, Alessandra; Jiménez-Jiménez, Víctor; Martínez-Sánchez, Luisa María; Wang, Yuting; Sales, Susanne; Shevchenko, Andrej; Poser, Steven W; Herbig, Maik; Otto, Oliver; Androutsellis-Theotokis, Andreas; Guck, Jochen; Gerl, Mathias J; Cannistraci, Carlo Vittorio
2017-03-13
Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.
Histogram of Oriented Principal Components for Cross-View Action Recognition.
Rahmani, Hossein; Mahmood, Arif; Huynh, Du; Mian, Ajmal
2016-12-01
Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process pointclouds for cross-view action recognition from unknown and unseen views. We propose the histogram of oriented principal components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations. At a 3D point, HOPC is computed by projecting the three scaled eigenvectors of the pointcloud within its local spatio-temporal support volume onto the vertices of a regular dodecahedron. HOPC is also used for the detection of spatio-temporal keypoints (STK) in 3D pointcloud sequences so that view-invariant STK descriptors (or Local HOPC descriptors) at these key locations only are used for action recognition. We also propose a global descriptor computed from the normalized spatio-temporal distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the performance of our proposed descriptors against nine existing techniques on two cross-view and three single-view human action recognition datasets. The experimental results show that our techniques provide significant improvement over state-of-the-art methods.
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
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
A Comparison of Autoclass and Principal Component Analysis as Applied to Asteroid Families
NASA Astrophysics Data System (ADS)
Harvell, T.; Ziffer, J.; Campins, H.; Arel, D.; Desfosses, R.; Reuillard, M.
2011-10-01
We present and compare the Sloan Digital Sky Survey (SDSS) colors of the Themis family, an older (2.5 Gya) outer-belt asteroid family, with that of the Beagle family (15 Mya), Themiss younger second-generation family. Comparing Beagle family colors with that of its older progenitor's family's colors allows a direct test of space weathering effects on C-complex asteroids. The question of space weathering among CComplex asteroids is an unsettled one. Nesvorny et al[1] found clear color differences between C-complex asteroids of different ages, which could be explained by space weathering processes. The trend with age that they determined based upon Principal Component Analysis (PCA) was anchored with the older Themis family at one extreme. Critical to the weathering hypothesis is the assumption that these asteroids would have similar un-weathered compositions. The catastrophic disruption of the Beagle family eliminates this question since its composition is that of its progenitor Themis member. The size of the SDSS dataset allows us to analyze these families using two statistical methods, PCA and Autoclass. Combined, these powerful methods provide a detailed picture of the role of space weathering among these asteroids
Spatial and temporal variation of total electron content as revealed by principal component analysis
NASA Astrophysics Data System (ADS)
Talaat, Elsayed R.; Zhu, Xun
2016-11-01
Eleven years of global total electron content (TEC) data derived from the assimilated thermosphere-ionosphere electrodynamics general circulation model 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 for 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 the EOF coefficients reveals how different mechanisms such as solar flux variation, change in the orbital declination, nonlinear mode coupling and geomagnetic activity are separated and expressed in different EOFs. This work demonstrates the usefulness of using the PCA technique to assimilate and monitor the global TEC field.
Tongue Motion Patterns in Post-Glossectomy and Typical Speakers: A Principal Components Analysis
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
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.
Adaptive elastic-net sparse principal component analysis for pathway association testing.
Chen, Xi
2011-10-24
Pathway or gene set analysis has become an increasingly popular approach for analyzing high-throughput biological experiments such as microarray gene expression studies. The purpose of pathway analysis is to identify differentially expressed pathways associated with outcomes. Important challenges in pathway analysis are selecting a subset of genes contributing most to association with clinical phenotypes and conducting statistical tests of association for the pathways efficiently. We propose a two-stage analysis strategy: (1) extract latent variables representing activities within each pathway using a dimension reduction approach based on adaptive elastic-net sparse principal component analysis; (2) integrate the latent variables with the regression modeling framework to analyze studies with different types of outcomes such as binary, continuous or survival outcomes. Our proposed approach is computationally efficient. For each pathway, because the latent variables are estimated in an unsupervised fashion without using disease outcome information, in the sample label permutation testing procedure, the latent variables only need to be calculated once rather than for each permutation resample. Using both simulated and real datasets, we show our approach performed favorably when compared with five other currently available pathway testing methods.
Schloemer, Sarah A; Thompson, Julie A; Silder, Amy; Thelen, Darryl G; Siston, Robert A
2017-03-01
Age-related increased hip extensor recruitment during gait is a proposed compensation strategy for reduced ankle power generation and may indicate a distal-to-proximal shift in muscle function with age. Extending beyond joint level analyses, identifying age-related changes at the muscle level could capture more closely the underlying mechanisms responsible for movement. The purpose of this study was to characterize and compare muscle forces and induced accelerations during gait in healthy older adults with those of young adults. Simulations of one gait cycle for ten older (73.9 ± 5.3 years) and six young (21.0 ± 2.1 years) adults walking at their self-selected speed were analyzed. Muscle force and induced acceleration waveforms, along with kinematic, kinetic, and muscle activation waveforms, were compared between age-groups using principal component analysis. Simulations of healthy older adults had greater gluteus maximus force and vertical support contribution, but smaller iliacus force, psoas force, and psoas vertical support contribution. There were no age-group differences in distal muscle force, contribution, or ankle torque magnitudes. Later peak dorsiflexion and peak ankle angular velocity in older adults may have contributed to their greater ankle power absorption during stance. These findings reveal the complex interplay between age-related changes in neuromuscular control, kinematics, and muscle function during gait.
Pourfarzad, Amir; Habibi Najafi, Mohammad B; Haddad Khodaparast, Mohammad H; Hassanzadeh Khayyat, Mohammad; Malekpour, Akbar
2014-06-15
The fructans, inulin and oligofructose, are known to exert many food and pharmaceutical applications and are widely used in functional foods throughout the world for their nutritional and techno-functional properties. In the present study, the Box-Behnken design was used to determine the optimal conditions for fructan precipitation from Eremurus spectabilis root powder (Serish) by adding ethanol that gave the maximum yield. Ethanol-to-syrup (E/S) ratio (2:1-15:1), precipitation temperature (30-60°C) and syrup concentration (10-40°B) were considered variables of fructan precipitation. The most compatible model among mean, linear and quadratic expressions was fitted to each response and the regression coefficients were determined using least square method. There was a good agreement between the experimental data and their predicted counterparts. The optimum conditions for fractionating fructan composition of Serish by ethanol were estimated to be E/S ratio of 8.56, temperature of 23.51°C and initial syrup concentration of 40°B. Precipitation under these optimized conditions achieved the best yield (85.81%), average chain length (12.92) and purity (80.18%). In addition, principal component analysis (PCA) allowed discriminating among precipitated fructan specialties.
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.
NASA Astrophysics Data System (ADS)
López-Alonso, José Manuel; Grumel, Eduardo; Cap, Nelly Lucía; Trivi, Marcelo; Rabal, Héctor; Alda, Javier
2016-12-01
Speckle is being used as a characterization tool for the analysis of the dynamics of slow-varying phenomena occurring in biological and industrial samples at the surface or near-surface regions. The retrieved data take the form of a sequence of speckle images. These images contain information about the inner dynamics of the biological or physical process taking place in the sample. Principal component analysis (PCA) is able to split the original data set into a collection of classes. These classes are related to processes showing different dynamics. In addition, statistical descriptors of speckle images are used to retrieve information on the characteristics of the sample. These statistical descriptors can be calculated in almost real time and provide a fast monitoring of the sample. On the other hand, PCA requires a longer computation time, but the results contain more information related to spatial-temporal patterns associated to the process under analysis. This contribution merges both descriptions and uses PCA as a preprocessing tool to obtain a collection of filtered images, where statistical descriptors are evaluated on each of them. The method applies to slow-varying biological and industrial processes.
A method for developing biomechanical response corridors based on principal component analysis.
Sun, W; Jin, J H; Reed, M P; Gayzik, F S; Danelson, K A; Bass, C R; Zhang, J Y; Rupp, J D
2016-10-03
The standard method for specifying target responses for human surrogates, such as crash test dummies and human computational models, involves developing a corridor based on the distribution of a set of empirical mechanical responses. These responses are commonly normalized to account for the effects of subject body shape, size, and mass on impact response. Limitations of this method arise from the normalization techniques, which are based on the assumptions that human geometry linearly scales with size and in some cases, on simple mechanical models. To address these limitations, a new method was developed for corridor generation that applies principal component (PC) analysis to align response histories. Rather than use normalization techniques to account for the effects of subject size on impact response, linear regression models are used to model the relationship between PC features and subject characteristics. Corridors are generated using Monte Carlo simulation based on estimated distributions of PC features for each PC. This method is applied to pelvis impact force data from a recent series of lateral impact tests to develop corridor bounds for a group of signals associated with a particular subject size. Comparing to the two most common methods for response normalization, the corridors generated by the new method are narrower and better retain the features in signals that are related to subject size and body shape.
Adaptive Elastic-Net Sparse Principal Component Analysis for Pathway Association Testing
Chen, Xi
2011-01-01
Pathway or gene set analysis has become an increasingly popular approach for analyzing high-throughput biological experiments such as microarray gene expression studies. The purpose of pathway analysis is to identify differentially expressed pathways associated with outcomes. Important challenges in pathway analysis are selecting a subset of genes contributing most to association with clinical phenotypes and conducting statistical tests of association for the pathways efficiently. We propose a two-stage analysis strategy: (1) extract latent variables representing activities within each pathway using a dimension reduction approach based on adaptive elastic-net sparse principal component analysis; (2) integrate the latent variables with the regression modeling framework to analyze studies with different types of outcomes such as binary, continuous or survival outcomes. Our proposed approach is computationally efficient. For each pathway, because the latent variables are estimated in an unsupervised fashion without using disease outcome information, in the sample label permutation testing procedure, the latent variables only need to be calculated once rather than for each permutation resample. Using both simulated and real datasets, we show our approach performed favorably when compared with five other currently available pathway testing methods. PMID:23089825
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.
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.
Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising
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
Principal component analysis of indicator PCB profiles in breast milk from Poland.
Skrbić, Biljana; Szyrwińska, Katarzyna; Durišić-Mladenović, Nataša; Nowicki, Piotr; Lulek, Janina
2010-11-01
Principal component analysis (PCA) was applied to a data set containing the levels of indicator polychlorinated biphenyls (PCBs) in human milk of mothers living in the Wielkopolska region, Poland, in order to investigate the information captured in the PCB patterns and to elucidate the relationship between PCB concentrations in milk and donor characteristics. According to the obtained PCA results milk fat content was the most influential factor affecting the PCB levels in milk of the Wielkopolska cohort. The lifestyle data collected from the questionnaire completed by the donors appeared to have no influence on PCB concentrations in breast milk. The score plots revealed the PCB contents of milk were quite low and uniform with a few outliers, without discrimination observed either between the primipareous and secundipareous females or between donors from the urban and rural areas. Comparison of the PCB levels and profiles of human milk from the Wielkopolska region and from various European and Asian locations made by PCA reflected a generally low background exposure and indicated the possible reasons for the outlying of some samples. In order to enhance the chances of observing the relationship between donor habits and PCB levels in breast milk it was suggested that the questionnaire be redesigned to gather information about vegetable product consumption and indoor air exposure.
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
Identification of PM sources by principal component analysis (PCA) coupled with wind direction data.
Viana, M; Querol, X; Alastuey, A; Gil, J I; Menéndez, M
2006-12-01
The effectiveness of combining principal component analysis (PCA) with multi-linear regression (MLRA) and wind direction data was demonstrated in this study. PM data from three grain-size fractions from a highly industrialised area in Northern Spain were analysed. Seven independent PM sources were identified by PCA: steel (Pb, Zn, Cd, Mn) and pigment (Cr, Mo, Ni) manufacture, road dust (Fe, Ba, Cd), traffic exhaust (P, OC + EC), regional-scale transport (, , V), crustal contributions (Al2O3, Sr, K) and sea spray (Na, Cl). The spatial distribution of the sources was obtained by coupling PCA with wind direction data, which helped identify regional drainage flows as the main source of crustal material. The same analysis showed that the contribution of motorway traffic to PM10 levels is 4-5 microg m-3 higher than that of local traffic. The coupling of PCA-MLRA with wind direction data proved thus to be useful in extracting further information on source contributions and locations. Correct identification and characterisation of PM sources is essential for the design and application of effective abatement strategies.
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.
Wang, Li; Liu, Hongzhi; Liu, Li; Wang, Qiang; Li, Shurong; Li, Qizhai
2017-03-01
Supervised principal component regression (SPCR) analysis was adopted to establish the evaluation model of peanut protein solubility. Sixty-six peanut varieties were analysed in the present study. Results showed there was intimate correlation between protein solubility and other indexes. At 0.05 level, these 11 indexes, namely crude fat, crude protein, total sugar, cystine, arginine, conarachin I, 37.5kDa, 23.5kDa, 15.5kDa, protein extraction rate, and kernel ratio, were correlated with protein solubility and were extracted to for establishing the SPCR model. At 0.01 level, a simper model was built between the four indexes (crude protein, cystine, conarachin I, and 15.5kDa) and protein solubility. Verification results showed that the coefficients between theoretical and experimental values were 0.815 (p<0.05) and 0.699 (p<0.01), respectively, which indicated both models can forecast the protein solubility effectively. The application of models was more convenient and efficient than traditional determination method.
Detecting Combustion and Flow Features In Situ Using Principal Component Analysis
Thompson, David; Grout, Ray W.; Fabian, Nathan D.; Bennett, Janine Camille
2009-03-01
This report presents progress on identifying and classifying features involving combustion in turbulent flow using principal component analysis (PCA) and k-means clustering using an in situ analysis framework. We describe a process for extracting temporally- and spatially-varying information from the simulation, classifying the information, and then applying the classification algorithm to either other portions of the simulation not used for training the classifier or further simulations. Because the regions classified as being of interest take up a small portion of the overall simulation domain, it will consume fewer resources to perform further analysis or save these regions at a higher fidelity than previously possible. The implementation of this process is partially complete and results obtained from PCA of test data is presented that indicates the process may have merit: the basis vectors that PCA provides are significantly different in regions where combustion is occurring and even when all 21 species of a lifted flame simulation are correlated the computational cost of PCA is minimal. What remains to be determined is whether k-means (or other) clustering techniques will be able to identify combined combustion and flow features with an accuracy that makes further characterization of these regions feasible and meaningful.
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.
A robust principal component analysis algorithm for EEG-based vigilance estimation.
Shi, Li-Chen; Duan, Ruo-Nan; Lu, Bao-Liang
2013-01-01
Feature dimensionality reduction methods with robustness have a great significance for making better use of EEG data, since EEG features are usually high-dimensional and contain a lot of noise. In this paper, a robust principal component analysis (PCA) algorithm is introduced to reduce the dimension of EEG features for vigilance estimation. The performance is compared with that of standard PCA, L1-norm PCA, sparse PCA, and robust PCA in feature dimension reduction on an EEG data set of twenty-three subjects. To evaluate the performance of these algorithms, smoothed differential entropy features are used as the vigilance related EEG features. Experimental results demonstrate that the robustness and performance of robust PCA are better than other algorithms for both off-line and on-line vigilance estimation. The average RMSE (root mean square errors) of vigilance estimation was 0.158 when robust PCA was applied to reduce the dimensionality of features, while the average RMSE was 0.172 when standard PCA was used in the same task.
Robust 2D principal component analysis: a structured sparsity regularized approach.
Yipeng Sun; Xiaoming Tao; Yang Li; Jianhua Lu
2015-08-01
Principal component analysis (PCA) is widely used to extract features and reduce dimensionality in various computer vision and image/video processing tasks. Conventional approaches either lack robustness to outliers and corrupted data or are designed for one-dimensional signals. To address this problem, we propose a robust PCA model for two-dimensional images incorporating structured sparse priors, referred to as structured sparse 2D-PCA. This robust model considers the prior of structured and grouped pixel values in two dimensions. As the proposed formulation is jointly nonconvex and nonsmooth, which is difficult to tackle by joint optimization, we develop a two-stage alternating minimization approach to solve the problem. This approach iteratively learns the projection matrices by bidirectional decomposition and utilizes the proximal method to obtain the structured sparse outliers. By considering the structured sparsity prior, the proposed model becomes less sensitive to noisy data and outliers in two dimensions. Moreover, the computational cost indicates that the robust two-dimensional model is capable of processing quarter common intermediate format video in real time, as well as handling large-size images and videos, which is often intractable with other robust PCA approaches that involve image-to-vector conversion. Experimental results on robust face reconstruction, video background subtraction data set, and real-world videos show the effectiveness of the proposed model compared with conventional 2D-PCA and other robust PCA algorithms.
Reduced order model based on principal component analysis for process simulation and optimization
Lang, Y.; Malacina, A.; Biegler, L.; Munteanu, S.; Madsen, J.; Zitney, S.
2009-01-01
It is well-known that distributed parameter computational fluid dynamics (CFD) models provide more accurate results than conventional, lumped-parameter unit operation models used in process simulation. Consequently, the use of CFD models in process/equipment co-simulation offers the potential to optimize overall plant performance with respect to complex thermal and fluid flow phenomena. Because solving CFD models is time-consuming compared to the overall process simulation, we consider the development of fast reduced order models (ROMs) based on CFD results to closely approximate the high-fidelity equipment models in the co-simulation. By considering process equipment items with complicated geometries and detailed thermodynamic property models, this study proposes a strategy to develop ROMs based on principal component analysis (PCA). Taking advantage of commercial process simulation and CFD software (for example, Aspen Plus and FLUENT), we are able to develop systematic CFD-based ROMs for equipment models in an efficient manner. In particular, we show that the validity of the ROM is more robust within well-sampled input domain and the CPU time is significantly reduced. Typically, it takes at most several CPU seconds to evaluate the ROM compared to several CPU hours or more to solve the CFD model. Two case studies, involving two power plant equipment examples, are described and demonstrate the benefits of using our proposed ROM methodology for process simulation and optimization.
NASA Astrophysics Data System (ADS)
Reichman, Daniel; Morton, Kenneth D.; Collins, Leslie M.; Torrione, Peter A.
2014-05-01
Ground Penetrating Radar (GPR) is a very promising technology for subsurface threat detection. A successful algorithm employing GPR should achieve high detection rates at a low false-alarm rate and do so at operationally relevant speeds. GPRs measure reflections at dielectric boundaries that occur at the interfaces between different materials. These boundaries may occur at any depth, within the sensor's range, and furthermore, the dielectric changes could be such that they induce a 180 degree phase shift in the received signal relative to the emitted GPR pulse. As a result of these time-of-arrival and phase variations, extracting robust features from target responses in GPR is not straightforward. In this work, a method to mitigate polarity and alignment variations based on an expectation-maximization (EM) principal-component analysis (PCA) approach is proposed. This work demonstrates how model-based target alignment can significantly improve detection performance. Performance is measured according to the improvement in the receiver operating characteristic (ROC) curve for classification before and after the data is properly aligned and phase-corrected.
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.
Ciucci, Sara; Ge, Yan; Durán, Claudio; Palladini, Alessandra; Jiménez-Jiménez, Víctor; Martínez-Sánchez, Luisa María; Wang, Yuting; Sales, Susanne; Shevchenko, Andrej; Poser, Steven W.; Herbig, Maik; Otto, Oliver; Androutsellis-Theotokis, Andreas; Guck, Jochen; Gerl, Mathias J.; Cannistraci, Carlo Vittorio
2017-01-01
Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics. PMID:28287094
NASA Astrophysics Data System (ADS)
Fan, Qimeng; Chen, Chaoyin; Huang, Zaiqiang; Zhang, Chunmei; Liang, Pengjuan; Zhao, Shenglan
2015-02-01
Rhizoma Gastrodiae (Tianma) of different variants and different geographical origins has vital difference in quality and physiological efficacy. This paper focused on the classification and identification of Tianma of six types (two variants from three different geographical origins) using three dimensional synchronous fluorescence spectroscopy (3D-SFS) coupled with principal component analysis (PCA). 3D-SF spectra of aqueous extracts, which were obtained from Tianma of the six types, were measured by a LS-50B luminescence spectrofluorometer. The experimental results showed that the characteristic fluorescent spectral regions of the 3D-SF spectra were similar, while the intensities of characteristic regions are different significantly. Coupled these differences in peak intensities with PCA, Tianma of six types could be discriminated successfully. In conclusion, 3D-SFS coupled with PCA, which has such advantages as effective, specific, rapid, non-polluting, has an edge for discrimination of the similar Chinese herbal medicine. And the proposed methodology is a useful tool to classify and identify Tianma of different variants and different geographical origins.
Kaistha, Nitin; Upadhyaya, Belle R.
2001-11-15
An integrated method for the detection and isolation of incipient faults in common field devices, such as sensors and actuators, using plant operational data is presented. The approach is based on the premise that data for normal operation lie on a surface and abnormal situations lead to deviations from the surface in a particular way. Statistically significant deviations from the surface result in the detection of faults, and the characteristic directions of deviations are used for isolation of one or more faults from the set of typical faults. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data and fit a hyperplane to the data. The fault direction for each of the scenarios is obtained using the singular value decomposition on the state and control function prediction errors, and fault isolation is then accomplished from projections on the fault directions. This approach is demonstrated for a simulated pressurized water reactor steam generator system and for a laboratory process control system under single device fault conditions. Enhanced fault isolation capability is also illustrated by incorporating realistic nonlinear terms in the PCA data matrix.
Yin, Jianhua; Xia, Yang
2011-01-01
Fourier transform infrared imaging (FT-IRI) technique and principal component regression (PCR) method were used to quantitatively determine collagen and proteoglycan concentrations in bovine nasal cartilage (BNC). An infrared spectral library was first established by obtaining eleven infrared spectra from a series of collagen and chondroitin 6-sulphate mixed in different ratios. FT-IR images were obtained from 6 μm thick sections of BNC specimens at 6.25 μm pixel size. The spectra from the FT-IR images were imported into a PCR program to obtain the relative concentrations of collagen and proteoglycan in BNC, based on the spectral library of pure chemicals. These PCR-determined concentrations agreed with the molecular concentrations determined biochemically using an enzyme digestion assay. Use of the imaging approach revealed that proteoglycan loss in the specimens occurs first at the surface of the tissue block when compared with the middle portion of the tissue block. The quantitative correlation of collagen and proteoglycan revealed that their infrared absorption peak-areas at 1338 and 1072-855 cm−1 can only be used as qualitative indicators of the molecular contents. The use of PCR in FT-IRI offers an accurate tool to spatially determine the distributions of macromolecular concentration in cartilage. PMID:21073787
GPS GDOP classification via improved neural network trainings and principal component analysis
NASA Astrophysics Data System (ADS)
Azami, Hamed; Sanei, Saeid
2014-09-01
Geometric dilution of precision (GDOP) is an engineering expression that denotes how well the constellation of global positioning system (GPS) satellites is organised geometrically. In the analysis of received signals, it is often essential to invert and transform the data matrices. This requires tremendous computational burden on the navigator's processor. Since classification of GPS GDOP is a non-linear problem, neural networks (NNs) can be used as an acceptable solution. Since the back propagation (BP) does not have sufficient speed to train a feed-forward NN, in this paper several improved NN trainings, including Levenberg-Marquardt (LM), modified LM, and resilient BP (RBP), scaled conjugate gradient, one-step secant (OSS) and quasi-Newton methods are proposed to classify the GPS GDOP. In this study, in order to have uncorrelated and informative features of the GPS GDOP, principal component analysis (PCA) is used as a pre-processing step. The simulation results show that using the RBP and PCA leads to greater accuracy and lower calculation time comparing with other existing and proposed methods and it can improve the classification accuracy of GPS satellites to about 99.65%. Moreover, the modified LM is the fastest algorithm that requires only 10 iterations for training the NN and it can be used in online applications.
Hirayama, Jun-ichiro; Hyvärinen, Aapo; Kiviniemi, Vesa; Kawanabe, Motoaki; Yamashita, Okito
2016-01-01
Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated “eigenconnectivity” patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra- and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module-based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods. PMID:28002474
Model reduction of cavity nonlinear optics for photonic logic: a quasi-principal components approach
NASA Astrophysics Data System (ADS)
Shi, Zhan; Nurdin, Hendra I.
2016-11-01
Kerr nonlinear cavities displaying optical thresholding have been proposed for the realization of ultra-low power photonic logic gates. In the ultra-low photon number regime, corresponding to energy levels in the attojoule scale, quantum input-output models become important to study the effect of unavoidable quantum fluctuations on the performance of such logic gates. However, being a quantum anharmonic oscillator, a Kerr-cavity has an infinite dimensional Hilbert space spanned by the Fock states of the oscillator. This poses a challenge to simulate and analyze photonic logic gates and circuits composed of multiple Kerr nonlinearities. For simulation, the Hilbert of the oscillator is typically truncated to the span of only a finite number of Fock states. This paper develops a quasi-principal components approach to identify important subspaces of a Kerr-cavity Hilbert space and exploits it to construct an approximate reduced model of the Kerr-cavity on a smaller Hilbert space. Using this approach, we find a reduced dimension model with a Hilbert space dimension of 15 that can closely match the magnitudes of the mean transmitted and reflected output fields of a conventional truncated Fock state model of dimension 75, when driven by an input coherent field that switches between two levels. For the same input, the reduced model also closely matches the magnitudes of the mean output fields of Kerr-cavity-based AND and NOT gates and a NAND latch obtained from simulation of the full 75 dimension model.
Egan, William; Morgan, Stephen L. Brewer, William E.
1999-02-01
The forensic determination of carboxyhemoglobin (COHb) in blood was performed by using an improved principal component regression (PCR) technique applied to UV-visible spectra. Calibration data were decomposed into principal components, and the principal components useful for prediction were selected by their correlation with calibration spectra. Cross-validation of prediction results was done by leverage-corrected residuals. Confidence and prediction intervals derived from classical regression theory were found to be reasonable in size. The results compared favorably to a comparison study conducted by using a CO Oximeter method. In analysis of forensic case study samples, the improved PCR method allowed detection of abnormal samples and successfully predicted percentages of COHb and methemoglobin (MetHb), and provided error estimates for those predictions. {copyright} {ital 1999} {ital Society for Applied Spectroscopy}
Pittelkow, Yvonne; Wilson, Susan R
2005-06-01
This note is in response to Wouters et al. (2003, Biometrics 59, 1131-1139) who compared three methods for exploring gene expression data. Contrary to their summary that principal component analysis is not very informative, we show that it is possible to determine principal component analyses that are useful for exploratory analysis of microarray data. We also present another biplot representation, the GE-biplot (Gene Expression biplot), that is a useful method for exploring gene expression data with the major advantage of being able to aid interpretation of both the samples and the genes relative to each other.
NASA Astrophysics Data System (ADS)
Nomoto, Yohei; Yamashita, Kazuhiko; Ohya, Tetsuya; Koyama, Hironori; Kawasumi, Masashi
There is the increasing concern of the society to prevent the fall of the aged. The improvement in aged people's the muscular strength of the lower-limb, postural control and walking ability are important for quality of life and fall prevention. The aim of this study was to develop multiple evaluation methods in order to advise for improvement and maintenance of lower limb function between aged and young. The subjects were 16 healthy young volunteers (mean ± S.D: 19.9 ± 0.6 years) and 10 healthy aged volunteers (mean ± S.D: 80.6 ± 6.1 years). Measurement items related to lower limb function were selected from the items which we have ever used. Selected measurement items of function of lower are distance of extroversion of the toe, angle of flexion of the toe, maximum width of step, knee elevation, moving distance of greater trochanter, walking balance, toe-gap force and rotation range of ankle joint. Measurement items summarized by the principal component analysis into lower ability evaluation methods including walking ability and muscle strength of lower limb and flexibility of ankle. The young group demonstrated the factor of 1.6 greater the assessment score of walking ability compared with the aged group. The young group demonstrated the factor of 1.4 greater the assessment score of muscle strength of lower limb compared with the aged group. The young group demonstrated the factor of 1.2 greater the assessment score of flexibility of ankle compared with the aged group. The results suggested that it was possible to assess the lower limb function of aged and young numerically and to advise on their foot function.
Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks.
Petersen, Alexander; Zhao, Jianyang; Carmichael, Owen; Müller, Hans-Georg
2016-09-01
In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis (FPCA) is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference. Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level. This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function-episodic memory and executive function-were investigated. The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings. To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores.
Cole, Jacqueline M; Cheng, Xie; Payne, Michael C
2016-11-07
The use of principal component analysis (PCA) to statistically infer features of local structure from experimental pair distribution function (PDF) data is assessed on a case study of rare-earth phosphate glasses (REPGs). Such glasses, codoped with two rare-earth ions (R and R') of different sizes and optical properties, are of interest to the laser industry. The determination of structure-property relationships in these materials is an important aspect of their technological development. Yet, realizing the local structure of codoped REPGs presents significant challenges relative to their singly doped counterparts; specifically, R and R' are difficult to distinguish in terms of establishing relative material compositions, identifying atomic pairwise correlation profiles in a PDF that are associated with each ion, and resolving peak overlap of such profiles in PDFs. This study demonstrates that PCA can be employed to help overcome these structural complications, by statistically inferring trends in PDFs that exist for a restricted set of experimental data on REPGs, and using these as training data to predict material compositions and PDF profiles in unknown codoped REPGs. The application of these PCA methods to resolve individual atomic pairwise correlations in t(r) signatures is also presented. The training methods developed for these structural predictions are prevalidated by testing their ability to reproduce known physical phenomena, such as the lanthanide contraction, on PDF signatures of the structurally simpler singly doped REPGs. The intrinsic limitations of applying PCA to analyze PDFs relative to the quality control of source data, data processing, and sample definition, are also considered. While this case study is limited to lanthanide-doped REPGs, this type of statistical inference may easily be extended to other inorganic solid-state materials and be exploited in large-scale data-mining efforts that probe many t(r) functions.
NASA Astrophysics Data System (ADS)
Zheng, Wei; Li, Hong
2017-01-01
For dark energy, the equation of state (EoS) is a critical parameter to depict its physical properties. In this paper, we mainly give constraints on the EoS of dark energy w with the latest observations of cosmic microwave background radiation (CMB) from Planck satellite, JLA Type Ia supernovae (SNIa) sample, baryon acoustic oscillation (BAO) and Hubble parameter measurements. We introduce a kind of parameterized dark energy model called "constant bin - w ", in which the whole redshift range is divided into several bins, and EoS w in each bin is assumed as an independent constant. The results show that EoS in all of the redshift bins are comparable with ΛCDM in the 2σ confidence regions, but some weak deviations from w = - 1 are still indicated. In particular, in the framework of 7 bins, a slight oscillation behavior is shown in the redshift 0 < z < 0.75, especially around the range of 4th bin (0.25 < z < 0.35) and 5th bin (0.35 < z < 0.51). Additionally, we adopt the principal component analysis (PCA) method to do the model-independent analysis, which includes normal PCA and localized PCA methods. By implementing so called normal PCA method, the original oscillation behavior of EoS indicated in the framework of 7 bins becomes more significant after the best reconstruction, but such result still supports ΛCDM within the margin of 2σ errors. To further reduce the errors of constraints on EoS, and confirm such deviations from the cosmological constant scenario, we hope for more precise observational data in the future.
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
Investigation of inversion polymorphisms in the human genome using principal components analysis.
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.
Fabris, Chiara; Facchinetti, Andrea; Fico, Giuseppe; Sambo, Francesco; Arredondo, Maria Teresa; Cobelli, Claudio
2015-01-01
Background: Abnormal glucose variability (GV) is a risk factor for diabetes complications, and tens of indices for its quantification from continuous glucose monitoring (CGM) time series have been proposed. However, the information carried by these indices is redundant, and a parsimonious description of GV can be obtained through sparse principal component analysis (SPCA). We have recently shown that a set of 10 metrics selected by SPCA is able to describe more than 60% of the variance of 25 GV indicators in type 1 diabetes (T1D). Here, we want to extend the application of SPCA to type 2 diabetes (T2D). Methods: A data set of CGM time series collected in 13 T2D subjects was considered. The 25 GV indices considered for T1D were evaluated. SPCA was used to select a subset of indices able to describe the majority of the original variance. Results: A subset of 10 indicators was selected and allowed to describe 83% of the variance of the original pool of 25 indices. Four metrics sufficient to describe 67% of the original variance turned out to be shared by the parsimonious sets of indices in T1D and T2D. Conclusions: Starting from a pool of 25 indices assessed from CGM time series in T2D subjects, reduced subsets of metrics virtually providing the same information content can be determined by SPCA. The fact that these indices also appear in the parsimonious description of GV in T1D may indicate that they could be particularly informative of GV in diabetes, regardless of the specific type of disease. PMID:26232371
Cole, Jacqueline M.; Cheng, Xie; Payne, Michael C.
2016-10-18
The use of principal component analysis (PCA) to statistically infer features of local structure from experimental pair distribution function (PDF) data is assessed on a case study of rare-earth phosphate glasses (REPGs). Such glasses, co-doped with two rare-earth ions (R and R’) of different sizes and optical properties, are of interest to the laser industry. The determination of structure-property relationships in these materials is an important aspect of their technological development. Yet, realizing the local structure of co-doped REPGs presents significant challenges relative to their singly-doped counterparts; specifically, R and R’ are difficult to distinguish in terms of establishing relativemore » material compositions, identifying atomic pairwise correlation profiles in a PDF that are associated with each ion, and resolving peak overlap of such profiles in PDFs. This study demonstrates that PCA can be employed to help overcome these structural complications, by statistically inferring trends in PDFs that exist for a restricted set of experimental data on REPGs, and using these as training data to predict material compositions and PDF profiles in unknown co-doped REPGs. The application of these PCA methods to resolve individual atomic pairwise correlations in t(r) signatures is also presented. The training methods developed for these structural predictions are pre-validated by testing their ability to reproduce known physical phenomena, such as the lanthanide contraction, on PDF signatures of the structurally simpler singly-doped REPGs. The intrinsic limitations of applying PCA to analyze PDFs relative to the quality control of source data, data processing, and sample definition, are also considered. Furthermore, while this case study is limited to lanthanide-doped REPGs, this type of statistical inference may easily be extended to other inorganic solid-state materials, and be exploited in large-scale data-mining efforts that probe many t
Food patterns measured by principal component analysis and obesity in the Nepalese adult
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
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.
Aydin, T; Bayrak, N; Baran, E; Cakir, A
2017-03-23
Insecticidal effects of the dichloromethane, ethyl acetate, acetone, ethanol and methanol extracts of Humulus lupulus (hops) L. cones and its principal components, xanthohumol was investigated on five stored pests, Sitophilus granarius (L.), Sitophilus oryzae (L.), Acanthoscelides obtectus (Say.), Tribolium castaneum (Herbst) and Lasioderma serricorne (F.). The mortality of adults of the insects treated with 2, 5, 5, 10 and 20 mg ml̠-1 concentrations of the extracts and xanthuhumol was counted after 24, 48, 72, 96 and 120 h. In order to determine the toxic effects of the substances tested against all tested insects, durations for 50% mortality of the adults, and LD50 values were also determined in the first 48 h by probit analysis. Our results also showed that xanthohumol was more toxic against the pests in comparison with the extracts applications. LD50 values for xanthohumol were found to be low dose as compared with the extracts. Xanthohumol was more toxic against S. granarius (L.) with 6.8 µg of LD50 value. Among the extracts, methanol extract was less effective than other extracts against all tested insects. The ethyl acetate extract of H. lupulus cones was the most effective extract against the tested pests. The quantitative amounts of xanthohumol in the extracts were determined using a high-performance liquid chromatography. The quantitative data indicated that amount of xanthohumol in the extracts increased with increase of polarity of the solvents used from methanol to dichloromethane. The methanol extract contained the high amount of xanthohumol with 5.74 g/100 g extract (0.46 g/100 g plant sample).
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
Mauldin, F William; Zhu, Hongtu T; Behler, Russell H; Nichols, Timothy C; Gallippi, Caterina M
2008-02-01
We introduce a new method for automatic classification of acoustic radiation force impulse (ARFI) displacement profiles using what have been termed "robust" methods for principal component analysis (PCA) and clustering. Unlike classical approaches, the robust methods are less sensitive to high variance outlier profiles and require no a priori information regarding expected tissue response to ARFI excitation. We first validate our methods using synthetic data with additive noise and/or outlier curves. Second, the robust techniques are applied to classifying ARFI displacement profiles acquired in an atherosclerotic familial hypercholesterolemic (FH) pig iliac artery in vivo. The in-vivo classification results are compared with parametric ARFI images showing peak induced displacement and time to 67% recovery and to spatially correlated immunohistochemistry. Our results support that robust techniques outperform conventional PCA and clustering approaches to classification when ARFI data are inclusive of low to relatively high noise levels (up to 5 dB average signal-to-noise [SNR] to amplitude) but no outliers: for example, 99.53% correct for robust techniques vs. 97.75% correct for the classical approach. The robust techniques also perform better than conventional approaches when ARFI data are inclusive of moderately high noise levels (10 dB average SNR to amplitude) in addition to a high concentration of outlier displacement profiles (10% outlier content): for example, 99.87% correct for robust techniques vs. 33.33% correct for the classical approach. This work suggests that automatic identification of tissue structures exhibiting similar displacement responses to ARFI excitation is possible, even in the context of outlier profiles. Moreover, this work represents an important first step toward automatic correlation of ARFI data to spatially matched immunohistochemistry.
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.
Jantzen, E; Sonesson, A; Tangen, T; Eng, J
1993-01-01
Twenty-nine species (76 strains) of members of the genus Legionella were analyzed for their cellular hydroxylated fatty acids (OH-FAs). The individual patterns were unusually complex and included both monohydroxylated and dihydroxylated chains of unbranched or branched (iso and anteiso) types. Comparison of the strain profiles by SIMCA (Soft Independent Modelling of Class Analogy) principal component analysis revealed four main groups. Group 1 included Legionella pneumophila plus L. israelensis strains, and group 2 included L. micdadei and L. maceacherneii strains. These two closely related groups were characterized by the occurrence of di-OH-FAs and differed mainly in the amounts of 3-OH-a21:0, 3-OH-n21:0, 3-OH-n22:0, and 3-OH-a23:0. Group 3 (13 species) was distinguished by i14:0 at less than 3%, 3-OH-3-OH-n14:0 at greater than 5%, 3-OH-n15:0 at greater than 2%, and minute amounts of OH-FAs with chains longer than 21:0. Group 4 (12 species) was heterogeneous. Its main characteristics were the presence of 3-OH-n12:0 and 3-OH-n13:0, 3-OH-i14:0 at greater than 5%, as well as significant amounts of 3-OH-a21:0 and 3-OH-n21:0. The groupings obtained by OH-FA profiles were found to reflect DNA-DNA homology groupings reasonably well, and the profiles appear to be useful for differentiation of Legionella species. PMID:8314981
Cole, Jacqueline M.; Cheng, Xie; Payne, Michael C.
2016-10-18
The use of principal component analysis (PCA) to statistically infer features of local structure from experimental pair distribution function (PDF) data is assessed on a case study of rare-earth phosphate glasses (REPGs). Such glasses, co-doped with two rare-earth ions (R and R’) of different sizes and optical properties, are of interest to the laser industry. The determination of structure-property relationships in these materials is an important aspect of their technological development. Yet, realizing the local structure of co-doped REPGs presents significant challenges relative to their singly-doped counterparts; specifically, R and R’ are difficult to distinguish in terms of establishing relative material compositions, identifying atomic pairwise correlation profiles in a PDF that are associated with each ion, and resolving peak overlap of such profiles in PDFs. This study demonstrates that PCA can be employed to help overcome these structural complications, by statistically inferring trends in PDFs that exist for a restricted set of experimental data on REPGs, and using these as training data to predict material compositions and PDF profiles in unknown co-doped REPGs. The application of these PCA methods to resolve individual atomic pairwise correlations in t(r) signatures is also presented. The training methods developed for these structural predictions are pre-validated by testing their ability to reproduce known physical phenomena, such as the lanthanide contraction, on PDF signatures of the structurally simpler singly-doped REPGs. The intrinsic limitations of applying PCA to analyze PDFs relative to the quality control of source data, data processing, and sample definition, are also considered. Furthermore, while this case study is limited to lanthanide-doped REPGs, this type of statistical inference may easily be extended to other inorganic solid-state materials, and be exploited in large-scale data-mining efforts that probe many t(r) functions.
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
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.
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
NASA Technical Reports Server (NTRS)
Hobbs, R. W.; Maran, S. P.; Kafatos, M.; Brown, L. W.
1977-01-01
The three principal emission components of Cygnus A were observed at 99 GHz, the highest frequency at which radio measurements of this source have been accomplished. The observations show no definite indication of a high-frequency cutoff in the spectrum of the compact central component, which perhaps may be attributed to an optically thin synchrotron source that peaks at a frequency of several hundred GHz.
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.
NASA Astrophysics Data System (ADS)
Barzin, Razieh; Shirvani, Amin; Lotfi, Hossein
2017-01-01
Downward shortwave radiation is a key quantity in the land-atmosphere interaction. Since the moderate resolution imaging spectroradiometer data has a coarse temporal resolution, which is not suitable for estimating daily average radiation, many efforts have been undertaken to estimate instantaneous solar radiation using moderate resolution imaging spectroradiometer data. In this study, the principal components analysis technique was applied to capture the information of moderate resolution imaging spectroradiometer bands, extraterrestrial radiation, aerosol optical depth, and atmospheric water vapour. A regression model based on the principal components was used to estimate daily average shortwave radiation for ten synoptic stations in the Fars province, Iran, for the period 2009-2012. The Durbin-Watson statistic and autocorrelation function of the residuals of the fitted principal components regression model indicated that the residuals were serially independent. The results indicated that the fitted principal components regression models accounted for about 86-96% of total variance of the observed shortwave radiation values and the root mean square error was about 0.9-2.04 MJ m-2 d-1. Also, the results indicated that the model accuracy decreased as the aerosol optical depth increased and extraterrestrial radiation was the most important predictor variable among all.
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...
Buzanskas, M E; Savegnago, R P; Grossi, D A; Venturini, G C; Queiroz, S A; Silva, L O C; Júnior, R A A Torres; Munari, D P; Alencar, M M
2013-01-01
Phenotypic data from female Canchim beef cattle were used to obtain estimates of genetic parameters for reproduction and growth traits using a linear animal mixed model. In addition, relationships among animal estimated breeding values (EBVs) for these traits were explored using principal component analysis. The traits studied in female Canchim cattle were age at first calving (AFC), age at second calving (ASC), calving interval (CI), and bodyweight at 420 days of age (BW420). The heritability estimates for AFC, ASC, CI and BW420 were 0.03±0.01, 0.07±0.01, 0.06±0.02, and 0.24±0.02, respectively. The genetic correlations for AFC with ASC, AFC with CI, AFC with BW420, ASC with CI, ASC with BW420, and CI with BW420 were 0.87±0.07, 0.23±0.02, -0.15±0.01, 0.67±0.13, -0.07±0.13, and 0.02±0.14, respectively. Standardised EBVs for AFC, ASC and CI exhibited a high association with the first principal component, whereas the standardised EBV for BW420 was closely associated with the second principal component. The heritability estimates for AFC, ASC and CI suggest that these traits would respond slowly to selection. However, selection response could be enhanced by constructing selection indices based on the principal components.
A Novel Principal Component Analysis-Based Acceleration Scheme for LES-ODT: An A Priori Study
NASA Astrophysics Data System (ADS)
Echekki, Tarek; Mirgolbabaei, Hessan
2012-11-01
A parameterization of the composition space based on principal component analysis (PCA) is proposed to represent the transport equations with the one-dimensional turbulence (ODT) solutions of a hybrid large-eddy simulation (LES) and ODT scheme. An a priori validation of the proposed approach is implemented based on stand-alone ODT solutions of the Sandia Flame F flame, which is characterized by different regimes of combustion starting with pilot stabilization, to extinction and reignition and self-stabilized combustion. The PCA analysis is carried out with a full set of the thermo-chemical scalars' vector as well as a subset of this vector. The subset is made up primarily of major species and temperature. The results show that the different regimes are reproduced using only three principal components for the thermo-chemical scalars based on the full and a subset of the thermo-chemical scalars' vector. Reproduction of the source term of the principal component represents a greater challenge. It is found that using the subset of the thermo-chemical scalars' vector both minor species and the first three principal components source terms are reasonably well predicted.
Morin, R.H.
1997-01-01
Returns from drilling in unconsolidated cobble and sand aquifers commonly do not identify lithologic changes that may be meaningful for Hydrogeologic investigations. Vertical resolution of saturated, Quaternary, coarse braided-slream deposits is significantly improved by interpreting natural gamma (G), epithermal neutron (N), and electromagnetically induced resistivity (IR) logs obtained from wells at the Capital Station site in Boise, Idaho. Interpretation of these geophysical logs is simplified because these sediments are derived largely from high-gamma-producing source rocks (granitics of the Boise River drainage), contain few clays, and have undergone little diagenesis. Analysis of G, N, and IR data from these deposits with principal components analysis provides an objective means to determine if units can be recognized within the braided-stream deposits. In particular, performing principal components analysis on G, N, and IR data from eight wells at Capital Station (1) allows the variable system dimensionality to be reduced from three to two by selecting the two eigenvectors with the greatest variance as axes for principal component scatterplots, (2) generates principal components with interpretable physical meanings, (3) distinguishes sand from cobble-dominated units, and (4) provides a means to distinguish between cobble-dominated units.
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 ...
González-Solís, José Luis; Martínez-Espinosa, Juan Carlos; Salgado-Román, Juan Manuel; Palomares-Anda, Pascual
2014-05-01
In this research, we used the Raman spectroscopy to distinguish between normal and leukemia blood serum and identify the different types of leukemia based on serum biochemistry. In addition, monitoring of patients under chemotherapy leukemia treatment (CHLT) was studied. Blood samples were obtained from seven patients who were clinically diagnosed with three leukemia types and 21 healthy volunteers. In addition, other five leukemia patients were monitored during the CHLT, two patients were declared healthy, one patient suspended it; the health of the other two patients worsened, and no improvement was observed along CHLT. The serum samples were put under an Olympus microscope integrated to the Raman system, and several points were chosen for the Raman measurement. The Horiba Jobin Yvon LabRAM HR800 Raman system is equipped with a liquid nitrogen-cooled detector and a laser of 830 nm with a power irradiation of 17 mW. It is shown that the serum samples from patient with leukemia and from the control group can be discriminated when multivariate statistical methods of principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to their Raman spectra obtaining two large clusters corresponding to the control and leukemia serum samples and three clusters inside the leukemia group associated with the three leukemia types. The major differences between leukemia and control spectra were at 1,338 (Trp, α-helix, phospholipids), 1,447 (lipids), 1,523 (β-carotene), 1,556 (Trp), 1,587 (protein, Tyr), 1,603 (Tyr, Phe), and 1,654 (proteins, amide I, α-helix, phospholipids) cm(-1), where these peaks were less intense in the leukemia spectrum. Minor differences occurred at 661 (glutathione), 890 (glutathione), 973 (glucosamine), 1,126 (protein, phospholipid C-C str), 1,160 (β-carotene), 1,174 (Trp, Phe), 1,208 (Trp), 1,246 (amide III), 1,380 (glucosamine), and 1,404 (glutathione) cm(-1). Leukemia spectrum showed a peak at 917 cm(-1) associated with
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
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.
Liang, Man; Huang, Fu-rong; He, Xue-jia; Chen, Xing-dan
2014-08-01
In order to explore rapid real-time algae detection methods, in the present study experiments were carried out to use fluorescence spectral imaging technology combined with a pattern recognition method for identification research of different types of algae. The fluorescence effect of algae samples is obvious during the detection. The fluorescence spectral imaging system was adopted to collect spectral images of 40 algal samples. Through image denoising, binarization processing and making sure the effective pixels, the spectral curves of each sample were drawn according to the spectral cube. The spectra in the 400-720 nm wavelength range were obtained. Then, two pattern recognition methods, i.e., hierarchical cluster analysis and principal component analysis, were used to process the spectral data. The hierarchical cluster analysis results showed that the Euclidean distance method and average weighted method were used to calculate the cluster distance between samples, and the samples could be correctly classified at a level of the distance L=2.452 or above, with an accuracy of 100%. The principal component analysis results showed that first-order derivative, second-order derivative, multiplicative scatter correction, standard normal variate and other pretreatments were carried out on raw spectral data, then principal component analysis was conducted, among which the identification effect after the second-order derivative pretreatment was shown to be the most effective, and eight types of algae samples were independently distributed in the principal component eigenspace. It was thus shown that it was feasible to use fluorescence spectral imaging technology combined with cluster analysis and principal component analysis for algae identification. The method had the characteristics of being easy to operate, fast and nondestructive.
Early detection of dental fluorosis using Raman spectroscopy and principal component analysis.
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
Zhang, Lei; Li, Jian; Pei, Yu-Fang; Liu, Yongjun; Deng, Hong-Wen
2009-11-01
Traditional transmission disequilibrium test (TDT) based methods for genetic association analyses are robust to population stratification at the cost of a substantial loss of power. We here describe a novel method for family-based association studies that corrects for population stratification with the use of an extension of principal component analysis (PCA). Specifically, we adopt PCA on unrelated parents in each family. We then infer principal components for children from those for their parents through a TDT-like strategy. Two test statistics within the variance-components model are proposed for association tests. Simulation results show that the proposed tests have correct type I error rates regardless of population stratification, and have greatly improved power over two popular TDT-based methods: QTDT and FBAT. The application to the Genetic Analysis Workshop 16 (GAW16) data sets attests to the feasibility of the proposed method.
40 CFR 62.14505 - What are the principal components of this subpart?
Code of Federal Regulations, 2013 CFR
2013-07-01
... POLLUTANTS Federal Plan Requirements for Commercial and Industrial Solid Waste Incineration Units That Commenced Construction On or Before November 30, 1999 Introduction § 62.14505 What are the principal...) through (k) of this section. (a) Increments of progress toward compliance. (b) Waste management plan....
ERIC Educational Resources Information Center
Friend, Jennifer; Watson, Robert
2014-01-01
This article examines comparative survey results for 16 principal preparation programs located in the Midwestern state of Missouri across a four-year time period from 2008 to 2012. The authors are founding members of a statewide Higher Education Evaluation Committee (HEEC), which has been meeting on a monthly basis since 2005, comprised of faculty…
Li, Boyan; Calvet, Amandine; Casamayou-Boucau, Yannick; Ryder, Alan G
2016-03-24
A new, fully automated, rapid method, referred to as kernel principal component analysis residual diagnosis (KPCARD), is proposed for removing cosmic ray artifacts (CRAs) in Raman spectra, and in particular for large Raman imaging datasets. KPCARD identifies CRAs via a statistical analysis of the residuals obtained at each wavenumber in the spectra. The method utilizes the stochastic nature of CRAs; therefore, the most significant components in principal component analysis (PCA) of large numbers of Raman spectra should not contain any CRAs. The process worked by first implementing kernel PCA (kPCA) on all the Raman mapping data and second accurately estimating the inter- and intra-spectrum noise to generate two threshold values. CRA identification was then achieved by using the threshold values to evaluate the residuals for each spectrum and assess if a CRA was present. CRA correction was achieved by spectral replacement where, the nearest neighbor (NN) spectrum, most spectroscopically similar to the CRA contaminated spectrum and principal components (PCs) obtained by kPCA were both used to generate a robust, best curve fit to the CRA contaminated spectrum. This best fit spectrum then replaced the CRA contaminated spectrum in the dataset. KPCARD efficacy was demonstrated by using simulated data and real Raman spectra collected from solid-state materials. The results showed that KPCARD was fast (<1 min per 8400 spectra), accurate, precise, and suitable for the automated correction of very large (>1 million) Raman datasets.
Principal component analysis of vertical profiles of Q[sub 1] and Q[sub 2] in the tropics
Alexander, G.D.; Young, G.S.; Ledvina, D.V. )
1993-02-01
Rotated principal component analysis (PCA) is applied to the combined vertical profiles of apparent heat source Q[sub 1] and apparent moisture sink Q[sub 2] from both disturbed and undisturbed periods of the Australian summer monsoon season. The data represent the heating and drying within two radiosonde arrays afforded by the Australian Monsoon Experiment (AMEX). The aim here is to identify dominant modes of variability in combined vertical profiles of Q[sub 1] and Q[sub 2]. Rotation of the principal components (PCs) - done to assure stable, physically meaningful components - yields several PCs, deemed here to be statistically significant. The variation of individual Q[sub 1] and Q[sub 2] profiles from the mean profile can be expressed as linear combinations of the PCs; therefore, determination of the relative importance of each PC (through examination of its score) during differing convective conditions provides insight into their physical meaning. For instance, the contribution of PC 1 (that mode of variability that explains the maximum amount of variance between the profiles) is largest when mature cloud-cluster coverage is most expansive. Therefore, this PC is attributable to that combination of deep convection and associated stratiform anvil typical of mature cloud clusters. The remaining PCs fall into two categories: those whose contributions vary with the evolution of a convective system and those whose contributions vary diurnally. Principal components of the former group represent the effects of convection from shallow cumulus to stratiform anvil precipitation. Principal components of the latter group, those that show heating and drying patterns confined to the extremities of the troposphere, are attributable to diabatic boundary-layer fluxes and radiative processes at the top of the troposphere. 64 refs., 10 figs., 2 tabs.
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
Ranamukhaarachchi, Sahan A; Peiris, Ramila H; Moresoli, Christine
2017-02-15
The potential of intrinsic fluorescence and principal component analysis (PCA) to characterize the antioxidant capacity of soy protein hydrolysates (SPH) during sequential ultrafiltration (UF) and nanofiltration (NF) was evaluated. SPH was obtained by enzymatic hydrolysis of soy protein isolate. Antioxidant capacity was measured by Oxygen Radical Absorbance Capacity (ORAC) and Folin Ciocalteau Reagent (FCR) assays together with fluorescence excitation-emission matrices (EEM). PCA of the fluorescence EEMs revealed two principal components (PC1-tryptophan, PC2-tyrosine) that captured significant variance in the fluorescence spectra. Regression models between antioxidant capacity and PC1 and PC2 displayed strong linear correlations for NF fractions and a weak linear correlation for UF fractions. Clustering of UF and NF fractions according to ORACFPCA and FCRFPCA was observed. The ability of this method to extract information on contributions by tryptophan and tyrosine amino acid residues to the antioxidant capacity of SPH fractions was demonstrated.
NASA Astrophysics Data System (ADS)
Simpson, I. J.; Choi, Y.; Blake, D. R.; Rowland, F. S.
2002-12-01
Principal Component Analysis (PCA) is a multivariate statistical technique that can be used to identify major pollution sources and their emission composition from within a large ambient measurement data set. Here we apply PCA to a regional whole air data set collected during the airborne Transport and Chemical Evolution over the Pacific (TRACE-P) field campaign (February-April, 2001). The data set comprises a matrix of 3458 samples by 47 compounds (nonmethane hydrocarbons, halocarbons, alkyl nitrates, and sulfur compounds), collected at altitudes between 150 m and 12 km. Between 25-46 N latitude, industrial tracers such as the CFC replacement compounds (HCFCs) dominated the variance within the data set. Photochemically processed urban air and air influenced by emissions from the People's Republic of China also featured strongly. Further south between 7-25 N, processed urban air dominated the observed variance. Tracers indicative of automobile exhaust and gasoline evaporation were also evident in the Principal Component Analysis.
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.
Ramli, Saifullah; Ismail, Noryati; Alkarkhi, Abbas Fadhl Mubarek; Easa, Azhar Mat
2010-01-01
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
Raine, Daniel; Langley, Philip; Shepherd, Ewen; Lord, Stephen; Murray, Stephen; Murray, Alan; Bourke, John P.
2015-01-01
Background Lead V1 is routinely analysed due to its large amplitude AF waveform. V1 correlates strongly with right atrial activity but only moderately with left atrial activity. Posterior lead V9 correlates strongest with left atrial activity. Aims (1) To establish whether surface dominant AF frequency (DAF) calculated using principal component analysis (PCA) of a modified 12-lead ECG (including posterior leads) has a stronger correlation with left atrial activity compared to the standard ECG. (2) To assess the contribution of individual ECG leads to the AF principal component in both ECG configurations. Methods Patients were assigned to modified or standard ECG groups. In the modified ECG, posterior leads V8 and V9 replaced V4 and V6. AF waveform was extracted from one-minute surface ECG recordings using PCA. Surface DAF was correlated with intracardiac DAF from the high right atrium (HRA), coronary sinus (CS) and pulmonary veins (PVs). Results 96 patients were studied. Surface DAF from the modified ECG did not have a stronger correlation with left atrial activity compared to the standard ECG. Both ECG configurations correlated strongly with HRA, CS and right PVs but only moderately with left PVs. V1 contributed most to the AF principal component in both ECG configurations. PMID:25619612
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.
Ceulemans, Eva; Kiers, Henk A L
2006-05-01
Several three-mode principal component models can be considered for the modelling of three-way, three-mode data, including the Candecomp/Parafac, Tucker3, Tucker2, and Tucker1 models. The following question then may be raised: given a specific data set, which of these models should be selected, and at what complexity (i.e. with how many components)? We address this question by proposing a numerical model selection heuristic based on a convex hull. Simulation results show that this heuristic performs almost perfectly, except for Tucker3 data arrays with at least one small mode and a relatively large amount of error.
NASA Astrophysics Data System (ADS)
Li, Yanfu; Liu, Hongli; Ma, Ziji
2016-10-01
Rail corrugation dynamic measurement techniques are critical to guarantee transport security and guide rail maintenance. During the inspection process, low-frequency trends caused by rail fluctuation are usually superimposed on rail corrugation and seriously affect the assessment of rail maintenance quality. In order to extract and remove the nonlinear and non-stationary trends from original mixed signals, a hybrid model based ensemble empirical mode decomposition (EEMD) and modified principal component analysis (MPCA) is proposed in this paper. Compared with the existing de-trending methods based on EMD, this method first considers low-frequency intrinsic mode functions (IMFs) thought to be underlying trend components that maybe contain some unrelated components, such as white noise and low-frequency signal itself, and proposes to use PCA to accurately extract the pure trends from the IMFs containing multiple components. On the other hand, due to the energy contribution ratio between trends and mixed signals is prior unknown, and the principal components (PCs) decomposed by PCA are arranged in order of energy reduction without considering frequency distribution, the proposed method modifies traditional PCA and just selects relevant low-frequency PCs to reconstruct the trends based on the zero-crossing numbers (ZCN) of each PC. Extensive tests are presented to illustrate the effectiveness of the proposed method. The results show the proposed EEMD-PCA-ZCN is an effective tool for trend extraction of rail corrugation measured dynamically.
Easy Absolute Values? Absolutely
ERIC Educational Resources Information Center
Taylor, Sharon E.; Mittag, Kathleen Cage
2015-01-01
The authors teach a problem-solving course for preservice middle-grades education majors that includes concepts dealing with absolute-value computations, equations, and inequalities. Many of these students like mathematics and plan to teach it, so they are adept at symbolic manipulations. Getting them to think differently about a concept that they…
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.
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
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.
Huang, Lei; Reiss, Philip T; Xiao, Luo; Zipunnikov, Vadim; Lindquist, Martin A; Crainiceanu, Ciprian M
2016-08-29
SummaryMany modern neuroimaging studies acquire large spatial images of the brain observed sequentially over time. Such data are often stored in the forms of matrices. To model these matrix-variate data we introduce a class of separable processes using explicit latent process modeling. To account for the size and two-way structure of the data, we extend principal component analysis to achieve dimensionality reduction at the individual level. We introduce necessary identifiability conditions for each model and develop scalable estimation procedures. The method is motivated by and applied to a functional magnetic resonance imaging study designed to analyze the relationship between pain and brain activity.
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.
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.
Ong, Yi Hong; Lim, Mayasari; Liu, Quan
2012-09-24
Raman spectroscopy has been explored as a promising label-free technique in discriminating apoptosis and necrosis induced cell death in leukemia cells. In addition to Principal component analysis (PCA) as commonly employed in Raman data analysis, another less commonly used but powerful method is Biochemical Component Analysis (BCA). In BCA, a Raman spectrum is decomposed into the contributions from several known basic biochemical components, such as proteins, lipid, nucleic acids and glycogen groups etc. The differences in terms of classification accuracy and interpretability of resulting data between these two methods in Raman spectroscopy have not been systematically investigated to our knowledge. In this study, we utilized both methods to analyze the Raman spectra measured from live cells, apoptotic and necrotic leukemia cells. The comparison indicates that two methods yield comparable accuracy in sample classification when the numbers of basic components are equal. The changes in the contributions of biochemical components in BCA can be interpreted by cell biology principles in apoptosis and necrosis. In contrast, the contributions of most principle components in PCA are difficult to interpret except the first one. The capability of BCA to unveil fine biochemical changes in cell spectra and excellent accuracy in classification can impel the broad application of Raman spectroscopy in biological research.
Gao, Boyan; Lu, Yingjian; Sheng, Yi; Chen, Pei; Yu, Liangli Lucy
2013-03-27
High-performance liquid chromatography (HPLC) and flow injection electrospray ionization with ion trap mass spectrometry (FIMS) fingerprints combined with principal component analysis (PCA) were examined for their potential in differentiating commercial organic and conventional sage samples. The individual components in the sage samples were also characterized with an ultraperformance liquid chromatograph with a quadrupole-time-of-flight mass spectrometer (UPLC Q-TOF MS). The results suggested that both HPLC and FIMS fingerprints combined with PCA could differentiate organic and conventional sage samples effectively. FIMS may serve as a quick test capable of distinguishing organic and conventional sages in 1 min and could potentially be developed for high-throughput applications, whereas HPLC fingerprints could provide more chemical composition information with a longer analytical time.
NASA Astrophysics Data System (ADS)
Kholodov, V. A.; Yaroslavtseva, N. V.; Lazarev, V. I.; Frid, A. S.
2016-09-01
Cluster analysis and principal component analysis (PCA) have been used for the interpretation of dry sieving data. Chernozems from the treatments of long-term field experiments with different land-use patterns— annually mowed steppe, continuous potato culture, permanent black fallow, and untilled fallow since 1998 after permanent black fallow—have been used. Analysis of dry sieving data by PCA has shown that the treatments of untilled fallow after black fallow and annually mowed steppe differ most in the series considered; the content of dry aggregates of 10-7 mm makes the largest contribution to the distribution of objects along the first principal component. This fraction has been sieved in water and analyzed by PCA. In contrast to dry sieving data, the wet sieving data showed the closest mathematical distance between the treatment of untilled fallow after black fallow and the undisturbed treatment of annually mowed steppe, while the untilled fallow after black fallow and the permanent black fallow were the most distant treatments. Thus, it may be suggested that the water stability of structure is first restored after the removal of destructive anthropogenic load. However, the restoration of the distribution of structural separates to the parameters characteristic of native soils is a significantly longer process.
NASA Astrophysics Data System (ADS)
Chattopadhyay, Surajit; Chattopadhyay, Goutami
2012-10-01
In the work discussed in this paper we considered total ozone time series over Kolkata (22°34'10.92″N, 88°22'10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.
Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong
2015-08-07
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.
Ghasemi, Jahan B; Zolfonoun, Ehsan
2013-11-01
A new multicomponent analysis method, based on principal component analysis-multivariate adaptive regression splines (PC-MARS) is proposed for the determination of dialkyltin compounds. In Tween-20 micellar media, dimethyl and dibutyltin react with morin to give fluorescent complexes with the maximum emission peaks at 527 and 520nm, respectively. The spectrofluorimetric matrix data, before building the MARS models, were subjected to principal component analysis and decomposed to PC scores as starting points for the MARS algorithm. The algorithm classifies the calibration data into several groups, in each a regression line or hyperplane is fitted. Performances of the proposed methods were tested in term of root mean square errors of prediction (RMSEP), using synthetic solutions. The results show the strong potential of PC-MARS, as a multivariate calibration method, to be applied to spectral data for multicomponent determinations. The effect of different experimental parameters on the performance of the method were studied and discussed. The prediction capability of the proposed method compared with GC-MS method for determination of dimethyltin and/or dibutyltin.
NASA Astrophysics Data System (ADS)
Ghasemi, Jahan B.; Zolfonoun, Ehsan
2013-11-01
A new multicomponent analysis method, based on principal component analysis-multivariate adaptive regression splines (PC-MARS) is proposed for the determination of dialkyltin compounds. In Tween-20 micellar media, dimethyl and dibutyltin react with morin to give fluorescent complexes with the maximum emission peaks at 527 and 520 nm, respectively. The spectrofluorimetric matrix data, before building the MARS models, were subjected to principal component analysis and decomposed to PC scores as starting points for the MARS algorithm. The algorithm classifies the calibration data into several groups, in each a regression line or hyperplane is fitted. Performances of the proposed methods were tested in term of root mean square errors of prediction (RMSEP), using synthetic solutions. The results show the strong potential of PC-MARS, as a multivariate calibration method, to be applied to spectral data for multicomponent determinations. The effect of different experimental parameters on the performance of the method were studied and discussed. The prediction capability of the proposed method compared with GC-MS method for determination of dimethyltin and/or dibutyltin.
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.
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.
Zhang, Jian; Hou, Dibo; Wang, Ke; Huang, Pingjie; Zhang, Guangxin; Loáiciga, Hugo
2017-04-01
The detection of organic contaminants in water distribution systems is essential to protect public health from potential harmful compounds resulting from accidental spills or intentional releases. Existing methods for detecting organic contaminants are based on quantitative analyses such as chemical testing and gas/liquid chromatography, which are time- and reagent-consuming and involve costly maintenance. This study proposes a novel procedure based on discrete wavelet transform and principal component analysis for detecting organic contamination events from ultraviolet spectral data. Firstly, the spectrum of each observation is transformed using discrete wavelet with a coiflet mother wavelet to capture the abrupt change along the wavelength. Principal component analysis is then employed to approximate the spectra based on capture and fusion features. The significant value of Hotelling's T(2) statistics is calculated and used to detect outliers. An alarm of contamination event is triggered by sequential Bayesian analysis when the outliers appear continuously in several observations. The effectiveness of the proposed procedure is tested on-line using a pilot-scale setup and experimental data.
McNabola, Aonghus; Broderick, Brian M; Gill, Laurence W
2009-10-01
Principal component analysis was used to examine air pollution personal exposure data of four urban commuter transport modes for their interrelationships between pollutants and relationships with traffic and meteorological data. Air quality samples of PM2.5 and VOCs were recorded during peak traffic congestion for the car, bus, cyclist and pedestrian between January 2005 and June 2006 on a busy route in Dublin, Ireland. In total, 200 personal exposure samples were recorded each comprising 17 variables describing the personal exposure concentrations, meteorological conditions and traffic conditions. The data reduction technique, principal component analysis (PCA), was used to create weighted linear combinations of the data and these were subsequently examined for interrelationships between the many variables recorded. The results of the PCA found that personal exposure concentrations in non-motorised forms of transport were influenced to a higher degree by wind speed, whereas personal exposure concentrations in motorised forms of transport were influenced to a higher degree by traffic congestion. The findings of the investigation show that the most effective mechanisms of personal exposure reduction differ between motorised and non-motorised modes of commuter transport.
Ghosh-Dastidar, Samanwoy; Adeli, Hojjat; Dadmehr, Nahid
2008-02-01
A novel principal component analysis (PCA)-enhanced cosine radial basis function neural network classifier is presented. The two-stage classifier is integrated with the mixed-band wavelet-chaos methodology, developed earlier by the authors, for accurate and robust classification of electroencephalogram (EEGs) into healthy, ictal, and interictal EEGs. A nine-parameter mixed-band feature space discovered in previous research for effective EEG representation is used as input to the two-stage classifier. In the first stage, PCA is employed for feature enhancement. The rearrangement of the input space along the principal components of the data improves the classification accuracy of the cosine radial basis function neural network (RBFNN) employed in the second stage significantly. The classification accuracy and robustness of the classifier are validated by extensive parametric and sensitivity analysis. The new wavelet-chaos-neural network methodology yields high EEG classification accuracy (96.6%) and is quite robust to changes in training data with a low standard deviation of 1.4%. For epilepsy diagnosis, when only normal and interictal EEGs are considered, the classification accuracy of the proposed model is 99.3%. This statistic is especially remarkable because even the most highly trained neurologists do not appear to be able to detect interictal EEGs more than 80% of the times.
NASA Astrophysics Data System (ADS)
Choi, Yunsoo; Elliott, Scott; Simpson, Isobel J.; Blake, Donald R.; Colman, Jonah J.; Dubey, Manvendra K.; Meinardi, Simone; Rowland, F. Sherwood; Shirai, Tomoko; Smith, Felisa A.
2003-03-01
Hydrocarbon and halocarbon measurements collected during the second airborne Biomass Burning and Lightning Experiment (BIBLE-B) were subjected to a principal component analysis (PCA), to test the capability for identifying intercorrelated compounds within a large whole air data set. The BIBLE expeditions have sought to quantify and understand the products of burning, electrical discharge, and general atmospheric chemical processes during flights arrayed along the western edge of the Pacific. Principal component analysis was found to offer a compact method for identifying the major modes of composition encountered in the regional whole air data set. Transecting the continental monsoon, urban and industrial tracers (e.g., combustion byproducts, chlorinated methanes and ethanes, xylenes, and longer chain alkanes) dominated the observed variability. Pentane enhancements reflected vehicular emissions. In general, ethyl and propyl nitrate groupings indicated oxidation under nitrogen oxide (NOx) rich conditions and hence city or lightning influences. Over the tropical ocean, methyl nitrate grouped with brominated compounds and sometimes with dimethyl sulfide and methyl iodide. Biomass burning signatures were observed during flights over the Australian continent. Strong indications of wetland anaerobics (methane) or liquefied petroleum gas leakage (propane) were conspicuous by their absence. When all flights were considered together, sources attributable to human activity emerged as the most important. We suggest that factor reductions in general and PCA in particular may soon play a vital role in the analysis of regional whole air data sets, as a complement to more familiar methods.
NASA Astrophysics Data System (ADS)
Pandžić, K.; Trninić, D.
1992-03-01
The relation between mesoscale (the Kupa river catchment area, Yugoslavia) discharge and precipitation field anomalies and macroscale (European-Northern Atlantic Region) surface pressure anomalies is considered. For this purpose the principal component analysis (PCA) technique has been applied. Mesoscale monthly discharge values for 25 stream flow measurement stations, precipitation and the Palmer 'drought index' data for 18 weather stations as well as the macroscale surface pressure for 12 × 19 grid points have been used. All refer to the period 1961-1980. Two principal components (PCs) are the most significant for each mesoscale field, which describe more than 85% of total field variance. In addition to a significant correlation indicated between mesoscale discharge and precipitation anomaly and the Palmer 'drought index' PCs, five space anomaly patterns were established for each field. A pattern of the macroscale surface pressure anomaly field corresponds to each of these patterns. Thus, an interpretation of the discharge anomaly field in the Kupa river basin in terms of macrocirculation is achieved.
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.
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.
NASA Astrophysics Data System (ADS)
Callies, Ulrich; Gaslikova, Lidia; Kapitza, Hartmut; Scharfe, Mirco
2016-08-01
Time variability of Eulerian residual currents in the German Bight (North Sea) is studied drawing on existing multi-decadal 2D barotropic simulations (1.6 km resolution) for the period Jan. 1958-Aug. 2015. Residual currents are calculated as 25 h means of velocity fields stored every hour. Principal component analysis (PCA) reveals that daily variations of these residual currents can be reasonably well represented in terms of only 2-3 degrees of freedom, partly linked to wind directions. The daily data refine monthly data already used in the past. Unlike existing classifications based on subjective assessment, numerical principal components (PCs) provide measures of strength and can directly be incorporated into more comprehensive statistical data analyses. Daily resolution in particular fits the time schedule of data sampled at the German Bight long-term monitoring station at Helgoland Roads. An example demonstrates the use of PCs and corresponding empirical orthogonal functions (EOFs) for the interpretation of short-term variations of these local observations. On the other hand, monthly averaging of the daily PCs enables to link up with previous studies on longer timescales.
NASA Astrophysics Data System (ADS)
Callies, Ulrich; Gaslikova, Lidia; Kapitza, Hartmut; Scharfe, Mirco
2017-04-01
Time variability of Eulerian residual currents in the German Bight (North Sea) is studied drawing on existing multi-decadal 2D barotropic simulations (1.6 km resolution) for the period Jan. 1958-Aug. 2015. Residual currents are calculated as 25 h means of velocity fields stored every hour. Principal component analysis (PCA) reveals that daily variations of these residual currents can be reasonably well represented in terms of only 2-3 degrees of freedom, partly linked to wind directions. The daily data refine monthly data already used in the past. Unlike existing classifications based on subjective assessment, numerical principal components (PCs) provide measures of strength and can directly be incorporated into more comprehensive statistical data analyses. Daily resolution in particular fits the time schedule of data sampled at the German Bight long-term monitoring station at Helgoland Roads. An example demonstrates the use of PCs and corresponding empirical orthogonal functions (EOFs) for the interpretation of short-term variations of these local observations. On the other hand, monthly averaging of the daily PCs enables to link up with previous studies on longer timescales.
Rapid cultivar identification of barley seeds through disjoint principal component modeling.
Whitehead, Iain; Munoz, Alicia; Becker, Thomas
2017-01-01
Classification of barley varieties is a crucial part of the control and assessment of barley seeds especially for the malting and brewing industry. The correct classification of barley is essential in that a majority of decisions made regarding process specifications, economic considerations, and the type of product produced with the cereal are made based on the barley variety itself. This fact combined with the need to promptly assess the cereal as it is delivered to a malt house or production facility creates the need for a technique to quickly identify a barley variety based on a sample. This work explores the feasibility of differentiating between barley varieties based on the protein spectrum of barley seeds. In order to produce a rapid analysis of the protein composition of the barley seeds, lab-on-a-chip micro fluid technology is used to analyze the protein composition. Classification of the barley variety is then made using disjoint principle component models. This work included 19 different barley varieties. The varieties consisted of both winter and summer barley types. In this work, it is demonstrated that this system can identify the most likely barley variety with an accuracy of 95.9% based on cross validation and can screen summer barley with an accuracy of 95.2% and a false positive rate of 0.0% based on cross validation. This demonstrates the feasibility of the method to provide a rapid and relatively inexpensive method to verify the heritage of barley seeds.
NASA Astrophysics Data System (ADS)
Ressler, Gerhard; Eicker, Annette; Lieb, Verena; Schmidt, Michael; Seitz, Florian; Shang, Kun; Shum, Che-Kwan
2015-04-01
Regionally changing hydrological conditions and their link to the availability of water for human consumption and agriculture is a challenging topic in the context of global change that is receiving increasing attention. Gravity field changes related to signals of land hydrology have been observed by the Gravity Recovery And Climate Experiment (GRACE) satellite mission over a period of more than 12 years. These changes are being analysed in our studies with respect to changing hydrological conditions, especially as a consequence of extreme weather situations and/or a change of climatic conditions. Typically, variations of the Earth's gravity field are modeled as a series expansion in terms of global spherical harmonics with time dependent harmonic coefficients. In order to investigate specific structures in the signal we alternatively apply a wavelet-based multi-resolution technique for the determination of regional spatiotemporal variations of the Earth's gravitational potential in combination with principal component analysis (PCA) for detailed evaluation of these structures. The multi-resolution representation (MRR) i.e. the composition of a signal considering different resolution levels is a suitable approach for spatial gravity modeling especially in case of inhomogeneous distribution of observation data on the one hand and because of the inhomogeneous structure of the Earth's gravity field itself on the other hand. In the MRR the signal is split into detail signals by applying low- and band-pass filters realized e.g. by spherical scaling and wavelet functions. Each detail signal is related to a specific resolution level and covers a certain part of the signal spectrum. Principal component analysis (PCA) enables for revealing specific signal patterns in the space as well as the time domain like trends and seasonal as well as semi seasonal variations. We apply the above mentioned combined technique to GRACE L1C residual potential differences that have been
NASA Astrophysics Data System (ADS)
Geminale, A.; Grassi, D.; Altieri, F.; Serventi, G.; Carli, C.; Carrozzo, F. G.; Sgavetti, M.; Orosei, R.; D'Aversa, E.; Bellucci, G.; Frigeri, A.
The aim of this work is to extract the surface contribution in the Martian visible/near-infrared spectra removing the atmospheric components by means of Principal Component Analysis (PCA) and target transformation (TT). The developed technique is suitable for separating spectral components in a data set large enough to enable an effective usage of statistical methods, in support to the more common approaches to remove the gaseous component. Data collected by imaging spectrometers, such as the OMEGA (Observatoire pour la Minéralogie, l'Eau, les Glaces et l'Activité) instrument on board the ESA mission Mars Express (MEx), are particularly suitable for this purpose since it includes in the same session of observation a large number of spectra with different content of aerosols, gases and mineralogy. The methodology presented in this work has been applied to the analysis of OMEGA sessions over Nili Fossae and Mawrth Vallis regions, which have been already widely studied because of the presence of hydrated minerals. Once the surface reflectance, free from atmospheric contributions, has been obtained, the Modified Gaussian Model (MGM) has been applied to spectra showing the hydrated phase. Silicates and iron-bearing hydrated minerals have been identified by means of the electronic transitions of Fe2+ between 0.8-1.2 mu m, while at longer wavelengths the hydrated mineralogy is identified by overtones of the OH group.
Siuly, Siuly; Li, Yan
2015-04-01
The aim of this study is to design a robust feature extraction method for the classification of multiclass EEG signals to determine valuable features from original epileptic EEG data and to discover an efficient classifier for the features. An optimum allocation based principal component analysis method named as OA_PCA is developed for the feature extraction from epileptic EEG data. As EEG data from different channels are correlated and huge in number, the optimum allocation (OA) scheme is used to discover the most favorable representatives with minimal variability from a large number of EEG data. The principal component analysis (PCA) is applied to construct uncorrelated components and also to reduce the dimensionality of the OA samples for an enhanced recognition. In order to choose a suitable classifier for the OA_PCA feature set, four popular classifiers: least square support vector machine (LS-SVM), naive bayes classifier (NB), k-nearest neighbor algorithm (KNN), and linear discriminant analysis (LDA) are applied and tested. Furthermore, our approaches are also compared with some recent research work. The experimental results show that the LS-SVM_1v1 approach yields 100% of the overall classification accuracy (OCA), improving up to 7.10% over the existing algorithms for the epileptic EEG data. The major finding of this research is that the LS-SVM with the 1v1 system is the best technique for the OA_PCA features in the epileptic EEG signal classification that outperforms all the recent reported existing methods in the literature.
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
Asamitsu, Yuko; Nakamura, Yoko; Ueda, Minoru; Kuwahara, Shigefumi; Kiyota, Hiromasa
2006-06-01
Synthesis of both enantiomers of methyl 4,5-didehydrojasmonate (1, Delta(4,5)-MJA; >99.8% ee), a constituent of jasmin absolute, established the absolute configuration of the natural product, and their odor quality was evaluated. The fragrance of the natural (3S,7R)-enantiomer (a fresh natural, sweet floral fruity odor, reminiscent of Jasmin and Ylang Ylang flower, more intensive and tenacious) was superior to that of the unnatural (3R,7S)-enantiomer (a floral green odor with slight metallic green aspect, less intensive than the natural form) and the racemate (green-floral note, having weak and less volume than methyl jasmonate). Odor difference between natural and unnatural enantiomers of methyl jasmonate (2) is also reported.
NASA Astrophysics Data System (ADS)
Anees, Amir; Khan, Waqar Ahmad; Gondal, Muhammad Asif; Hussain, Iqtadar
2013-07-01
The aim of this work is to make use of the mean of absolute deviation (MAD) method for the evaluation process of substitution boxes used in the advanced encryption standard. In this paper, we use the MAD technique to analyze some popular and prevailing substitution boxes used in encryption processes. In particular, MAD is applied to advanced encryption standard (AES), affine power affine (APA), Gray, Lui J., Residue Prime, S8 AES, SKIPJACK, and Xyi substitution boxes.
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.
NASA Astrophysics Data System (ADS)
Virtanen, Jaakko; Noponen, Tommi; Meriläinen, Pekka
2009-09-01
Near-infrared spectroscopy (NIRS) is a method for noninvasive estimation of cerebral hemodynamic changes. Principal component analysis (PCA) and independent component analysis (ICA) can be used for decomposing a set of signals to underlying components. Our objective is to determine whether PCA or ICA is more efficient in identifying and removing scalp blood flow interference from multichannel NIRS signals. Concentration changes of oxygenated (HbO2) and deoxygenated (HbR) hemoglobin are measured on the forehead with multichannel NIRS during hyper- and hypocapnia. PCA and ICA are used separately to identify and remove signal contribution from extracerebral tissue, and the resulting estimates of cerebral responses are compared to the expected cerebral responses. Both methods were able to reduce extracerebral contribution to the signals, but PCA typically performs equal to or better than ICA. The improvement in 3-cm signal quality achieved with both methods is comparable to increasing the source-detector separation from 3 to 5 cm. Especially PCA appears to be well suited for use in NIRS applications where the cerebral activation is diffuse, such as monitoring of global cerebral oxygenation and hemodynamics. Performance differences between PCA and ICA could be attributed primarily to different criteria for identifying the surface effect.
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.
NASA Astrophysics Data System (ADS)
Ranjbar, H.; Hassanzadeh, H.; Torabi, M.; Ilaghi, O.
2001-08-01
This paper describes a methodology for the integrated interpretation of airborne magnetic and airborne γ-ray spectrometer data. The Darrehzar porphyry copper deposit is situated in the Urumieh-Dokhtar magmatic assemblage of Central Iran. Phyllic and propylitic alterations are pervasive in the area but potassic and argillic alterations are not readily recognized on the surface. The spatial distributions of geophysical data resemble the lithological and alteration patterns in the area. The Darrehzar porphyry copper deposit is considered as a control site for determination of the degrees that the geophysical data is correlated with the mineralization zone. Airborne magnetic/radiometric, and geochemical/alteration data sets have been integrated and analyzed using principal component analysis. This technique is found to be useful for the delineation of hydrothermally altered areas and data compression.
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.
García, M C; Otero, A; García, M L; Moreno, B
1987-06-01
Principal component analysis was used to examine the correlations between two sets of variables, one representing physicochemical characteristics (pH, aw and NaCl, moisture and fat content) of Manchego (36 samples) and Burgos (36 samples) cheeses, and the other representing counts of several microbial groups (mesophiles, psychrotrophs, lactic acid bacteria, coliforms, enterococci, staphylococci and molds and yeasts). Thermonuclease content was also included. In addition to the expected relationships (NaCl content, moisture, aw, etc.), significant correlations between some compositional characteristics and levels of certain microorganisms were found. These correlations were dependent on the type of cheese. Thermonuclease content was positively related to enterococci and ripening (only in Manchego cheese). In contrast to former observations, no relationships were observed between coliforms and enterococci counts.
NASA Astrophysics Data System (ADS)
Van Huffel, Sabine; Wang, Yu; Vanhamme, Leentje; Van Hecke, Paul
2002-09-01
Several algorithms for automatic frequency alignment and quantitation of single resonances in multiple magnetic resonance (MR) spectra are investigated. First, a careful comparison between the complex principal component analysis (PCA) and the Hankel total least squares-based methods for quantifying the resonances in the spectral sets of magnetic resonance spectroscopy imaging (MRSI) spectra is presented. Afterward, we discuss a method based on complex PCA plus linear regression and a method based on cross correlation of the magnitude spectra for correcting frequency shifts of resonances in sets of MR spectra. Their advantages and limitations are demonstrated on simulated MR data sets as well as on an in vivo MRSI data set of the human brain.
Soeiro, Bruno T; Boen, Thaís R; Wagner, Roger; Lima-Pallone, Juliana A
2009-01-01
The aim of the present work was to determine parameters of the corn and wheat flour matrix, such as protein, lipid, moisture, ash and carbohydrates, folic acid and iron contents. Three principal components explained 91% of the total variance. Wheat flours were characterized by high protein and moisture content. On the other hand, the corn flours had the greater carbohydrates, lipids and folic acid levels. The concentrations of folic acid were lower than the issued value for wheat flours. Nevertheless, corn flours presented extremely high values. The iron concentration was higher than that recommended in Brazilian legislation. Poor homogenization of folic acid and iron was observed in enriched flours. This study could be useful to help the governmental authorities in the enriched food programs evaluation.
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.
Sun, Wei; Sun, Jingyi
2017-03-01
Increased attention has been paid to PM2.5 pollution in China. Due to its detrimental effects on environment and health, it is important to establish a PM2.5 concentration forecasting model with high precision for its monitoring and controlling. This paper presents a novel hybrid model based on principal component analysis (PCA) and least squares support vector machine (LSSVM) optimized by cuckoo search (CS). First PCA is adopted to extract original features and reduce dimension for input selection. Then LSSVM is applied to predict the daily PM2.5 concentration. The parameters in LSSVM are fine-tuned by CS to improve its generalization. An experiment study reveals that the proposed approach outperforms a single LSSVM model with default parameters and a general regression neural network (GRNN) model in PM2.5 concentration prediction. Therefore the established model presents the potential to be applied to air quality forecasting systems.
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.
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%.
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.
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.
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.
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.
Yin, Jianhua; Xia, Yang
2014-01-01
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. PMID:25000570
Møller, Stina Frosch; Jørgensen, Bo M
2007-04-01
Gas chromatograms of fatty acid methyl esters and of volatile lipid oxidation products from fish lipid extracts are analyzed by multivariate data analysis [principal component analysis (PCA)]. Peak alignment is necessary in order to include all sampled points of the chromatograms in the data set. The ability of robust algorithms to deal with outlier problems, including both sample-wise and element-wise outliers, and the advantages and drawbacks of two robust PCA methods, robust PCA (ROBPCA) and robust singular value decomposition when analysing these GC data were investigated. The results show that the usage of ROPCA is advantageous, compared with traditional PCA, when analysing the entire profile of chromatographic data in cases of sub-optimally aligned data. It also demonstrates how choosing the most robust PCA (sample or element-wise) depends on the type of outliers present in the data set.
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.
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
Yu, Marcia M L; Sandercock, P Mark L
2012-01-01
During the forensic examination of textile fibers, fibers are usually mounted on glass slides for visual inspection and identification under the microscope. One method that has the capability to accurately identify single textile fibers without subsequent demounting is Raman microspectroscopy. The effect of the mountant Entellan New on the Raman spectra of fibers was investigated to determine if it is suitable for fiber analysis. Raman spectra of synthetic fibers mounted in three different ways were collected and subjected to multivariate analysis. Principal component analysis score plots revealed that while spectra from different fiber classes formed distinct groups, fibers of the same class formed a single group regardless of the mounting method. The spectra of bare fibers and those mounted in Entellan New were found to be statistically indistinguishable by analysis of variance calculations. These results demonstrate that fibers mounted in Entellan New may be identified directly by Raman microspectroscopy without further sample preparation.
Jiang, Miaomiao; Jiao, Yujiao; 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.
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.
Mack, Cina M; Lin, Bryant J; Turner, James D; Johnstone, Andrew F M; Burgoon, Lyle D; Shafer, Timothy J
2014-01-01
Microelectrode arrays (MEAs) can be used to 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 (PCA) and chemical class prediction using support vector machines (SVMs) to classify chemical effects based on MEA data from 16 chemicals. Spontaneous firing rate in primary cortical cultures was increased by bicuculline (BIC), lindane (LND), RDX and picrotoxin (PTX); not changed by nicotine (NIC), acetaminophen (ACE), and glyphosate (GLY); and decreased by muscimol (MUS), verapamil (VER), fipronil (FIP), fluoxetine (FLU), chlorpyrifos oxon (CPO), domoic acid (DA), deltamethrin (DELT) and dimethyl phthalate (DMP). PCA was performed on mean firing rate, bursting parameters and synchrony data for concentrations above each chemical's EC50 for mean firing rate. The first three principal components accounted for 67.5, 19.7, and 6.9% of the data variability and were used to identify separation between chemical classes visually through spatial proximity. In the PCA, there was clear separation of GABAA antagonists BIC, LND, and RDX from other chemicals. For the SVM prediction model, the experiments were classified into the three chemical classes of increasing, decreasing or no change in activity with a mean accuracy of 83.8% under a radial kernel with 10-fold cross-validation. The separation of different chemical classes through PCA and high prediction accuracy in SVM of a small dataset indicates that MEA data may be useful for separating chemicals into effects classes using these or other related approaches.
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.
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.
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.
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
Okada, Kaori; Gogami, Yoshitaka; Oikawa, Tadao
2013-02-01
We performed sensory evaluations on 141 bottles of sake and analyzed the relationship between the D-amino acid concentrations, and the taste of the sake using principal component analysis, which yielded seven principal components (PC1-7) that explained 100 % of the total variance in the data. PC1, which explains 33.6 % of the total variance, correlates most positively with strong taste and most negatively with balanced tastes. PC2, which explains 54.4 % of the total variance, correlates most positively with a sweet taste and most negatively with bitter and sour tastes. Sakes brewed with "Kimoto yeast starter" and "Yamahaimoto" had high scores for PC1 and PC2, and had strong taste in comparison with sakes brewed with "Sokujo-moto". When present at concentrations below 50 μM, D-Ala did not affect the PC1 score, but all the sakes showed a high PC1 score, when the D-Ala was above 100 μM. Similar observations were found for the D-Asp and D-Glu concentrations with regard to PC1, and the threshold concentrations of D-Asp and D-Glu that affected the taste were 33.8 and 33.3 μM, respectively. Certain bacteria present in sake, especially lactic acid bacteria, produce D-Ala, D-Asp and D-Glu during storage, and these D-amino acids increased the PC1 score and produced a strong taste (Nojun). When D- and L-Ala were added to the sakes, the value for the umami taste in the sensory evaluation increased, with the effect of D-Ala being much stronger than that of L-Ala. The addition of 50-5,000 μM DL-Ala did not effect on the aroma of the sakes at all.
Garcia, Mario; Li, Wen-Whai; Yang, Hongling; Amaya, Maria A.; Myers, Orrin; Burchiel, Scott W.; Berwick, Marianne; Pingitore, Nicholas E.
2012-01-01
The use of land-use regression (LUR) techniques for modeling small-scale variations of intraurban air pollution has been increasing in the last decade. The most appealing feature of LUR techniques is the economical monitoring requirements. In this study, principal component analysis (PCA) was employed to optimize an LUR model for PM2.5. The PM2.5 monitoring network consisted of 13 sites, which constrained the regression model to a maximum of one independent variable. An optimized surrogate of vehicle emissions was produced by PCA and employed as the predictor variable in the model. The vehicle emissions surrogate consisted of a linear combination of several traffic variables (e.g., vehicle miles traveled, speed, traffic demand, road length, and time) obtained from a road network used for traffic modeling. The vehicle-emissions surrogate produced by the PCA had a predictive capacity greater (R2 = .458) than the traffic variable, Traffic Demand summarized for a 1 km buffer, with best predictive capacity (R2 = .341). The PCA-based method employed in this study was effective at increasing the fit of an ordinary LUR model by optimizing the utilization of a PM2.5 dataset from small-n monitoring network. In general, the method used can contribute to LUR techniques in two major ways: 1) by improving the predictive power of the input variable, by substituting a principal component for a single variable and 2) by creating an orthogonal set of predictor variables, and thus fulfilling the no colinearity assumption of the linear regression methods. The proposed PCA method, should be universally applicable to LUR methods and will expand their economical attractiveness. PMID:22464030
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
Gao, Wen; Wang, Rui; Li, Dan; Liu, Ke; Chen, Jun; Li, Hui-Jun; Xu, Xiaojun; Li, Ping; Yang, Hua
2016-01-05
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.
NASA Astrophysics Data System (ADS)
Otero, Federico; Norte, Federico; Araneo, Diego
2016-10-01
The aim of this work is to obtain an index for predicting the probability of occurrence of zonda event at surface level from sounding data at Mendoza city, Argentine. To accomplish this goal, surface zonda wind events were previously found with an objective classification method (OCM) only considering the surface station values. Once obtained the dates and the onset time of each event, the prior closest sounding for each event was taken to realize a principal component analysis (PCA) that is used to identify the leading patterns of the vertical structure of the atmosphere previously to a zonda wind event. These components were used to construct the index model. For the PCA an entry matrix of temperature (T) and dew point temperature (Td) anomalies for the standard levels between 850 and 300 hPa was build. The analysis yielded six significant components with a 94 % of the variance explained and the leading patterns of favorable weather conditions for the development of the phenomenon were obtained. A zonda/non-zonda indicator c can be estimated by a logistic multiple regressions depending on the PCA component loadings, determining a zonda probability index widehat{c} calculable from T and Td profiles and it depends on the climatological features of the region. The index showed 74.7 % efficiency. The same analysis was performed by adding surface values of T and Td from Mendoza Aero station increasing the index efficiency to 87.8 %. The results revealed four significantly correlated PCs with a major improvement in differentiating zonda cases and a reducing of the uncertainty interval.
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
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.
Onat, Burcu; Alver Şahin, Ülkü; Bayat, Cuma
2012-05-01
In this study, the size distribution of airborne particles and related heavy metals Co, Cd, Sn, Cu, Ni, Cr, Pb and V in two urban areas in Istanbul