[Source apportionment of aerosol lead in Beijing using absolute principal component analysis].
Li, Yu-wu; Liu, Xian-de; Li, Bing; Yang, Hong-xia; Dong, Shu-ping; Zhang, Ting; Guo, Jing
2008-12-01
From 18 September 2005 to 13 September 2006 in Beijing, 166 TSP samples were collected at three sites in southern suburb, downtown and northern suburb, respectively. Lead and other 28 elements were determined for those samples by ICP-AES and ICP-MS methods. The lead average concentration of one year in southern suburb, downtown and northern suburb was 179, 142 and 102 ng x m(-3), respectively. Source identification and apportionment were carried out using absolute principal component analysis (APCA) method. Five groups of sources were recognized. The sources are nonferrous metallurgy (55.6%), coal combustion (16.9%), construction dust (11.8%) and soil dust (10.2%), respectively. Major sources of aerosol lead in Beijing were from nonferrous metallurgy industry. Lead associated with coal combustion and other sources were evenly distributed over three sites. However, industrial leadwas apparently at different levels at three sites, higher in southern suburb and lower in northern suburb. The lead concentration originated from nonferrous metallurgy at three sites from southern suburb, downtown to northern suburb was 113, 77.0 and 44.2 ng x m(-3), respectively, implying that the major sources for industrial lead were from the south to the Beijing. There is considerable space for reduction of aerosol lead concentration in Beijing. The main object for lead pollution discharge reduction and environmental management measures should aim at nonferrous metallurgy, especially for the field of lead metallurgy located at the south area to the Beijing. The calculation result based on the same chemical analysis data also shows that major source for TSP in Beijing is of soil dust and construction dust, accounting for 72.3%. The other sources such as coal combustion and nonferrous metallurgy account for 13.4% and 9.5%, respectively.
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.
2016-10-01
Accurate determination of stellar atmospheric parameters and elemental abundances is crucial for Galactic archeology 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 LAMOST spectra with a multivariate regression method based on kernel-based principal component analysis, using stars in common with other surveys (Hipparcos, Kepler, APOGEE) 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 the 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
Kernel Near Principal Component Analysis
MARTIN, SHAWN B.
2002-07-01
We propose a novel algorithm based on Principal Component Analysis (PCA). First, we present an interesting approximation of PCA using Gram-Schmidt orthonormalization. Next, we combine our approximation with the kernel functions from Support Vector Machines (SVMs) to provide a nonlinear generalization of PCA. After benchmarking our algorithm in the linear case, we explore its use in both the linear and nonlinear cases. We include applications to face data analysis, handwritten digit recognition, and fluid flow.
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.
Interpretable functional principal component analysis.
Lin, Zhenhua; Wang, Liangliang; Cao, Jiguo
2016-09-01
Functional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations. However, these intervals are often hard for naïve users to identify, because of the vague definition of "significant values". In this article, we develop a novel penalty-based method to derive FPCs that are only nonzero precisely in the intervals where the values of FPCs are significant, whence the derived FPCs possess better interpretability than the FPCs derived from existing methods. To compute the proposed FPCs, we devise an efficient algorithm based on projection deflation techniques. We show that the proposed interpretable FPCs are strongly consistent and asymptotically normal under mild conditions. Simulation studies confirm that with a competitive performance in explaining variations of sample curves, the proposed FPCs are more interpretable than the traditional counterparts. This advantage is demonstrated by analyzing two real datasets, namely, electroencephalography data and Canadian weather data.
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
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…
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 implementation in Java
NASA Astrophysics Data System (ADS)
Wójtowicz, Sebastian; Belka, Radosław; Sławiński, Tomasz; Parian, Mahnaz
2015-09-01
In this paper we show how PCA (Principal Component Analysis) method can be implemented using Java programming language. We consider using PCA algorithm especially in analysed data obtained from Raman spectroscopy measurements, but other applications of developed software should also be possible. Our goal is to create a general purpose PCA application, ready to run on every platform which is supported by Java.
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...
Principal Component Analysis With Sparse Fused Loadings
Guo, Jian; James, Gareth; Levina, Elizaveta; Michailidis, George; Zhu, Ji
2014-01-01
In this article, we propose a new method for principal component analysis (PCA), whose main objective is to capture natural “blocking” structures in the variables. Further, the method, beyond selecting different variables for different components, also encourages the loadings of highly correlated variables to have the same magnitude. These two features often help in interpreting the principal components. To achieve these goals, a fusion penalty is introduced and the resulting optimization problem solved by an alternating block optimization algorithm. The method is applied to a number of simulated and real datasets and it is shown that it achieves the stated objectives. The supplemental materials for this article are available online. PMID:25878487
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.
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
Hockey sticks, principal components, and spurious significance
NASA Astrophysics Data System (ADS)
McIntyre, Stephen; McKitrick, Ross
2005-02-01
The ``hockey stick'' shaped temperature reconstruction of Mann et al. (1998, 1999) has been widely applied. However it has not been previously noted in print that, prior to their principal components (PCs) analysis on tree ring networks, they carried out an unusual data transformation which strongly affects the resulting PCs. Their method, when tested on persistent red noise, nearly always produces a hockey stick shaped first principal component (PC1) and overstates the first eigenvalue. In the controversial 15th century period, the MBH98 method effectively selects only one species (bristlecone pine) into the critical North American PC1, making it implausible to describe it as the ``dominant pattern of variance''. Through Monte Carlo analysis, we show that MBH98 benchmarks for significance of the Reduction of Error (RE) statistic are substantially under-stated and, using a range of cross-validation statistics, we show that the MBH98 15th century reconstruction lacks statistical significance.
Two stage principal component analysis of color.
Lenz, Reiner
2002-01-01
We introduce a two-stage analysis of color spectra. In the first processing stage, correlation with the first eigenvector of a spectral database is used to measure the intensity of a color spectrum. In the second step, a perspective projection is used to map the color spectrum to the hyperspace of spectra with first eigenvector coefficient equal to unity. The location in this hyperspace describes the chromaticity of the color spectrum. In this new projection space, a second basis of eigenvectors is computed and the projected spectrum is described by the expansion in this chromaticity basis. This description is possible since the space of color spectra is conical. We compare this two-stage process with traditional principal component analysis and find that the results of the new structure are closer to the structure of traditional chromaticity descriptors than traditional principal component analysis.
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. PMID:24872597
Multilevel sparse functional principal component analysis
Di, Chongzhi; Crainiceanu, Ciprian M.; Jank, Wolfgang S.
2014-01-01
We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions. PMID:24872597
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
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.
Dynamic heart rate estimation using principal component analysis.
Yu, Yong-Poh; Raveendran, P; Lim, Chern-Loon; Kwan, Ban-Hoe
2015-11-01
In this paper, facial images from various video sequences are used to obtain a heart rate reading. In this study, a video camera is used to capture the facial images of eight subjects whose heart rates vary dynamically, between 81 and 153 BPM. Principal component analysis (PCA) is used to recover the blood volume pulses (BVP) which can be used for the heart rate estimation. An important consideration for accuracy of the dynamic heart rate estimation is to determine the shortest video duration that realizes it. This video duration is chosen when the six principal components (PC) are least correlated amongst them. When this is achieved, the first PC is used to obtain the heart rate. The results obtained from the proposed method are compared to the readings obtained from the Polar heart rate monitor. Experimental results show the proposed method is able to estimate the dynamic heart rate readings using less computational requirements when compared to the existing method. The mean absolute error and the standard deviation of the absolute errors between experimental readings and actual readings are 2.18 BPM and 1.71 BPM respectively.
Dynamic heart rate estimation using principal component analysis
Yu, Yong-Poh; Raveendran, P.; Lim, Chern-Loon; Kwan, Ban-Hoe
2015-01-01
In this paper, facial images from various video sequences are used to obtain a heart rate reading. In this study, a video camera is used to capture the facial images of eight subjects whose heart rates vary dynamically, between 81 and 153 BPM. Principal component analysis (PCA) is used to recover the blood volume pulses (BVP) which can be used for the heart rate estimation. An important consideration for accuracy of the dynamic heart rate estimation is to determine the shortest video duration that realizes it. This video duration is chosen when the six principal components (PC) are least correlated amongst them. When this is achieved, the first PC is used to obtain the heart rate. The results obtained from the proposed method are compared to the readings obtained from the Polar heart rate monitor. Experimental results show the proposed method is able to estimate the dynamic heart rate readings using less computational requirements when compared to the existing method. The mean absolute error and the standard deviation of the absolute errors between experimental readings and actual readings are 2.18 BPM and 1.71 BPM respectively. PMID:26601022
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.
Principal Components Analysis In Medical Imaging
NASA Astrophysics Data System (ADS)
Weaver, J. B.; Huddleston, A. L.
1986-06-01
Principal components analysis, PCA, is basically a data reduction technique. PCA has been used in several problems in diagnostic radiology: processing radioisotope brain scans (Ref.1), automatic alignment of radionuclide images (Ref. 2), processing MRI images (Ref. 3,4), analyzing first-pass cardiac studies (Ref. 5) correcting for attenuation in bone mineral measurements (Ref. 6) and in dual energy x-ray imaging (Ref. 6,7). This paper will progress as follows; a brief introduction to the mathematics of PCA will be followed by two brief examples of how PCA has been used in the literature. Finally my own experience with PCA in dual-energy x-ray imaging will be given.
Principal component analysis of scintimammographic images.
Bonifazzi, Claudio; Cinti, Maria Nerina; Vincentis, Giuseppe De; Finos, Livio; Muzzioli, Valerio; Betti, Margherita; Nico, Lanconelli; Tartari, Agostino; Pani, Roberto
2006-01-01
The recent development of new gamma imagers based on scintillation array with high spatial resolution, has strongly improved the possibility of detecting sub-centimeter cancer in Scintimammography. However, Compton scattering contamination remains the main drawback since it limits the sensitivity of tumor detection. Principal component image analysis (PCA), recently introduced in scintimam nographic imaging, is a data reduction technique able to represent the radiation emitted from chest, breast healthy and damaged tissues as separated images. From these images a Scintimammography can be obtained where the Compton contamination is "removed". In the present paper we compared the PCA reconstructed images with the conventional scintimammographic images resulting from the photopeak (Ph) energy window. Data coming from a clinical trial were used. For both kinds of images the tumor presence was quantified by evaluating the t-student statistics for independent sample as a measure of the signal-to-noise ratio (SNR). Since the absence of Compton scattering, the PCA reconstructed images shows a better noise suppression and allows a more reliable diagnostics in comparison with the images obtained by the photopeak energy window, reducing the trend in producing false positive. PMID:17646004
Perturbational formulation of principal component analysis in molecular dynamics simulation
NASA Astrophysics Data System (ADS)
Koyama, Yohei M.; Kobayashi, Tetsuya J.; Tomoda, Shuji; Ueda, Hiroki R.
2008-10-01
apply the PEPCA to an alanine dipeptide molecule in vacuum as a minimal model of a nonsingle dominant conformational biomolecule. The first and second principal components clearly characterize two stable states and the transition state between them. Positive and negative components with larger absolute values of the first and second eigenvectors identify the electrostatic interactions, which stabilize or destabilize each stable state and the transition state. Our result therefore indicates that PCA can be applied, by carefully selecting the perturbation functions, not only to identify the molecular conformational fluctuation but also to predict the conformational distribution change by the perturbation beyond the limitation of the previous methods.
Censored-Data Correlation and Principal Component Dynamic Programming
NASA Astrophysics Data System (ADS)
Saad, Maarouf; Turgeon, Andre; Stedinger, Jery R.
1992-08-01
A principal component stochastic dynamic programming algorithm for stochastic multireservoir hydropower system operation optimization was proposed by Saad and Turgeon (1988). The dimension of the state space of the system is reduced by using only the major principal components of the system's state as determined by a principal component analysis of the results of a deterministic optimization. A significant refinement of the procedure is to employ censored-data principal component analysis. The more sophisticated statistical analysis provides a better model of the full dynamic range of the reservoir system's state, explains more observed variability of the reservoir volumes, and provides more accurate estimates of the statistical description's parameters. In an example presented of a four-reservoir system, the first component of the censored-data principal component analysis, for a given month, explained all but 7% of the uncensored system's variability, whereas the standard analysis for the same month left 12% of the observed system's variability unexplained.
NASA Astrophysics Data System (ADS)
Kenfack, S. C.; Mkankam, K. F.; Alory, G.; du Penhoat, Y.; Hounkonnou, N. M.; Vondou, D. A.; Bawe, G. N.
2014-03-01
Principal Component Analysis (PCA) is one of the popular statistical methods for feature extraction. The neural network model has been performed on the PCA to obtain nonlinear principal component analysis (NLPCA), which allows the extraction of nonlinear features in the dataset missed by the PCA. NLPCA is applied to the monthly Sea Surface Temperature (SST) data from the eastern tropical Atlantic Ocean (29° W-21° E, 25° S-7° N) for the period 1982-2005. The focus is on the differences between SST inter-annual variability patterns; either extracted through traditional PCA or the NLPCA methods.The first mode of NLPCA explains 45.5% of the total variance of SST anomaly compared to 42% explained by the first PCA. Results from previous studies that detected the Atlantic cold tongue (ACT) as the main mode are confirmed. It is observed that the maximum signal in the Gulf of Guinea (GOG) is located along coastal Angola. In agreement with composite analysis, NLPCA exhibits two types of ACT, referred to as weak and strong Atlantic cold tongues. These two events are not totally symmetrical. NLPCA thus explains the results given by both PCA and composite analysis. A particular area observed along the northern boundary between 13 and 5° W vanishes in the strong ACT case and reaches maximum extension to the west in the weak ACT case. It is also observed that the original SST data correlates well with NLPCA and PCA, but with a stronger correlation on ACT area for NLPCA and southwest in the case of PCA.
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…
Least Principal Components Analysis (LPCA): An Alternative to Regression Analysis.
ERIC Educational Resources Information Center
Olson, Jeffery E.
Often, all of the variables in a model are latent, random, or subject to measurement error, or there is not an obvious dependent variable. When any of these conditions exist, an appropriate method for estimating the linear relationships among the variables is Least Principal Components Analysis. Least Principal Components are robust, consistent,…
A principal component analysis of transmission spectra of wine distillates
NASA Astrophysics Data System (ADS)
Rogovaya, M. V.; Sinitsyn, G. V.; Khodasevich, M. A.
2014-11-01
A chemometric method of decomposing multidimensional data into a small-sized space, the principal component method, has been applied to the transmission spectra of vintage Moldovan wine distillates. A sample of 42 distillates aged from four to 7 years from six producers has been used to show the possibility of identifying a producer in a two-dimensional space of principal components describing 94.5% of the data-matrix dispersion. Analysis of the loads into the first two principal components has shown that, in order to measure the optical characteristics of the samples under study using only two wavelengths, it is necessary to select 380 and 540 nm, instead of the standard 420 and 520 nm, to describe the variability of the distillates by one principal component or 370 and 520 nm to describe the variability by two principal components.
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.
Online kernel principal component analysis: a reduced-order model.
Honeine, Paul
2012-09-01
Kernel principal component analysis (kernel-PCA) is an elegant nonlinear extension of one of the most used data analysis and dimensionality reduction techniques, the principal component analysis. In this paper, we propose an online algorithm for kernel-PCA. To this end, we examine a kernel-based version of Oja's rule, initially put forward to extract a linear principal axe. As with most kernel-based machines, the model order equals the number of available observations. To provide an online scheme, we propose to control the model order. We discuss theoretical results, such as an upper bound on the error of approximating the principal functions with the reduced-order model. We derive a recursive algorithm to discover the first principal axis, and extend it to multiple axes. Experimental results demonstrate the effectiveness of the proposed approach, both on synthetic data set and on images of handwritten digits, with comparison to classical kernel-PCA and iterative kernel-PCA.
EXAFS and principal component analysis : a new shell game.
Wasserman, S.
1998-10-28
The use of principal component (factor) analysis in the analysis EXAFS spectra is described. The components derived from EXAFS spectra share mathematical properties with the original spectra. As a result, the abstract components can be analyzed using standard EXAFS methodology to yield the bond distances and other coordination parameters. The number of components that must be analyzed is usually less than the number of original spectra. The method is demonstrated using a series of spectra from aqueous solutions of uranyl ions.
Longitudinal functional principal component modeling via Stochastic Approximation Monte Carlo
Martinez, Josue G.; Liang, Faming; Zhou, Lan; Carroll, Raymond J.
2010-01-01
The authors consider the analysis of hierarchical longitudinal functional data based upon a functional principal components approach. In contrast to standard frequentist approaches to selecting the number of principal components, the authors do model averaging using a Bayesian formulation. A relatively straightforward reversible jump Markov Chain Monte Carlo formulation has poor mixing properties and in simulated data often becomes trapped at the wrong number of principal components. In order to overcome this, the authors show how to apply Stochastic Approximation Monte Carlo (SAMC) to this problem, a method that has the potential to explore the entire space and does not become trapped in local extrema. The combination of reversible jump methods and SAMC in hierarchical longitudinal functional data is simplified by a polar coordinate representation of the principal components. The approach is easy to implement and does well in simulated data in determining the distribution of the number of principal components, and in terms of its frequentist estimation properties. Empirical applications are also presented. PMID:20689648
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
Applications of Nonlinear Principal Components Analysis to Behavioral Data.
ERIC Educational Resources Information Center
Hicks, Marilyn Maginley
1981-01-01
An empirical investigation of the statistical procedure entitled nonlinear principal components analysis was conducted on a known equation and on measurement data in order to demonstrate the procedure and examine its potential usefulness. This method was suggested by R. Gnanadesikan and based on an early paper of Karl Pearson. (Author/AL)
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.
Spatially Weighted Principal Component Analysis for Imaging Classification
Guo, Ruixin; Ahn, Mihye; Zhu, Hongtu
2014-01-01
The aim of this paper is to develop a supervised dimension reduction framework, called Spatially Weighted Principal Component Analysis (SWPCA), for high dimensional imaging classification. Two main challenges in imaging classification are the high dimensionality of the feature space and the complex spatial structure of imaging data. In SWPCA, we introduce two sets of novel weights including global and local spatial weights, which enable a selective treatment of individual features and incorporation of the spatial structure of imaging data and class label information. We develop an e cient two-stage iterative SWPCA algorithm and its penalized version along with the associated weight determination. We use both simulation studies and real data analysis to evaluate the finite-sample performance of our SWPCA. The results show that SWPCA outperforms several competing principal component analysis (PCA) methods, such as supervised PCA (SPCA), and other competing methods, such as sparse discriminant analysis (SDA). PMID:26089629
Removing Milky Way from airglow images using principal component analysis
NASA Astrophysics Data System (ADS)
Li, Zhenhua; Liu, Alan; Sivjee, Gulamabas G.
2014-04-01
Airglow imaging is an effective way to obtain atmospheric gravity wave information in the airglow layers in the upper mesosphere and the lower thermosphere. Airglow images are often contaminated by the Milky Way emission. To extract gravity wave parameters correctly, the Milky Way must be removed. The paper demonstrates that principal component analysis (PCA) can effectively represent the dominant variation patterns of the intensity of airglow images that are associated with the slow moving Milky Way features. Subtracting this PCA reconstructed field reveals gravity waves that are otherwise overwhelmed by the strong spurious waves associated with the Milky Way. Numerical experiments show that nonstationary gravity waves with typical wave amplitudes and persistences are not affected by the PCA removal because the variances contributed by each wave event are much smaller than the ones in the principal components.
Applications Of Nonlinear Principal Components Analysis To Behavioral Data.
Hicks, M M
1981-07-01
A quadratic function was derived from variables believed to be nonlinearly related. The method was suggested by Gnanadesikan (1977) and based on an early paper of Karl Pearson (1901) (which gave rise to principal components), in which Pearson demonstrated that a plane of best fit to a system of points could be elicited from the elements of the eigenvector associated with the smallest eigenvalue of the covariance matrix. PMID:26815595
Phase-shifting interferometry based on principal component analysis.
Vargas, J; Quiroga, J Antonio; Belenguer, T
2011-04-15
An asynchronous phase-shifting method based on principal component analysis (PCA) is presented. No restrictions about the background, modulation, and phase shifts are necessary. The presented method is very fast and needs very low computational requirements, so it can be used with very large images and/or very large image sets. The method is based on obtaining two quadrature signals by the PCA algorithm. We have applied the proposed method to simulated and experimental interferograms, obtaining satisfactory results.
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 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.
Principal Component Analysis of Dynamically distinct D-Type Asteroids.
NASA Astrophysics Data System (ADS)
Nedic, Sanja; Ziffer, J.; Campins, H.; Fernandez, Y. R.; Walker, M.
2008-09-01
Principal Component Analysis (PCA), a common statistically based classification technique, has been used to classify asteroids into broad spectral categories. In some cases, a spectral superclass considered in isolation may undergo sub-classification (e.g. S-type subclasses). Since D-type asteroids populate at least three distinct dynamical regions in the asteroid belt -- namely Hilda, L4 Trojans and L5 Trojans, and since the recently-developed "Nice” model (Morbidelli et al. 2005. Nature 435, 462; Levison et al. 2008, ACM 2008 abstract #8156) hypothesizes that these regions may share a common origin, examining the appropriateness of a D-type sub-classification scheme is warranted. Toward this end, we performed PCA on the D-type L4, L5, and Hilda asteroids. Our PCA was based on the Sloan Digital Sky Survey broadband colors (u - g, g - r, r - i, and i - z) of 31 L4, 24 L5, and 32 Hilda asteroids with radii ranging from approximately 5 to 45 km. PCA showed 90.2% of the variance in the spectra could be condensed into the first two principal components, PC1 and PC2, with the first and second component accounting for 50.7% and 39.4% respectively. No significant clustering is observed on a PC1 vs. PC2 plot suggesting the D-type L4, L5, and Hilda asteroids do not form three independent groups, but rather are spectrally indistinguishable. We performed several statistical analyses of the means and variances of the principal components to test the validity of this conclusion. No statistically significant difference in the means among the three groups was found, nor was there any such difference in the variances, although the statistic comparing the L4 Trojans and Hildas was close to the critical value. Further measurements of colors of both large and small Trojans and Hildas will let us continue to investigate the spectral diversity of these objects.
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
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. PMID:23020106
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.
Multivariate concentration determination using principal component regression with residual analysis
Keithley, Richard B.; Heien, Michael L.; Wightman, R. Mark
2009-01-01
Data analysis is an essential tenet of analytical chemistry, extending the possible information obtained from the measurement of chemical phenomena. Chemometric methods have grown considerably in recent years, but their wide use is hindered because some still consider them too complicated. The purpose of this review is to describe a multivariate chemometric method, principal component regression, in a simple manner from the point of view of an analytical chemist, to demonstrate the need for proper quality-control (QC) measures in multivariate analysis and to advocate the use of residuals as a proper QC method. PMID:20160977
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.
Assessment of depression in Kuwait by principal component analysis.
el-Islam, M F; Moussa, M A; Malasi, T H; Suleiman, M A; Mirza, I A
1988-01-01
One hundred depressed inpatients were examined by the WHO schedule for Standardized Assessment of Depressive Disorders (SADD). A common core of symptoms is shared with patients in other studies from Western, Middle-Eastern and international studies. However, pathoplastic cultural influences are manifest in a number of symptoms, notable among which are metaphorical descriptions of symptom intensity by the overwhelmed patient, infrequency of feelings of hopelessness and suicidal attempts, masking of guilt feelings by a front of somatization and a linkage of body weight and sexual functions to health in general. Evidence is provided for a continuum-type unimodal distribution of the principal components studied.
Weighted EMPCA: Weighted Expectation Maximization Principal Component Analysis
NASA Astrophysics Data System (ADS)
Bailey, Stephen
2016-09-01
Weighted EMPCA performs principal component analysis (PCA) on noisy datasets with missing values. Estimates of the measurement error are used to weight the input data such that the resulting eigenvectors, when compared to classic PCA, are more sensitive to the true underlying signal variations rather than being pulled by heteroskedastic measurement noise. Missing data are simply limiting cases of weight = 0. The underlying algorithm is a noise weighted expectation maximization (EM) PCA, which has additional benefits of implementation speed and flexibility for smoothing eigenvectors to reduce the noise contribution.
Efficient training of multilayer perceptrons using principal component analysis
Bunzmann, Christoph; Urbanczik, Robert; Biehl, Michael
2005-08-01
A training algorithm for multilayer perceptrons is discussed and studied in detail, which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix computed from the example inputs and their target outputs. Typical properties of the training procedure are investigated by means of a statistical physics analysis in models of learning regression and classification tasks. We demonstrate that the procedure requires by far fewer examples for good generalization than traditional online training. For networks with a large number of hidden units we derive the training prescription which achieves, within our model, the optimal generalization behavior.
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.
Acceleration of dynamic fluorescence molecular tomography with principal component analysis
Zhang, Guanglei; He, Wei; Pu, Huangsheng; Liu, Fei; Chen, Maomao; Bai, Jing; Luo, Jianwen
2015-01-01
Dynamic fluorescence molecular tomography (FMT) is an attractive imaging technique for three-dimensionally resolving the metabolic process of fluorescent biomarkers in small animal. When combined with compartmental modeling, dynamic FMT can be used to obtain parametric images which can provide quantitative pharmacokinetic information for drug development and metabolic research. However, the computational burden of dynamic FMT is extremely huge due to its large data sets arising from the long measurement process and the densely sampling device. In this work, we propose to accelerate the reconstruction process of dynamic FMT based on principal component analysis (PCA). Taking advantage of the compression property of PCA, the dimension of the sub weight matrix used for solving the inverse problem is reduced by retaining only a few principal components which can retain most of the effective information of the sub weight matrix. Therefore, the reconstruction process of dynamic FMT can be accelerated by solving the smaller scale inverse problem. Numerical simulation and mouse experiment are performed to validate the performance of the proposed method. Results show that the proposed method can greatly accelerate the reconstruction of parametric images in dynamic FMT almost without degradation in image quality. PMID:26114027
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.
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.
Sparse principal component analysis in medical shape modeling
NASA Astrophysics Data System (ADS)
Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus
2006-03-01
Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.
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.
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.
The neurocognitive components of pitch processing: insights from absolute pitch.
Wilson, Sarah J; Lusher, Dean; Wan, Catherine Y; Dudgeon, Paul; Reutens, David C
2009-03-01
The natural variability of pitch naming ability in the population (known as absolute pitch or AP) provides an ideal method for investigating individual differences in pitch processing and auditory knowledge formation and representation. We have demonstrated the involvement of different cognitive processes in AP ability that reflects varying skill expertise in the presence of similar early age of onset of music tuition. These processes were related to different regions of brain activity, including those involved in pitch working memory (right prefrontal cortex) and the long-term representation of pitch (superior temporal gyrus). They reflected expertise through the use of context dependent pitch cues and the level of automaticity of pitch naming. They impart functional significance to structural asymmetry differences in the planum temporale of musicians and establish a neurobiological basis for an AP template. More generally, they indicate variability of knowledge representation in the presence of environmental fostering of early cognitive development that translates to differences in cognitive ability. PMID:18663250
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.
Samsonovich, Alexei V; 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 ( approximately 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 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.
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.
Functional principal components analysis of workload capacity functions
Burns, Devin M.; Houpt, Joseph W.; Townsend, James T.; Endres, Michael J.
2013-01-01
Workload capacity, an important concept in many areas of psychology, describes processing efficiency across changes in workload. The capacity coefficient is a function across time that provides a useful measure of this construct. Until now, most analyses of the capacity coefficient have focused on the magnitude of this function, and often only in terms of a qualitative comparison (greater than or less than one). This work explains how a functional extension of principal components analysis can capture the time-extended information of these functional data, using a small number of scalar values chosen to emphasize the variance between participants and conditions. This approach provides many possibilities for a more fine-grained study of differences in workload capacity across tasks and individuals. PMID:23475829
Undersampled dynamic magnetic resonance imaging using kernel principal component analysis.
Wang, Yanhua; Ying, Leslie
2014-01-01
Compressed sensing (CS) is a promising approach to accelerate dynamic magnetic resonance imaging (MRI). Most existing CS methods employ linear sparsifying transforms. The recent developments in non-linear or kernel-based sparse representations have been shown to outperform the linear transforms. In this paper, we present an iterative non-linear CS dynamic MRI reconstruction framework that uses the kernel principal component analysis (KPCA) to exploit the sparseness of the dynamic image sequence in the feature space. Specifically, we apply KPCA to represent the temporal profiles of each spatial location and reconstruct the images through a modified pre-image problem. The underlying optimization algorithm is based on variable splitting and fixed-point iteration method. Simulation results show that the proposed method outperforms conventional CS method in terms of aliasing artifact reduction and kinetic information preservation. PMID:25570262
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.
Diagnosis of nonlinear systems using kernel principal component analysis
NASA Astrophysics Data System (ADS)
Kallas, M.; Mourot, G.; Maquin, D.; Ragot, J.
2014-12-01
Technological advances in the process industries during the past decade have resulted in increasingly complicated processes, systems and products. Therefore, recent researches consider the challenges in their design and management for successful operation. While principal component analysis (PCA) technique is widely used for diagnosis, its structure cannot describe nonlinear related variables. Thus, an extension to the case of nonlinear systems is presented in a feature space for process monitoring. Working in a high-dimensional feature space, it is necessary to get back to the original space. Hence, an iterative pre-image technique is derived to provide a solution for fault diagnosis. The relevance of the proposed technique is illustrated on artificial and real dataset.
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 Astrophysics Data System (ADS)
Johnson, P. E.; Smith, M. O.; Adams, J. B.
1985-02-01
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.
Spatial control of groundwater contamination, using principal component analysis
NASA Astrophysics Data System (ADS)
Rao, N. Subba
2014-06-01
A study on the geochemistry of groundwater was carried out in a river basin of Andhra Pradesh to probe into the spatial controlling processes of groundwater contamination, using principal component analysis (PCA). The PCA transforms the chemical variables, pH, EC, Ca2+, Mg2+, Na+, K+, HCO, Cl-, SO, NO and F-, into two orthogonal principal components (PC1 and PC2), accounting for 75% of the total variance of the data matrix. PC1 has high positive loadings of EC, Na+, Cl-, SO, Mg2+ and Ca2+, representing a salinity controlled process of geogenic (mineral dissolution, ion exchange, and evaporation), anthropogenic (agricultural activities and domestic wastewaters), and marine (marine clay) origin. The PC2 loadings are highly positive for HCO , F-, pH and NO, attributing to the alkalinity and pollution controlled processes of geogenic and anthropogenic origins. The PC scores reflect the change of groundwater quality of geogenic origin from upstream to downstream area with an increase in concentration of chemical variables, which is due to anthropogenic and marine origins with varying topography, soil type, depth of water levels, and water usage. Thus, the groundwater quality shows a variation of chemical facies from Na+ > Ca2+ > Mg2+ > K+: HCO > Cl- > SO NO > F-at high topography to Na+ > Mg2+ > Ca2+ > K+: Cl- > HCO > SO NO > F- at low topography. With PCA, an effective tool for the spatial controlling processes of groundwater contamination, a subset of explored wells is indexed for continuous monitoring to optimize the expensive effort.
Zhang, Li-qing; Wu, Xiao-hua; Tang, Xi; Zhu, Xian-liang; Su, Wen-ting
2002-06-01
Principal component regression (PCR) method is used to analyse five components: acetaminophen, p-aminophenol, caffeine, chlorphenamine maleate and guaifenesin. The basic principle and the analytical step of the approach are described in detail. The computer program of LHG is based on VB language. The experimental result shows that the PCR method has no systematical error as compared to classical method. The experimental result shows that the average recovery of each component is all in the range from 96.43% to 107.14%. Each component obtains satisfactory result without any pre-separation. The approach is simple, rapid and suitable for the computer-aid analysis. PMID:12938324
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.
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
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 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.
Kernel principal component analysis for stochastic input model generation
Ma Xiang; Zabaras, Nicholas
2011-08-10
Highlights: {yields} KPCA is used to construct a reduced order stochastic model of permeability. {yields} A new approach is proposed to solve the pre-image problem in KPCA. {yields} Polynomial chaos is used to provide a parametric stochastic input model. {yields} Flow in porous media with channelized permeability is considered. - Abstract: Stochastic analysis of random heterogeneous media provides useful information only if realistic input models of the material property variations are used. These input models are often constructed from a set of experimental samples of the underlying random field. To this end, the Karhunen-Loeve (K-L) expansion, also known as principal component analysis (PCA), is the most popular model reduction method due to its uniform mean-square convergence. However, it only projects the samples onto an optimal linear subspace, which results in an unreasonable representation of the original data if they are non-linearly related to each other. In other words, it only preserves the first-order (mean) and second-order statistics (covariance) of a random field, which is insufficient for reproducing complex structures. This paper applies kernel principal component analysis (KPCA) to construct a reduced-order stochastic input model for the material property variation in heterogeneous media. KPCA can be considered as a nonlinear version of PCA. Through use of kernel functions, KPCA further enables the preservation of higher-order statistics of the random field, instead of just two-point statistics as in the standard Karhunen-Loeve (K-L) expansion. Thus, this method can model non-Gaussian, non-stationary random fields. In this work, we also propose a new approach to solve the pre-image problem involved in KPCA. In addition, polynomial chaos (PC) expansion is used to represent the random coefficients in KPCA which provides a parametric stochastic input model. Thus, realizations, which are statistically consistent with the experimental data, can be
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...
REDUCTION OF ECHO DECORRELATION VIA COMPLEX PRINCIPAL COMPONENT FILTERING
Mauldin, F. William; Viola, Francesco; Walker, William F.
2009-01-01
Ultrasound motion estimation is a fundamental component of clinical and research techniques that include color flow Doppler, spectral Doppler, radiation force imaging and ultrasound-based elasticity estimation. In each of these applications, motion estimates are corrupted by signal decorrelation that originates from nonuniform target motion across the acoustic beam. In this article, complex principal component filtering (PCF) is demonstrated as a filtering technique for dramatically reducing echo decorrelation in blood flow estimation and radiation force imaging. We present simulation results from a wide range of imaging conditions that illustrate a dramatic improvement over simple bandpass filtering in terms of overall echo decorrelation (≤99.9% reduction), root mean square error (≤97.3% reduction) and the standard deviation of displacement estimates (≤97.4% reduction). A radiation force imaging technique, termed sonorheometry, was applied to fresh whole blood during coagulation, and complex PCF operated on the returning echoes. Sonorheometry was specifically chosen as an example radiation force imaging technique in which echo decorrelation corrupts motion estimation. At 2 min after initiation of blood coagulation, the average echo correlation for sonorheometry improved from 0.996 to 0.9999, which corresponded to a 41.0% reduction in motion estimation variance as predicted by the Cramer-Rao lower bound under reasonable imaging conditions. We also applied complex PCF to improve blood velocity estimates from the left carotid artery of a healthy 23-year-old male. At the location of peak blood velocity, complex PCF improved the correlation of consecutive echo signals from an average correlation of 0.94 to 0.998. The improved echo correlation for both sonorheometry and blood flow estimation yielded motion estimates that exhibited more consistent responses with less noise. Complex PCF reduces speckle decorrelation and improves the performance of ultrasonic motion
Inverse spatial principal component analysis for geophysical survey data interpolation
NASA Astrophysics Data System (ADS)
Li, Qingmou; Dehler, Sonya A.
2015-04-01
The starting point for data processing, visualization, and overlay with other data sources in geological applications often involves building a regular grid by interpolation of geophysical measurements. Typically, the sampling interval along survey lines is much higher than the spacing between survey lines because the geophysical recording system is able to operate with a high sampling rate, while the costs and slower speeds associated with operational platforms limit line spacing. However, currently available interpolating methods often smooth data observed with higher sampling rate along a survey line to accommodate the lower spacing across lines, and much of the higher resolution information is not captured in the interpolation process. In this approach, a method termed as the inverse spatial principal component analysis (isPCA) is developed to address this problem. In the isPCA method, a whole profile observation as well as its line position is handled as an entity and a survey collection of line entities is analyzed for interpolation. To test its performance, the developed isPCA method is used to process a simulated airborne magnetic survey from an existing magnetic grid offshore the Atlantic coast of Canada. The interpolation results using the isPCA method and other methods are compared with the original survey grid. It is demonstrated that the isPCA method outperforms the Inverse Distance Weighting (IDW), Kriging (Geostatistical), and MINimum Curvature (MINC) interpolation methods in retaining detailed anomaly structures and restoring original values. In a second test, a high resolution magnetic survey offshore Cape Breton, Nova Scotia, Canada, was processed and the results are compared with other geological information. This example demonstrates the effective performance of the isPCA method in basin structure identification.
Covariate selection with iterative principal component analysis for predicting physical
Technology Transfer Automated Retrieval System (TEKTRAN)
Local and regional soil data can be improved by coupling new digital soil mapping techniques with high resolution remote sensing products to quantify both spatial and absolute variation of soil properties. The objective of this research was to advance data-driven digital soil mapping techniques for ...
NASA Astrophysics Data System (ADS)
Baglivo, Fabricio Hugo; Arini, Pedro David
2011-12-01
Electrocardiographic repolarization abnormalities can be detected by Principal Components Analysis of the T-wave. In this work we studied the efect of signal averaging on the mean value and reproducibility of the ratio of the 2nd to the 1st eigenvalue of T-wave (T21W) and the absolute and relative T-wave residuum (TrelWR and TabsWR) in the ECG during ischemia induced by Percutaneous Coronary Intervention. Also, the intra-subject and inter-subject variability of T-wave parameters have been analyzed. Results showed that TrelWR and TabsWR evaluated from the average of 10 complexes had lower values and higher reproducibility than those obtained from 1 complex. On the other hand T21W calculated from 10 complexes did not show statistical diferences versus the T21W calculated on single beats. The results of this study corroborate that, with a signal averaging technique, the 2nd and the 1st eigenvalue are not afected by noise while the 4th to 8th eigenvalues are so much afected by this, suggesting the use of the signal averaged technique before calculation of absolute and relative T-wave residuum. Finally, we have shown that T-wave morphology parameters present high intra-subject stability.
Mining gene expression data by interpreting principal components
Roden, Joseph C; King, Brandon W; Trout, Diane; Mortazavi, Ali; Wold, Barbara J; Hart, Christopher E
2006-01-01
Background There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis. Results We present a method for automatically identifying such candidate sets of biologically relevant genes using a combination of principal components analysis and information theoretic metrics. To enable easy use of our methods, we have developed a data analysis package that facilitates visualization and subsequent data mining of the independent sources of significant variation present in gene microarray expression datasets (or in any other similarly structured high-dimensional dataset). We applied these tools to two public datasets, and highlight sets of genes most affected by specific subsets of conditions (e.g. tissues, treatments, samples, etc.). Statistically significant associations for highlighted gene sets were shown via global analysis for Gene Ontology term enrichment. Together with covariate associations, the tool provides a basis for building testable hypotheses about the biological or experimental causes of observed variation. Conclusion We provide an unsupervised data mining technique for diverse microarray expression datasets that is distinct from major methods now in routine use. In test uses, this method, based on publicly available gene annotations, appears to identify numerous sets of biologically relevant genes. It has proven especially
NASA Astrophysics Data System (ADS)
Chattopadhyay, Goutami; Chattopadhyay, Surajit; Chakraborthy, Parthasarathi
2012-07-01
The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser-Meyer-Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott's indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots.
Principal Component Analysis of Long-Lag,Wide-Pulse Gamma-Ray Burst Data
NASA Astrophysics Data System (ADS)
Peng, Zhao-Yang; Liu, Wen-Shuai
2014-09-01
We have carried out a Principal Component Analysis (PCA) of the temporal and spectral variables of 24 long-lag, wide-pulse gamma-ray bursts (GRBs) presented by Norris et al. (2005). Taking all eight temporal and spectral parameters into account, our analysis shows that four principal components are enough to describe the variation of the temporal and spectral data of long-lag bursts. In addition, the first-two principal components are dominated by the temporal variables while the third and fourth principal components are dominated by the spectral parameters.
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.
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...
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...
NASA Astrophysics Data System (ADS)
Sharma, S. K.; Gajbhiye, S.; Tignath, S.
2015-03-01
Principal component analysis has been applied to 13 dimensionless geomorphic parameters on 8 sub-watersheds of Kanhiya Nala watershed tributary of Tons River located in Part of Panna and Satna district of Madhya Pradesh, India, to group the parameters under different components based on significant correlations. Results of principal component analysis of 13 geomorphic parameters clearly reveal that some of these parameters are strongly correlated with the components but texture ratio and hypsometric integral do not show correlation with any of the component. So they have been screened out of analysis. The principal component loading matrix obtained using correlation matrix of eleven parameters reveals that first three components together account for 93.71 % of the total explained variance. Therefore, principal component loading is applied to get better correlation and clearly group the parameters in physically significant components. Based on the properties of the geomorphic parameters, three principal components were defined as drainage, slope or steepness and shape components. One parameter each from the significant components may form a set of independent parameters at a time in modeling the hydrologic responses such as runoff and sediment yield from small watersheds.
Identification of the isomers using principal component analysis (PCA) method
NASA Astrophysics Data System (ADS)
Kepceoǧlu, Abdullah; Gündoǧdu, Yasemin; Ledingham, Kenneth William David; Kilic, Hamdi Sukur
2016-03-01
In this work, we have carried out a detailed statistical analysis for experimental data of mass spectra from xylene isomers. Principle Component Analysis (PCA) was used to identify the isomers which cannot be distinguished using conventional statistical methods for interpretation of their mass spectra. Experiments have been carried out using a linear TOF-MS coupled to a femtosecond laser system as an energy source for the ionisation processes. We have performed experiments and collected data which has been analysed and interpreted using PCA as a multivariate analysis of these spectra. This demonstrates the strength of the method to get an insight for distinguishing the isomers which cannot be identified using conventional mass analysis obtained through dissociative ionisation processes on these molecules. The PCA results dependending on the laser pulse energy and the background pressure in the spectrometers have been presented in this work.
Roemer, R A; Josiassen, R C; Shagass, C
1990-01-01
Principal components analysis of evoked potentials differing between groups presents an interpretive problem, particularly in psychiatric research. Two sets of principal components and associated factor scores may appear to differ. The issue is to determine the extent to which visually differing principal components and resultant factor scores span the same factor space. Sets of evoked potentials from controls and from schizophrenics were each subjected to principal components analysis, from which factor score coefficients were computed for all subjects. This allowed determination of the extent to which (1) the two sets of basis waves were similar and (2) the factor scores resulting from the set of basis waves derived from principal components analysis of the control subjects' evoked potential data adequately represented those of the schizophrenics and vice-versa. Canonical correlation analyses indicated substantial similarities between the principal component structures (sets of basis waves). Multiple correlational analyses confirmed that the basis waves from either group spanned the other group's factor space. Factor scores from either set of basis waves were highly correlated. These results suggest that principal component structures derived from evoked potentials of a control group may be used in computing evoked potential factor scores of psychiatrically diverse populations even though the average evoked potentials of the groups may differ in several ways.
Foch, Eric; Milner, Clare E
2014-01-01
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.
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
Principal Component and Linkage Analysis of Cardiovascular Risk Traits in the Norfolk Isolate
Cox, Hannah C.; Bellis, Claire; Lea, Rod A.; Quinlan, Sharon; Hughes, Roger; Dyer, Thomas; Charlesworth, Jac; Blangero, John; Griffiths, Lyn R.
2009-01-01
Objective(s) An individual's risk of developing cardiovascular disease (CVD) is influenced by genetic factors. This study focussed on mapping genetic loci for CVD-risk traits in a unique population isolate derived from Norfolk Island. Methods This investigation focussed on 377 individuals descended from the population founders. Principal component analysis was used to extract orthogonal components from 11 cardiovascular risk traits. Multipoint variance component methods were used to assess genome-wide linkage using SOLAR to the derived factors. A total of 285 of the 377 related individuals were informative for linkage analysis. Results A total of 4 principal components accounting for 83% of the total variance were derived. Principal component 1 was loaded with body size indicators; principal component 2 with body size, cholesterol and triglyceride levels; principal component 3 with the blood pressures; and principal component 4 with LDL-cholesterol and total cholesterol levels. Suggestive evidence of linkage for principal component 2 (h2 = 0.35) was observed on chromosome 5q35 (LOD = 1.85; p = 0.0008). While peak regions on chromosome 10p11.2 (LOD = 1.27; p = 0.005) and 12q13 (LOD = 1.63; p = 0.003) were observed to segregate with principal components 1 (h2 = 0.33) and 4 (h2 = 0.42), respectively. Conclusion(s): This study investigated a number of CVD risk traits in a unique isolated population. Findings support the clustering of CVD risk traits and provide interesting evidence of a region on chromosome 5q35 segregating with weight, waist circumference, HDL-c and total triglyceride levels. PMID:19339786
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.
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...
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 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)
De Souza-Rossetto, E. A.; Rocha-Pinto, H. J.
2010-01-01
The globular cluster luminosity function distribution shows a peak at MV ≈ -7.5 mag. There are some indications that the kinematic parameters are correlated with luminosity. In particular, Alfaro et al. (2001) have studied the properties of the Galactic globular cluster system and they found a correlation between spatial-velocity component and globular cluster absolute magnitude. The authors assumed that the globular clusters can be separated into two groups. The first is composed of globular clusters with MV < -7.5 mag and moving preferentially towards the north Galactic pole, while the faintest globular clusters, composing the second group, move towards the Galactic disk. We have selected a sample of globular clusters using the same criteria as Alfaro et al. (2001) and have checked that this apparent relation indeed exists. Nevertheless, we decided to investigate whether it could be a fortuitous relation or an intrinsic property by checking its validity for eight different epochs at past and future times. The orbital parameters for the globular clusters at these eight epochs were found by orbital integration using a typical Galactic potential. We show that this relation between the vertical velocity component and the absolute magnitude among globular clusters is not coherent with time and the velocity distribution does not support the hypothesis of Alfaro et al. for the existence of two dynamical groups of globular clusters.
A unified self-stabilizing neural network algorithm for principal and minor components extraction.
Kong, Xiangyu; Hu, Changhua; Ma, Hongguang; Han, Chongzhao
2012-02-01
Recently, many unified learning algorithms have been developed for principal component analysis and minor component analysis. These unified algorithms can be used to extract principal components and, if altered simply by the sign, can also serve as a minor component extractor. This is of practical significance in the implementations of algorithms. This paper proposes a unified self-stabilizing neural network learning algorithm for principal and minor components extraction, and studies the stability of the proposed unified algorithm via the fixed-point analysis method. The proposed unified self-stabilizing algorithm for principal and minor components extraction is extended for tracking the principal subspace (PS) and minor subspace (MS). The averaging differential equation and the energy function associated with the unified algorithm for tracking PS and MS are given. It is shown that the averaging differential equation will globally asymptotically converge to an invariance set, and the corresponding energy function exhibit a unique global minimum attained if and only if its state matrices span the PS or MS of the autocorrelation matrix of a vector data stream. It is concluded that the proposed unified algorithm for tracking PS and MS can efficiently track an orthonormal basis of the PS or MS. Simulations are carried out to further illustrate the theoretical results achieved.
Principal component analysis of satellite passive microwave data over sea ice
NASA Astrophysics Data System (ADS)
Rothrock, D. A.; Thomas, Donald R.; Thorndike, Alan S.
1988-03-01
The 10 channels of scanning multichannel microwave radiometer data for the Arctic are examined by correlation, multiple regression, and principal component analyses. Data from April, August, and December 1979 are analyzed separately. Correlations are greater than 0.8 for all pairs of channels except some of those involving the 37-GHz channels. Multiple regression shows a high degree of redundancy in the data; three channels can explain between 94.0 and 99.6% of the total variance. A principal component analysis of the covariance matrix shows that the first two eigenvalues contain 99.7% of the variance. Only the first two principal components contain variance due to the mixture of surface types. Three component mixtures (water, first-year ice, and multiyear ice) can be resolved in two dimensions. The presence of other ice types, such as second-year ice or wet ice, makes determination of ice age ambiguous in some geographic regions. Winds and surface temperature variations cause variations in the first three principal components. The confounding of these variables with mixture of surface types is a major source of error in resolving the mixture. The variance in principal components 3 through 10 is small and entirely due to variability in the pure type signatures. Determination of winds and surface temperature, as well as other variables, from this information is limited by instrument noise and presently unknown large-scale variability in the emissivity of sea ice.
ERIC Educational Resources Information Center
Lautenschlager, Gary J.
The parallel analysis method for determining the number of components to retain in a principal components analysis has received a recent resurgence of support and interest. However, researchers and practitioners desiring to use this criterion have been hampered by the required Monte Carlo analyses needed to develop the criteria. Two recent…
40 CFR 60.2998 - What are the principal components of the model rule?
Code of Federal Regulations, 2011 CFR
2011-07-01
... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule? The model rule contains nine major components, as follows: (a) Compliance schedule. (b)...
40 CFR 60.2570 - What are the principal components of the model rule?
Code of Federal Regulations, 2010 CFR
2010-07-01
... the model rule? 60.2570 Section 60.2570 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Construction On or Before November 30, 1999 Use of Model Rule § 60.2570 What are the principal components of the model rule? The model rule contains the eleven major components listed in paragraphs (a)...
40 CFR 60.2998 - What are the principal components of the model rule?
Code of Federal Regulations, 2010 CFR
2010-07-01
... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule? The model rule contains nine major components, as follows: (a) Compliance schedule. (b)...
Principal component structure and sport-specific differences in the running one-leg vertical jump.
Laffaye, G; Bardy, B G; Durey, A
2007-05-01
The aim of this study is to identify the kinetic principal components involved in one-leg running vertical jumps, as well as the potential differences between specialists from different sports. The sample was composed of 25 regional skilled athletes who play different jumping sports (volleyball players, handball players, basketball players, high jumpers and novices), who performed a running one-leg jump. A principal component analysis was performed on the data obtained from the 200 tested jumps in order to identify the principal components summarizing the six variables extracted from the force-time curve. Two principal components including six variables accounted for 78 % of the variance in jump height. Running one-leg vertical jump performance was predicted by a temporal component (that brings together impulse time, eccentric time and vertical displacement of the center of mass) and a force component (who brings together relative peak of force and power, and rate of force development). A comparison made among athletes revealed a temporal-prevailing profile for volleyball players, and a force-dominant profile for Fosbury high jumpers. Novices showed an ineffective utilization of the force component, while handball and basketball players showed heterogeneous and neutral component profiles. Participants will use a jumping strategy in which variables related to either the magnitude or timing of force production will be closely coupled; athletes from different sporting backgrounds will use a jumping strategy that reflects the inherent demands of their chosen sport.
Optimized Principal Component Analysis on Coronagraphic Images of the Fomalhaut System
NASA Astrophysics Data System (ADS)
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 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. Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere, Chile under program number 087.C-0901(B).
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.
NASA Astrophysics Data System (ADS)
Cuell, Charles; Bonsal, Barrie
2009-10-01
A common method of automated synoptic typing for climatological investigations involves data reduction by principal component analysis followed by the application of a clustering method. The number of eigenvectors kept in the principal component analysis is usually determined by a threshold value of relative variance retained, typically 85% to 95%, under the implicit assumption that varying this relative variance will not affect the resultant synoptic catalogue. This assumption is tested using daily 500-mb geopotential heights over northwest Canada during the winter period (December to February) from 1948 to 2006. Results show that the synoptic catalogue and associated surface climatological characteristics undergo changes for values of relative variance retained over 99%, indicating the typical thresholds are too low and calling into question the validity of performing principal component analysis prior to objective clustering.
Savage, J.C.
1988-01-01
Geodetic measurements of deformation at Long Valley caldera provide two examples of the application of principal component analysis. A 40-line trilateration network surrounding the caldera was surveyed in midsummer 1983, 1984, 1985, 1986, and 1987. Principal component analysis indicates that the observed deformation can be represented by a single coherent source. The time dependence for that source displays a rapid rate of deformation in 1983-1984 followed by less rapid but uniform rate in the 1984-1987 interval. The spatial factor seems consistent with expansion of a magma chamber beneath the caldera plus some shallow right-lateral slip on a vertical fault in the south moat of the caldera. An independent principal component analysis of the 1982, 1983, 1984, 1985, 1986, and 1987 leveling across the caldera requires two self-coherent sources to explain the deformation. -from Author
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.
Ghosh, Debasree; Chattopadhyay, Parimal
2012-06-01
The objective of the work was to use the method of quantitative descriptive analysis (QDA) to describe the sensory attributes of the fermented food products prepared with the incorporation of lactic cultures. Panellists were selected and trained to evaluate various attributes specially color and appearance, body texture, flavor, overall acceptability and acidity of the fermented food products like cow milk curd and soymilk curd, idli, sauerkraut and probiotic ice cream. Principal component analysis (PCA) identified the six significant principal components that accounted for more than 90% of the variance in the sensory attribute data. Overall product quality was modelled as a function of principal components using multiple least squares regression (R (2) = 0.8). The result from PCA was statistically analyzed by analysis of variance (ANOVA). These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring the fermented food product attributes that are important for consumer acceptability. PMID:23729852
Giesen, E B W; Ding, M; Dalstra, M; van Eijden, T M G J
2003-09-01
As several morphological parameters of cancellous bone express more or less the same architectural measure, we applied principal components analysis to group these measures and correlated these to the mechanical properties. Cylindrical specimens (n = 24) were obtained in different orientations from embalmed mandibular condyles; the angle of the first principal direction and the axis of the specimen, expressing the orientation of the trabeculae, ranged from 10 degrees to 87 degrees. Morphological parameters were determined by a method based on Archimedes' principle and by micro-CT scanning, and the mechanical properties were obtained by mechanical testing. The principal components analysis was used to obtain a set of independent components to describe the morphology. This set was entered into linear regression analyses for explaining the variance in mechanical properties. The principal components analysis revealed four components: amount of bone, number of trabeculae, trabecular orientation, and miscellaneous. They accounted for about 90% of the variance in the morphological variables. The component loadings indicated that a higher amount of bone was primarily associated with more plate-like trabeculae, and not with more or thicker trabeculae. The trabecular orientation was most determinative (about 50%) in explaining stiffness, strength, and failure energy. The amount of bone was second most determinative and increased the explained variance to about 72%. These results suggest that trabecular orientation and amount of bone are important in explaining the anisotropic mechanical properties of the cancellous bone of the mandibular condyle. PMID:14667134
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
[Effects of nutrient deficiency on principal components of ginseng root exudates].
Li, Yong; Huang, Xiao-fang; Ding, Wan-long
2008-08-01
By the method of solution culture, the effects of N, P, and K deficiency on the principol components in root exudates of ginseng at its early growth stage were studied. The results showed that in treatments N and K deficiency and control, no significant difference was observed in the principal components of ginseng root exudates, and 28, 29, and 27 principal chromatographic peaks were detected by GC-MS, respectively; while in treatment P deficiency, only 22 principal chromatographic peaks were detected. Furthermore compounds in the root exudates from treatments N, P, and K deficiency and control were identified, respectively. Compared with control, treatments N and K deficiency had more kinds of organic acids and phenolic acids in root exudates, while treatment P deficiency was in adverse, which suggested that at early growth stages, ginseng had more requirement to N and K than P, and N and K deficiency would accelerate the exudation of organic acids and phenolic acids by roots.
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…
Gabor feature-based apple quality inspection using kernel principal component analysis
Technology Transfer Automated Retrieval System (TEKTRAN)
Automated inspection of apple quality involves computer recognition of good apples and blemished apples based on geometric or statistical features derived from apple images. This paper introduces a Gabor feature-based kernel, principal component analysis (PCA) method; by combining Gabor wavelet rep...
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…
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.
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,…
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
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…
Analysis of the principal component algorithm in phase-shifting interferometry.
Vargas, J; Quiroga, J Antonio; Belenguer, T
2011-06-15
We recently presented a new asynchronous demodulation method for phase-sampling interferometry. The method is based in the principal component analysis (PCA) technique. In the former work, the PCA method was derived heuristically. In this work, we present an in-depth analysis of the PCA demodulation method.
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…
ERIC Educational Resources Information Center
Linting, Marielle; Meulman, Jacqueline J.; Groenen, Patrick J. F.; van der Kooij, Anita J.
2007-01-01
Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate…
40 CFR 60.1580 - What are the principal components of the model rule?
Code of Federal Regulations, 2010 CFR
2010-07-01
... 40 Protection of Environment 6 2010-07-01 2010-07-01 false What are the principal components of the model rule? 60.1580 Section 60.1580 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small...
Principal Components versus Principle Axis Factors: When Will We Ever Learn?
ERIC Educational Resources Information Center
Thompson, Bruce; Vidal-Brown, Sherry A.
Analysts differ quite heatedly over the use of principal components rather than principle axis factor analysis. The difference between the two approaches involves the entries used on the diagonal of the matrix of associations that is analyzed. This paper uses an actual data set (N=539; variables =98) to illustrate that these two methods converge…
The Use of Exploratory Factor Analysis and Principal Components Analysis in Communication Research.
ERIC Educational Resources Information Center
Park, Hee Sun; Dailey, Rene; Lemus, Daisy
2002-01-01
Discusses the distinct purposes of principal components analysis (PCA) and exploratory factor analysis (EFA), using two data sets as examples. Reviews the use of each technique in three major communication journals: "Communication Monographs,""Human Communication Research," and "Communication Research." Finds that the use of EFA and PCA indicates…
Laser Scanning Tomography in the EPIC-Norfolk Eye Study: Principal Components and Associations
Khawaja, Anthony P.; Chan, Michelle P. Y.; Broadway, David C.; Garway-Heath, David F.; Luben, Robert; Yip, Jennifer L. Y.; Hayat, Shabina; Khaw, Kay-Tee; Foster, Paul J.
2013-01-01
Purpose. To describe Heidelberg Retina Tomograph (HRT) measures, their principal components, and their associations in a British population. Methods. The European Prospective Investigation of Cancer (EPIC)-Norfolk Eye Study is nested within a multicenter cohort study. Measurements were taken with the HRT-2 and the software subsequently updated to yield HRT-3 parameters. Principal components analysis (PCA) was used to identify distinct components of the HRT variables. Generalized estimating equation models were used to examine associations of these components with age, sex, height, body mass index (BMI), blood pressure, social class, education, alcohol intake, smoking status, axial length, IOP, and lens status. Results. Complete data were available from 10,859 eyes of 6430 participants with a mean age of 68 years. Principal components analysis identified three components with an eigenvalue greater than 1, explaining 79.9% of the variance of all the HRT measures. These were named cup, retinal nerve fiber layer (RNFL), and rim based on the factor loadings they were most correlated with. Older age was significantly associated with a greater cup (P = 0.003), smaller RNFL (P < 0.001), and smaller rim (P < 0.001). Female sex (P = 0.001), higher education (P < 0.001), and shorter axial length (P < 0.001) were associated with a greater RNFL. Lower BMI and higher IOP were associated with a greater cup (both, P < 0.001) and a smaller rim (BMI, P = 0.001; IOP, P < 0.001). Conclusions. Heidelberg Retina Tomograph measures in this cohort were largely explained by three principal components related to optic disc cup, RNFL, and rim. Associations with cup and rim were distinct to associations with RNFL, suggesting different underlying determinants. PMID:24030456
Balanovskaia, E V; Nurbaev, S D
1997-12-01
On the basis of maps of principal components ("synthetic maps"), populations were arranged in the space of principal components. In terms of the applied model, nodes of a dense, uniform grid represented human populations. For each node, the frequency of a given gene was interpolated from these values for all original populations. Principal components were estimated and mapped on the basis of maps for all genes. Each population (grid node) was assigned a marker of an ethnic or some other group of populations and was positioned in the space of principal components according to the values from the original maps. The resultant "ethnic clouds" of populations and "ethnic centroids" of principal components provide some new possibilities for explaining the patterns of changes in gene pools. The maps of reliability of principal components allow the researcher to eliminate the information on populations which is unreliable and turn to the "reliable" space of principal components. The method was tested with the use of the maps of principal components for the gene pool of the East European population. Eastern Slavonic (Russians, Ukrainians, and Belarussians) and western and eastern Finno-Ugrian (Estonians and Mordovians, respectively) ethnic groups were mapped to the space of principal components. The relative positions of the populations of these ethnic groups was analyzed in the spaces of the first and the second, the first and the third, and the second and the third principal components of the East European gene pool.
Principal Component Analyses of Topographic Profiles of Terrestrial and Venusian Uplifts
NASA Astrophysics Data System (ADS)
Stoddard, P. R.; Jurdy, D. M.
2013-12-01
Topographic data can provide valuable insight into some of the fundamental surface features and processes for our nearest terrestrial neighbor, Venus. Many of its topographic features remain enigmatic, even after two decades of post-Magellan study. Past examination of topographic profiles showed some similarities between certain classes of features (hotspots and regiones; rifts and chasmata), but further quantitative analysis could better define correlations between terrestrial and venusian features. We undertake such a quantitative comparison of topographic features on Venus and Earth through Principal Component Analysis (PCA). In Principal Component Analysis, the correlation coefficients for pairs of features are determined from their normalized average topographic profiles. These correlation coefficients are then arranged in a covariance matrix, diagonalized to find the eigenvalues, or principal components, which can be displayed graphically as profiles. The principal components assess the degree of similarity and variability of the shapes of the average profiles. PCA thus offers an independent and objective mode of comparison. In a preliminary comparison of uplifts on the two planets, PCA was applied for four terrestrial hotspots and three venusian regiones. The trace of the covariance matrix summed to 700, and the first three principal components (values 472, 160, 51) together accounted for over 97% the shape of the profiles. Thus, the topographic profiles of the 7 uplifts on Venus and Earth can be very nearly described with just 3 independent components. The shapes of the major principal components give some insight into the uplift process on each planet. The first, (i.e. largest) component, corresponding to a simple uplift is positive for all 7 features. The second component, however, differs by planet: negative for the 4 terrestrial hotspots, but positive for the venusian regiones. Indeed, the shape of this component's profile (central low flanked by two
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.
Principal Component Analysis of Spectroscopic Imaging Data in Scanning Probe Microscopy
Jesse, Stephen; Kalinin, Sergei V
2009-01-01
The approach for data analysis in band excitation family of scanning probe microscopies based on principal component analysis (PCA) is explored. PCA utilizes the similarity between spectra within the image to select the relevant response components. For small signal variations within the image, the PCA components coincide with the results of deconvolution using simple harmonic oscillator model. For strong signal variations, the PCA allows effective approach to rapidly process, de-noise and compress the data. The extension of PCA for correlation function analysis is demonstrated. The prospects of PCA as a universal tool for data analysis and representation in multidimensional SPMs are discussed.
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
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.
Dascălu, Cristina Gena; Antohe, Magda Ecaterina
2009-01-01
Based on the eigenvalues and the eigenvectors analysis, the principal component analysis has the purpose to identify the subspace of the main components from a set of parameters, which are enough to characterize the whole set of parameters. Interpreting the data for analysis as a cloud of points, we find through geometrical transformations the directions where the cloud's dispersion is maximal--the lines that pass through the cloud's center of weight and have a maximal density of points around them (by defining an appropriate criteria function and its minimization. This method can be successfully used in order to simplify the statistical analysis on questionnaires--because it helps us to select from a set of items only the most relevant ones, which cover the variations of the whole set of data. For instance, in the presented sample we started from a questionnaire with 28 items and, applying the principal component analysis we identified 7 principal components--or main items--fact that simplifies significantly the further data statistical analysis. PMID:21495371
Jirayucharoensak, Suwicha; Pan-Ngum, Setha; Israsena, Pasin
2014-01-01
Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers. PMID:25258728
Dascălu, Cristina Gena; Antohe, Magda Ecaterina
2009-01-01
Based on the eigenvalues and the eigenvectors analysis, the principal component analysis has the purpose to identify the subspace of the main components from a set of parameters, which are enough to characterize the whole set of parameters. Interpreting the data for analysis as a cloud of points, we find through geometrical transformations the directions where the cloud's dispersion is maximal--the lines that pass through the cloud's center of weight and have a maximal density of points around them (by defining an appropriate criteria function and its minimization. This method can be successfully used in order to simplify the statistical analysis on questionnaires--because it helps us to select from a set of items only the most relevant ones, which cover the variations of the whole set of data. For instance, in the presented sample we started from a questionnaire with 28 items and, applying the principal component analysis we identified 7 principal components--or main items--fact that simplifies significantly the further data statistical analysis.
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)
Yan, Li; Liu, Li
2010-07-01
A new method for accurate measurement of content of textile mixture by use of Fourier transform near infrared spectroscopy is put forward. The near infrared spectra of 56 samples with different cotton and polyester contents were obtained, in which 41 samples, 10 samples and 5 samples were used for the calibration set, validation set and prediction set respectively. Principal component analysis (PCA) was utilized for the spectra data compression. Principal component regression (PCR) model was developed. It indicates that the MAE is within 2.9% and the RMSE is less than 3.6% for the validation samples, which is suitable for the prediction of unknown samples. The PCR model was applied to predict unknown samples. Experimental results show that this approach by use of Fourier transform Near Infrared Spectroscopy can be used to quantitative analysis for textile fiber.
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.
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.
Schultz, T.W.; Moulton, M.P.
1985-01-01
Currently the best method of quantitatively predicting biological activity is by regression analysis. Regression analysis assumes that biological activity is a function of physiochemical properties and chemical structure. There is, however, a problem associated with this methodoloy. The problem arises from the fact that substituent constants (SC) are often intercorrelated (i.e. nonorthogonal). Since the SC/sub i/ are not orthogonal, the order in which they enter the regression model is critically important. This problem becomes increasingly complex with the addition of independent variables. An alternative to this statistical quagmire is Principal Components Analysis (PCA). In PCA, the original variables are redefined by new variables (i.e. the principal components) which are linear combinations of the original variables. It is the purpose of this investigation to examine the interrelationship between seven substituent constants using PCA, and to attempt to predict toxicity of a series of napthalene derivates to the growth of tetrahymena.
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.
Obtaining a linear combination of the principal components of a matrix on quantum computers
NASA Astrophysics Data System (ADS)
Daskin, Ammar
2016-07-01
Principal component analysis is a multivariate statistical method frequently used in science and engineering to reduce the dimension of a problem or extract the most significant features from a dataset. In this paper, using a similar notion to the quantum counting, we show how to apply the amplitude amplification together with the phase estimation algorithm to an operator in order to procure the eigenvectors of the operator associated to the eigenvalues defined in the range [ a, b] , where a and b are real and 0 ≤ a ≤ b ≤ 1 . This makes possible to obtain a combination of the eigenvectors associated with the largest eigenvalues and so can be used to do principal component analysis on quantum computers.
An efficient classification method based on principal component and sparse representation.
Zhai, Lin; Fu, Shujun; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang
2016-01-01
As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition. PMID:27386281
Nonlinear feature extraction using kernel principal component analysis with non-negative pre-image.
Kallas, Maya; Honeine, Paul; Richard, Cedric; Amoud, Hassan; Francis, Clovis
2010-01-01
The inherent physical characteristics of many real-life phenomena, including biological and physiological aspects, require adapted nonlinear tools. Moreover, the additive nature in some situations involve solutions expressed as positive combinations of data. In this paper, we propose a nonlinear feature extraction method, with a non-negativity constraint. To this end, the kernel principal component analysis is considered to define the most relevant features in the reproducing kernel Hilbert space. These features are the nonlinear principal components with high-order correlations between input variables. A pre-image technique is required to get back to the input space. With a non-negative constraint, we show that one can solve the pre-image problem efficiently, using a simple iterative scheme. Furthermore, the constrained solution contributes to the stability of the algorithm. Experimental results on event-related potentials (ERP) illustrate the efficiency of the proposed method.
Nonlinear real-life signal detection with a supervised principal components analysis.
Zhou, C T; Cai, T X; Cai, T F
2007-03-01
A novel strategy named supervised principal components analysis for the detection of a target signal of interest embedded in an unknown noisy environment has been investigated. There are two channels in our detection scheme. Each channel consists of a nonlinear phase-space reconstructor (for embedding a data matrix using the received time series) and a principal components analyzer (for feature extraction), respectively. The output error time series, which results from the difference of both eigenvectors of the correlation data matrices from these two channels, is then analyzed using time-frequency tools, for example, frequency spectrum or Wigner-Ville distribution. Experimental results based on real-life electromagnetic data are presented to demonstrate the detection performance of our algorithm. It is found that weak signals hidden beneath the noise floor can be detected. Furthermore, the robustness of the detection performance clearly illustrated that signal frequencies can be extracted when the signal power is not too low.
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)
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.
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. PMID:15016469
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.
Duforet-Frebourg, Nicolas; Luu, Keurcien; Laval, Guillaume; Bazin, Eric; Blum, Michael G.B.
2016-01-01
To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult. PMID:26715629
NASA Astrophysics Data System (ADS)
Yan, D.; Cecil, T.; Gades, L.; Jacobsen, C.; Madden, T.; Miceli, A.
2016-07-01
We present a method using principal component analysis (PCA) to process x-ray pulses with severe shape variation where traditional optimal filter methods fail. We demonstrate that PCA is able to noise-filter and extract energy information from x-ray pulses despite their different shapes. We apply this method to a dataset from an x-ray thermal kinetic inductance detector which has severe pulse shape variation arising from position-dependent absorption.
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.
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)
Jakovels, Dainis; Lihacova, Ilze; Kuzmina, Ilona; Spigulis, Janis
2013-11-01
Non-invasive and fast primary diagnostics of pigmented skin lesions is required due to frequent incidence of skin cancer - melanoma. Diagnostic potential of principal component analysis (PCA) for distant skin melanoma recognition is discussed. Processing of the measured clinical multi-spectral images (31 melanomas and 94 nonmalignant pigmented lesions) in the wavelength range of 450-950 nm by means of PCA resulted in 87 % sensitivity and 78 % specificity for separation between malignant melanomas and pigmented nevi.
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. PMID:25135154
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.
The Use of Principal Components for Creating Improved Imagery for Geometric Control Point Selection
NASA Technical Reports Server (NTRS)
Imhoff, M. L.
1982-01-01
A directed principal component (PC) analysis and its transformation was applied to 7-channel thematic mapper simulator (TMS) data and 4-channel LANDSAT multispectral scanner system (MSS) data collected over the city of Lancaster, Pennsylvania, to create improved imagery for geometric control point selection for image to image registration. Nineteen temporally stable geometric control points, such as road interactions and bridges, were selected for a 236 sq km area. The control points were visible on both the TMS and MSS imagery. On the first attempt the corresponding image control points were selected on both data sets without using the principal components transformation. Many of the road intersection locations were visible but the actual road crossings could not be distinguished. As a result, mensuration errors using raw data exceeded the equivalent of two (79 x 79 m) pixels. The application of a guided principal components transformation yielded TMS and MSS single band images showing improved detail in the scene's urban and residential infrastructure. The PC transformed data sets were then utilized for the reselection of geometric control points. By shown greater detail, control points on both the TMS and MSS imagery could be located with greater precision using the PC transformed data.
Wang, Jinjia; Yang, Liang
2015-06-01
The brain computer interface (BCI) can be used to control external devices directly through electroencephalogram (EEG) information. A multi-linear principal component analysis (MPCA) framework was used for the limitations of tensor form of multichannel EEG signals processing based on traditional principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA). Based on MPCA, we used the projection of tensor-matrix to achieve the goal of dimensionality reduction and features exaction. Then we used the Fisher linear classifier to classify the features. Furthermore, we used this novel method on the BCI competition II dataset 4 and BCI competition N dataset 3 in the experiment. The second-order tensor representation of time-space EEG data and the third-order tensor representation of time-space-frequency BEG data were used. The best results that were superior to those from other dimensionality reduction methods were obtained by much debugging on parameter P and testQ. For two-order tensor, the highest accuracy rates could be achieved as 81.0% and 40.1%, and for three-order tensor, the highest accuracy rates were 76.0% and 43.5%, respectively.
NASA Astrophysics Data System (ADS)
Dovbeshko, G. I.; Repnytska, O. P.; Pererva, T.; Miruta, A.; Kosenkov, D.
2004-07-01
Conformation analysis of mutated DNA-bacteriophages (PLys-23, P23-2, P47- the numbers have been assigned by T. Pererva) induced by MS2 virus incorporated in Ecoli AB 259 Hfr 3000 has been done. Surface enhanced infrared absorption (SEIRA) spectroscopy and principal component analysis has been applied for solving this problem. The nucleic acids isolated from the mutated phages had a form of double stranded DNA with different modifications. The nucleic acid from phage P47 was undergone the structural rearrangement in the most degree. The shape and position ofthe fine structure of the Phosphate asymmetrical band at 1071cm-1 as well as the stretching OH vibration at 3370-3390 cm-1 has indicated to the appearance ofadditional OH-groups. The Z-form feature has been found in the base vibration region (1694 cm-1) and the sugar region (932 cm-1). A supposition about modification of structure of DNA by Z-fragments for P47 phage has been proposed. The P23-2 and PLys-23 phages have showed the numerous minor structural changes also. On the basis of SEIRA spectra we have determined the characteristic parameters of the marker bands of nucleic acid used for construction of principal components. Contribution of different spectral parameters of nucleic acids to principal components has been estimated.
Generalized Multilevel Function-on-Scalar Regression and Principal Component Analysis
Goldsmith, Jeff; Zipunnikov, Vadim; Schrack, Jennifer
2015-01-01
Summary 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 six hundred subjects over five 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 twenty-four hour period; in addition, the principal components analysis identifies the patterns of activity that distinguish subjects and days within subjects. PMID:25620473
Multiple regression and principal components analysis of puberty and growth in cattle.
Baker, J F; Stewart, T S; Long, C R; Cartwright, T C
1988-09-01
Multiple regression and principal components analyses were employed to examine relationships among pubertal and growth characters. Records used were from 424 bulls and 475 heifers produced by a diallel mating of Angus, Brahman, Hereford, Holstein and Jersey breeds. Characters studied were age, weight and height at puberty and measurements of weight and hip height from 9 to 21 mo of age; pelvic measurements of heifers also were included. Measurements of weight and height near 1 yr of age were related most highly to pubertal age, weight adn height. Larger size near 1 yr of age was associated with younger, larger animals at puberty. Growth rate was associated with pubertal characters before, but not after, adjustment for effects of breed-type. Principal components of the variation of pubertal and growth characters among animals were strongly related to both weight and height. The majority of the variation among breed-types was due to height. Characteristic vectors of principal components describing the variation of bulls and heifers were strikingly similar. The variance-covariance structure of pubertal characters was essentially the same for both sexes even though the mean values of the characters differed. PMID:3170369
NASA Astrophysics Data System (ADS)
Melbourne, A.; Atkinson, D.; White, M. J.; Collins, D.; Leach, M.; Hawkes, D.
2007-09-01
Registration of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) of soft tissue is difficult. Conventional registration cost functions that depend on information content are compromised by the changing intensity profile, leading to misregistration. We present a new data-driven model of uptake patterns formed from a principal components analysis (PCA) of time-series data, avoiding the need for a physiological model. We term this process progressive principal component registration (PPCR). Registration is performed repeatedly to an artificial time series of target images generated using the principal components of the current best-registered time-series data. The aim is to produce a dataset that has had random motion artefacts removed but long-term contrast enhancement implicitly preserved. The procedure is tested on 22 DCE-MRI datasets of the liver. Preliminary assessment of the images is by expert observer comparison with registration to the first image in the sequence. The PPCR is preferred in all cases where a preference exists. The method requires neither segmentation nor a pharmacokinetic uptake model and can allow successful registration in the presence of contrast enhancement.
Changes in human meibum lipid with meibomian gland dysfunction using principal component analysis.
Borchman, Douglas; Yappert, Marta C; Foulks, Gary N
2010-08-01
Changes in the phase transition temperatures and conformation of human meibum lipid with age and meibomian gland dysfunction have been quantified but with analysis of less than 1% of the infrared spectral range. The remaining 99% of the spectral range was analyzed with principal component analysis (PCA) and confirms our previous studies and reveal further insights into changes that occur in meibum with age and disease. Infrared spectra of meibum from 41 patients diagnosed with meibomian gland dysfunction (Md) and 32 normal donors (Mn) were measured. Principal component analysis (PCA) was used to quantify the variance among the spectra and meibum protein was quantified using the infrared carbonyl and amide I and II bands. A training set of spectra was used to discriminate between Mn and Md with an accuracy of 93%. This shows that certain spectral regions (eigenvectors) contain compositional and structural information about the changes that occur with the principal component (variable), meibomian gland dysfunction. The spectral features of the major eigenvector indicate that Md contains more protein and relatively less CH(3) and cis = CH band intensity compared to Mn. The amount of protein was confirmed from relative infrared band intensities. Our study supports the idea that compositional differences result in meibum that is less fluid and more viscous with meibomian gland dysfunction so that less lipid flows out of the meibomian gland orifice as observed clinically. This study also demonstrates the power of the combination of infrared spectroscopy and PCA as a diagnostic tool that discriminates between Mn and Md.
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%.
Principal component-based radiative transfer model for hyperspectral sensors: theoretical concept.
Liu, Xu; Smith, William L; Zhou, Daniel K; Larar, Allen
2006-01-01
Modern infrared satellite sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-Track Infrared Sounder (CrIS), the Tropospheric Emission Spectrometer (TES), the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS), and the 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, superfast radiative transfer models are needed. We present 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 the properties of PC scores and instrument line-shape functions. The PCRTM is accurate and flexible. Because of its high speed and compressed spectral information format, it has great potential for superfast one-dimensional physical retrieval and for numerical weather prediction large volume radiance data assimilation applications. The model has been successfully developed for the 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.
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.
Héberger, K; Keszler, A; Gude, M
1999-01-01
Decomposition of hydroperoxides in sunflower oil under strictly oxygen-free conditions was followed by measuring peroxide values against time, absorbance values at 232 and 268 nm, para-anisidine values, and by quantitative analyses of volatile products using various additives. The results were arranged in a matrix form and subjected to principal component analysis. Three principal components explained 89-97% of the total variance in the data. The measured quantities and the effect of additives were closely related. Characteristic plots showed similarities among the measured quantities (loading plots) and among the additives (score plots). Initial decomposition rate of hydroperoxides and the amount of volatile products formed were similar to each other. The outliers, the absorbance values, were similar to each other but carried independent information from the other quantities. Para-anisidine value (PAV) was a unique parameter. Since PAV behaved differently during the course of hydroperoxide degradation, it served as a kinetic indicator. Most additives were similar in their effects on the mentioned quantities, but two outliers were also observed. Rotation of the principal component axes did not change the dominant patterns observed. The investigations clearly showed which variables were worth measuring to evaluate different additives.
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.
Principal component analysis of the memory load effect in a change detection task.
Zhou, Li; Thomas, Robin D
2015-05-01
Previous research using the change detection task has found little or no relationship between P3 amplitude and working memory load. This contrasts with findings from other paradigms that indicate a decrease in P3 amplitude with increases in working memory load. We adopted a principal component analysis strategy to resolve this discrepancy. After ERPs were decomposed, the P3 component decreased in amplitude when the memory load increased. Its amplitude was also associated with individuals' working memory capacity. In conclusion, P3 plays a critical role in change detection task as it does in other working memory tasks.
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.
Jankovic, Marko; Ogawa, Hidemitsu
2004-10-01
Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method. PMID:15593379
Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy.
Seo, Jihye; An, Yuri; Lee, Jungsul; Ku, Taeyun; Kang, Yujung; Ahn, Chulwoo; Choi, Chulhee
2016-04-30
Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features. PMID:27071414
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.
PCA of PCA: principal component analysis of partial covering absorption in NGC 1365
NASA Astrophysics Data System (ADS)
Parker, M. L.; Walton, D. J.; Fabian, A. C.; Risaliti, G.
2014-06-01
We analyse 400 ks of XMM-Newton data on the active galactic nucleus NGC 1365 using principal component analysis (PCA) to identify model-independent spectral components. We find two significant components and demonstrate that they are qualitatively different from those found in MCG-6-30-15 using the same method. As the variability in NGC 1365 is known to be due to changes in the parameters of a partial covering neutral absorber, this shows that the same mechanism cannot be the driver of variability in MCG-6-30-15. By examining intervals where the spectrum shows relatively low absorption we separate the effects of intrinsic source variability, including signatures of relativistic reflection, from variations in the intervening absorption. We simulate the principal components produced by different physical variations, and show that PCA provides a clear distinction between absorption and reflection as the drivers of variability in AGN spectra. The simulations are shown to reproduce the PCA spectra of both NGC 1365 and MCG-6-30-15, and further demonstrate that the dominant cause of spectral variability in these two sources requires a qualitatively different mechanism.
Kosaka, Masashi; Sekiguchi, Satoshi; Naito, Junpei; Uemura, Makoto; Kuwahara, Shunsuke; Watanabe, Masataka; Harada, Nobuyuki; Hiroi, Kunio
2005-05-01
Enantiopure phthalides 2 and 5-8 were synthesized via enantioresolution of the corresponding alcohols with a chiral auxiliary of camphorsultam dichlorophthalic acid, (1S,2R,4R)-(-)-CSDP acid 3, followed by solvolysis with KOH in MeOH and the catalytic oxidation of chiral glycols with iridium complex 28. The absolute configurations of phthalides 2 and 5-8 were determined by applying the (1)H-NMR anisotropy method of MalphaNP acid (4), 2-methoxy-2-(1-naphthyl)propionic acid, to the chiral synthetic precursory alcohols. In the case of 3-phenylphthalide (R)-(-)-7, the absolute configuration determined by the (1)H-NMR anisotropy method using MalphaNP acid 4 agreed with that by the X-ray crystallographic method. By applying these methods, 3-butylphthalide (S)-(-)-2, a fragrance component of essential oil of celery, has been synthesized in an enantiopure form, and its absolute configuration was unambiguously determined.
Bai, Libing; Gao, Bin; Tian, Shulin; Cheng, Yuhua; Chen, Yifan; Tian, Gui Yun; Woo, W L
2013-10-01
Eddy Current Pulsed Thermography (ECPT), an emerging Non-Destructive Testing and Evaluation technique, has been applied for a wide range of materials. The lateral heat diffusion leads to decreasing of temperature contrast between defect and defect-free area. To enhance the flaw contrast, different statistical methods, such as Principal Component Analysis and Independent Component Analysis, have been proposed for thermography image sequences processing in recent years. However, there is lack of direct and detailed independent comparisons in both algorithm implementations. The aim of this article is to compare the two methods and to determine the optimized technique for flaw contrast enhancement in ECPT data. Verification experiments are conducted on artificial and thermal fatigue nature crack detection.
NASA Astrophysics Data System (ADS)
Bai, Libing; Gao, Bin; Tian, Shulin; Cheng, Yuhua; Chen, Yifan; Tian, Gui Yun; Woo, W. L.
2013-10-01
Eddy Current Pulsed Thermography (ECPT), an emerging Non-Destructive Testing and Evaluation technique, has been applied for a wide range of materials. The lateral heat diffusion leads to decreasing of temperature contrast between defect and defect-free area. To enhance the flaw contrast, different statistical methods, such as Principal Component Analysis and Independent Component Analysis, have been proposed for thermography image sequences processing in recent years. However, there is lack of direct and detailed independent comparisons in both algorithm implementations. The aim of this article is to compare the two methods and to determine the optimized technique for flaw contrast enhancement in ECPT data. Verification experiments are conducted on artificial and thermal fatigue nature crack detection.
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.
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
McKeown, M J; Ramsay, D A
1996-12-01
The classification of astrocytomas, astrocytomas with anaplastic foci and glioblastoma multiformes is not always straightforward because the tumors form a histological continuum. The use of principal component analysis (PCA) and neural nets in the classification of these tumors is explored. PCA was performed on 14 histological features recorded from 52 gliomas classified by the Radiation Therapy Oncology Group method (17 astrocytomas, 18 astrocytomas with anaplastic foci, 17 glioblastoma multiformes). Four of the 14 possible 'scores' derived from this analysis were selected to summarize the histological variability seen in all the tumors. These scores were mostly significantly different between tumor types and were thus used to successfully train a neural net to correctly classify these tumors. The first principal component (score) supported the use of increasing cellularity, mitoses, endothelial proliferation, and necrosis in differentiating between the tumor categories, but accounted for only 39% of the variability seen. Other histological features that were significant components of the other scores included the presence of multinucleated or giant cells, gemistocytes, atypical mitoses and changes in nuclear chromatin. Computer programs derived from the methodology described provide a way of standardizing glioma diagnosis and may be extended to assist with management decisions.
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.
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.
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
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.
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.
Li, Zhen-Hao; Liu, Pei; Qian, Da-Wei; Li, Wei; Shang, Er-Xin; Duan, Jin-Ao
2013-06-01
The objective of the present study was to establish a method based on principal component analysis (PCA) for the study of transdermal delivery of multiple components in Chinese medicine, and to choose the best penetration enhancers for the active fraction of Xiangfusiwu decoction (BW) with this method. Improved Franz diffusion cells with isolated rat abdomen skins were carried out to experiment on the transdermal delivery of six active components, including ferulic acid, paeoniflorin, albiflorin, protopine, tetrahydropalmatine and tetrahydrocolumbamine. The concentrations of these components were determined by LC-MS/MS, then the total factor scores of the concentrations at different times were calculated using PCA and were employed instead of the concentrations to compute the cumulative amounts and steady fluxes, the latter of which were considered as the indexes for optimizing penetration enhancers. The results showed that compared to the control group, the steady fluxes of the other groups increased significantly and furthermore, 4% azone with 1% propylene glycol manifested the best effect. The six components could penetrate through skin well under the action of penetration enhancers. The method established in this study has been proved to be suitable for the study of transdermal delivery of multiple components, and it provided a scientific basis for preparation research of Xiangfusiwu decoction and moreover, it could be a reference for Chinese medicine research. PMID:23984531
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
Zhang, Yiwei; Pan, Wei
2015-03-01
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, which is perhaps the 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 an 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 vs. 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 lifestyle), the PCs may be able to implicitly and effectively adjust for the confounder whereas the LMM cannot. Accordingly, to adjust for both population structures and nongenetic 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.
Scanning Probe Microscope Imaging with Principal Component Analysis of Cell Types
NASA Astrophysics Data System (ADS)
Ayres, V. M.; Goolsby, B.; Salam, F.; Yu, M.-M.; Xi, Ning; Wang, D.
2002-03-01
Scanning Probe Microscopy provides high resolution imaging of specimens, including biological specimens. Scanning Probe Microscope-based nanomanipulation is a newly emerging area that offers an orders-of-magnitude improvement over current manipulation capabilities. Together, the two offer the possibility of site-specific direct investigations of biological events. We present our research toward the development of a landmark recognition scheme for use within an adaptive nonlinear neural network controller, for high end control of the X-Y motion of an SPM tip. Our goal is sensing/landmark recognition within an overall feedback control formulation that will ultimately be used to accurately steer the probes tip along a prescribed trajectory to a designated biological site. In a different approach than haptic feedback-based nanomanipulation, the human operator is eliminated except for high end control and a training algorithm is substituted instead. Principal Component Analysis is used for landmark recognition of specific biological features. Principal Component Analysis is a pattern recognition technique that selects/extracts key features from a data set. The feature selection process transforms the data space into the feature space by reducing the dimensionality of the data set. The reduced data set is comprised of the most effective features that contain the intrinsic information of the data. In this work, Principal Component Analysis is applied to recognition of leukocytes (white blood cells) and erythrocytes (red blood cells), and further distinguishing between neutrophilic and lymphocytic leukocyte varieties. We find that that information from an initial 512x512 (xyz) SPM data set can be effectively represented by eight eigenvectors.
McIlroy, John W; Smith, Ruth Waddell; McGuffin, Victoria L
2015-12-01
Following publication of the National Academy of Sciences report "Strengthening Forensic Science in the United States: A Path Forward", there has been increasing interest in the application of multivariate statistical procedures for the evaluation of forensic evidence. However, prior to statistical analysis, variance from sources other than the sample must be minimized through application of data pretreatment procedures. This is necessary to ensure that subsequent statistical analysis of the data provides meaningful results. The purpose of this work was to evaluate the effect of pretreatment procedures on multivariate statistical analysis of chromatographic data obtained for a reference set of diesel fuels. Diesel was selected due to its chemical complexity and forensic relevance, both for fire debris and environmental forensic applications. Principal components analysis (PCA) was applied to the untreated chromatograms to assess association of replicates and discrimination among the different diesel samples. The chromatograms were then pretreated by sequentially applying the following procedures: background correction, smoothing, retention-time alignment, and normalization. The effect of each procedure on association and discrimination was evaluated based on the association of replicates in the PCA scores plot. For these data, background correction and smoothing offered minimal improvement, whereas alignment and normalization offered the greatest improvement in the association of replicates and discrimination among highly similar samples. Further, prior to pretreatment, the first principal component accounted for only non-sample sources of variance. Following pretreatment, these sources were minimized and the first principal component accounted for significant chemical differences among the diesel samples. These results highlight the need for pretreatment procedures and provide a metric to assess the effect of pretreatment on subsequent multivariate statistical
McIlroy, John W; Smith, Ruth Waddell; McGuffin, Victoria L
2015-12-01
Following publication of the National Academy of Sciences report "Strengthening Forensic Science in the United States: A Path Forward", there has been increasing interest in the application of multivariate statistical procedures for the evaluation of forensic evidence. However, prior to statistical analysis, variance from sources other than the sample must be minimized through application of data pretreatment procedures. This is necessary to ensure that subsequent statistical analysis of the data provides meaningful results. The purpose of this work was to evaluate the effect of pretreatment procedures on multivariate statistical analysis of chromatographic data obtained for a reference set of diesel fuels. Diesel was selected due to its chemical complexity and forensic relevance, both for fire debris and environmental forensic applications. Principal components analysis (PCA) was applied to the untreated chromatograms to assess association of replicates and discrimination among the different diesel samples. The chromatograms were then pretreated by sequentially applying the following procedures: background correction, smoothing, retention-time alignment, and normalization. The effect of each procedure on association and discrimination was evaluated based on the association of replicates in the PCA scores plot. For these data, background correction and smoothing offered minimal improvement, whereas alignment and normalization offered the greatest improvement in the association of replicates and discrimination among highly similar samples. Further, prior to pretreatment, the first principal component accounted for only non-sample sources of variance. Following pretreatment, these sources were minimized and the first principal component accounted for significant chemical differences among the diesel samples. These results highlight the need for pretreatment procedures and provide a metric to assess the effect of pretreatment on subsequent multivariate statistical
Spectral principal component analysis of mid-infrared spectra of a sample of PG QSOs
NASA Astrophysics Data System (ADS)
Bian, Wei-Hao; He, Zhi-Cheng; Green, Richard; Shi, Yong; Ge, Xue; Liu, Wen-Shuai
2016-03-01
A spectral principal component (SPC) analysis of a sample of 87 Palomar-Green (PG) QSOs at z < 0.5 is presented for their mid-infrared spectra from Spitzer Space Telescope. We have derived the first five eigenspectra, which account for 85.2 per cent of the mid-infrared spectral variation. It is found that the first eigenspectrum represents the mid-infrared slope, forbidden emission line strength and 9.7 μm silicate feature; the 3rd and 4th eigenspectra represent the silicate features at 18 and 9.7 μm, respectively. With the principal components (PC) from optical principal component analysis, we find that there is a medium strong correlation between spectral SPC1 and PC2 (accretion rate). It suggests that more nuclear contribution to the near-IR spectrum leads to the change of mid-IR slope. We find mid-IR forbidden lines are suppressed with higher accretion rate. A medium strong correlation between SPC3 and PC1 (Eddington ratio) suggests a connection between the silicate feature at 18 μm and the Eddington ratio. For the ratio of the silicate strength at 9.7 μm to that at 18 μm, we find a strong correlation with PC2 (accretion rate or QSO luminosity). We also find that there is a medium strong correlation between the star formation rate (SFR) and PC2. It implies a correlation between SFR and the central accretion rate in PG QSOs.
Identification of differentially expressed genes in microarray data in a principal component space.
Ospina, Luis; López-Kleine, Liliana
2013-12-01
Microarray experiments are often conducted in order to compare gene expression between two conditions. Tests to detected mean differential expression of genes between conditions are conducted applying correction for multiple testing. Seldom, relationships between gene expression and microarray conditions are investigated in a multivariate approach. Here we propose determining the relationship between genes and conditions using a Principal Component Analysis (PCA) space and classifying genes to one of two biological conditions based on their position relative to a direction on the PC space representing each condition.
Addressing misallocation of variance in principal components analysis of event-related potentials.
Dien, J
1998-01-01
Interpretation of evoked response potentials is complicated by the extensive superposition of multiple electrical events. The most common approach to disentangling these features is principal components analysis (PCA). Critics have demonstrated a number of caveats that complicate interpretation, notably misallocation of variance and latency jitter. This paper describes some further caveats to PCA as well as using simulations to evaluate three potential methods for addressing them: parallel analysis, oblique rotations, and spatial PCA. An improved simulation model is introduced for examining these issues. It is concluded that PCA is an essential statistical tool for event-related potential analysis, but only if applied appropriately.
Reducibility of invertible tuples to the principal component in commutative Banach algebras
NASA Astrophysics Data System (ADS)
Mortini, Raymond; Rupp, Rudolf
2016-10-01
Let A be a complex, commutative unital Banach algebra. We introduce two notions of exponential reducibility of Banach algebra tuples and present an analogue to the Corach-Suárez result on the connection between reducibility in A and in C(M(A)). Our methods are of an analytical nature. Necessary and sufficient geometric/topological conditions are given for reducibility (respectively reducibility to the principal component of Un(A)) whenever the spectrum of A is homeomorphic to a subset of {C}n.
FPGA-based real-time blind source separation with principal component analysis
NASA Astrophysics Data System (ADS)
Wilson, Matthew; Meyer-Baese, Uwe
2015-05-01
Principal component analysis (PCA) is a popular technique in reducing the dimension of a large data set so that more informed conclusions can be made about the relationship between the values in the data set. Blind source separation (BSS) is one of the many applications of PCA, where it is used to separate linearly mixed signals into their source signals. This project attempts to implement a BSS system in hardware. Due to unique characteristics of hardware implementation, the Generalized Hebbian Algorithm (GHA), a learning network model, is used. The FPGA used to compile and test the system is the Altera Cyclone III EP3C120F780I7.
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.
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.
Using principal component analysis to monitor spatial and temporal changes in water quality.
Bengraïne, Karim; Marhaba, Taha F
2003-06-27
Chemical, biological and physical data monitored at 12 locations along the Passaic River, New Jersey, during the year 1998 are analyzed. Principal component analysis (PCA) was used: (i) to extract the factors associated with the hydrochemistry variability; (ii) to obtain the spatial and temporal changes in the water quality. Solute content, temperature, nutrients and organics were the main patterns extracted. The spatial analysis isolated two stations showing a possible point or non-point source of pollution. This study shows the importance of environmental monitoring associated with simple but powerful statistics to better understand a complex water system. PMID:12835021
Fatigue among caregivers of chronic renal failure patients: a principal components analysis.
Schneider, Robert A
2003-12-01
Quality of life for caregivers of ESRD patients has not been well addressed. The physical and psychological status of this overlooked group can be important in the recovery or adaptation of patients with chronic renal failure. One particular symptom of a reduced quality of life of such caregivers is that of fatigue. The study tested the reliability of both existing and newer fatigue measures. Measures with high reliability yielded a single construct of fatigue in a principal components analysis in this study of 99 caregivers. Implications for practice are addressed. Potential for further study is recommended. PMID:14730783
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.
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.
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: .
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. PMID:27227487
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.
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.
Islam, M. A.; Alam, M. K.; Islam, M. N.; Khan, M. A. S.; Ekeberg, D.; Rukke, E. O.; Vegarud, G. E.
2014-01-01
The aim of the present study was to get a total physical and chemical characterization and comparison of the principal components in Bangladeshi buffalo (B), Holstein cross (HX), Indigenous cattle (IC) and Red Chittagong Cattle (RCC) milk. Protein and casein (CN) composition and type, casein micellar size (CMS), naturally occurring peptides, free amino acids, fat, milk fat globule size (MFGS), fatty acid composition, carbohydrates, total and individual minerals were analyzed. These components are related to technological and nutritional properties of milk. Consequently, they are important for the dairy industry and in the animal feeding and breeding strategies. Considerable variation in most of the principal components of milk were observed among the animals. The milk of RCC and IC contained higher protein, CN, β-CN, whey protein, lactose, total mineral and P. They were more or less similar in most of the all other components. The B milk was found higher in CN number, in the content of αs2-, κ-CN and α-lactalbumin, free amino acids, unsaturated fatty acids, Ca and Ca:P. The B milk was also lower in β-lactoglobulin content and had the largest CMS and MFGS. Proportion of CN to whey protein was lower in HX milk and this milk was found higher in β-lactoglobulin and naturally occuring peptides. Considering the results obtained including the ratio of αs1-, αs2-, β- and κ-CN, B and RCC milk showed best data both from nutritional and technological aspects. PMID:25050028
Principal component analysis for the research of TEC variations' dynamic in the Ionosphere
NASA Astrophysics Data System (ADS)
Maslennikova, Yulia; Bochkarev, Vladimir
Principal Component Analysis (it is also known as Proper Orthogonal Decomposition (POD)) is one of the basic ways to research of spatial structure of various geophysical parameters. POD involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. However it is requires considering the features both physical processes in investigated system and form of representation of experimental data for this method to be used. Problems that appear through using POD and some possible ways of solution of those problems (through example of variations of the Total Electronic Content (TEC) of ionosphere) are represented in that report. The maps of TEC are given by the Jet Propulsion Laboratory (JPL) during the 1998-2009 years. When PCA is used it is necessary to consider the dominating periodicity in observable date (first of all daily and seasonal). The proposed solution of that problem is using of co-moving frame of reference in which the "subsolar" point remains motionless. That trick allows a better way to investigate the spatial structure of the first components than using a geographical coordinate system. It appears that a type of scalar product of a data vectors has an influence on the decomposition components' orthogonality definition. One of the main goals of this research is to investigate the influence of scalar multiplication's type on adequacy of decomposition in consideration of a relative position of graticule points and of a TEC data' preprocessing method. The TEC decomposition's reproducibility checking is made by using a statistical modeling. Another goal of this research is to investigate the influence of the distribution type of geophysical parameters' fluctuation on quality of a mode separation. Decomposition is performed using nonparametric estimation of degree of correlation. It is shown that the proposed method makes it possible to improve a mode
Sousa, C C; Damasceno-Silva, K J; Bastos, E A; Rocha, M M
2015-01-01
Vigna unguiculata (L.) Walp (cowpea) is a food crop with high nutritional value that is cultivated throughout tropical and subtropical regions of the world. The main constraint on high productivity of cowpea is water deficit, caused by the long periods of drought that occur in these regions. The aim of the present study was to select elite cowpea genotypes with enhanced drought tolerance, by applying principal component analysis to 219 first-cycle progenies obtained in a recurrent selection program. The experimental design comprised a simple 15 x 15 lattice with 450 plots, each of two rows of 10 plants. Plants were grown under water-deficit conditions by applying a water depth of 205 mm representing one-half of that required by cowpea. Variables assessed were flowering, maturation, pod length, number and mass of beans/pod, mass of 100 beans, and productivity/plot. Ten elite cowpea genotypes were selected, in which principal components 1 and 2 encompassed variables related to yield (pod length, beans/pod, and productivity/plot) and life precocity (flowering and maturation), respectively. PMID:26662390
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.
Early forest fire detection using principal component analysis of infrared video
NASA Astrophysics Data System (ADS)
Saghri, John A.; Radjabi, Ryan; Jacobs, John T.
2011-09-01
A land-based early forest fire detection scheme which exploits the infrared (IR) temporal signature of fire plume is described. Unlike common land-based and/or satellite-based techniques which rely on measurement and discrimination of fire plume directly from its infrared and/or visible reflectance imagery, this scheme is based on exploitation of fire plume temporal signature, i.e., temperature fluctuations over the observation period. The method is simple and relatively inexpensive to implement. The false alarm rate is expected to be lower that of the existing methods. Land-based infrared (IR) cameras are installed in a step-stare-mode configuration in potential fire-prone areas. The sequence of IR video frames from each camera is digitally processed to determine if there is a fire within camera's field of view (FOV). The process involves applying a principal component transformation (PCT) to each nonoverlapping sequence of video frames from the camera to produce a corresponding sequence of temporally-uncorrelated principal component (PC) images. Since pixels that form a fire plume exhibit statistically similar temporal variation (i.e., have a unique temporal signature), PCT conveniently renders the footprint/trace of the fire plume in low-order PC images. The PC image which best reveals the trace of the fire plume is then selected and spatially filtered via simple threshold and median filter operations to remove the background clutter, such as traces of moving tree branches due to wind.
IMPROVED SEARCH OF PRINCIPAL COMPONENT ANALYSIS DATABASES FOR SPECTRO-POLARIMETRIC INVERSION
Casini, R.; Lites, B. W.; Ramos, A. Asensio
2013-08-20
We describe a simple technique for the acceleration of spectro-polarimetric inversions based on principal component analysis (PCA) of Stokes profiles. This technique involves the indexing of the database models based on the sign of the projections (PCA coefficients) of the first few relevant orders of principal components of the four Stokes parameters. In this way, each model in the database can be attributed a distinctive binary number of 2{sup 4n} bits, where n is the number of PCA orders used for the indexing. Each of these binary numbers (indices) identifies a group of ''compatible'' models for the inversion of a given set of observed Stokes profiles sharing the same index. The complete set of the binary numbers so constructed evidently determines a partition of the database. The search of the database for the PCA inversion of spectro-polarimetric data can profit greatly from this indexing. In practical cases it becomes possible to approach the ideal acceleration factor of 2{sup 4n} as compared to the systematic search of a non-indexed database for a traditional PCA inversion. This indexing method relies on the existence of a physical meaning in the sign of the PCA coefficients of a model. For this reason, the presence of model ambiguities and of spectro-polarimetric noise in the observations limits in practice the number n of relevant PCA orders that can be used for the indexing.
Trevizani, Gabriela A; Nasario-Junior, Olivassé; Benchimol-Barbosa, Paulo R; Silva, Lilian P; Nadal, Jurandir
2016-07-01
The purpose of this study was to investigate the application of the principal component analysis (PCA) technique on power spectral density function (PSD) of consecutive normal RR intervals (iRR) aiming at assessing its ability to discriminate healthy women according to age groups: young group (20-25 year-old) and middle-aged group (40-60 year-old). Thirty healthy and non-smoking female volunteers were investigated (13 young [mean ± SD (median): 22·8 ± 0·9 years (23·0)] and 17 Middle-aged [51·7 ± 5·3 years (50·0)]). The iRR sequence was collected during ten minutes, breathing spontaneously, in supine position and in the morning, using a heart rate monitor. After selecting an iRR segment (5 min) with the smallest variance, an auto regressive model was used to estimate the PSD. Five principal component coefficients, extracted from PSD signals, were retained for analysis according to the Mahalanobis distance classifier. A threshold established by logistic regression allowed the separation of the groups with 100% specificity, 83·2% sensitivity and 93·3% total accuracy. The PCA appropriately classified two groups of women in relation to age (young and Middle-aged) based on PSD analysis of consecutive normal RR intervals.
Improved Search of Principal Component Analysis Databases for Spectro-polarimetric Inversion
NASA Astrophysics Data System (ADS)
Casini, R.; Asensio Ramos, A.; Lites, B. W.; López Ariste, A.
2013-08-01
We describe a simple technique for the acceleration of spectro-polarimetric inversions based on principal component analysis (PCA) of Stokes profiles. This technique involves the indexing of the database models based on the sign of the projections (PCA coefficients) of the first few relevant orders of principal components of the four Stokes parameters. In this way, each model in the database can be attributed a distinctive binary number of 24n bits, where n is the number of PCA orders used for the indexing. Each of these binary numbers (indices) identifies a group of "compatible" models for the inversion of a given set of observed Stokes profiles sharing the same index. The complete set of the binary numbers so constructed evidently determines a partition of the database. The search of the database for the PCA inversion of spectro-polarimetric data can profit greatly from this indexing. In practical cases it becomes possible to approach the ideal acceleration factor of 24n as compared to the systematic search of a non-indexed database for a traditional PCA inversion. This indexing method relies on the existence of a physical meaning in the sign of the PCA coefficients of a model. For this reason, the presence of model ambiguities and of spectro-polarimetric noise in the observations limits in practice the number n of relevant PCA orders that can be used for the indexing.
Li, Zhengbang; Zhang, Wei; Pan, Dongdong; Li, Qizhai
2016-01-01
Principal component analysis (PCA) is a useful tool to identify important linear combination of correlated variables in multivariate analysis and has been applied to detect association between genetic variants and human complex diseases of interest. How to choose adequate number of principal components (PCs) to represent the original system in an optimal way is a key issue for PCA. Note that the traditional PCA, only using a few top PCs while discarding the other PCs, might significantly lose power in genetic association studies if all the PCs contain non-ignorable signals. In order to make full use of information from all PCs, Aschard and his colleagues have proposed a multi-step combined PCs method (named mCPC) recently, which performs well especially when several traits are highly correlated. However, the power superiority of mCPC has just been illustrated by simulation, while the theoretical power performance of mCPC has not been studied yet. In this work, we attempt to investigate theoretical properties of mCPC and further propose a novel and efficient strategy to combine PCs. Extensive simulation results confirm that the proposed method is more robust than existing procedures. A real data application to detect the association between gene TRAF1-C5 and rheumatoid arthritis further shows good performance of the proposed procedure. PMID:27189724
A Principal Component Analysis of global images of Jupiter obtained by Cassini ISS
NASA Astrophysics Data System (ADS)
Ordóñez Etxeberria, I.; Hueso, R.; Sánchez-Lavega, A.
2014-04-01
The Cassini spacecraft flybied Jupiter in December 2000. The Imaging Science Subsystem (ISS) cameras acquired a large number of images at different spatial resolution in several filters sensitive to different altitudes and to cloud color. We have used these images to build high-resolution multi-wavelength nearly full maps of the planet in cylindrical and polar projections. The images have been analyzed by means of a principal component analysis technique (PCA) which looks for spatial covariances in different filtered images and proposes a new set of images (Principal Components, PC) which contains most of the spatial variability. The goal of this research is triple since we: 1) explore correlations between the ammonia cloud layer observed in most filters and the upper hazes observed in methane band images and UV, 2) we explore the spatial distribution of chromophores similarly to previous studies using HST images [1, 2]; 3) we look for image combinations that could be useful for cloud features sharpening. Furthermore, we study a global characterization of reletive altimetry of clouds and hazes from synthetic indexes between images with different contributions from the methane absorption bands (CB1, CB2, CB3, MT1, MT2, MT3).
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.
Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao
2015-01-01
Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383
NASA Astrophysics Data System (ADS)
Skibba, Ramin A.; Macciò, Andrea V.
2011-09-01
We study the correlations between the structural parameters of dark matter haloes using principal component analysis. We consider a set of eight parameters, six of which are commonly used to characterize dark matter halo properties: mass, concentration, spin, shape, overdensity and the angle (ΦL) between the major axis and the angular momentum vector. Two additional parameters (xoff and ρrms) are used to describe the degree of 'relaxedness' of the halo. We find that we can account for much of the variance of these properties with halo mass and concentration, on the one hand, and halo relaxedness on the other. None the less, three principal components are usually required to account for most of the variance. We argue that halo mass is not as dominant as expected, which is a challenge for halo occupation models and semi-analytic models that assume that mass determines other halo (and galaxy) properties. In addition, we find that the angle ΦL is not significantly correlated with other halo parameters, which may present a difficulty for models in which galaxy discs are oriented in haloes in a particular way. Finally, at fixed mass, we find that a halo's environment (quantified by the large-scale overdensity) is relatively unimportant.
A Principal Components-Based Clustering Method to Identify Variants Associated with Complex Traits
Black, Mary Helen; Watanabe, Richard M.
2011-01-01
Background Multivariate methods ranging from joint SNP to principal components analysis (PCA) have been developed for testing multiple markers in a region for association with disease and disease-related traits. However, these methods suffer from low power and/or the inability to identify the subset of markers contributing to evidence for association under various scenarios. Methods We introduce or-thoblique principal components-based clustering (OPCC) as an alternative approach to identify specific subsets of markers showing association with a quantitative outcome of interest. We demonstrate the utility of OPCC using simulation studies and an example from the literature on type 2 diabetes. Results Compared to traditional methods, OPCC has similar or improved power under various scenarios of linkage disequilibrium structure and genotype availability. Most importantly, our simulations show how OPCC accurately parses large numbers of markers to a subset containing the causal variant or its proxy. Conclusion OPCC is a powerful and efficient data reduction method for detecting associations between gene variants and disease-related traits. Unlike alternative methodologies, OPCC has the ability to isolate the effect of causal SNP(s) from among large sets of markers in a candidate region. Therefore, OPCC is an improvement over PCA for testing multiple SNP associations With phenotypes Of interest. PMID:21389731
Zhang, Yiwei; Shen, Xiaotong; Pan, Wei
2013-12-01
Population stratification is of primary interest in genetic studies to infer human evolution history and to avoid spurious findings in association testing. Although it is well studied with high-density single nucleotide polymorphisms (SNPs) in genome-wide association studies (GWASs), next-generation sequencing brings both new opportunities and challenges to uncovering population structures in finer scales. Several recent studies have noticed different confounding effects from variants of different minor allele frequencies (MAFs). In this paper, using a low-coverage sequencing dataset from the 1000 Genomes Project, we compared a popular method, principal component analysis (PCA), with a recently proposed spectral clustering technique, called spectral dimensional reduction (SDR), in detecting and adjusting for population stratification at the level of ethnic subgroups. We investigated the varying performance of adjusting for population stratification with different types and sets of variants when testing on different types of variants. One main conclusion is that principal components based on all variants or common variants were generally most effective in controlling inflations caused by population stratification; in particular, contrary to many speculations on the effectiveness of rare variants, we did not find much added value with the use of only rare variants. In addition, SDR was confirmed to be more robust than PCA, especially when applied to rare variants. PMID:24123217
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.
Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao
2015-01-01
Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383
Butler, Rebecca A.
2014-01-01
Stroke aphasia is a multidimensional disorder in which patient profiles reflect variation along multiple behavioural continua. We present a novel approach to separating the principal aspects of chronic aphasic performance and isolating their neural bases. Principal components analysis was used to extract core factors underlying performance of 31 participants with chronic stroke aphasia on a large, detailed battery of behavioural assessments. The rotated principle components analysis revealed three key factors, which we labelled as phonology, semantic and executive/cognition on the basis of the common elements in the tests that loaded most strongly on each component. The phonology factor explained the most variance, followed by the semantic factor and then the executive-cognition factor. The use of principle components analysis rendered participants’ scores on these three factors orthogonal and therefore ideal for use as simultaneous continuous predictors in a voxel-based correlational methodology analysis of high resolution structural scans. Phonological processing ability was uniquely related to left posterior perisylvian regions including Heschl’s gyrus, posterior middle and superior temporal gyri and superior temporal sulcus, as well as the white matter underlying the posterior superior temporal gyrus. The semantic factor was uniquely related to left anterior middle temporal gyrus and the underlying temporal stem. The executive-cognition factor was not correlated selectively with the structural integrity of any particular region, as might be expected in light of the widely-distributed and multi-functional nature of the regions that support executive functions. The identified phonological and semantic areas align well with those highlighted by other methodologies such as functional neuroimaging and neurostimulation. The use of principle components analysis allowed us to characterize the neural bases of participants’ behavioural performance more robustly and
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.
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.
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
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.
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
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.
Brown, Niklas; Bichler, Sebastian; Fiedler, Meike; Alt, Wilfried
2016-06-01
Detection of neuro-muscular fatigue in strength training is difficult, due to missing criterion measures and the complexity of fatigue. Thus, a variety of methods are used to determine fatigue. The aim of this study was to use a principal component analysis (PCA) on a multifactorial data-set based on kinematic measurements to determine fatigue. Twenty participants (strength training experienced, 60% male) executed 3 sets of 3 exercises with 50 (12 repetitions), 75 (12 repetitions) and 100%-12 RM (RM). Data were collected with a 3D accelerometer and analysed by a newly developed algorithm to evaluate parameters for each repetition. A PCA with six variables was carried out on the results. A fatigue factor was computed based on the loadings on the first component. One-way ANOVA with Bonferroni post hoc analysis was calculated to test for differences between the intensity levels. All six input variables had high loadings on the first component. The ANOVA showed a significant difference between intensities (p < 0.001). Post-hoc analysis revealed a difference between 100% and the lower intensities (p < 0.05) and no difference between 50 and 75%-12RM. Based on these results, it is possible to distinguish between fatigued and non-fatigued sets of strength training. PMID:27111008
Liver DCE-MRI Registration in Manifold Space Based on Robust Principal Component Analysis
Feng, Qianjin; Zhou, Yujia; Li, Xueli; Mei, Yingjie; Lu, Zhentai; Zhang, Yu; Feng, Yanqiu; Liu, Yaqin; Yang, Wei; Chen, Wufan
2016-01-01
A technical challenge in the registration of dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging in the liver is intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, a manifold-based registration framework for liver DCE-MR time series is proposed. We assume that liver DCE-MR time series are located on a low-dimensional manifold and determine intrinsic similarities between frames. Based on the obtained manifold, the large deformation of two dissimilar images can be decomposed into a series of small deformations between adjacent images on the manifold through gradual deformation of each frame to the template image along the geodesic path. Furthermore, manifold construction is important in automating the selection of the template image, which is an approximation of the geodesic mean. Robust principal component analysis is performed to separate motion components from intensity changes induced by contrast agents; the components caused by motion are used to guide registration in eliminating the effect of contrast enhancement. Visual inspection and quantitative assessment are further performed on clinical dataset registration. Experiments show that the proposed method effectively reduces movements while preserving the topology of contrast-enhancing structures and provides improved registration performance. PMID:27681452
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.
Zeitzer, Jamie M.; David, Renaud; Friedman, Leah; Mulin, Emmanuel; Garcia, René; Wang, Jia; Yesavage, Jerome A.; Robert, Philippe H.; Shannon, William
2012-01-01
Objectives To determine if there is a specific pattern of gross motor activity associated with apathy in individuals with Alzheimer disease (AD). Design Examination of ad libitum 24-hour ambulatory gross motor activity patterns. Setting Community-dwelling, outpatient. Participants Ninety-two individuals with AD, 35 of whom had apathy. Measurements Wrist actigraphy data were collected and examined using functional principal component analysis (fPCA). Results Individuals with apathy have a different pattern of gross motor activity than those without apathy (first fPCA component, p <0.0001, t = 5.73, df = 90, t test) such that there is a pronounced decline in early afternoon activity in those with apathy. This change in activity is independent of depression (p = 0.68, F[1, 89] = 0.05, analysis of variance). The decline in activity is consistent with an increase in napping. Those with apathy also have an early wake and bedtime (second fPCA component, t = 2.53, df = 90, p <0.05, t test). Conclusions There is a signature activity pattern in individuals with apathy and AD that is distinct from those without apathy and those with depression. Actigraphy may be a useful adjunctive measurement in the clinical diagnosis of apathy in the context of AD. PMID:23498386
Using the robust principal component analysis algorithm to remove RF spike artifacts from MR images
Atkinson, David; Nagy, Zoltan; Chan, Rachel W.; Josephs, Oliver; Lythgoe, Mark F.; Ordidge, Roger J.; Thomas, David L.
2015-01-01
Purpose Brief bursts of RF noise during MR data acquisition (“k‐space spikes”) cause disruptive image artifacts, manifesting as stripes overlaid on the image. RF noise is often related to hardware problems, including vibrations during gradient‐heavy sequences, such as diffusion‐weighted imaging. In this study, we present an application of the Robust Principal Component Analysis (RPCA) algorithm to remove spike noise from k‐space. Methods: Corrupted k‐space matrices were decomposed into their low‐rank and sparse components using the RPCA algorithm, such that spikes were contained within the sparse component and artifact‐free k‐space data remained in the low‐rank component. Automated center refilling was applied to keep the peaked central cluster of k‐space from misclassification in the sparse component. Results: This algorithm was demonstrated to effectively remove k‐space spikes from four data types under conditions generating spikes: (i) mouse heart T1 mapping, (ii) mouse heart cine imaging, (iii) human kidney diffusion tensor imaging (DTI) data, and (iv) human brain DTI data. Myocardial T1 values changed by 86.1 ± 171 ms following despiking, and fractional anisotropy values were recovered following despiking of DTI data. Conclusion: The RPCA despiking algorithm will be a valuable postprocessing method for retrospectively removing stripe artifacts without affecting the underlying signal of interest. Magn Reson Med 75:2517–2525, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMID:26193125
NASA Astrophysics Data System (ADS)
Zhang, Xing; Wen, Gongjian
2014-10-01
Research on target detection in hyperspectral imagery (HSI) has drawn much attention recently in many areas. Due to the limitation of the HSI sensor's spatial resolution, the target of interest normally occupies only a few pixels, sometimes are even present as subpixels. This may increase the difficulties in target detection. Moreover, in some cases, such as in the rescue and surveillance tasks, small targets are the most significant information. Therefore, it is very difficult but important to effectively detect the interested small target. Using a three-dimensional tensor to model an HSI data cube can preserve as many as possible the original spatial-spectral constraint structures, which is conducive to utilize the whole information for small target detection. This paper proposes a novel and effective algorithm for small target detection in HSI based on three-dimensional principal component analysis (3D-PCA). According to the 3D-PCA, the significant components usually contain most information of imagery, in contrast, the details of small targets exist in the insignificant components. So, after 3D-PCA implemented on the HSI, the significant components which indicate the background of HSI are removed and the insignificant components are used to detect small targets. The algorithm is outstanding thanks to the tensor-based method which is applied to process the HSI directly, making full use of spatial and spectral information, by employing multilinear algebra. Experiments with a real HSI show that the detection probability of interested small targets improved greatly compared to the classical RX detector.
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)
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.
Research of power plant parameter based on the principal component analysis method
NASA Astrophysics Data System (ADS)
Yang, Yang; Zhang, Di
2012-01-01
With the development of power technology and the expansion of power plants, plant operation monitoring points are increasing at the same time. A large number of data parameters let technicians obtain more information about unit running, but adjusting and processing the data processing are inconvenient. Principal Component Analysis was used for the real-time data analysis in the thermal power plant unit running. New variables can be obtained from the multi-parameter indicators by knowledge mining. Since the new variables are pairwise uncorrelated which can reflect most of original data information, they can provide the basis for optimal operation and adjustment of the actual production units. It will also play an important role in the factory data processing and related fields.
Ferrero, A; Campos, J; Rabal, A M; Pons, A; Hernanz, M L; Corróns, A
2011-09-26
The Bidirectional Reflectance Distribution Function (BRDF) is essential to characterize an object's reflectance properties. This function depends both on the various illumination-observation geometries as well as on the wavelength. As a result, the comprehensive interpretation of the data becomes rather complex. In this work we assess the use of the multivariable analysis technique of Principal Components Analysis (PCA) applied to the experimental BRDF data of a ceramic colour standard. It will be shown that the result may be linked to the various reflection processes occurring on the surface, assuming that the incoming spectral distribution is affected by each one of these processes in a specific manner. Moreover, this procedure facilitates the task of interpolating a series of BRDF measurements obtained for a particular sample.
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.
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
Zarzo, Manuel; Stanton, David T
2006-10-01
Many classifications of odors have been proposed, but none of them have yet gained wide acceptance. Odor sensation is usually described by means of odor character descriptors. If these semantic profiles are obtained for a large diversity of compounds, the resulting database can be considered representative of odor perception space. Few of these comprehensive databases are publicly available, being a valuable source of information for fragrance research. Their statistical analysis has revealed that the underlying structure of odor space is high dimensional and not governed by a few primary odors. In a new effort to study the underlying sensory dimensions of the multivariate olfactory perception space, we have applied principal component analysis to a database of 881 perfume materials with semantic profiles comprising 82 odor descriptors. The relationships identified between the descriptors are consistent with those reported in similar studies and have allowed their classification into 17 odor classes.
Lenz, Michael; Müller, Franz-Josef; Zenke, Martin; Schuppert, Andreas
2016-01-01
Principal components analysis (PCA) is a common unsupervised method for the analysis of gene expression microarray data, providing information on the overall structure of the analyzed dataset. In the recent years, it has been applied to very large datasets involving many different tissues and cell types, in order to create a low dimensional global map of human gene expression. Here, we reevaluate this approach and show that the linear intrinsic dimensionality of this global map is higher than previously reported. Furthermore, we analyze in which cases PCA fails to detect biologically relevant information and point the reader to methods that overcome these limitations. Our results refine the current understanding of the overall structure of gene expression spaces and show that PCA critically depends on the effect size of the biological signal as well as on the fraction of samples containing this signal. PMID:27254731
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 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.
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.
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.
Turner, Dee A; Goodpaster, John V
2012-01-01
Ignitable liquid residues recovered from a fire scene will often show signs of weathering as a result of exposure to the heat of the fire. In addition, when the substrate is rich in organic matter, both weathering and microbial degradation may be observed. In this study, 20 μL aliquots of fresh gasoline samples were intentionally weathered and also subjected to microbial degradation in potting soil. These samples were then analyzed using a passive adsorption-elution recovery method and gas chromatography/mass spectrometry. Peak areas from compounds of interest were normalized and autoscaled and then subjected to principal components analysis. This analysis showed that while lower boiling compounds are subject to weathering, a different set of compounds are subject to microbial degradation. Of the compounds studied, heptane, octane, toluene, and ethylbenzene were the most vulnerable to both weathering and microbial degradation. In contrast, 1,3,5-trimethylbenzene and 2-ethyltoluene were the most resistant to both phenomena.
Elsawy, Amr S; Eldawlatly, Seif; Taher, Mohamed; Aly, Gamal M
2014-01-01
The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.
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.
NASA Astrophysics Data System (ADS)
Lin, Chia-Yu; Suhalim, Jeffrey L.; Nien, Chyong Ly; Miljković, Miloš D.; Diem, Max; Jester, James V.; Potma, Eric. O.
2011-02-01
The lipid distribution in the mouse meibomian gland was examined with picosecond spectral anti-Stokes Raman scattering (CARS) imaging. Spectral CARS data sets were generated by imaging specific localized regions of the gland within tissue sections at consecutive Raman shifts in the CH2 stretching vibrational range. Spectral differences between the location specific CARS spectra obtained in the lipid-rich regions of the acinus and the central duct were observed, which were confirmed with a Raman microspectroscopic analysis, and attributed to meibum lipid modifications within the gland. A principal component analysis of the spectral data set reveals changes in the CARS spectrum when transitioning from the acini to the central duct. These results demonstrate the utility of picosecond spectral CARS imaging combined with multivariate analysis for assessing differences in the distribution and composition of lipids in tissues.
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.
Iranmanesh, Vahab; Ahmad, Sharifah Mumtazah Syed; Adnan, Wan Azizun Wan; Yussof, Salman; Arigbabu, Olasimbo Ayodeji; Malallah, Fahad Layth
2014-01-01
One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%. PMID:25133227
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.
Bozorgzadeh, Bardia; Covey, Daniel P; Garris, Paul A; Mohseni, Pedram
2015-01-01
This paper reports on field-programmable gate array (FPGA) implementation of a digital signal processing (DSP) unit for real-time processing of neurochemical data obtained by fast-scan cyclic voltammetry (FSCV) at a carbonfiber microelectrode (CFM). The DSP unit comprises a decimation filter and two embedded processors to process the FSCV data obtained by an oversampling recording front-end and differentiate the target analyte from interferents in real time with a chemometrics algorithm using principal component regression (PCR). Interfaced with an integrated, FSCV-sensing front-end, the DSP unit successfully resolves the dopamine response from that of pH change and background-current drift, two common dopamine interferents, in flow injection analysis involving bolus injection of mixed solutions, as well as in biological tests involving electrically evoked, transient dopamine release in the forebrain of an anesthetized rat. PMID:26737451
Classification of chili powders by thin-layer chromatography and principal component analysis.
Cserháti, T; Forgács, E; Morais, H; Mota, T
2000-09-11
Silica gel, aluminium oxide, diatomaceous earth, polyamide, cyano, diol and amino plates have been tested for their capacity to separate the color pigments of six chili powders of different origin by both adsorption and reversed-phase thin-layer chromatography. The plates were evaluated at 340 and 440 nm wavelengths. Best separation of color pigments was obtained on impregnated diatomaceous earth layer using acetone-water 17:3 v/v eluent. It was found that the pigment composition of chili powders showed marked differences. Principal component analysis employed for the classification of the chili powders according to their pigment composition indicated that these differences can be used for the determination of the similarity or dissimilarity of the chili powders.
Koch, C.D.; Pirkle, F.L.; Schmidt, J.S.
1981-01-01
A Principal Components Analysis (PCA) has been written to aid in the interpretation of multivariate aerial radiometric data collected by the US Department of Energy (DOE) under the National Uranium Resource Evaluation (NURE) program. The variations exhibited by these data have been reduced and classified into a number of linear combinations by using the PCA program. The PCA program then generates histograms and outlier maps of the individual variates. Black and white plots can be made on a Calcomp plotter by the application of follow-up programs. All programs referred to in this guide were written for a DEC-10. From this analysis a geologist may begin to interpret the data structure. Insight into geological processes underlying the data may be obtained.
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.
Determination principal component content of seed oils by THz-TDS
NASA Astrophysics Data System (ADS)
Li, Jiu-sheng; Li, Xiang-jun
2009-07-01
The terahertz transmission spectra of seed oils are measured in the frequency range extending from 0.2 to 1.4 THz using terahertz time-domain spectroscopy (THz-TDS). The absorption spectra of three acid compounds (octadecanoic acid, octadecenoic acid and octadecadienoic acid) in seed oils are recorded and simulated using both THz-TDS and density functional theory (DFT) methods. Support vector regression (SVR) model using the raw measured terahertz spectral data directly as input of the principal component is established and is employed to determinate three acid compounds content for the terahertz time-domain spectroscopy. Comparison of the experimental data using liquid chromatography with predictions based on support vector regression, respectively, exhibits excellent agreement.
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%.
Lenz, Michael; Müller, Franz-Josef; Zenke, Martin; Schuppert, Andreas
2016-01-01
Principal components analysis (PCA) is a common unsupervised method for the analysis of gene expression microarray data, providing information on the overall structure of the analyzed dataset. In the recent years, it has been applied to very large datasets involving many different tissues and cell types, in order to create a low dimensional global map of human gene expression. Here, we reevaluate this approach and show that the linear intrinsic dimensionality of this global map is higher than previously reported. Furthermore, we analyze in which cases PCA fails to detect biologically relevant information and point the reader to methods that overcome these limitations. Our results refine the current understanding of the overall structure of gene expression spaces and show that PCA critically depends on the effect size of the biological signal as well as on the fraction of samples containing this signal. PMID:27254731
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.
Ghosh, Antara; Barman, Soma
2016-06-01
Gene systems are extremely complex, heterogeneous, and noisy in nature. Many statistical tools which are used to extract relevant feature from genes provide fuzzy and ambiguous information. High-dimensional gene expression database available in public domain usually contains thousands of genes. Efficient prediction method is demanding nowadays for accurate identification of such database. Euclidean distance measurement and principal component analysis methods are applied on such databases to identify the genes. In both methods, prediction algorithm is based on homology search approach. Digital Signal Processing technique along with statistical method is used for analysis of genes in both cases. A two-level decision logic is used for gene classification as healthy or cancerous. This binary logic minimizes the prediction error and improves prediction accuracy. Superiority of the method is judged by receiver operating characteristic curve. PMID:26877227
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.
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.
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.
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.
NASA Astrophysics Data System (ADS)
Tarvainen, Mika P.; Laitinen, Tomi; Lyyra-Laitinen, Tiina; Niskanen, Juha-Pekka; Karjalainen, Pasi A.
2007-12-01
Ventricular repolarization duration (VRD) is affected by heart rate and autonomic control, and thus VRD varies in time in a similar way as heart rate. VRD variability is commonly assessed by determining the time differences between successive R- and T-waves, that is, RT intervals. Traditional methods for RT interval detection necessitate the detection of either T-wave apexes or offsets. In this paper, we propose a principal-component-regression- (PCR-) based method for estimating RT variability. The main benefit of the method is that it does not necessitate T-wave detection. The proposed method is compared with traditional RT interval measures, and as a result, it is observed to estimate RT variability accurately and to be less sensitive to noise than the traditional methods. As a specific application, the method is applied to exercise electrocardiogram (ECG) recordings.
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.
Biffi, Alessandro; Anderson, Christopher D.; Nalls, Michael A.; Rahman, Rosanna; Sonni, Akshata; Cortellini, Lynelle; Rost, Natalia S.; Matarin, Mar; Hernandez, Dena G.; Plourde, Anna; de Bakker, Paul I.W.; Ross, Owen A.; Greenberg, Steven M.; Furie, Karen L.; Meschia, James F.; Singleton, Andrew B.; Saxena, Richa; Rosand, Jonathan
2010-01-01
Although inherited mitochondrial genetic variation can cause human disease, no validated methods exist for control of confounding due to mitochondrial population stratification (PS). We sought to identify a reliable method for PS assessment in mitochondrial medical genetics. We analyzed mitochondrial SNP data from 1513 European American individuals concomitantly genotyped with the use of a previously validated panel of 144 mitochondrial markers as well as the Affymetrix 6.0 (n = 432), Illumina 610-Quad (n = 458), or Illumina 660 (n = 623) platforms. Additional analyses were performed in 938 participants in the Human Genome Diversity Panel (HGDP) (Illumina 650). We compared the following methods for controlling for PS: haplogroup-stratified analyses, mitochondrial principal-component analysis (PCA), and combined autosomal-mitochondrial PCA. We computed mitochondrial genomic inflation factors (mtGIFs) and test statistics for simulated case-control and continuous phenotypes (10,000 simulations each) with varying degrees of correlation with mitochondrial ancestry. Results were then compared across adjustment methods. We also calculated power for discovery of true associations under each method, using a simulation approach. Mitochondrial PCA recapitulated haplogroup information, but haplogroup-stratified analyses were inferior to mitochondrial PCA in controlling for PS. Correlation between nuclear and mitochondrial principal components (PCs) was very limited. Adjustment for nuclear PCs had no effect on mitochondrial analysis of simulated phenotypes. Mitochondrial PCA performed with the use of data from commercially available genome-wide arrays correlated strongly with PCA performed with the use of an exhaustive mitochondrial marker panel. Finally, we demonstrate, through simulation, no loss in power for detection of true associations with the use of mitochondrial PCA. PMID:20537299
Chen, Wan-hui; Liu, Xu-hua; He, Xiong-kui; Min, Shun-geng; Zhang, Lu-da
2010-11-01
Elastic net is an improvement of the least-squares method by introducing in L1 and L2 penalties, and it has the advantages of the variable selection. The quantitative analysis model build by Elastic net can improve the prediction accuracy. Using 89 wheat samples as the experiment material, the spectrum principal components of the samples were selected by Elastic net. The analysis model was established for the near-infrared spectrum and the wheat's protein content, and the feasibility of using Elastic net to establish the quantitative analysis model was confirmed. In experiment, the 89 wheat samples were randomly divided into two groups, with 60 samples being the model set and 29 samples being the prediction set. The 60 samples were used to build analysis model to predict the protein contents of the 29 samples, and correlation coefficient (R) of the predicted value and chemistry observed value was 0. 984 9, with the mean relative error being 2.48%. To further investigate the feasibility and stability of the model, the 89 samples were randomly selected five times, with 60 samples to be model set and 29 samples to be prediction set. The five groups of principal components which were selected by Elastic net for building model were basically consistent, and compared with the PCR and PLS method, the model prediction accuracies were all better than PCR and similar with PLS. In view of the fact that Elastic net can realize the variable selection and the model has good prediction, it was shown that Elastic net is suitable method for building chemometrics quantitative analysis model. PMID:21284156
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.
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.
NASA Astrophysics Data System (ADS)
Lee, J. H.; Kitanidis, P. K.
2014-12-01
The geostatistical approach (GA) to inversion has been applied to many engineering applications to estimate unknown parameter functions and quantify the uncertainty in estimation. Thanks to recent advances in sensor technology, large-scale/joint inversions have become more common and the implementation of the traditional GA algorithm would require thousands of expensive numerical simulation runs, which would be computationally infeasible. To overcome the computational challenges, we present the Principal Component Geostatistical Approach (PCGA) that makes use of leading principal components of the prior information to avoid expensive sensitivity computations and obtain an approximate GA solution and its uncertainty with a few hundred numerical simulation runs. As we show in this presentation, the PCGA estimate is close to, even almost same as the estimate obtained from full-model implemented GA while one can reduce the computation time by the order of 10 or more in most practical cases. Furthermore, our method is "black-box" in the sense that any numerical simulation software can be linked to PCGA to perform the geostatistical inversion. This enables a hassle-free implementation of GA to multi-physics problems and joint inversion with different types of measurements such as hydrologic, chemical, and geophysical data obviating the need to explicitly compute the sensitivity of measurements through expensive coupled numerical simulations. Lastly, the PCGA is easily implemented to run the numerical simulations in parallel, thus taking advantage of high performance computing environments. We show the effectiveness and efficiency of our method with several examples such as 3-D transient hydraulic tomography, joint inversion of head and tracer data and geochemical heterogeneity identification.
Use of a Principal Components Analysis for the Generation of Daily Time Series.
NASA Astrophysics Data System (ADS)
Dreveton, Christine; Guillou, Yann
2004-07-01
A new approach for generating daily time series is considered in response to the weather-derivatives market. This approach consists of performing a principal components analysis to create independent variables, the values of which are then generated separately with a random process. Weather derivatives are financial or insurance products that give companies the opportunity to cover themselves against adverse climate conditions. The aim of a generator is to provide a wider range of feasible situations to be used in an assessment of risk. Generation of a temperature time series is required by insurers or bankers for pricing weather options. The provision of conditional probabilities and a good representation of the interannual variance are the main challenges of a generator when used for weather derivatives. The generator was developed according to this new approach using a principal components analysis and was applied to the daily average temperature time series of the Paris-Montsouris station in France. The observed dataset was homogenized and the trend was removed to represent correctly the present climate. The results obtained with the generator show that it represents correctly the interannual variance of the observed climate; this is the main result of the work, because one of the main discrepancies of other generators is their inability to represent accurately the observed interannual climate variance—this discrepancy is not acceptable for an application to weather derivatives. The generator was also tested to calculate conditional probabilities: for example, the knowledge of the aggregated value of heating degree-days in the middle of the heating season allows one to estimate the probability if reaching a threshold at the end of the heating season. This represents the main application of a climate generator for use with weather derivatives.
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.
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
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
Shin, Eui-Cheol; Hwang, Chung Eun; Lee, Byong Won; Kim, Hyun Tae; Ko, Jong Min; Baek, In Youl; Lee, Yang-Bong; Choi, Jin Sang; Cho, Eun Ju; Seo, Weon Taek; Cho, Kye Man
2012-09-01
The purpose of this study was to investigate the fatty acid profiles in 18 soybean cultivars grown in Korea. A total of eleven fatty acids were identified in the sample set, which was comprised of myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1, ω7), stearic (C18:0), oleic (C18:1, ω9), linoleic (C18:2, ω6), linolenic (C18:3, ω3), arachidic (C20:0), gondoic (C20:1, ω9), behenic (C22:0), and lignoceric (C24:0) acids by gas-liquid chromatography with flame ionization detector (GC-FID). Based on their color, yellow-, black-, brown-, and green-colored cultivars were denoted. Correlation coefficients (r) between the nine major fatty acids identified (two trace fatty acids, myristic and palmitoleic, were not included in the study) were generated and revealed an inverse association between oleic and linoleic acids (r=-0.94, p<0.05), while stearic acid was positively correlated to arachidic acid (r=0.72, p<0.05). Principal component analysis (PCA) of the fatty acid data yielded four significant principal components (PCs; i.e., eigenvalues>1), which together account for 81.49% of the total variance in the data set; with PC1 contributing 28.16% of the total. Eigen analysis of the correlation matrix loadings of the four significant PCs revealed that PC1 was mainly contributed to by oleic, linoleic, and gondoic acids, PC2 by stearic, linolenic and arachidic acids, PC3 by behenic and lignoceric acids, and PC4 by palmitic acid. The score plots generated between PC1-PC2 and PC3-PC4 segregated soybean cultivars based on fatty acid composition. PMID:24471082
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.
Smith, Andrew D A C; Emmett, Pauline M; Newby, P K; Northstone, Kate
2013-05-28
Principal components analysis (PCA) is a popular method for deriving dietary patterns. A number of decisions must be made throughout the analytic process, including how to quantify the input variables of the PCA. The present study aims to compare the effect of using different input variables on the patterns extracted using PCA on 3-d diet diary data collected from 7473 children, aged 10 years, in the Avon Longitudinal Study of Parents and Children. Four options were examined: weight consumed of each food group (g/d), energy-adjusted weight, percentage contribution to energy of each food group and binary intake (consumed/not consumed). Four separate PCA were performed, one for each intake measurement. Three or four dietary patterns were obtained from each analysis, with at least one component that described 'more healthy' and 'less healthy' diets and one component that described a diet with high consumption of meat, potatoes and vegetables. There were no obvious differences between the patterns derived using percentage energy as a measurement and adjusting weight for total energy intake, compared to those derived using gram weights. Using binary input variables yielded a component that loaded positively on reduced fat and reduced sugar foods. The present results suggest that food intakes quantified by gram weights or as binary variables both resulted in meaningful dietary patterns and each method has distinct advantages: weight takes into account the amount of each food consumed and binary intake appears to describe general food preferences, which are potentially easier to modify and useful in public health settings. PMID:22950853
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
Comparison of Fourier, principal component and wavelet analyses for high speed flame measurements
NASA Astrophysics Data System (ADS)
Wickersham, Andrew J.; Li, Xuesong; Ma, Lin
2014-04-01
The continuing advancement of high speed, combustion diagnostics calls for mathematical techniques that can extract key information from large datasets. This paper therefore describes a case study to compare the characterization of combustion dynamics behind a V-gutter flame holder using three mathematical methods: Fourier analysis, principal component analysis, (PCA), and wavelet analysis (WA). The comparison focuses on the analysis of the characteristic frequencies of flow-flame interactions, with a particular emphasis on the analysis of transient and unsteady combustion procedures, such as lean blow off. Experimental data obtained under a range of conditions were analyzed using all three methods, and several observations were made. When applied to the analysis of stable combustion processes, all three methods reported frequency characteristics that were similar both quantitatively and qualitatively. Under unstable and transient combustion conditions, the WA method is capable of revealing the dynamics of the frequency components in the measurements, while traditional Fourier and PCA methods encounter application restrictions. Lastly, these applications also demonstrated WA's suitability for practical combustion measurements beyond chemiluminescence, such as its applicability to discrete signals, insensitivity to the choice of wavelet basis, and insensitivity to the target signal extracted from the raw measurements.
Principal Component Analysis of Chinese Porcelains from the Five Dynasties to the Qing Dynasty
NASA Astrophysics Data System (ADS)
Yap, C. T.; Hua, Younan
1992-10-01
This is a study of the possibility of identifying antique Chinese porcelains according to the period or dynasty, using major and minor chemical components (SiO2 , Al2O3 , Fe2O3 , K2O, Na2O, CaO and MgO) from the body of the porcelain. Principal component analysis is applied to published data on 66 pieces of Chinese procelains made in Jingdezhen during the Five Dynasties and the Song, Yuan, Ming and Qing Dynasties. It is shown that porcelains made during the Five Dynasties and the Yuan (or Ming) and Qing Dynasties can be segregated completely without any overlap. However, there is appreciable overlap between the Five Dynasties and the Song Dynasty, some overlap between the Song and Ming Dynasties and also between the Yuan and Ming Dynasties. Interestingly, Qing procelains are well separated from all the others. The percentage of silica in the porcelain body decreases and that of alumina increases with recentness with the exception of the Yuan and Ming Dynasties, where this trend is reversed.
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
Satellite image fusion based on principal component analysis and high-pass filtering.
Metwalli, Mohamed R; Nasr, Ayman H; Allah, Osama S Farag; El-Rabaie, S; Abd El-Samie, Fathi E
2010-06-01
This paper presents an integrated method for the fusion of satellite images. Several commercial earth observation satellites carry dual-resolution sensors, which provide high spatial resolution or simply high-resolution (HR) panchromatic (pan) images and low-resolution (LR) multi-spectral (MS) images. Image fusion methods are therefore required to integrate a high-spectral-resolution MS image with a high-spatial-resolution pan image to produce a pan-sharpened image with high spectral and spatial resolutions. Some image fusion methods such as the intensity, hue, and saturation (IHS) method, the principal component analysis (PCA) method, and the Brovey transform (BT) method provide HR MS images, but with low spectral quality. Another family of image fusion methods, such as the high-pass-filtering (HPF) method, operates on the basis of the injection of high frequency components from the HR pan image into the MS image. This family of methods provides less spectral distortion. In this paper, we propose the integration of the PCA method and the HPF method to provide a pan-sharpened MS image with superior spatial resolution and less spectral distortion. The experimental results show that the proposed fusion method retains the spectral characteristics of the MS image and, at the same time, improves the spatial resolution of the pan-sharpened image. PMID:20508708
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.
Chavez, P.S., Jr.; Kwarteng, A.Y.
1989-01-01
A challenge encountered with Landsat Thematic Mapper (TM) data, which includes data from size reflective spectral bands, is displaying as much information as possible in a three-image set for color compositing or digital analysis. Principal component analysis (PCA) applied to the six TM bands simultaneously is often used to address this problem. However, two problems that can be encountered using the PCA method are that information of interest might be mathematically mapped to one of the unused components and that a color composite can be difficult to interpret. "Selective' PCA can be used to minimize both of these problems. The spectral contrast among several spectral regions was mapped for a northern Arizona site using Landsat TM data. Field investigations determined that most of the spectral contrast seen in this area was due to one of the following: the amount of iron and hematite in the soils and rocks, vegetation differences, standing and running water, or the presence of gypsum, which has a higher moisture retention capability than do the surrounding soils and rocks. -from Authors
Linting, Mariëlle; Meulman, Jacqueline J; Groenen, Patrick J F; van der Kooij, Anita J
2007-09-01
Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate normality. For nonlinear PCA, however, standard options for establishing stability are not provided. The authors use the nonparametric bootstrap procedure to assess the stability of nonlinear PCA results, applied to empirical data. They use confidence intervals for the variable transformations and confidence ellipses for the eigenvalues, the component loadings, and the person scores. They discuss the balanced version of the bootstrap, bias estimation, and Procrustes rotation. To provide a benchmark, the same bootstrap procedure is applied to linear PCA on the same data. On the basis of the results, the authors advise using at least 1,000 bootstrap samples, using Procrustes rotation on the bootstrap results, examining the bootstrap distributions along with the confidence regions, and merging categories with small marginal frequencies to reduce the variance of the bootstrap results.
Medina, José M; Díaz, José A; Vukusic, Pete
2015-04-20
Iridescent structural colors in biology exhibit sophisticated spatially-varying reflectance properties that depend on both the illumination and viewing angles. The classification of such spectral and spatial information in iridescent structurally colored surfaces is important to elucidate the functional role of irregularity and to improve understanding of color pattern formation at different length scales. In this study, we propose a non-invasive method for the spectral classification of spatial reflectance patterns at the micron scale based on the multispectral imaging technique and the principal component analysis similarity factor (PCASF). We demonstrate the effectiveness of this approach and its component methods by detailing its use in the study of the angle-dependent reflectance properties of Pavo cristatus (the common peacock) feathers, a species of peafowl very well known to exhibit bright and saturated iridescent colors. We show that multispectral reflectance imaging and PCASF approaches can be used as effective tools for spectral recognition of iridescent patterns in the visible spectrum and provide meaningful information for spectral classification of the irregularity of the microstructure in iridescent plumage. PMID:25969062
NASA Astrophysics Data System (ADS)
Bernat, Edward M.; Nelson, Lindsay D.; Holroyd, Clay B.; Gehring, William J.; Patrick, Christopher J.
2008-08-01
Measurement of EEG event-related potential (ERP) data has been most commonly undertaken in the time-domain, which can be complicated to interpret when separable activity overlaps in time. When the overlapping activity has distinct frequency characteristics, however, time-frequency (TF) signal processing techniques can be useful. The current report utilized ERP data from a cognitive task producing typical feedback-related negativity (FRN) and P300 ERP components which overlap in time. TF transforms were computed using the binomial reduced interference distribution (RID), and the resulting TF activity was then characterized using principal components analysis (PCA). Consistent with previous work, results indicate that the FRN was more related to theta activity (3-7 Hz) and P300 more to delta activity (below 3 Hz). At the same time, both time-domain measures were shown to be mixtures of TF theta and delta activity, highlighting the difficulties with overlapping activity. The TF theta and delta measures, on the other hand, were largely independent from each other, but also independently indexed the feedback stimulus parameters investigated. Results support the view that TF decomposition can greatly improve separation of overlapping EEG/ERP activity relevant to cognitive models of performance monitoring.
Aviyente, Selin; Bernat, Edward M.; Malone, Stephen M.; Iacono, William G.
2010-01-01
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. PMID:20730031
Agudelo-Gómez, D A; Pelicioni Savegnago, R; Buzanskas, M E; Ferraudo, A S; Prado Munari, D; Cerón-Muñoz, M F
2015-08-01
A multitrait model (MC) and 5 reduced-rank models with principal component structure (components PC, PC, PC, PC, and PC) were compared. The objectives were to determine the most appropriate model for estimating genetic parameters and to evaluate the genetic progress of dual-purpose buffaloes in Colombia using that model. The traits evaluated were weaning weight (WW), yearling weight (W12), weight at 18 mo of age (W18), weight at 2 yr of age (W24), age at first calving (AFC), and milk yield at 270 d of first lactation (MY270). Genealogy and productive information from 34,326 buffaloes born in Colombia between 1997 and 2014 were used. Colombian Association of Buffalo Breeders (ACB) provided the data. Direct additive genetic and residual random effects were included for all the traits. In addition, the maternal additive genetic effect and permanent environmental random effect were included for WW, while a maternal additive genetic effect was included for W12. The fixed effects were contemporary group (farm, year, and calving season: January to April, May to August, or September to December; for all traits) and sex (for WW, W12, W18, and W24). Additionally, parity was included as a fixed effect for WW and W12. Age at weighing was used as a covariate for WW, W12, W18, and W24. Genetic progress of all traits was analyzed using a generalized smooth model (GAM). According to the Akaike information criteria (AIC), the best model was the one with reduced rank and first 3 principal components (PC). This model maintained 100% of the original variance. Genetic parameters estimated with this model were similar to those estimated by MC, but with smaller standard errors. Heritability for weight-related traits ranged between 0.23 and 0.44. Heritabilities for AFC and MY270 were 0.14 and 0.24, respectively. The genetic correlations obtained between all weights (WW, W12, W18, and W24) were positive and high. Correlations between all weights with AFC were negative and moderate
Bright-field cell image segmentation by principal component pursuit with an Ncut penalization
NASA Astrophysics Data System (ADS)
Chen, Yuehuan; Wan, Justin W. L.
2015-03-01
Segmentation of cells in time-lapse bright-field microscopic images is crucial in understanding cell behaviours for oncological research. However, the complex nature of the cells makes it difficult to segment cells accurately. Furthermore, poor contrast, broken cell boundaries and the halo artifact pose additional challenges to this problem. Standard segmentation techniques such as edged-based methods, watershed, or active contours result in poor segmentation. Other existing methods for bright-field images cannot provide good results without localized segmentation steps. In this paper, we present two robust mathematical models to segment bright-field cells automatically for the entire image. These models treat cell image segmentation as a background subtraction problem, which can be formulated as a Principal Component Pursuit (PCP) problem. Our first segmentation model is formulated as a PCP with nonnegative constraints. We exploit the sparse component of the PCP solution for identifying the cell pixels. However, there is no control on the quality of the sparse component and the nonzero entries can scatter all over the image, resulting in a noisy segmentation. The second model is an improvement of the first model by combining PCP with spectral clustering. Seemingly unrelated approaches, we combine the two techniques by incorporating normalized-cut in the PCP as a measure for the quality of the segmentation. These two models have been applied to a set of C2C12 cells obtained from bright-field microscopy. Experimental results demonstrate that the proposed models are effective in segmenting cells from bright-field images.
Modified Diatomaceous earth as a principal stationary phase component in TLC.
Ergül, Soner; Kadan, Imdat; Savaşci, Sahin; Ergül, Suzan
2005-09-01
Modified natural diatomaceous earth (DE) is a principal component of the stationary phase in normal thin-layer chromatography (TLC) applications and is mixed with commercial silica gel 60GF254 (Si-60GF254). Modification is carried out by flux calcination and refluxing with acid. Natural DE, modified DEs [flux calcinated (FC)DE and FCDE-I), and Si-60GF254 are characterized by scanning electron microscopy and Fourier-transform-IR spectroscopy. Particle size, specific surface area, pore distribution, pore volume, and surface hydroxyl group density parameters of materials are determined by various techniques. FCDE-I and Si-60GF254 are investigated for their usefulness in the stationary phase of TLC both individually and in composition. Commercially available red and blue ink samples are run on layers of Si-60GF254 and FCDE-I individually, and on various FCDE-I and Si-60GF254 mixtures. Butanol-ethanol-2M ammonia (3:1:1, v/v) and butanol-acetic acid-water (12:3:5, v/v) mixtures are used as mobile phases. The polarities of stationary phases decrease, and the retention factor (Rf) values of ink components increase when the FCDE-I content of the stationary phase increases. The properties of the stationary phase can be optimized by adding FCDE-I to Si-60GF254. This study may be useful in understanding both the systematic effects of stationary phase properties [e.g., specific surface area and surface hydroxyl group density, aOH(s)] and those of the mobile phase (e.g., polarity and acidity) on Rf values and the separability of components.
NASA Astrophysics Data System (ADS)
Maslennikova, Yu S.; Bochkarev, V. V.; Voloskov, D. S.
2015-01-01
In this paper we describe results of the principal components analysis of the dynamics of Total Electronic Content (TEC) data with the use of global maps presented by the Jet Propulsion Laboratory (NASA, USA) for the period of 2007-2011. We show that the result of the decomposition in principal components essentially depends on the method used for preprocessing the data, their representation (the used coordinate system), and the data centering technique (e.g., daily and seasonal components extracting). The use of momentarily co-moving frame of reference and other special techniques provide opportunity for the detailed analysis of the ionospheric equatorial anomaly. The covariance matrix of decomposition was calculated using Spearman's rank correlation coefficient that allows reducing statistical relationship between principal components.
Robust principal component analysis-based four-dimensional computed tomography
NASA Astrophysics Data System (ADS)
Gao, Hao; Cai, Jian-Feng; Shen, Zuowei; Zhao, Hongkai
2011-06-01
The purpose of this paper for four-dimensional (4D) computed tomography (CT) is threefold. (1) A new spatiotemporal model is presented from the matrix perspective with the row dimension in space and the column dimension in time, namely the robust PCA (principal component analysis)-based 4D CT model. That is, instead of viewing the 4D object as a temporal collection of three-dimensional (3D) images and looking for local coherence in time or space independently, we perceive it as a mixture of low-rank matrix and sparse matrix to explore the maximum temporal coherence of the spatial structure among phases. Here the low-rank matrix corresponds to the 'background' or reference state, which is stationary over time or similar in structure; the sparse matrix stands for the 'motion' or time-varying component, e.g., heart motion in cardiac imaging, which is often either approximately sparse itself or can be sparsified in the proper basis. Besides 4D CT, this robust PCA-based 4D CT model should be applicable in other imaging problems for motion reduction or/and change detection with the least amount of data, such as multi-energy CT, cardiac MRI, and hyperspectral imaging. (2) A dynamic strategy for data acquisition, i.e. a temporally spiral scheme, is proposed that can potentially maintain similar reconstruction accuracy with far fewer projections of the data. The key point of this dynamic scheme is to reduce the total number of measurements, and hence the radiation dose, by acquiring complementary data in different phases while reducing redundant measurements of the common background structure. (3) An accurate, efficient, yet simple-to-implement algorithm based on the split Bregman method is developed for solving the model problem with sparse representation in tight frames.
Sheppard, P S; Stevenson, J M; Graham, R B
2016-05-01
The objective of the present study was to determine if there is a sex-based difference in lifting technique across increasing-load conditions. Eleven male and 14 female participants (n = 25) with no previous history of low back disorder participated in the study. Participants completed freestyle, symmetric lifts of a box with handles from the floor to a table positioned at 50% of their height for five trials under three load conditions (10%, 20%, and 30% of their individual maximum isometric back strength). Joint kinematic data for the ankle, knee, hip, and lumbar and thoracic spine were collected using a two-camera Optotrak motion capture system. Joint angles were calculated using a three-dimensional Euler rotation sequence. Principal component analysis (PCA) and single component reconstruction were applied to assess differences in lifting technique across the entire waveforms. Thirty-two PCs were retained from the five joints and three axes in accordance with the 90% trace criterion. Repeated-measures ANOVA with a mixed design revealed no significant effect of sex for any of the PCs. This is contrary to previous research that used discrete points on the lifting curve to analyze sex-based differences, but agrees with more recent research using more complex analysis techniques. There was a significant effect of load on lifting technique for five PCs of the lower limb (PC1 of ankle flexion, knee flexion, and knee adduction, as well as PC2 and PC3 of hip flexion) (p < 0.005). However, there was no significant effect of load on the thoracic and lumbar spine. It was concluded that when load is standardized to individual back strength characteristics, males and females adopted a similar lifting technique. In addition, as load increased male and female participants changed their lifting technique in a similar manner. PMID:26851478
Robust principal component analysis-based four-dimensional computed tomography.
Gao, Hao; Cai, Jian-Feng; Shen, Zuowei; Zhao, Hongkai
2011-06-01
The purpose of this paper for four-dimensional (4D) computed tomography (CT) is threefold. (1) A new spatiotemporal model is presented from the matrix perspective with the row dimension in space and the column dimension in time, namely the robust PCA (principal component analysis)-based 4D CT model. That is, instead of viewing the 4D object as a temporal collection of three-dimensional (3D) images and looking for local coherence in time or space independently, we perceive it as a mixture of low-rank matrix and sparse matrix to explore the maximum temporal coherence of the spatial structure among phases. Here the low-rank matrix corresponds to the 'background' or reference state, which is stationary over time or similar in structure; the sparse matrix stands for the 'motion' or time-varying component, e.g., heart motion in cardiac imaging, which is often either approximately sparse itself or can be sparsified in the proper basis. Besides 4D CT, this robust PCA-based 4D CT model should be applicable in other imaging problems for motion reduction or/and change detection with the least amount of data, such as multi-energy CT, cardiac MRI, and hyperspectral imaging. (2) A dynamic strategy for data acquisition, i.e. a temporally spiral scheme, is proposed that can potentially maintain similar reconstruction accuracy with far fewer projections of the data. The key point of this dynamic scheme is to reduce the total number of measurements, and hence the radiation dose, by acquiring complementary data in different phases while reducing redundant measurements of the common background structure. (3) An accurate, efficient, yet simple-to-implement algorithm based on the split Bregman method is developed for solving the model problem with sparse representation in tight frames. PMID:21540490
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
NASA Astrophysics Data System (ADS)
Casoni, Dorina; Sârbu, Costel
2014-01-01
A novel chemometric approach is described for evaluating the radical scavenging activity of biogenic amine related compounds by using the 2,2-diphenyl-1-picrylhydrazyl (DPPHrad ) procedure and principal component analysis (PCA) tool. By a comprehensive chemometric investigation of variations in the radical scavenging profiles provided by the full-range UV-Vis spectra for different test duration and different relative concentrations (different molar ratio - [AH]/[DPPHrad ]) of the investigated compounds, new antioxidant activity evaluation parameters were proposed. The new proposed parameters (PC1, mPC1, maxPC1) are in good agreement with the reference DPPHrad results (% RSA and IC50 derived from the reference DPPHrad test), obtained for the investigated amines and reference antioxidants. Much more, the PCA profiles are better patterns for the comprehensive characterization of radical scavenging activity of compounds, allowing visualization of complex information by a simple graphical representation and underlying the (dis)similarity of compounds related both to the reaction kinetics and compounds concentration.
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.
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.
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. PMID:18238065
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
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.
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.
Magnetic Flux Leakage and Principal Component Analysis for metal loss approximation in a pipeline
NASA Astrophysics Data System (ADS)
Ruiz, M.; Mujica, L. E.; Quintero, M.; Florez, J.; Quintero, S.
2015-07-01
Safety and reliability of hydrocarbon transportation pipelines represent a critical aspect for the Oil an Gas industry. Pipeline failures caused by corrosion, external agents, among others, can develop leaks or even rupture, which can negatively impact on population, natural environment, infrastructure and economy. It is imperative to have accurate inspection tools traveling through the pipeline to diagnose the integrity. In this way, over the last few years, different techniques under the concept of structural health monitoring (SHM) have continuously been in development. This work is based on a hybrid methodology that combines the Magnetic Flux Leakage (MFL) and Principal Components Analysis (PCA) approaches. The MFL technique induces a magnetic field in the pipeline's walls. The data are recorded by sensors measuring leakage magnetic field in segments with loss of metal, such as cracking, corrosion, among others. The data provide information of a pipeline with 15 years of operation approximately, which transports gas, has a diameter of 20 inches and a total length of 110 km (with several changes in the topography). On the other hand, PCA is a well-known technique that compresses the information and extracts the most relevant information facilitating the detection of damage in several structures. At this point, the goal of this work is to detect and localize critical loss of metal of a pipeline that are currently working.
NASA Astrophysics Data System (ADS)
Zhao, Hong; Li, Changjun; Li, Hongping; Lv, Kebo; Zhao, Qinghui
2016-06-01
The sea surface salinity (SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity (SMOS) satellite data. Based on the principal component regression (PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea (in the area of 4°-25°N, 105°-125°E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu (practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.
Recognition of grasp types through principal components of DWT based EMG features.
Kakoty, Nayan M; Hazarika, Shyamanta M
2011-01-01
With the advancement in machine learning and signal processing techniques, electromyogram (EMG) signals have increasingly gained importance in man-machine interaction. Multifingered hand prostheses using surface EMG for control has appeared in the market. However, EMG based control is still rudimentary, being limited to a few hand postures based on higher number of EMG channels. Moreover, control is non-intuitive, in the sense that the user is required to learn to associate muscle remnants actions to unrelated posture of the prosthesis. Herein lies the promise of a low channel EMG based grasp classification architecture for development of an embedded intelligent prosthetic controller. This paper reports classification of six grasp types used during 70% of daily living activities based on two channel forearm EMG. A feature vector through principal component analysis of discrete wavelet transform coefficients based features of the EMG signal is derived. Classification is through radial basis function kernel based support vector machine following preprocessing and maximum voluntary contraction normalization of EMG signals. 10-fold cross validation is done. We have achieved an average recognition rate of 97.5%.
NASA Astrophysics Data System (ADS)
Yuchechen, Adrián E.; Canziani, Pablo O.; Bischoff, Susana A.
2016-05-01
To understand how the tropopause annual evolution relates to the troposphere and lower stratosphere over southern South America, the study analyzes the joint behavior of single and double thermal tropopauses with the 500 and 100 hPa levels in the region. Radiosonde data spanning the period 1973-2014 were used. Geopotential height time series that were filtered known cycles were used as input for an unrotated S-mode principal components analysis. The first three leading modes of variability were analyzed. The first one has a strong semi-annual behavior, linked to wind cycles, with maximum activity in the center of the study region on the lee of the Andes. It appears to be linked to the vertical propagation of planetary and gravity waves. Semi-annual and terannual cycles dominate the second mode, the associated spatial patterns having strong resemblance with the occurrence of cold fronts. The annual time series for the third mode are coupled to a blocking index over the South Atlantic, and the associated spatial structures are also similar to blocking patterns. Results are in good agreement with observations, showing that the use of thermal tropopauses is a valid tool for studying different phenomena taking place in the region.
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
Keithley, Richard B; Wightman, R Mark
2011-06-01
Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586
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
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.
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.
NASA Astrophysics Data System (ADS)
Li, J.; Zhang, Y.
2006-12-01
With the growing use of large-scale sensor networks, huge volumes of sensor data are being generated from structural health monitoring systems. Vibration sensor data often constitute a large portion of the monitoring data from a structural health monitoring system. Efficient transmission and management of large-size vibration sensor datasets are becoming an increasingly important aspect of structural health monitoring systems. To address this problem of emerging importance, this paper presents a novel method for interactive retrieval and management of sensor network data. Pre-defined features obtained from principal components analysis (PCA) are proposed for the detection of changes in the monitored structure. The PCA transform and linear predictor are also used in the data compression scheme to allow users to retrieve data progressively with significantly reduced data size. The results of a case study involving wireless sensor network data collected from a five-story model building are presented to demonstrate the potential use of the proposed method in the transmission and management of sensor network data. The proposed method is believed to provide data users with the flexibility to select data and retrieve data at multi-resolution levels, reducing raw data size, relaxing the communication bandwidth requirement, and speeding up the data transmission process.
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
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.
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.
Desdouits, Nathan; Nilges, Michael; Blondel, Arnaud
2015-02-01
Protein conformation has been recognized as the key feature determining biological function, as it determines the position of the essential groups specifically interacting with substrates. Hence, the shape of the cavities or grooves at the protein surface appears to drive those functions. However, only a few studies describe the geometrical evolution of protein cavities during molecular dynamics simulations (MD), usually with a crude representation. To unveil the dynamics of cavity geometry evolution, we developed an approach combining cavity detection and Principal Component Analysis (PCA). This approach was applied to four systems subjected to MD (lysozyme, sperm whale myoglobin, Dengue envelope protein and EF-CaM complex). PCA on cavities allows us to perform efficient analysis and classification of the geometry diversity explored by a cavity. Additionally, it reveals correlations between the evolutions of the cavities and structures, and can even suggest how to modify the protein conformation to induce a given cavity geometry. It also helps to perform fast and consensual clustering of conformations according to cavity geometry. Finally, using this approach, we show that both carbon monoxide (CO) location and transfer among the different xenon sites of myoglobin are correlated with few cavity evolution modes of high amplitude. This correlation illustrates the link between ligand diffusion and the dynamic network of internal cavities.
Principal component analysis of proteolytic profiles as markers of authenticity of PDO cheeses.
Guerreiro, Joana Santos; Barros, Mário; Fernandes, Paulo; Pires, Preciosa; Bardsley, Ronald
2013-02-15
The casein fraction of 13 Portuguese PDO cheeses were analysed using Urea-PAGE and reverse phase-high performance liquid chromatography (RP-HPLC) and then subjected to chemometric evaluation. The chemometric techniques of cluster analysis (CA) and principal component analysis (PCA) were used for the classification studies. Peptide mapping using Urea-PAGE followed by CA revealed two major clusters according to the similarity of the proteolytic profile of the cheeses. PCA results were in accordance with the grouping performed using CA. CA of RP-HPLC results of the matured cheeses revealed the presence of one major cluster comprising samples manufactured with only ovine milk or milk admixtures. When the results of CA technique were compared with the two PCA approaches performed, it was found that the grouping of the samples was similar. Both approaches, revealed the potential of proteolytic profiles (which is an essential aspect of cheese maturation) as markers of authenticity of PDO cheeses in terms of ripening time and milk admixtures not mentioned on the label.
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.
Principal component analysis in the wavelet domain: new features for underwater object recognition
NASA Astrophysics Data System (ADS)
Okimoto, Gordon S.; Lemonds, David W.
1999-08-01
Principal component analysis (PCA) in the wavelet domain provides powerful features for underwater object recognition applications. The multiresolution analysis of the Morlet wavelet transform (MWT) is used to pre-process echo returns from targets ensonified by biologically motivated broadband signal. PCA is then used to compress and denoise the resulting time-scale signal representation for presentation to a hierarchical neural network for object classification. Wavelet/PCA features combined with multi-aspect data fusion and neural networks have resulted in impressive underwater object recognition performance using backscatter data generated by simulate dolphin echolocation clicks and bat- like linear frequency modulated upsweeps. For example, wavelet/PCA features extracted from LFM echo returns have resulted in correct classification rates of 98.6 percent over a six target suite, which includes two mine simulators and four clutter objects. For the same data, ROC analysis of the two-class mine-like versus non-mine-like problem resulted in a probability of detection of 0.981 and a probability of false alarm of 0.032 at the 'optimal' operating point. The wavelet/PCA feature extraction algorithm is currently being implemented in VLSI for use in small, unmanned underwater vehicles designed for mine- hunting operations in shallow water environments.
Sergeant, C J; Starkey, E N; Bartz, K K; Wilson, M H; Mueter, F J
2016-04-01
To design sustainable water quality monitoring programs, practitioners must choose meaningful variables, justify the temporal and spatial extent of measurements, and demonstrate that program objectives are successfully achieved after implementation. Consequently, data must be analyzed across several variables and often from multiple sites and seasons. Multivariate techniques such as ordination are common throughout the water quality literature, but methods vary widely and could benefit from greater standardization. We have found little clear guidance and open source code for efficiently conducting ordination to explore water quality patterns. Practitioners unfamiliar with techniques such as principal components analysis (PCA) are faced with a steep learning curve to summarize expansive data sets in periodic reports and manuscripts. Here, we present a seven-step framework for conducting PCA and associated tests. The last step is dedicated to conducting Procrustes analysis, a valuable but rarely used test within the water quality field that describes the degree of concordance between separate multivariate data matrices and provides residual values for similar points across each matrix. We illustrate the utility of these tools using three increasingly complex water quality case studies in US parklands. The case studies demonstrate how PCA and Procrustes analysis answer common applied monitoring questions such as (1) do data from separate monitoring locations describe similar water quality regimes, and (2) what time periods exhibit the greatest water quality regime variability? We provide data sets and annotated R code for recreating case study results and as a base for crafting new code for similar monitoring applications. PMID:27021692
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.
Martínez, Arturo; Alcaraz, Raúl; Rieta, José J
2011-01-01
Ectopic beats are early heart beats with remarkable large amplitude that provoke serious disturbances in the analysis of electrocardiograms (ECG). These beats are very common in atrial fibrillation (AF) and are the source of important residua when the QRST is intended to be removed. Given that QRST cancellation is a binding step in the appropriate analysis of atrial activity (AA) in AF, a method for ventricular ectopic beats cancellation is proposed as a previous step to the application of any QRST removal technique. First, the method discriminates between normal and ectopic beats with an accuracy higher than 99% through QRS morphological characterization. Next, the most similar ectopic beats to the one under cancellation are clustered and serve to get their eigenvector matrix by principal component analysis. Finally, the highest variance eigenvector is used as cancellation template. The reduction ectopic rate (RER) has been defined to evaluate the method's performance by using templates generated with 5, 10, 20, 40 or 80 ectopics. Optimal results were reached with the 5 most similar complexes, yielding a RER higher than 5.5. In addition, a decreasing RER trend was noticed as the number of considered ectopics for cancellation increased. As conclusion, given that ectopics presented a remarkable variability in their morphology, the proposed cancellation approach is a robust ectopic remover and can notably facilitate the later application of any QRST cancellation technique to extract the AA in the best conditions. PMID:22255385
Desdouits, Nathan; Nilges, Michael; Blondel, Arnaud
2015-02-01
Protein conformation has been recognized as the key feature determining biological function, as it determines the position of the essential groups specifically interacting with substrates. Hence, the shape of the cavities or grooves at the protein surface appears to drive those functions. However, only a few studies describe the geometrical evolution of protein cavities during molecular dynamics simulations (MD), usually with a crude representation. To unveil the dynamics of cavity geometry evolution, we developed an approach combining cavity detection and Principal Component Analysis (PCA). This approach was applied to four systems subjected to MD (lysozyme, sperm whale myoglobin, Dengue envelope protein and EF-CaM complex). PCA on cavities allows us to perform efficient analysis and classification of the geometry diversity explored by a cavity. Additionally, it reveals correlations between the evolutions of the cavities and structures, and can even suggest how to modify the protein conformation to induce a given cavity geometry. It also helps to perform fast and consensual clustering of conformations according to cavity geometry. Finally, using this approach, we show that both carbon monoxide (CO) location and transfer among the different xenon sites of myoglobin are correlated with few cavity evolution modes of high amplitude. This correlation illustrates the link between ligand diffusion and the dynamic network of internal cavities. PMID:25424655
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
A performance measure based on principal component analysis for ceramic armor integrity
NASA Astrophysics Data System (ADS)
Rollins, D. K., Sr.; Stiehl, C. K.; Kotz, K.; Beverlin, L.; Brasche, L.
2012-05-01
Principal Component Analysis (PCA) has been applied to thru-transmission ultrasound data taken on ceramic armor. PCA will help find and accentuate differences within the tile, making it easier to find differences. First, the thru-transmission ultrasound data was analyzed. As the ultrasound transducer moves along the surface of the tile, the signal from the sound wave is measured as it reaches the receiver, giving a time signal at each tile location. The information from this time signal is dissected into ten equal segments, and the maximum peak is measured within each segment, or gate. This gives ten measurements at each tile location that correspond to tile depth An image can be made for each of the ten gate measurements. PCA was applied to this data for all of the tile samples, and a performance measure was developed from the loading information. A performance measure was developed and tested on six samples from each of the panels. When these performance measures are compared to the results of the ballistics tests, it can be seen that the performance measure correlates well to the penetration velocities found from the ballistics tests.
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
NASA Astrophysics Data System (ADS)
Hoell, S.; Omenzetter, P.
2015-07-01
The utilization of vibration signals for structural damage detection (SDD) is appealing due to the strong theoretical foundation of such approaches, ease of data acquisition and processing efficiency. Different methods are available for defining damage sensitive features (DSFs) based on vibrations, such as modal analysis or time series methods. The present paper proposes the use of partial autocorrelation coefficients of acceleration responses as DSFs. Principal component (PC) analysis is used to transform the initial DSFs to scores. The resulting scores from the healthy and damaged states are used to select the PCs which are most sensitive to damage. These are then used for making decisions about the structural state by means of statistical hypothesis testing conducted on the scores. The approach is applied to experiments with a laboratory scale wind turbine blade (WTB) made of glass-fibre reinforced epoxy composite. Damage is non-destructively simulated by attaching small masses and the WTB is excited with the help of an electrodynamic shaker using band-limited white noise. The SDD results for the selected subsets of PCs show a clear improvement of the detectability of early damages compared to other DSF selections.
Yin, Jianhua; Xia, Yang
2010-11-01
Fourier transform infrared imaging (FT-IRI) and principal component regression (PCR) 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-sulfate 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 with FT-IRI offers an accurate tool to spatially determine the distributions of macromolecular concentration in cartilage.
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.
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.
Principal component analysis of the reionization history from Planck 2015 data
NASA Astrophysics Data System (ADS)
Dai, Wei-Ming; Guo, Zong-Kuan; Cai, Rong-Gen
2015-12-01
The simple assumption of an instantaneous reionization of the Universe may bias estimates of cosmological parameters. In this paper a model-independent principal component method for the reionization history is applied to give constraints on the cosmological parameters from recent Planck 2015 data. We find that the Universe is not completely reionized at redshifts z ≥8.5 at 95% C.L. Both the reionization optical depth and matter fluctuation amplitude are higher than but consistent with those obtained in the standard instantaneous reionization scheme. The high estimated value of the matter fluctuation amplitude strengthens the tension between Planck cosmic microwave background observations and some astrophysical data, such as cluster counts and weak lensing. The tension can be significantly relieved if the neutrino masses are allowed to vary. Thanks to a high scalar spectral index, the low-scale spontaneously broken supersymmetry inflationary model can fit the data well, which is marginally disfavored at 95% C.L. in the Planck analysis.
Pipeline monitoring using acoustic principal component analysis recognition with the Mel scale
NASA Astrophysics Data System (ADS)
Wan, Chunfeng; Mita, Akira
2009-05-01
In modern cities, many important pipelines are laid underground. In order to prevent these lifeline infrastructures from accidental damage, monitoring systems are becoming indispensable. Third party activities were shown by recent reports to be a major cause of pipeline damage. Potential damage threat to the pipeline can be identified by detecting dangerous construction equipment nearby by studying the surrounding noise. Sound recognition technologies are used to identify them by their sounds, which can easily be captured by small sensors deployed along the pipelines. Pattern classification methods based on principal component analysis (PCA) were used to recognize the sounds from road cutters. In this paper, a Mel residual, i.e. the PCA residual in the Mel scale, is proposed to be the recognition feature. Determining if a captured sound belongs to a road cutter only requires checking how large its Mel residual is. Experiments were conducted and results showed that the proposed Mel-residual-based PCA recognition worked very well. The proposed Mel PCA residual recognition method will be very useful for pipeline monitoring systems to prevent accidental breakage and to ensure the safety of underground lifeline infrastructures.
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.
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 breast DCE-MRI Adjusted with a Model Based Method
Eyal, Erez.; Badikhi, Daria; Furman-Haran, Edna; Kelcz, Fredrick; Kirshenbaum, Kevin J.; Degani, Hadassa
2010-01-01
Purpose To investigate a fast, objective and standardized method for analyzing breast DCE-MRI applying principal component analysis (PCA) adjusted with a model based method. Materials and Methods 3D gradient-echo dynamic contrast-enhanced breast images of 31 malignant and 38 benign lesions, recorded on a 1.5 Tesla scanner were retrospectively analyzed by PCA and by the model based three-time-point (3TP) method. Results Intensity scaled (IS) and enhancement scaled (ES) datasets were reduced by PCA yielding a 1st IS-eigenvector that captured the signal variation between fat and fibroglandular tissue; two IS-eigenvectors and the two first ES-eigenvectors that captured contrast-enhanced changes, whereas the remaining eigenvectors captured predominantly noise changes. Rotation of the two contrast related eigenvectors led to a high congruence between the projection coefficients and the 3TP parameters. The ES-eigenvectors and the rotation angle were highly reproducible across malignant lesions enabling calculation of a general rotated eigenvector base. ROC curve analysis of the projection coefficients of the two eigenvectors indicated high sensitivity of the 1st rotated eigenvector to detect lesions (AUC>0.97) and of the 2nd rotated eigenvector to differentiate malignancy from benignancy (AUC=0.87). Conclusion PCA adjusted with a model-based method provided a fast and objective computer-aided diagnostic tool for breast DCE-MRI. PMID:19856419
Dony, R D; Coblentz, C L; Nabmias, C; Haykin, S
1996-11-01
The performance of a new, neural network-based image compression method was evaluated on digital radiographs for use in an educational environment. The network uses a mixture of principal components (MPC) representation to effect optimally adaptive transform coding of an image and has significant computational advantages over other techniques. Nine representative digital chest radiographs were compressed 10:1, 20:1, 30:1, and 40:1 with the MPC method. The five versions of each image, including the original, were shown simultaneously, in random order, to each of seven radiologists, who rated each one on a five-point scale for image quality and visibility of pathologic conditions. One radiologist also ranked four versions of each of the nine images in terms of the severity of distortion: The four versions represented 30:1 and 40:1 compression with the MPC method and with the classic Karhunen-Loève transform (KLT). Only for the images compressed 40:1 with the MPC method were there any unacceptable ratings. Nevertheless, the images compressed 40:1 received a top score in 26%-33% of the evaluations. Images compressed with the MPC method were rated better than or as good as images compressed with the KLT technique 17 of 18 times. Four of nine times, images compressed 40:1 with the MPC method were rated as good as or better than images compressed 30:1 with the KLT technique.
Multi-point accelerometric detection and principal component analysis of heart sounds.
De Panfilis, S; Moroni, C; Peccianti, M; Chiru, O M; Vashkevich, V; Parisi, G; Cassone, R
2013-03-01
Heart sounds are a fundamental physiological variable that provide a unique insight into cardiac semiotics. However a deterministic and unambiguous association between noises in cardiac dynamics is far from being accomplished yet due to many and different overlapping events which contribute to the acoustic emission. The current computer-based capacities in terms of signal detection and processing allow one to move from the standard cardiac auscultation, even in its improved forms like electronic stethoscopes or hi-tech phonocardiography, to the extraction of information on the cardiac activity previously unexplored. In this report, we present a new equipment for the detection of heart sounds, based on a set of accelerometric sensors placed in contact with the chest skin on the precordial area, and are able to measure simultaneously the vibration induced on the chest surface by the heart's mechanical activity. By utilizing advanced algorithms for the data treatment, such as wavelet decomposition and principal component analysis, we are able to condense the spatially extended acoustic information and to provide a synthetical representation of the heart activity. We applied our approach to 30 adults, mixed per gender, age and healthiness, and correlated our results with standard echocardiographic examinations. We obtained a 93% concordance rate with echocardiography between healthy and unhealthy hearts, including minor abnormalities such as mitral valve prolapse.
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.
Aggregate eco-efficiency indices for New Zealand--a principal components analysis.
Jollands, Nigel; Lermit, Jonathan; Patterson, Murray
2004-12-01
Eco-efficiency has emerged as a management response to waste issues associated with current production processes. Despite the popularity of the term in both business and government circles, limited attention has been paid to measuring and reporting eco-efficiency to government policy makers. Aggregate measures of eco-efficiency are needed, to complement existing measures and to help highlight important patterns in eco-efficiency data. This paper aims to develop aggregate measures of eco-efficiency for use by policy makers. Specifically, this paper provides a unique analysis by applying principal components analysis (PCA) to eco-efficiency indicators in New Zealand. The study reveals that New Zealand's overall eco-efficiency improved for two out of the five aggregate measures over the period 1994/1995-1997/1998. The worsening of the other aggregate measures reflects, among other things, the relatively poor performance of the primary production and related processing sectors. These results show PCA is an effective approach for aggregating eco-efficiency indicators and assisting decision makers by reducing redundancy in an eco-efficiency indicators matrix.
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}
NASA Astrophysics Data System (ADS)
Medina, José M.; Díaz, José A.
2013-05-01
We have applied principal component analysis to examine trial-to-trial variability of reflectances of automotive coatings that contain effect pigments. Reflectance databases were measured from different color batch productions using a multi-angle spectrophotometer. A method to classify the principal components was used based on the eigenvalue spectra. It was found that the eigenvalue spectra follow distinct power laws and depend on the detection angle. The scaling exponent provided an estimation of the correlation between reflectances and it was higher near specular reflection, suggesting a contribution from the deposition of effect pigments. Our findings indicate that principal component analysis can be a useful tool to classify different sources of spectral variability in color engineering.
NASA Astrophysics Data System (ADS)
Zahedi, Javad; Rounaghi, Mohammad Mahdi
2015-11-01
Stock price changes are receiving the increasing attention of investors, especially those who have long-term aims. The present study intends to assess the predictability of prices on Tehran Stock Exchange through the application of artificial neural network models and principal component analysis method and using 20 accounting variables. Finally, goodness of fit for principal component analysis has been determined through real values, and the effective factors in Tehran Stock Exchange prices have been accurately predicted and modeled in the form of a new pattern consisting of all variables.
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.
Principal components analysis of FT-Raman spectra of ex vivo basal cell carcinoma
NASA Astrophysics Data System (ADS)
Martin, Airton A.; Bitar Carter, Renata A.; de Oliveira Nunes, Lilian; Loschiavo Arisawa, Emilia A.; Silveira, Landulfo, Jr.
2004-07-01
FT-Raman spectroscopy is a modern analytical tool and it is believed that its use for skin cancer diagnosis will lead to several advantages for patients, e.g., faster results and a minimization of invasivity. This article reports results of an ex Vivo study of the FT-Raman spectra regarding differentiation between non-diseased and malignant human skin lesions, Basal Cell Carcinoma (BCC). A Nd: YAG laser at 1064nm was used as the excitation source in the FT-Raman, RFS 100/S Spectrometer, Bruker. Thirty-nine sets of human skin samples, 18 histopathologically diagnosed as non-diseased, and 21 as BCC, were obtained during routine therapeutic procedures required by the primary disease. No sample preparation was needed to promote the FT-Raman spectra collection. The main spectral features, which may differentiate the sample, were found in the shift region of Amide I (1640 to 1680 cm-1), Amide III (1220 to 1330cm-1), proteins and lipids (1400 to 1500 cm-1), amino acids (939 to 940 cm-1) and deoxyribonucleic acid (1600 to 1620cm-1). Principal Components Analysis (PCA) was applied to FT-Raman spectra of Basal Cell Carcinoma. Analysis was performed on mean-normalized and mean-centered data of the non-diseased skin and BCC spectra. The dynamic loading of PCA was expanded into 2D contour by calculating a variance-covariance matrix. PCA was used to verify the statistical differences in the sample. This technique applied over all samples identified tissue type within 83% of sensitivity and 100% specificity. The PCA technique proved efficient for analysis in skin tissue ex vivo, results were significant and coherent.
Race, Alan M; Steven, Rory T; Palmer, Andrew D; Styles, Iain B; Bunch, Josephine
2013-03-19
A memory efficient algorithm for the computation of principal component analysis (PCA) of large mass spectrometry imaging data sets is presented. Mass spectrometry imaging (MSI) enables two- and three-dimensional overviews of hundreds of unlabeled molecular species in complex samples such as intact tissue. PCA, in combination with data binning or other reduction algorithms, has been widely used in the unsupervised processing of MSI data and as a dimentionality reduction method prior to clustering and spatial segmentation. Standard implementations of PCA require the data to be stored in random access memory. This imposes an upper limit on the amount of data that can be processed, necessitating a compromise between the number of pixels and the number of peaks to include. With increasing interest in multivariate analysis of large 3D multislice data sets and ongoing improvements in instrumentation, the ability to retain all pixels and many more peaks is increasingly important. We present a new method which has no limitation on the number of pixels and allows an increased number of peaks to be retained. The new technique was validated against the MATLAB (The MathWorks Inc., Natick, Massachusetts) implementation of PCA (princomp) and then used to reduce, without discarding peaks or pixels, multiple serial sections acquired from a single mouse brain which was too large to be analyzed with princomp. Then, k-means clustering was performed on the reduced data set. We further demonstrate with simulated data of 83 slices, comprising 20,535 pixels per slice and equaling 44 GB of data, that the new method can be used in combination with existing tools to process an entire organ. MATLAB code implementing the memory efficient PCA algorithm is provided.
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.
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
Vavougios, George D; George D, George; Pastaka, Chaido; Zarogiannis, Sotirios G; Gourgoulianis, Konstantinos I
2016-02-01
Phenotyping obstructive sleep apnea syndrome's comorbidity has been attempted for the first time only recently. The aim of our study was to determine phenotypes of comorbidity in obstructive sleep apnea syndrome patients employing a data-driven approach. Data from 1472 consecutive patient records were recovered from our hospital's database. Categorical principal component analysis and two-step clustering were employed to detect distinct clusters in the data. Univariate comparisons between clusters included one-way analysis of variance with Bonferroni correction and chi-square tests. Predictors of pairwise cluster membership were determined via a binary logistic regression model. The analyses revealed six distinct clusters: A, 'healthy, reporting sleeping related symptoms'; B, 'mild obstructive sleep apnea syndrome without significant comorbidities'; C1: 'moderate obstructive sleep apnea syndrome, obesity, without significant comorbidities'; C2: 'moderate obstructive sleep apnea syndrome with severe comorbidity, obesity and the exclusive inclusion of stroke'; D1: 'severe obstructive sleep apnea syndrome and obesity without comorbidity and a 33.8% prevalence of hypertension'; and D2: 'severe obstructive sleep apnea syndrome with severe comorbidities, along with the highest Epworth Sleepiness Scale score and highest body mass index'. Clusters differed significantly in apnea-hypopnea index, oxygen desaturation index; arousal index; age, body mass index, minimum oxygen saturation and daytime oxygen saturation (one-way analysis of variance P < 0.0001). Binary logistic regression indicated that older age, greater body mass index, lower daytime oxygen saturation and hypertension were associated independently with an increased risk of belonging in a comorbid cluster. Six distinct phenotypes of obstructive sleep apnea syndrome and its comorbidities were identified. Mapping the heterogeneity of the obstructive sleep apnea syndrome may help the early identification of at
Modeling of gas absorption cross sections by use of principal-component-analysis model parameters.
Bak, Jimmy
2002-05-20
Monitoring the amount of gaseous species in the atmosphere and exhaust gases by remote infrared spectroscopic methods calls for the use of a compilation of spectral data, which can be used to match spectra measured in a practical application. Model spectra are based on time-consuming line-by-line calculations of absorption cross sections in databases by use of temperature as input combined with path length and partial and total pressure. It is demonstrated that principal component analysis (PCA) can be used to compress the spectrum of absorption cross sections, which depend strongly on temperature, into a reduced representation of score values and loading vectors. The temperature range from 300 to 1000 K is studied. This range is divided into two subranges (300-650 K and 650-1000K), and separate PCA models are constructed for each. The relationship between the scores and the temperature values is highly nonlinear. It is shown, however, that because the score-temperature relationships are smooth and continuous, they can be modeled by polynomials of varying degrees. The accuracy of the data compression method is validated with line-by-line-calculated absorption data of carbon monoxide and water vapor. Relative deviations between the absorption cross sections reconstructed from the PCA model parameters and the line-by-line-calculated values are found to be smaller than 0.15% for cross sections exceeding 1.27 x 10(-21) cm(-1) atm(-1) (CO) and 0.20% for cross sections exceeding 4.03 x 10(-21) cm(-1) atm(-1) (H2O). The computing time is reduced by a factor of 10(4). PMID:12027171
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. PMID:27267074
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
An extended echo state network using Volterra filtering and principal component analysis.
Boccato, Levy; Lopes, Amauri; Attux, Romis; Von Zuben, Fernando J
2012-08-01
Echo state networks (ESNs) can be interpreted as promoting an encouraging compromise between two seemingly conflicting objectives: (i) simplicity of the resulting mathematical model and (ii) capability to express a wide range of nonlinear dynamics. By imposing fixed weights to the recurrent connections, the echo state approach avoids the well-known difficulties faced by recurrent neural network training strategies, but still preserves, to a certain extent, the potential of the underlying structure due to the existence of feedback loops within the dynamical reservoir. Moreover, the overall training process is relatively simple, as it amounts essentially to adapting the readout, which usually corresponds to a linear combiner. However, the linear nature of the output layer may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. In this work, we present a novel architecture for an ESN in which the linear combiner is replaced by a Volterra filter structure. Additionally, the principal component analysis technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. The proposed architecture is then analyzed in the context of a set of representative information extraction problems, more specifically supervised and unsupervised channel equalization, and blind separation of convolutive mixtures. The obtained results, when compared to those produced by already proposed ESN versions, highlight the benefits brought by the novel network proposal and characterize it as a promising tool to deal with challenging signal processing tasks.
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
Genetic parameters and principal component analysis for egg production from White Leghorn hens.
Venturini, G C; Savegnago, R P; Nunes, B N; Ledur, M C; Schmidt, G S; El Faro, L; Munari, D P
2013-09-01
The objectives of this study were to estimate genetic parameters for accumulated egg production over 3-wk periods and for total egg production over 54 wk of egg-laying, and using principal component analysis (PCA), to explore the relationships among the breeding values of these traits to identify the possible genetic relationships present among them and hence to observe which of them could be used as selection criteria for improving egg production. Egg production was measured among 1,512 females of a line of White Leghorn laying hens. The traits analyzed were the number of eggs produced over partial periods of 3 wk, thus totaling 18 partial periods (P1 to P18), and the total number of eggs produced over the period between the 17 and 70 wk of age (PTOT), thus totaling 54 wk of egg production. Estimates of genetic parameters were obtained by means of the restricted maximum likelihood method, using 2-trait animal models. The PCA was done using the breeding values of partial and total egg production. The heritability estimates ranged from 0.05 ± 0.03 (P1 and P8) to 0.27 ± 0.06 (P4) in the 2-trait analysis. The genetic correlations between PTOT and partial periods ranged from 0.19 ± 0.31 (P1) to 1.00 ± 0.05 (P10, P11, and P12). Despite the high genetic correlation, selection of birds based on P10, P11, and P12 did not result in an increase in PTOT because of the low heritability estimates for these periods (0.06 ± 0.03, 0.12 ± 0.04, and 0.10 ± 0.04, respectively). The PCA showed that egg production can be divided genetically into 4 periods, and that P1 and P2 are independent and have little genetic association with the other periods.
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
Malmquist, Linus M V; Olsen, Rasmus R; Hansen, Asger B; Andersen, Ole; Christensen, Jan H
2007-09-14
Detailed characterization and understanding of oil weathering at the molecular level is an essential part of tiered approaches for forensic oil spill identification, for risk assessment of terrestrial and marine oil spills, and for evaluating effects of bioremediation initiatives. Here, a chemometric-based method is applied to data from two in vitro experiments in order to distinguish the effects of evaporation and dissolution processes on oil composition. The potential of the method for obtaining detailed chemical information of the effects from evaporation and dissolution processes, to determine weathering state and to distinguish between various weathering processes is investigated and discussed. The method is based on comprehensive and objective chromatographic data processing followed by principal component analysis (PCA) of concatenated sections of gas chromatography-mass spectrometry chromatograms containing homologue series of n-alkanes (m/z 85) and alkyltoluenes (m/z 105). The PCA model based solely on in vitro samples and validated by samples from an authentic marine oil spill gives a detailed description of the temporal changes in n-alkane and alkyltoluene compositions. The PCA model is able to distinguish the two physical weathering processes both with respect to removal rate and relative changes. The model shows that evaporation has a large impact on both the alkyltoluenes and on the n-alkanes (e.g., nC-18 is completely removed after 192 days of in vitro evaporation). Dissolution, however, is shown to be a much slower process for weathering of heavy fuel oils with only limited impact on the alkyltoluenes, and no effects on the n-alkane distribution.
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. PMID:22808122
Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD.
Sidhu, Gagan S; Asgarian, Nasimeh; Greiner, Russell; Brown, Matthew R G
2012-01-01
This study explored various feature extraction methods for use in automated diagnosis of Attention-Deficit Hyperactivity Disorder (ADHD) from functional Magnetic Resonance Image (fMRI) data. Each participant's data consisted of a resting state fMRI scan as well as phenotypic data (age, gender, handedness, IQ, and site of scanning) from the ADHD-200 dataset. We used machine learning techniques to produce support vector machine (SVM) classifiers that attempted to differentiate between (1) all ADHD patients vs. healthy controls and (2) ADHD combined (ADHD-c) type vs. ADHD inattentive (ADHD-i) type vs. controls. In different tests, we used only the phenotypic data, only the imaging data, or else both the phenotypic and imaging data. For feature extraction on fMRI data, we tested the Fast Fourier Transform (FFT), different variants of Principal Component Analysis (PCA), and combinations of FFT and PCA. PCA variants included PCA over time (PCA-t), PCA over space and time (PCA-st), and kernelized PCA (kPCA-st). Baseline chance accuracy was 64.2% produced by guessing healthy control (the majority class) for all participants. Using only phenotypic data produced 72.9% accuracy on two class diagnosis and 66.8% on three class diagnosis. Diagnosis using only imaging data did not perform as well as phenotypic-only approaches. Using both phenotypic and imaging data with combined FFT and kPCA-st feature extraction yielded accuracies of 76.0% on two class diagnosis and 68.6% on three class diagnosis-better than phenotypic-only approaches. Our results demonstrate the potential of using FFT and kPCA-st with resting-state fMRI data as well as phenotypic data for automated diagnosis of ADHD. These results are encouraging given known challenges of learning ADHD diagnostic classifiers using the ADHD-200 dataset (see Brown et al., 2012).
GA-fisher: A new LDA-based face recognition algorithm with selection of principal components.
Zheng, Wei-Shi; Lai, Jian-Huang; Yuen, Pong C
2005-10-01
This paper addresses the dimension reduction problem in Fisherface for face recognition. When the number of training samples is less than the image dimension (total number of pixels), the within-class scatter matrix (Sw) in Linear Discriminant Analysis (LDA) is singular, and Principal Component Analysis (PCA) is suggested to employ in Fisherface for dimension reduction of Sw so that it becomes nonsingular. The popular method is to select the largest nonzero eigenvalues and the corresponding eigenvectors for LDA. To attenuate the illumination effect, some researchers suggested removing the three eigenvectors with the largest eigenvalues and the performance is improved. However, as far as we know, there is no systematic way to determine which eigenvalues should be used. Along this line, this paper proposes a theorem to interpret why PCA can be used in LDA and an automatic and systematic method to select the eigenvectors to be used in LDA using a Genetic Algorithm (GA). A GA-PCA is then developed. It is found that some small eigenvectors should also be used as part of the basis for dimension reduction. Using the GA-PCA to reduce the dimension, a GA-Fisher method is designed and developed. Comparing with the traditional Fisherface method, the proposed GA-Fisher offers two additional advantages. First, optimal bases for dimensionality reduction are derived from GA-PCA. Second, the computational efficiency of LDA is improved by adding a whitening procedure after dimension reduction. The Face Recognition Technology (FERET) and Carnegie Mellon University Pose, Illumination, and Expression (CMU PIE) databases are used for evaluation. Experimental results show that almost 5 % improvement compared with Fisherface can be obtained, and the results are encouraging.
The Langat River water quality index based on principal component analysis
NASA Astrophysics Data System (ADS)
Mohd Ali, Zalina; Ibrahim, Noor Akma; Mengersen, Kerrie; Shitan, Mahendran; Juahir, Hafizan
2013-04-01
River Water Quality Index (WQI) is calculated using an aggregation function of the six water quality sub-indices variables, together with their relative importance or weights respectively. The formula is used by the Department of Environment to indicate a general status of the rivers in Malaysia. The six elected water quality variables used in the formula are, namely: suspended solids (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO) and pH. The sub-indices calculations, determined by quality rating curve and their weights, were based on expert opinions. However, the use of sub-indices and the relative importance established in the formula is very subjective in nature and does not consider the inter-relationships among the variables. The relationships of the variables are important due to the nature of multi-dimensionality and complex characteristics found in river water. Therefore, a well-known multivariate technique, i.e. Principal Component Analysis (PCA) was proposed to re-calculate the waterquality index specifically in Langat River based on the inter-relationship approach. The application of this approach is not well-studied in river water quality index development studies in Malaysia. Hence, the approach in the study is relevant and important since the first river water quality development took place in 1981. The PCA results showed that the weights obtained indicate the difference in ranking of the relative importance for particular variables compared to the classical approaches used in WQI-DOE. Based on the new weights, the Langat River water quality index was calculated and the comparison between both indexes was also discussed in this paper.
Applications of gauge duality in robust principal component analysis and semidefinite programming
NASA Astrophysics Data System (ADS)
Ma, ShiQian; Yang, JunFeng
2016-08-01
Gauge duality theory was originated by Freund [Math. Programming, 38(1):47-67, 1987] and was recently further investigated by Friedlander, Mac{\\^e}do and Pong [SIAM J. Optm., 24(4):1999-2022, 2014]. When solving some matrix optimization problems via gauge dual, one is usually able to avoid full matrix decompositions such as singular value and/or eigenvalue decompositions. In such an approach, a gauge dual problem is solved in the first stage, and then an optimal solution to the primal problem can be recovered from the dual optimal solution obtained in the first stage. Recently, this theory has been applied to a class of \\emph{semidefinite programming} (SDP) problems with promising numerical results [Friedlander and Mac{\\^e}do, SIAM J. Sci. Comp., to appear, 2016]. In this paper, we establish some theoretical results on applying the gauge duality theory to robust \\emph{principal component analysis} (PCA) and general SDP. For each problem, we present its gauge dual problem, characterize the optimality conditions for the primal-dual gauge pair, and validate a way to recover a primal optimal solution from a dual one. These results are extensions of [Friedlander and Mac{\\^e}do, SIAM J. Sci. Comp., to appear, 2016] from nuclear norm regularization to robust PCA and from a special class of SDP which requires the coefficient matrix in the linear objective to be positive definite to SDP problems without this restriction. Our results provide further understanding in the potential advantages and disadvantages of the gauge duality theory.
Lee, Hyun Jung; Suh, Hyung Joo; Han, Sung Hee; Hong, Jungil; Choi, Hyeon-Son
2016-06-01
In this study, we report the optimal extraction conditions for obtaining organosulfur compounds, such as cycloalliin, from garlic by using principal component analysis (PCA). Extraction variables including temperature (40~80°C), time (0.5~12 h), and pH (4~12) were investigated for the highest cycloalliin yields. The cycloalliin yield (5.5 mmol/mL) at pH 10 was enhanced by ~40% relative to those (~3.9 mmol/mL) at pH 4 and pH 6. The cycloalliin level at 80°C showed the highest yield among the tested temperatures (5.05 mmol/mL). Prolonged extraction times also increased cycloalliin yield; the yield after 12 h was enhanced ~2-fold (4 mmol/mL) compared to the control. Isoalliin and cycloalliin levels were inversely correlated, whereas a direct correlation between polyphenol and cycloalliin levels was observed. In storage for 30 days, garlic stored at 60°C (11 mmol/mL) showed higher levels of cycloalliin and polyphenols than those at 40°C, with the maximum cycloalliin level (13 mmol/mL) on day 15. Based on the PCA analysis, the isoalliin level depended on the extraction time, while cycloalliin amounts were influenced not only by extraction time, but also by pH and temperature. Taken together, extraction of garlic at 80°C, with an incubation time of 12 h, at pH 10 afforded the maximum yield of cycloalliin. PMID:27390731
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.
Cluster and Principal Component Analysis of Human Glioblastoma Multiforme (GBM) Tumor Proteome
Pooladi, Mehdi; Rezaei-Tavirani, Mostafa; Hashemi, Mehrdad; Hesami-Tackallou, Saeed; Khaghani-Razi-Abad, Solmaz; Moradi, Afshin; Zali, Ali Reza; Mousavi, Masoumeh; Firozi-Dalvand, Leila; Rakhshan, Azadeh; Zamanian Azodi, Mona
2014-01-01
Background Glioblastoma Multiforme (GBM) or grade IV astrocytoma is the most common and lethal adult malignant brain tumor. Several of the molecular alterations detected in gliomas may have diagnostic and/or prognostic implications. Proteomics has been widely applied in various areas of science, ranging from the deciphering of molecular pathogen nests of discuses. Methods In this study proteins were extracted from the tumor and normal brain tissues and then the protein purity was evaluated by Bradford test and spectrophotometry. In this study, proteins were separated by 2-Dimensional Gel (2DG) electrophoresis method and the spots were then analyzed and compared using statistical data and specific software. Protein clustering analysis was performed on the list of proteins deemed significantly altered in glioblastoma tumors (t-test and one-way ANOVA; P< 0.05). Results The 2D gel showed totally 876 spots. We reported, 172 spots were exhibited differently in expression level (fold > 2) for glioblastoma. On each analytical 2D gel, an average of 876 spots was observed. In this study, 188 spots exhibited up regulation of expression level, whereas the remaining 232 spots were decreased in glioblastoma tumor relative to normal tissue. Results demonstrate that functional clustering (up and down regulated) and Principal Component Analysis (PCA) has considerable merits in aiding the interpretation of proteomic data. Conclusion 2D gel electrophoresis is the core of proteomics which permitted the separation of thousands of proteins. High resolution 2DE can resolve up to 5,000 proteins simultaneously. Using cluster analysis, we can also form groups of related variables, similar to what is practiced in factor analysis. PMID:25250155
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 Astrophysics Data System (ADS)
Kistenev, Yu. V.; Shapovalov, A. V.; Borisov, A. V.; Vrazhnov, D. A.; Nikolaev, V. V.; Nikiforova, O. Yu.
2015-11-01
The comparison results of different mother wavelets used for de-noising of model and experimental data which were presented by profiles of absorption spectra of exhaled air are presented. The impact of wavelets de-noising on classification quality made by principal component analysis are also discussed.
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...
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.
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 ...
Badawi, Ahmed M.; Weiss, Elisabeth; Sleeman, William C. IV; Yan Chenyu; Hugo, Geoffrey D.
2010-09-15
Purpose: To optimize modeling of interfractional anatomical variation during active breath-hold radiotherapy in lung cancer using principal component analysis (PCA). Methods: In 12 patients analyzed, weekly CT sessions consisting of three repeat intrafraction scans were acquired with active breathing control at the end of normal inspiration. The gross tumor volume (GTV) and lungs were delineated and reviewed on the first week image by physicians and propagated to all other images using deformable image registration. PCA was used to model the target and lung variability during treatment. Four PCA models were generated for each specific patient: (1) Individual models for the GTV and each lung from one image per week (week to week, W2W); (2) a W2W composite model of all structures; (3) individual models using all images (weekly plus repeat intrafraction images, allscans); and (4) composite model with all images. Models were reconstructed retrospectively (using all available images acquired) and prospectively (using only data acquired up to a time point during treatment). Dominant modes representing at least 95% of the total variability were used to reconstruct the observed anatomy. Residual reconstruction error between the model-reconstructed and observed anatomy was calculated to compare the accuracy of the models. Results: An average of 3.4 and 4.9 modes was required for the allscans models, for the GTV and composite models, respectively. The W2W model required one less mode in 40% of the patients. For the retrospective composite W2W model, the average reconstruction error was 0.7{+-}0.2 mm, which increased to 1.1{+-}0.5 mm when the allscans model was used. Individual and composite models did not have significantly different errors (p=0.15, paired t-test). The average reconstruction error for the prospective models of the GTV stabilized after four measurements at 1.2{+-}0.5 mm and for the composite model after five measurements at 0.8{+-}0.4 mm. Conclusions
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.
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
NASA Astrophysics Data System (ADS)
Heyer, Mark H.; Peter Schloerb, F.
1997-01-01
The multivariate statistical technique of principal component analysis (PCA) is described and demonstrated to be a valuable tool to consolidate the large amount of information obtained with spectroscopic imaging observations of the interstellar medium. Simple interstellar cloud models with varying degrees of complexity and Gaussian noise are constructed and analyzed to demonstrate the ability of PCA to statistically extract physical features and phenomena from the data and to gauge the effects of random noise upon the analysis. Principal components are calculated for high spatial dynamic range 12CO and 13CO data cubes of the Sh 155 (Cep OB3) cloud complex. These identify the three major emission components within the cloud and the spatial differences between 12CO and 13CO emissions. Higher order eigenimages identify small velocity fluctuations and therefore provide spatial information to the turbulent velocity field within the cloud. A size line width relationship δv ~ Rα is derived from spatial and kinematic characterizations of the principal components of 12CO emission from the Sh 155, Sh 235, Sh 140, and Gem OB1 cloud complexes. The power-law indices for these clouds range from 0.42 to 0.55 and are similar to those derived from an ensemble of clouds within the Galaxy found by Larson (1981) and Solomon et al. (1987). The size-line width relationship within a given cloud provides an important diagnostic to the variation of kinetic energy with size scale within turbulent flows of the interstellar medium.
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
Rosen, C; Yuan, Z
2001-01-01
In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy o-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.
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. PMID:27664660
Ghosh, Debarchana; Manson, Steven M
2008-01-01
In this paper, we present a hybrid approach, robust principal component geographically weighted regression (RPCGWR), in examining urbanization as a function of both extant urban land use and the effect of social and environmental factors in the Twin Cities Metropolitan Area (TCMA) of Minnesota. We used remotely sensed data to treat urbanization via the proxy of impervious surface. We then integrated two different methods, robust principal component analysis (RPCA) and geographically weighted regression (GWR) to create an innovative approach to model urbanization. The RPCGWR results show significant spatial heterogeneity in the relationships between proportion of impervious surface and the explanatory factors in the TCMA. We link this heterogeneity to the "sprawling" nature of urban land use that has moved outward from the core Twin Cities through to their suburbs and exurbs.
Sodre, Fernando Fabriz; dos Anjos, Vanessa Egea; Prestes, Ellen Christine; Grassi, Marco Tadeu
2005-06-01
The goal of this work was to identify the sources of copper loads in surface urban waters using principal component analysis under the aquatic parameters data evaluation approach. Water samples from the Irai and Iguacu rivers were collected monthly during a 12-month period at two points located upstream and downstream of a metropolitan region. pH, total alkalinity, dissolved chloride, total suspended solids, dissolved organic matter, total recoverable copper, temperature, and precipitation data provided some reliable information concerning the characteristics and water quality of both rivers. Principal component analysis indicated seasonal and spatial effects on copper concentration and loads in both environments. During the rainy season, non-point sources such as urban run-off are believed to be the major source of copper in both cases. In contrast, during the lower precipitation period, the discharge of raw sewage seems to be the primary source of copper to the Iguacu River, which also exhibited higher total metal concentrations.
NASA Astrophysics Data System (ADS)
Tiilikainen, J.; Tilli, J.-M.; Bosund, V.; Mattila, M.; Hakkarainen, T.; Airaksinen, V.-M.; Lipsanen, H.
2007-01-01
Two novel genetic algorithms implementing principal component analysis and an adaptive nonlinear fitness-space-structure technique are presented and compared with conventional algorithms in x-ray reflectivity analysis. Principal component analysis based on Hessian or interparameter covariance matrices is used to rotate a coordinate frame. The nonlinear adaptation applies nonlinear estimates to reshape the probability distribution of the trial parameters. The simulated x-ray reflectivity of a realistic model of a periodic nanolaminate structure was used as a test case for the fitting algorithms. The novel methods had significantly faster convergence and less stagnation than conventional non-adaptive genetic algorithms. The covariance approach needs no additional curve calculations compared with conventional methods, and it had better convergence properties than the computationally expensive Hessian approach. These new algorithms can also be applied to other fitting problems where tight interparameter dependence is present.
NASA Astrophysics Data System (ADS)
Li, Shao-Meng; Anlauf, K. G.; Wiebe, H. A.; Bottenheim, J. W.; Puckett, K. J.
Using a principal component analysis technique and data on atmospheric gases and aerosols at a rural site in Ontario, Canada from the Eulerian model evaluation field study (EMEFS), the Eulerian acid deposition and oxidant model (ADOM) is evaluated. Seventy-nine and 76% of the variances in the data and model output, respectively, are explained by three principal components. They are a chemically aged/ transported component, a diurnal cycle component, and an area emission component, all characterized by their ratios of gases and temporal variation patterns. The ADOM component contributions to sulphur species are in general agreement with the EMEFS components, but with notable differences for key photochemical species including O 3. The temporal variations of the ADOM components are close to those of the EMEFS components. The EMEFS chemically aged/transported component shows a high degree of photochemical processing, with the ratios [NO x]/[TNO y]=0.3 and [O 3]/([TNO y]-[NO x])=9±1. The corresponding ADOM component predicts lower G[NO x]/[TNO y] and [NO 3]/([TNO y]-[NO x]) ratios, probably caused by a chemical mechanism in the model that is too fast, and lower contributions to O 3, NO 2, TNO 3, PAN, TNO y, and HCHO, probably caused by model grid dilution or lower model emissions. The EMEFS diurnal component owes its variance to the daily photochemistry and nighttime dry deposition of the chemical species. In comparison, the matching ADOM component underpredicts the ratio [O 3]/([TNO y]-[NO x]) and the NO 2 consumption and O 3 production but overpredicts the contributions to the other species. The EMEFS emission component represents emissions from local/regional area sources. The corresponding ADOM component underpredicts TNO y by 44% and the fraction of TNO y as NO x compared to the EMEFS component, suggesting that the model has lower emissions of NO x and a photochemical mechanism that converts NO x faster than indicated by the EMEFS results.
Nylk, J; Kristensen, M V G; Mazilu, M; Thayil, A K; Mitchell, C A; Campbell, E C; Powis, S J; Gunn-Moore, F J; Dholakia, K
2015-04-01
We demonstrate a miniaturized single beam fiber optical trapping probe based on a high numerical aperture graded index (GRIN) micro-objective lens. This enables optical trapping at a distance of 200μm from the probe tip. The fiber trapping probe is characterized experimentally using power spectral density analysis and an original approach based on principal component analysis for accurate particle tracking. Its use for biomedical microscopy is demonstrated through optically mediated immunological synapse formation.
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.
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
Qi, Danyi; Roe, Brian E
2016-01-01
We estimate models of consumer food waste awareness and attitudes using responses from a national survey of U.S. residents. Our models are interpreted through the lens of several theories that describe how pro-social behaviors relate to awareness, attitudes and opinions. Our analysis of patterns among respondents' food waste attitudes yields a model with three principal components: one that represents perceived practical benefits households may lose if food waste were reduced, one that represents the guilt associated with food waste, and one that represents whether households feel they could be doing more to reduce food waste. We find our respondents express significant agreement that some perceived practical benefits are ascribed to throwing away uneaten food, e.g., nearly 70% of respondents agree that throwing away food after the package date has passed reduces the odds of foodborne illness, while nearly 60% agree that some food waste is necessary to ensure meals taste fresh. We identify that these attitudinal responses significantly load onto a single principal component that may represent a key attitudinal construct useful for policy guidance. Further, multivariate regression analysis reveals a significant positive association between the strength of this component and household income, suggesting that higher income households most strongly agree with statements that link throwing away uneaten food to perceived private benefits.
Qi, Danyi; Roe, Brian E
2016-01-01
We estimate models of consumer food waste awareness and attitudes using responses from a national survey of U.S. residents. Our models are interpreted through the lens of several theories that describe how pro-social behaviors relate to awareness, attitudes and opinions. Our analysis of patterns among respondents' food waste attitudes yields a model with three principal components: one that represents perceived practical benefits households may lose if food waste were reduced, one that represents the guilt associated with food waste, and one that represents whether households feel they could be doing more to reduce food waste. We find our respondents express significant agreement that some perceived practical benefits are ascribed to throwing away uneaten food, e.g., nearly 70% of respondents agree that throwing away food after the package date has passed reduces the odds of foodborne illness, while nearly 60% agree that some food waste is necessary to ensure meals taste fresh. We identify that these attitudinal responses significantly load onto a single principal component that may represent a key attitudinal construct useful for policy guidance. Further, multivariate regression analysis reveals a significant positive association between the strength of this component and household income, suggesting that higher income households most strongly agree with statements that link throwing away uneaten food to perceived private benefits. PMID:27441687
Skala, Marketa; Rosewall, Tara; Dawson, Laura; Divanbeigi, Lorella; Lockwood, Gina; Thomas, Christopher; Crook, Juanita; Chung, Peter; Warde, Padraig; Catton, Charles . E-mail: charles.catton@rmp.uhn.on.ca
2007-07-01
Purpose: The aims of this study were to determine the incidence of patient-assessed late toxicity after high-dose, image-guided radiation therapy in a cohort of men with prostate cancer; and to correlate toxicity with conventional dosimetric parameters and rectal and bladder dose-volume histograms (DVH) reduced using principal component analysis. Methods and Materials: Toxicity questionnaires were sent to 690 men treated for localized prostate cancer to 75.6 Gy or 79.8 Gy using three-dimensional conformal radiation therapy (3DCRT) or intensity-modulated radiation therapy (IMRT) between 1997 and 2003 at the Princess Margaret Hospital. Toxicity was graded according to the modified Radiation Therapy Oncology Group (RTOG)-late effects normal tissue (LENT) scoring system. Late rectal and bladder toxicity scores were dichotomized as < Grade 2 and {>=} Grade 2, and correlated with dosimetric parameters and with the first three principal components of rectal and bladder DVHs. Results: In all, 63% of the patients completed the questionnaire. At a median follow-up of 37 months, the incidence of late rectal toxicity RTOG Grades 1, 2, and 3 was 25.2%, 2.5%, and 0.7% respectively. The incidence of late urinary toxicity RTOG Grade 1, 2, and 3 was 16.5%, 8.8%, and 0.9% respectively. Maintenance of erectile function sufficient for intercourse was reported in 68%. No dosimetric parameter analyzed, including principal component analysis reduction of DVHs, correlated with late toxicity. Conclusions: Postal questionnaire was effective for collection of patient-assessed late toxicity data. The incidence of late toxicity was low, with a lack of correlation to dosimetric parameters. We attribute this to the use of conformal techniques and daily image guidance.
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.
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.
Melquiades, F L; Andreoni, L F S; Thomaz, E L
2013-07-01
Differences in composition and chemical elemental concentration are important information for soil samples classification. The objective of this study is to present a direct methodology, that is non-destructive and without complex sample preparation, in order to discriminate different land-use types and soil degradation, employing energy dispersive X-ray fluorescence and multivariate analysis. Sample classification results from principal component analysis, utilizing spectral data and elemental concentration values demonstrate that the methodology is efficient to discriminate different land-use types.
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)
Yin, Jianhua; Xia, Yang; Lu, Mei
2012-03-01
Fourier-transform infrared imaging (FT-IRI) technique with the principal component regression (PCR) method was used to quantitatively determine the 2D images and the depth-dependent concentration profiles of two principal macromolecular components (collagen and proteoglycan) in articular cartilage. Ten 6 μm thick sections of canine humeral cartilage were imaged at a pixel size of 6.25 μm in FT-IRI. The infrared spectra extracted from FT-IRI experiments were imported into a PCR program to calculate the quantitative distributions of both collagen and proteoglycan in dry cartilage, which were subsequently converted into the wet-weight based concentration profiles. The proteoglycan profiles by FT-IRI and PCR significantly correlated in linear regression with the proteoglycan profiles by the non-destructive μMRI (the goodness-of-fit 0.96 and the Pearson coefficient 0.98). Based on these concentration relationships, the concentration images of collagen and proteoglycan in both healthy and lesioned articular cartilage were successfully constructed two dimensionally. The simultaneous construction of both collagen and proteoglycan concentration images demonstrates that this combined imaging and chemometrics approach could be used as a sensitive tool to accurately resolve and visualize the concentration distributions of macromolecules in biological tissues.
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.
Murphy, T. E.; Lin, Y.; Tsui, K.-L.; Chen, V. C. P.; Allen, J. K.
2012-01-01
Engineering design often involves the determination of design variable settings to optimize competing performance requirements. In the early design stages we propose narrowing down the domain of design solutions using metamodels of principal components of the multiple performance levels that have been scaled by a multivariate quadratic loss function. The multivariate quadratic loss function is often used as the objective function in reaching optimal solutions because it utilizes the correlation structure of the design’s performance metrics and penalizes off-target performance in a symmetrical manner. We also compare the computational performance of these loss-scaled principal components when used to solve for the design with minimal expected multivariate quadratic loss under three modeling approaches: response surface methodology, multivariate adaptive regression splines, and spatial point modeling. We demonstrate the technique on the design of the mechanical frame of an electric vehicle with six desired performance levels determined simultaneously by the dimensions of eight mechanical design elements. The method is the focus in this work. PMID:23125480
Rodeberg, Nathan T; Johnson, Justin A; Cameron, Courtney M; Saddoris, Michael P; Carelli, Regina M; Wightman, R Mark
2015-11-17
Principal component regression, a multivariate calibration technique, is an invaluable tool for the analysis of voltammetric data collected in vivo with acutely implanted microelectrodes. This method utilizes training sets to separate cyclic voltammograms into contributions from multiple electroactive species. The introduction of chronically implanted microelectrodes permits longitudinal measurements at the same electrode and brain location over multiple recordings. The reliability of these measurements depends on a consistent calibration methodology. One published approach has been the use of training sets built with data from separate electrodes and animals to evaluate neurochemical signals in multiple subjects. Alternatively, responses to unpredicted rewards have been used to generate calibration data. This study addresses these approaches using voltammetric data from three different experiments in freely moving rats obtained with acutely implanted microelectrodes. The findings demonstrate critical issues arising from the misuse of principal component regression that result in significant underestimates of concentrations and improper statistical model validation that, in turn, can lead to inaccurate data interpretation. Therefore, the calibration methodology for chronically implanted microelectrodes needs to be revisited and improved before measurements can be considered reliable.
Chen, Pei-Yi; Yu, Jhe-Wei; Lu, Fen-Ling; Lin, Mei-Chih; Cheng, Hwei-Fang
2016-09-01
Cinnamon bark (Rou Gui in Chinese), cinnamon twig (Gui Zhi) and shaved cinnamon bark (Gui Sin) have been widely used as spices and in traditional Chinese medicine since ancient times. On-going issues related to quality and authenticity necessitate the development of analytical methods capable of providing an objective evaluation of samples. In this study, chemical fingerprints of cinnamon bark, cinnamon twigs and shaved cinnamon bark were established using liquid chromatography quadruple time-of-flight mass spectrometry in conjunction with principal component analysis (PCA). From 125 samples of cinnamon, we identified the following eight compounds and their the detection ratios: coumarin, cinnamaldehyde, cinnamyl alcohol, cinnamic acid, 2-hydroxycinnamaldehyde, 2-hydroxycinnamic acid, 2-methoxycinnamaldehyde and 4-methoxycinnamaldehyde. Of these, 4-methoxycinnamaldehyde presented the largest variations in detection ratio, making up 64.0, 97.4 and 50.0% in cinnamon bark, cinnamon twig, and shaved cinnamon bark, respectively. The quantities of cinnamyl alcohol, coumarin and cinnamaldehyde also varied between the three parts of the plant. Chemical fingerprints of the three cinnamon samples were established using principal component analysis, the results of which indicate that cinnamon bark and shaved cinnamon bark could be easily differentiated, despite a marked similarity in outward appearance. Cinnamon twig was also shown to depart from the other clusters. The proposed method provides a fast and efficient means of identifying cinnamon herbs for quality control purposes. Copyright © 2016 John Wiley & Sons, Ltd.
Li, Miaoyun; Li, Yuanhui; Huang, Xianqing; Zhao, Gaiming; Tian, Wei
2014-06-01
The growth of Pseudomonas of pallet-packaged seasoned prepared chicken products under selected storage temperatures (5°°C, 10°°C, 15°°C, 20°°C and 25°°C) has been studied in this paper. The modified Gompertz, Baranyi and Huang models were used for data fitting. Statistical criteria such as residual sum of squares, mean square error, Akaike's information criterion, Pseudo-R(2) were used to evaluate model performance. Results showed that RSS (Residual sum of squares) index contribution rate was more than 90% of the variability, which could be explained by the first principal components analyzed by the principal component analysis (PCA). The index values reported in Sichuan-style chicken skewers and chicken flesh and bones were about 94.85% and 93.345% respectively, and both the rate were better than the standard (85%). Therefore, RSS can be used as the main evaluating index to analyze and compare the difference of those three models. With the smallest average values of RSS and the biggest pseudo-R2 at most temperatures, the Baranyi model was more suitable to fit the data of Pseudomonas obtained from the two prepared chicken products than Gompertz model and Huang model. PMID:24549196
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)
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.
Chen, Pei-Yi; Yu, Jhe-Wei; Lu, Fen-Ling; Lin, Mei-Chih; Cheng, Hwei-Fang
2016-09-01
Cinnamon bark (Rou Gui in Chinese), cinnamon twig (Gui Zhi) and shaved cinnamon bark (Gui Sin) have been widely used as spices and in traditional Chinese medicine since ancient times. On-going issues related to quality and authenticity necessitate the development of analytical methods capable of providing an objective evaluation of samples. In this study, chemical fingerprints of cinnamon bark, cinnamon twigs and shaved cinnamon bark were established using liquid chromatography quadruple time-of-flight mass spectrometry in conjunction with principal component analysis (PCA). From 125 samples of cinnamon, we identified the following eight compounds and their the detection ratios: coumarin, cinnamaldehyde, cinnamyl alcohol, cinnamic acid, 2-hydroxycinnamaldehyde, 2-hydroxycinnamic acid, 2-methoxycinnamaldehyde and 4-methoxycinnamaldehyde. Of these, 4-methoxycinnamaldehyde presented the largest variations in detection ratio, making up 64.0, 97.4 and 50.0% in cinnamon bark, cinnamon twig, and shaved cinnamon bark, respectively. The quantities of cinnamyl alcohol, coumarin and cinnamaldehyde also varied between the three parts of the plant. Chemical fingerprints of the three cinnamon samples were established using principal component analysis, the results of which indicate that cinnamon bark and shaved cinnamon bark could be easily differentiated, despite a marked similarity in outward appearance. Cinnamon twig was also shown to depart from the other clusters. The proposed method provides a fast and efficient means of identifying cinnamon herbs for quality control purposes. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26873449
Lee, Yeonjung; Ha, Sun-Yong; Park, Hae-Kyung; Han, Myung-Soo; Shin, Kyung-Hoon
2015-04-01
To understand the factors controlling algal production in two lakes located on the Han River in South Korea, Lake Cheongpyeong and Lake Paldang, a principal component regression model study was conducted using environmental monitoring and primary productivity data. Although the two lakes were geographically close and located along the same river system, the main factors controlling primary productivity in each lake were different: hydraulic retention time and light conditions predominantly influenced algal productivity in Lake Cheongpyeong, while hydraulic retention time, chlorophyll a-specific productivity, and zooplankton grazing rate were most important in Lake Paldang. This investigation confirmed the utility of principal component regression analysis using environmental monitoring data for predicting complex biological processes such as primary productivity; in addition, the study also increased the understanding of explicit interactions between environmental variables. The findings obtained in this research will be useful for the adaptive management of water reservoirs. The results will also aid in the development of management strategies for water resources, thereby improving total environmental conservation.
2012-01-01
Background The chemotherapeutic agent paclitaxel arrests cell division by binding to the hetero-dimeric protein tubulin. Subtle differences in tubulin sequences, across eukaryotes and among β-tubulin isotypes, can have profound impact on paclitaxel-tubulin binding. To capture the experimentally observed paclitaxel-resistance of human βIII tubulin isotype and yeast β-tubulin, within a common theoretical framework, we have performed structural principal component analyses of β-tubulin sequences across eukaryotes. Results The paclitaxel-resistance of human βIII tubulin isotype and yeast β-tubulin uniquely mapped on to the lowest two principal components, defining the paclitaxel-binding site residues of β-tubulin. The molecular mechanisms behind paclitaxel-resistance, mediated through key residues, were identified from structural consequences of characteristic mutations that confer paclitaxel-resistance. Specifically, Ala277 in βIII isotype was shown to be crucial for paclitaxel-resistance. Conclusions The present analysis captures the origin of two apparently unrelated events, paclitaxel-insensitivity of yeast tubulin and human βIII tubulin isotype, through two common collective sequence vectors. PMID:22849332
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.
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.
Lunar polymict breccia 14321 - A compositional study of its principal components
NASA Technical Reports Server (NTRS)
Duncan, A. R.; Mckay, S. M.; Stoeser, J. W.; Lindstrom, M. M.; Lindstrom, D. J.; Fruchter, J. S.; Goles, G. G.
1975-01-01
Forty-nine sub-samples of the polymict breccia 14321,184 have been excavated from the rock and analyzed by instrumental activation analysis techniques, specially modified to allow examination of small samples. Two distinct types of microbreccia clasts were analysed. A mare-type basalt of unusual composition is present as several discrete clasts. Mixing models for the clastic components of the breccia illustrate that at least three stages of assembly may be distinguished on compositional grounds. The first was at a site such that KREEP-rich materials dominated the clastic rocks formed, although a variety of lithic fragments were apparently present. The second assembly stage may have been primarily that of comminution and mixing of the more primitive materials, with addition of mare-type basalt clasts. The third stage saw addition of clasts of 14321-type basalt. In the final assembly stage, the light matrix was apparently formed entirely by mutual abrasion of the pre-existing clasts, resulting in little or no change in bulk composition.
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.
Marques, Rejane C; Bernardi, José V E; Dórea, José G; Bastos, Wanderley R; Malm, Olaf
2008-10-01
The variance of variables associated with neurodevelopment at 180 days, pre-natal variables (Hg in placenta, blood and hair) and post-natal Hg exposure (including Thimerosal-containing vaccines, TCV) were examined in 82 exclusively breastfed infants using principal component analysis (PCA). This multivariate method was applied to identify hierarchy and sets of interrelated variables. The PCA yielded a two-factor solution, explaining 92% of variance and summarizing most of the relevant information in the dataset matrix: the first component represented birth weight and vaccine (first doses of Hepatitis B and DTP) variability and explained 57% of variance; the second component represented a gradient of neurodevelopment (Gesell scores) and explained 35% of variance. The third component explained only 3% of the remaining 8% variance. Beside CNS priming by breastfeeding, infant development (birth weight) and time of immunization with TCV should be considered in epidemiological studies. PCA can classify sets of variables related to vaccination and neuromotor development schedules, clearly discriminating between earlier and later TCV exposures of exclusively breastfed infants. In conclusion, the incommensurable concept of the chance of toxic risk caused by TCV-EtHg exposure against the proven benefit of immunization is in no way disputed here. However, infant neurodevelopmental (ND) disorders linked to Thimerosal-Hg stands in need of proof, but PCA points to the possibility of identifying exposure risk variables associated with ND schedules.
Maktabdar Oghaz, Mahdi; Maarof, Mohd Aizaini; Zainal, Anazida; Rohani, Mohd Foad; Yaghoubyan, S Hadi
2015-01-01
Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications. PMID:26267377
Maktabdar Oghaz, Mahdi; Maarof, Mohd Aizaini; Zainal, Anazida; Rohani, Mohd Foad; Yaghoubyan, S Hadi
2015-01-01
Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications.
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
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.
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.
Razifar, Pasha; Muhammed, Hamid Hamed; Engbrant, Fredrik; Svensson, Per-Edvin; Olsson, Johan; Bengtsson, Ewert; Långström, Bengt; Bergström, Mats
2009-01-01
Multivariate image analysis tools are used for analyzing dynamic or multidimensional Positron Emission Tomography, PET data with the aim of noise reduction, dimension reduction and signal separation. Principal Component Analysis is one of the most commonly used multivariate image analysis tools, applied on dynamic PET data. Independent Component Analysis is another multivariate image analysis tool used to extract and separate signals. Because of the presence of high and variable noise levels and correlation in the different PET images which may confound the multivariate analysis, it is essential to explore and investigate different types of pre-normalization (transformation) methods that need to be applied, prior to application of these tools. In this study, we explored the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) to extract signals and reduce noise, thereby increasing the Signal to Noise Ratio (SNR) in a dynamic sequence of PET images, where the features of the noise are different compared with some other medical imaging techniques. Applications on computer simulated PET images were explored and compared. Application of PCA generated relatively similar results, with some minor differences, on the images with different noise characteristics. However, clear differences were seen with respect to the type of pre-normalization. ICA on images normalized using two types of normalization methods also seemed to perform relatively well but did not reach the improvement in SNR as PCA. Furthermore ICA seems to have a tendency under some conditions to shift over information from IC1 to other independent components and to be more sensitive to the level of noise. PCA is a more stable technique than ICA and creates better results both qualitatively and quantitatively in the simulated PET images. PCA can extract the signals from the noise rather well and is not sensitive to type of noise, magnitude and correlation, when the input
NASA Astrophysics Data System (ADS)
Bauer, J. D.; Jackson, Andrew; Skwarchuk, Mark; Zelefsky, Michael
2006-10-01
We investigate the utility of principal component analysis as a tool for obtaining dose-volume combinations related to rectal bleeding after radiotherapy for prostate cancer. A direct implementation of principal component analysis reduces the number of degrees of freedom from the patient's dose-volume histograms that are associated with bleeding. However, when low-variance principal components are strongly correlated to outcome, their interpretation is problematic. A Varimax rotation is employed to aid in interpretability of the low-variance principal components. This procedure brings us closer to finding unique dose-volume combinations related to outcome but reintroduces correlation, requiring analysis of the overlap of information contained in such modes. Finally, we present examples of cost-benefit analyses for candidate dose-volume constraints for use in treatment planning.
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.
Bacon, A M
2000-04-01
The shape of the distal humerus in Homo, Pan (P. paniscus and P. troglodytes), Gorilla, and six australopithecines is compared using a geometric approach (Procrustes superimposition of landmarks). Fourteen landmarks are defined on the humerus in a two-dimensional space. Principal components analysis (PCA) is performed on all superimposed coordinates. I have chosen to discuss the precise place of KNM-KP 271 variously assigned to Australopithecus anamensis, Homo sp., or Praeanthropus africanus, in comparison with a sample of australopithecines. AL 288-1, AL 137-48 (Hadar), STW 431 (Sterkfontein), and TM 1517 (Kromdraai) are commonly attributed to Australopithecus afarensis (the two former), Australopithecus africanus, and Paranthropus robustus, respectively, while the taxonomic place of KNM-ER 739 (Homo or Paranthropus?) is not yet clearly defined. The analysis does not emphasize a particular affinity between KNM-KP 271 and modern Homo, nor with A. afarensis, as previously demonstrated (Lague and Jungers [1996] PMID:10727967
Tenke, Craig E; Kayser, Jürgen; Stewart, Jonathan W; Bruder, Gerard E
2010-01-01
We previously reported a novelty P3 reduction in depressed patients compared to healthy controls (n=20 per group) in a novelty oddball task using a 31-channel montage. In an independent replication and extension using a 67-channel montage (n=49 per group), reference-free current source density (CSD) waveforms were simplified and quantified by a temporal, covariance-based principal components analysis (PCA) (unrestricted Varimax rotation), yielding factor solutions consistent with other oddball tasks. A factor with a loadings peak at 343 ms summarized the target P3b source as well as a secondary midline frontocentral source for novels and targets. An earlier novelty vertex source (NVS) at 241 ms was present for novels, but not targets, and was reduced in patients. Compatible CSD-PCA findings were also confirmed for the original low-density sample. Results are consistent with a reduced novelty response in clinical depression, involving the early phase of the frontocentral novelty P3.
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.
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)
Xiong, Ling; Li, Kaihan; Tang, Jianqiao; Ma, Jie
2015-12-01
The matching area selection is the foundation of gravity gradient aided navigation. In this paper, a gravity gradient matching area selection criterion is proposed, based on the principal component analysis (PCA) and analytic hierarchy process (AHP). Firstly, the features of gravity gradient are extracted and nine gravity gradient characteristic parameters are obtained. Secondly, combining PCA with AHP, a PA model is built and the nine characteristic parameters are fused based on it. At last, the gravity gradient matching area selection criterion is given. By using this criterion, gravity gradient area can be divided into matching area and non-matching area. The simulation results show that gravity gradient position effect in the selected matching area is superior to the matching area, and the matching rate is greater than 90%, the position error is less than a gravity gradient grid.
NASA Astrophysics Data System (ADS)
Zhang, Yu; Xia, Tian
2016-04-01
Crack detection is an important application for Ground penetrating radar (GPR) to examine the concrete road or building structure conditions. The layer of rebars or utility pipes that typically exist inside the concrete structure can generate stronger scattering than small concrete cracks to affect detection effectiveness. In GPR image, the signature patterns of regularly distributed rebars or pipes can be deemed as correlated background signals, while for the small size cracks, their image features are typically irregularly and sparsely distributed. To effectively detect the cracks in concrete structure, the robust principal component analysis algorithm is developed to characterize the rank and sparsity of GPR image. For performance evaluations, simulations are conducted with various configurations.
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.
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.
Chauhan, Valentina Singh; Sharma, Alka
2003-04-01
The population which is below the poverty line is devoid of nutritious diet. Egg and milk are categorized as complete foods. The defensive organizations are situated in such remote places where fresh food material is not available. Keeping in view these problems, the study of organoleptic variables, viz., color, appearance, aroma, texture and taste in the food products of cake, omelet doughnut, coconut macaroon and mayonnaise from fresh egg and egg powder, was conducted. Principal component analysis was carried out. Organoleptic properties of doughnut prepared from egg powder were superior compared to fresh egg which had better sensory traits for coconut macaroon. The sensory traits like taste, texture and aroma were the most influential traits studied to pronouncing as a panel decision. It is proposed that fresh egg and egg powder should be preferred in the process of preparation of coconut macaroon and doughnut, respectively. PMID:12744287
Serra, Francesca; Guillou, Claude G; Reniero, Fabiano; Ballarin, Luciano; Cantagallo, Maria I; Wieser, Michael; Iyer, Sundaram S; Héberger, Károly; Vanhaecke, Frank
2005-01-01
In this study we show that the continental origin of coffee can be inferred on the basis of coupling the isotope ratios of several elements determined in green beans. The combination of the isotopic fingerprints of carbon, nitrogen and boron, used as integrated proxies for environmental conditions and agricultural practices, allows discrimination among the three continental areas producing coffee (Africa, Asia and America). In these continents there are countries producing 'specialty coffees', highly rated on the market that are sometimes mislabeled further on along the export-sale chain or mixed with cheaper coffees produced in other regions. By means of principal component analysis we were successful in identifying the continental origin of 88% of the samples analyzed. An intra-continent discrimination has not been possible at this stage of the study, but is planned in future work. Nonetheless, the approach using stable isotope ratios seems quite promising, and future development of this research is also discussed. PMID:15988730
Wang, Binyu; Xu, Haisong; Luo, M Ronnier; Guo, Jinyi
2011-07-01
The replacement of used-up ink cartridges is unavoidable, but it makes the existing characterization model far from accurate, while recharacterization is labor intensive. In this study, we propose a new correction method for cellular Yule-Nielsen spectral Neugebauer (CYNSN) models based on principal component analysis (PCA). First, a small set of correction samples are predicted, printed using new ink cartridges, and then measured. Second, the link between the predicted and measured reflectance weights, generated by PCA, is determined. The experimental results show that the proposed method provides a significant and robust improvement, since not only the color change between original and new inks but also the systemic error of CYNSN modelsis taken into account in the method.
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)
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%.
Hoang, Tuan; Tran, Dat; Huang, Xu
2013-01-01
Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.
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
NASA Astrophysics Data System (ADS)
Baronti, S.; Casini, A.; Lotti, F.; Porcinai, S.
1998-03-01
Image spectroscopy (IS) is an important tool for the noninvasive analysis of works of art. It generates a wide sequence of multispectral images from which a reflectance spectrum for each imaged point can be recovered. In addition, digital processing techniques can be employed to divide the images into areas of similar spectral behavior. An IS system designed and developed in our laboratory is described. The methodology used to process the acquired data integrates spectral analysis with statistical image processing: in particular, the potential of principal-component analysis applied in this area is investigated. A selection of the results obtained from a sixteenth-century oil-painted panel by Luca Signorelli is also reported.
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.
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.
Mourka, A.; Mazilu, M.; Wright, E. M.; Dholakia, K.
2013-01-01
The modal characterization of various families of beams is a topic of current interest. We recently reported a new method for the simultaneous determination of both the azimuthal and radial mode indices for light fields possessing orbital angular momentum. The method is based upon probing the far-field diffraction pattern from a random aperture and using the recorded data as a ‘training set'. We then transform the observed data into uncorrelated variables using the principal component analysis (PCA) algorithm. Here, we show the generic nature of this approach for the simultaneous determination of the modal parameters of Hermite-Gaussian and Bessel beams. This reinforces the widespread applicability of this method for applications including information processing, spectroscopy and manipulation. Additionally, preliminary results demonstrate reliable decomposition of superpositions of Laguerre-Gaussians, yielding the intensities and relative phases of each constituent mode. Thus, this approach represents a powerful method for characterizing the optical multi-dimensional Hilbert space. PMID:23478330
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. PMID:3268292
Lima, Marcelo A.; Rudd, Timothy R.; de Farias, Eduardo H. C.; Ebner, Lyvia F.; Gesteira, Tarsis F.; de Souza, Lauro M.; Mendes, Aline; Córdula, Carolina R.; Martins, João R. M.; Hoppensteadt, Debra; Fareed, Jawed; Sassaki, Guilherme L.; Yates, Edwin A.; Tersariol, Ivarne L. S.; Nader, Helena B.
2011-01-01
The year 2007 was marked by widespread adverse clinical responses to heparin use, leading to a global recall of potentially affected heparin batches in 2008. Several analytical methods have since been developed to detect impurities in heparin preparations; however, many are costly and dependent on instrumentation with only limited accessibility. A method based on a simple UV-scanning assay, combined with principal component analysis (PCA), was developed to detect impurities, such as glycosaminoglycans, other complex polysaccharides and aromatic compounds, in heparin preparations. Results were confirmed by NMR spectroscopy. This approach provides an additional, sensitive tool to determine heparin purity and safety, even when NMR spectroscopy failed, requiring only standard laboratory equipment and computing facilities. PMID:21267460
Andrade, J M; Kubista, M; Carlosena, A; Prada, D
2007-11-01
Three different approaches for 3-way analyses, namely, Procrustes rotation, parallel factor analysis (PARAFAC) and matrix-augmented principal component analysis, have been compared considering a four-seasons study on soil pollution. Each sampling season comprised 92 roadsoil samples and 12 analytical variables (heavy metals, loss on ignition, pH and humidity). Results show that the three chemometric techniques lead to essentially the same conclusions. Hence, Procrustes rotation, a mathematical technique scarcely applied in analytical chemistry, revealed as a useful tool for 3-way data analysis with potential advantages, including its conceptual simplicity and straightforward interpretation of the results. A novel application of the consensus vectors allowed definition of "consensus scores" so that visualization of the samples and temporal patterns can be made. Results also suggested that the trilinearity assumption imbedded in PARAFAC is essentially fulfilled when studying the temporal evolution of an environmental system where no new pollution sources appear during the course of the study. PMID:17950053
Lucas, L; Jauzein, M
2008-01-01
This work aims at evaluating spatial distribution patterns of concentration variations for chlorinated solvents in groundwater, based on principal component analysis and geographic information system (GIS) tools. The study investigates long-time series of chlorinated solvent concentrations in groundwater measured for 18 contaminated industrial sites. The characterization of contaminant plumes and delineation of pollutant sources are essential for choosing appropriate monitoring and remediation strategies, as contaminated groundwaters are characterized by complex patterns of spatial and temporal concentration variability, with wide unpredictable fluctuations over time. The present work describes the results of a new exploratory statistical method called the Variability Index Method (VIM) applied to environmental data to assess the performance of using concentration variations as molecular tracers to reveal aquifer dynamics, industrial impacts, and point sources for contamination plumes. The application of this method provides a useful assessment of controls over contaminant concentration variations as well as support for remediation techniques.
Soehn, Matthias Alber, Markus; Yan Di
2007-09-01
Purpose: The variability of dose-volume histogram (DVH) shapes in a patient population can be quantified using principal component analysis (PCA). We applied this to rectal DVHs of prostate cancer patients and investigated the correlation of the PCA parameters with late bleeding. Methods and Materials: PCA was applied to the rectal wall DVHs of 262 patients, who had been treated with a four-field box, conformal adaptive radiotherapy technique. The correlated changes in the DVH pattern were revealed as 'eigenmodes,' which were ordered by their importance to represent data set variability. Each DVH is uniquely characterized by its principal components (PCs). The correlation of the first three PCs and chronic rectal bleeding of Grade 2 or greater was investigated with uni- and multivariate logistic regression analyses. Results: Rectal wall DVHs in four-field conformal RT can primarily be represented by the first two or three PCs, which describe {approx}94% or 96% of the DVH shape variability, respectively. The first eigenmode models the total irradiated rectal volume; thus, PC1 correlates to the mean dose. Mode 2 describes the interpatient differences of the relative rectal volume in the two- or four-field overlap region. Mode 3 reveals correlations of volumes with intermediate doses ({approx}40-45 Gy) and volumes with doses >70 Gy; thus, PC3 is associated with the maximal dose. According to univariate logistic regression analysis, only PC2 correlated significantly with toxicity. However, multivariate logistic regression analysis with the first two or three PCs revealed an increased probability of bleeding for DVHs with more than one large PC. Conclusions: PCA can reveal the correlation structure of DVHs for a patient population as imposed by the treatment technique and provide information about its relationship to toxicity. It proves useful for augmenting normal tissue complication probability modeling approaches.
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
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.
Jung, Brian C.; Choi, Soo I.; Du, Annie X.; Cuzzocreo, Jennifer L.; Geng, Zhuo Z.; Ying, Howard S.; Perlman, Susan L.; Toga, Arthur W.; Prince, Jerry L.
2014-01-01
Although “cerebellar ataxia” is often used in reference to a disease process, presumably there are different underlying pathogenetic mechanisms for different subtypes. Indeed, spinocerebellar ataxia (SCA) types 2 and 6 demonstrate complementary phenotypes, thus predicting a different anatomic pattern of degeneration. Here, we show that an unsupervised classification method, based on principal component analysis (PCA) of cerebellar shape characteristics, can be used to separate SCA2 and SCA6 into two classes, which may represent disease-specific archetypes. Patients with SCA2 (n=11) and SCA6 (n=7) were compared against controls (n=15) using PCA to classify cerebellar anatomic shape characteristics. Within the first three principal components, SCA2 and SCA6 differed from controls and from each other. In a secondary analysis, we studied five additional subjects and found that these patients were consistent with the previously defined archetypal clusters of clinical and anatomical characteristics. Secondary analysis of five subjects with related diagnoses showed that disease groups that were clinically and pathophysiologically similar also shared similar anatomic characteristics. Specifically, Archetype #1 consisted of SCA3 (n=1) and SCA2, suggesting that cerebellar syndromes accompanied by atrophy of the pons may be associated with a characteristic pattern of cerebellar neurodegeneration. In comparison, Archetype #2 was comprised of disease groups with pure cerebellar atrophy (episodic ataxia type 2 (n=1), idiopathic late-onset cerebellar ataxias (n=3), and SCA6). This suggests that cerebellar shape analysis could aid in discriminating between different pathologies. Our findings further suggest that magnetic resonance imaging is a promising imaging biomarker that could aid in the diagnosis and therapeutic management in patients with cerebellar syndromes. PMID:22258915
Olvera, Hector A; Garcia, Mario; Li, Wen-Whai; Yang, Hongling; Amaya, Maria A; Myers, Orrin; Burchiel, Scott W; Berwick, Marianne; Pingitore, Nicholas E
2012-05-15
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.
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.
Jung, Brian C; Choi, Soo I; Du, Annie X; Cuzzocreo, Jennifer L; Geng, Zhuo Z; Ying, Howard S; Perlman, Susan L; Toga, Arthur W; Prince, Jerry L; Ying, Sarah H
2012-12-01
Although "cerebellar ataxia" is often used in reference to a disease process, presumably there are different underlying pathogenetic mechanisms for different subtypes. Indeed, spinocerebellar ataxia (SCA) types 2 and 6 demonstrate complementary phenotypes, thus predicting a different anatomic pattern of degeneration. Here, we show that an unsupervised classification method, based on principal component analysis (PCA) of cerebellar shape characteristics, can be used to separate SCA2 and SCA6 into two classes, which may represent disease-specific archetypes. Patients with SCA2 (n=11) and SCA6 (n=7) were compared against controls (n=15) using PCA to classify cerebellar anatomic shape characteristics. Within the first three principal components, SCA2 and SCA6 differed from controls and from each other. In a secondary analysis, we studied five additional subjects and found that these patients were consistent with the previously defined archetypal clusters of clinical and anatomical characteristics. Secondary analysis of five subjects with related diagnoses showed that disease groups that were clinically and pathophysiologically similar also shared similar anatomic characteristics. Specifically, Archetype #1 consisted of SCA3 (n=1) and SCA2, suggesting that cerebellar syndromes accompanied by atrophy of the pons may be associated with a characteristic pattern of cerebellar neurodegeneration. In comparison, Archetype #2 was comprised of disease groups with pure cerebellar atrophy (episodic ataxia type 2 (n=1), idiopathic late-onset cerebellar ataxias (n=3), and SCA6). This suggests that cerebellar shape analysis could aid in discriminating between different pathologies. Our findings further suggest that magnetic resonance imaging is a promising imaging biomarker that could aid in the diagnosis and therapeutic management in patients with cerebellar syndromes. PMID:22258915
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.
Gao, Wen; Wang, Rui; Li, Dan; Liu, Ke; Chen, Jun; Li, Hui-Jun; Xu, Xiaojun; Li, Ping; Yang, Hua
2016-01-01
The flowers of Lonicera japonica Thunb. were extensively used to treat many diseases. As the demands for L. japonica increased, some related Lonicera plants were often confused or misused. Caffeoylquinic acids were always regarded as chemical markers in the quality control of L. japonica, but they could be found in all Lonicera species. Thus, a simple and reliable method for the evaluation of different Lonicera flowers is necessary to be established. In this work a method based on single standard to determine multi-components (SSDMC) combined with principal component analysis (PCA) for control and distinguish of Lonicera species flowers have been developed. Six components including three caffeoylquinic acids and three iridoid glycosides were assayed simultaneously using chlorogenic acid as the reference standard. The credibility and feasibility of the SSDMC method were carefully validated and the results demonstrated that there were no remarkable differences compared with external standard method. Finally, a total of fifty-one batches covering five Lonicera species were analyzed and PCA was successfully applied to distinguish the Lonicera species. This strategy simplifies the processes in the quality control of multiple-componential herbal medicine which effectively adapted for improving the quality control of those herbs belonging to closely related species.
Gao, Wen; Wang, Rui; Li, Dan; Liu, Ke; Chen, Jun; Li, Hui-Jun; Xu, Xiaojun; Li, Ping; Yang, Hua
2016-01-01
The flowers of Lonicera japonica Thunb. were extensively used to treat many diseases. As the demands for L. japonica increased, some related Lonicera plants were often confused or misused. Caffeoylquinic acids were always regarded as chemical markers in the quality control of L. japonica, but they could be found in all Lonicera species. Thus, a simple and reliable method for the evaluation of different Lonicera flowers is necessary to be established. In this work a method based on single standard to determine multi-components (SSDMC) combined with principal component analysis (PCA) for control and distinguish of Lonicera species flowers have been developed. Six components including three caffeoylquinic acids and three iridoid glycosides were assayed simultaneously using chlorogenic acid as the reference standard. The credibility and feasibility of the SSDMC method were carefully validated and the results demonstrated that there were no remarkable differences compared with external standard method. Finally, a total of fifty-one batches covering five Lonicera species were analyzed and PCA was successfully applied to distinguish the Lonicera species. This strategy simplifies the processes in the quality control of multiple-componential herbal medicine which effectively adapted for improving the quality control of those herbs belonging to closely related species. PMID:26432385
Metsalu, Tauno; Vilo, Jaak
2015-07-01
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/.
NASA Technical Reports Server (NTRS)
Dong, D.; Fang, P.; Bock, F.; Webb, F.; Prawirondirdjo, L.; Kedar, S.; Jamason, P.
2006-01-01
Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering.We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.
Fitzpatrick, Clare K; Baldwin, Mark A; Rullkoetter, Paul J; Laz, Peter J
2011-01-01
Many aspects of biomechanics are variable in nature, including patient geometry, joint mechanics, implant alignment and clinical outcomes. Probabilistic methods have been applied in computational models to predict distributions of performance given uncertain or variable parameters. Sensitivity analysis is commonly used in conjunction with probabilistic methods to identify the parameters that most significantly affect the performance outcome; however, it does not consider coupled relationships for multiple output measures. Principal component analysis (PCA) has been applied to characterize common modes of variation in shape and kinematics. In this study, a novel, combined probabilistic and PCA approach was developed to characterize relationships between multiple input parameters and output measures. To demonstrate the benefits of the approach, it was applied to implanted patellofemoral (PF) mechanics to characterize relationships between femoral and patellar component alignment and loading and the resulting joint mechanics. Prior studies assessing PF sensitivity have performed individual perturbation of alignment parameters. However, the probabilistic and PCA approach enabled a more holistic evaluation of sensitivity, including identification of combinations of alignment parameters that most significantly contributed to kinematic and contact mechanics outcomes throughout the flexion cycle, and the predictive capability to estimate joint mechanics based on alignment conditions without requiring additional analysis. The approach showed comparable results for Monte Carlo sampling with 500 trials and the more efficient Latin Hypercube sampling with 50 trials. The probabilistic and PCA approach has broad applicability to biomechanical analysis and can provide insight into the interdependencies between implant design, alignment and the resulting mechanics.
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.
Fitzpatrick, Clare K; Baldwin, Mark A; Rullkoetter, Paul J; Laz, Peter J
2011-01-01
Many aspects of biomechanics are variable in nature, including patient geometry, joint mechanics, implant alignment and clinical outcomes. Probabilistic methods have been applied in computational models to predict distributions of performance given uncertain or variable parameters. Sensitivity analysis is commonly used in conjunction with probabilistic methods to identify the parameters that most significantly affect the performance outcome; however, it does not consider coupled relationships for multiple output measures. Principal component analysis (PCA) has been applied to characterize common modes of variation in shape and kinematics. In this study, a novel, combined probabilistic and PCA approach was developed to characterize relationships between multiple input parameters and output measures. To demonstrate the benefits of the approach, it was applied to implanted patellofemoral (PF) mechanics to characterize relationships between femoral and patellar component alignment and loading and the resulting joint mechanics. Prior studies assessing PF sensitivity have performed individual perturbation of alignment parameters. However, the probabilistic and PCA approach enabled a more holistic evaluation of sensitivity, including identification of combinations of alignment parameters that most significantly contributed to kinematic and contact mechanics outcomes throughout the flexion cycle, and the predictive capability to estimate joint mechanics based on alignment conditions without requiring additional analysis. The approach showed comparable results for Monte Carlo sampling with 500 trials and the more efficient Latin Hypercube sampling with 50 trials. The probabilistic and PCA approach has broad applicability to biomechanical analysis and can provide insight into the interdependencies between implant design, alignment and the resulting mechanics. PMID:20825941
Vesprini, Danny; Sia, Michael; Lockwood, Gina; Moseley, Douglas; Rosewall, Tara; Bayley, Andrew; Bristow, Robert; Chung, Peter; Menard, Cynthia; Milosevic, Michael; Warde, Padraig; Catton, Charles
2011-11-15
Purpose: To determine if principal component analysis (PCA) and standard parameters of rectal and bladder wall dose-volume histograms (DVHs) of prostate cancer patients treated with hypofractionated image-guided intensity-modulated radiotherapy (hypo-IMRT) can predict acute and late gastrointestinal (GI) toxicity. Methods and Materials: One hundred twenty-one patients underwent hypo-IMRT at 3 Gy/fraction, 5 days/week to either 60 Gy or 66 Gy, with daily online image guidance. Acute and late GI and genitourinary (GU) toxicity were recorded weekly during treatment and at each follow-up. All Radiation Therapy Oncology Group (RTOG) criteria toxicity scores were dichotomized as <2 and {>=}2. Standard dosimetric parameters and the first five to six principal components (PCs) of bladder and rectal wall DVHs were tested for association with the dichotomized toxicity outcomes, using logistic regression. Results: Median follow-up of all patients was 47 months (60 Gy cohort= 52 months; 66 Gy cohort= 31 months). The incidence rates of {>=}2 acute GI and GU toxicity were 14% and 29%, respectively, with no Grade {>=}3 acute GU toxicity. Late GI and GU toxicity scores {>=}2 were 16% and 15%, respectively. There was a significant difference in late GI toxicity {>=}2 when comparing the 66 Gy to the 60 Gy cohort (38% vs. 8%, respectively, p = 0.0003). The first PC of the rectal DVH was associated with late GI toxicity (odds ratio [OR], 6.91; p < 0.001), though it was not significantly stronger than standard DVH parameters such as Dmax (OR, 6.9; p < 0.001) or percentage of the organ receiving a 50% dose (V50) (OR, 5.95; p = 0 .001). Conclusions: Hypofractionated treatment with 60 Gy in 3 Gy fractions is well tolerated. There is a steep dose response curve between 60 Gy and 66 Gy for RTOG Grade {>=}2 GI effects with the dose constraints employed. Although PCA can predict late GI toxicity for patients treated with hypo-IMRT for prostate cancer, it provides no additional information
Furihata, Chie; Watanabe, Takashi; Suzuki, Takayoshi; Hamada, Shuichi; Nakajima, Madoka
2016-01-01
Toxicogenomics is a rapidly developing discipline focused on the elucidation of the molecular and cellular effects of chemicals on biological systems. As a collaborative study group of Toxicogenomics/JEMS·MMS, we conducted studies on hepatocarcinogens in rodent liver in which 100 candidate marker genes were selected to discriminate genotoxic hepatocarcinogens from non-genotoxic hepatocarcinogens. Differential gene expression induced by 13 chemicals were examined using DNA microarray and quantitative real-time PCR (qPCR), including eight genotoxic hepatocarcinogens [o-aminoazotoluene, chrysene, dibenzo[a,l]pyrene, diethylnitrosamine (DEN), 7,12-dimethylbenz[a]anthracene, dimethylnitrosamine, dipropylnitrosamine and ethylnitrosourea (ENU)], four non-genotoxic hepatocarcinogens [carbon tetrachloride, di(2-ethylhexyl)phthalate (DEHP), phenobarbital and trichloroethylene] and a non-genotoxic non-hepatocarcinogen [ethanol]. Using qPCR, 30 key genes were extracted from mouse livers at 4 h and 28 days following dose-dependent gene expression alteration induced by DEN and ENU: the most significant changes in gene expression were observed at 4 h. Next, we selected key point times at 4 and 48 h from changes in time-dependent gene expression during the acute phase following administration of chrysene by qPCR. We successfully showed discrimination of eight genotoxic hepatocarcinogens [2-acetylaminofluorene, 2,4-diaminotoluene, diisopropanolnitrosamine, 4-dimethylaminoazobenzene, 4-(methylnitsosamino)-1-(3-pyridyl)-1-butanone, N-nitrosomorpholine, quinoline and urethane] from four non-genotoxic hepatocarcinogens [1,4-dichlorobenzene, dichlorodiphenyltrichloroethane, DEHP and furan] using qPCR and principal component analysis. Additionally, we successfully identified two rat genotoxic hepatocarcinogens [DEN and 2,6-dinitrotoluene] from a nongenotoxic-hepatocarcinogen [DEHP] and a non-genotoxic non-hepatocarcinogen [phenacetin] at 4 and 48 h. The subsequent gene pathway
Deng, Xian-Mei; Yu, Jiang-Yong; Ding, Meng-Jin; Zhao, Ming; Xue, Xing-Yang; Che, Chun-Tao; Wang, Shu-Mei; Zhao, Bin; Meng, Jiang
2016-01-01
Background: Raw Moutan Cortex (RMC), derived from the root bark of Paeonia suffruticosa, and Processed Moutan Cortex (PMC) is obtained from RMC by undergoing a stir-frying process. Both of them are indicated for different pharmacodynamic action in traditional Chinese medicine, and they have been used in China and other Asian countries for thousands of years. Objective: To establish a method to study the RMC and PMC, revealing their different chemical composition by fingerprint, qualitative, and quantitative ways. Materials and Methods: High-performance liquid chromatography coupled with diode array detector and electrospray mass spectrometry (HPLC-DAD-ESIMS) were used for the analysis. Therefore, the analytes were separated on an Ultimate TM XB-C18 analytical column (250 mm × 4.6 mm, 5.0 μm) with a gradient elution program by a mobile phase consisting of acetonitrile and 0.1% (v/v) formic acid water solution. The flow rate, injection volume, detection wavelength, and column temperature were set at 1.0 mL/min, 10 μL, 254 nm, and 30°C, respectively. Besides, principal components analysis and the test of significance were applied in data analysis. Results: The results clearly showed a significant difference among RMC and PMC, indicating the significant changes in their chemical compositions before and after the stir-frying process. Conclusion: The HPLC-DAD-ESIMS coupled with chemometrics analysis could be used for comprehensive quality evaluation of raw and processed Moutan Cortex. SUMMARY The experiment study the RMC and PMC by HPLC-DAD-ESIMS couple with chemometrics analysis. The results of their fingerprints, qualitative, and quantitative all clearly showed significant changes in their chemical compositions before and after stir-frying processed. Abbreviation used: HPLC-DAD-ESIMS: High-performance Liquid Chromatography-Diode Array Detector-Electrospray Mass Spectrometry, RMC: Raw moutan cortex, PMC: Processed moutan cortex, TCM: Traditional Chinese medicine
Furihata, Chie; Watanabe, Takashi; Suzuki, Takayoshi; Hamada, Shuichi; Nakajima, Madoka
2016-01-01
Toxicogenomics is a rapidly developing discipline focused on the elucidation of the molecular and cellular effects of chemicals on biological systems. As a collaborative study group of Toxicogenomics/JEMS·MMS, we conducted studies on hepatocarcinogens in rodent liver in which 100 candidate marker genes were selected to discriminate genotoxic hepatocarcinogens from non-genotoxic hepatocarcinogens. Differential gene expression induced by 13 chemicals were examined using DNA microarray and quantitative real-time PCR (qPCR), including eight genotoxic hepatocarcinogens [o-aminoazotoluene, chrysene, dibenzo[a,l]pyrene, diethylnitrosamine (DEN), 7,12-dimethylbenz[a]anthracene, dimethylnitrosamine, dipropylnitrosamine and ethylnitrosourea (ENU)], four non-genotoxic hepatocarcinogens [carbon tetrachloride, di(2-ethylhexyl)phthalate (DEHP), phenobarbital and trichloroethylene] and a non-genotoxic non-hepatocarcinogen [ethanol]. Using qPCR, 30 key genes were extracted from mouse livers at 4 h and 28 days following dose-dependent gene expression alteration induced by DEN and ENU: the most significant changes in gene expression were observed at 4 h. Next, we selected key point times at 4 and 48 h from changes in time-dependent gene expression during the acute phase following administration of chrysene by qPCR. We successfully showed discrimination of eight genotoxic hepatocarcinogens [2-acetylaminofluorene, 2,4-diaminotoluene, diisopropanolnitrosamine, 4-dimethylaminoazobenzene, 4-(methylnitsosamino)-1-(3-pyridyl)-1-butanone, N-nitrosomorpholine, quinoline and urethane] from four non-genotoxic hepatocarcinogens [1,4-dichlorobenzene, dichlorodiphenyltrichloroethane, DEHP and furan] using qPCR and principal component analysis. Additionally, we successfully identified two rat genotoxic hepatocarcinogens [DEN and 2,6-dinitrotoluene] from a nongenotoxic-hepatocarcinogen [DEHP] and a non-genotoxic non-hepatocarcinogen [phenacetin] at 4 and 48 h. The subsequent gene pathway
2011-01-01
Background The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA). Method Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence. Results and Discussion The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD. Conclusion This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and
Pes, Giovanni Mario; Delitala, Alessandro Palmerio; Errigo, Alessandra; Delitala, Giuseppe; Dore, Maria Pina
2016-06-01
Latent autoimmune diabetes in adults (LADA) which accounts for more than 10 % of all cases of diabetes is characterized by onset after age 30, absence of ketoacidosis, insulin independence for at least 6 months, and presence of circulating islet-cell antibodies. Its marked heterogeneity in clinical features and immunological markers suggests the existence of multiple mechanisms underlying its pathogenesis. The principal component (PC) analysis is a statistical approach used for finding patterns in data of high dimension. In this study the PC analysis was applied to a set of variables from a cohort of Sardinian LADA patients to identify a smaller number of latent patterns. A list of 11 variables including clinical (gender, BMI, lipid profile, systolic and diastolic blood pressure and insulin-free time period), immunological (anti-GAD65, anti-IA-2 and anti-TPO antibody titers) and genetic features (predisposing gene variants previously identified as risk factors for autoimmune diabetes) retrieved from clinical records of 238 LADA patients referred to the Internal Medicine Unit of University of Sassari, Italy, were analyzed by PC analysis. The predictive value of each PC on the further development of insulin dependence was evaluated using Kaplan-Meier curves. Overall 4 clusters were identified by PC analysis. In component PC-1, the dominant variables were: BMI, triglycerides, systolic and diastolic blood pressure and duration of insulin-free time period; in PC-2: genetic variables such as Class II HLA, CTLA-4 as well as anti-GAD65, anti-IA-2 and anti-TPO antibody titers, and the insulin-free time period predominated; in PC-3: gender and triglycerides; and in PC-4: total cholesterol. These components explained 18, 15, 12, and 12 %, respectively, of the total variance in the LADA cohort. The predictive power of insulin dependence of the four components was different. PC-2 (characterized mostly by high antibody titers and presence of predisposing genetic markers
Newbern, Dorothee; Balikcioglu, Metin; Bain, James; Muehlbauer, Michael; Stevens, Robert; Ilkayeva, Olga; Dolinsky, Diana; Armstrong, Sarah; Irizarry, Krystal; Freemark, Michael
2014-01-01
Objective: Obesity and insulin resistance (IR) predispose to type 2 diabetes mellitus. Yet only half of obese adolescents have IR and far fewer progress to type 2 diabetes mellitus. We hypothesized that amino acid and fatty acid metabolites may serve as biomarkers or determinants of IR in obese teens. Research Design and Methods: Fasting blood samples were analyzed by tandem mass spectrometry in 82 obese adolescents. A principal components analysis and multiple linear regression models were used to correlate metabolic components with surrogate measures of IR: homeostasis model assessment index of insulin resistance (HOMA-IR), adiponectin, and triglyceride (TG) to high-density lipoprotein (HDL) ratio. Results: Branched-chain amino acid (BCAA) levels and products of BCAA catabolism were higher (P < .01) in males than females with comparable body mass index (BMI) z-score. In multivariate analyses, HOMA-IR in males correlated positively with BMI z-score and a metabolic signature containing BCAA, uric acid, and long-chain acylcarnitines and negatively with byproducts of complete fatty acid oxidation (R2 = 0.659, P < .0001). In contrast, only BMI z-score correlated with HOMA-IR in females. Adiponectin correlated inversely with BCAA and uric acid (R2 = 0.268, P = .0212) in males but not females. TG to HDL ratio correlated with BMI z-score and the BCAA signature in females but not males. Conclusions: BCAA levels and byproducts of BCAA catabolism are higher in obese teenage boys than girls of comparable BMI z-score. A metabolic signature comprising BCAA and uric acid correlates positively with HOMA-IR in males and TG to HDL ratio in females and inversely with adiponectin in males but not females. Likewise, byproducts of fatty acid oxidation associate inversely with HOMA-IR in males but not females. Our findings underscore the roles of sex differences in metabolic function and outcomes in pediatric obesity. PMID:25202817
NASA Astrophysics Data System (ADS)
Borowiak, Klaudia; Zbierska, Janina; Budka, Anna; Kayzer, Dariusz
2014-06-01
Three plant species were assessed in this study - ozone-sensitive and -resistant tobacco, ozone-sensitive petunia and bean. Plants were exposed to ambient air conditions for several weeks in two sites differing in tropospheric ozone concentrations in the growing season of 2009. Every week chlorophyll contents were analysed. Cumulative ozone effects on the chlorophyll content in relation to other meteorological parameters were evaluated using principal component analysis, while the relation between certain days of measurements of the plants were analysed using multivariate analysis of variance. Results revealed variability between plant species response. However, some similarities were noted. Positive relations of all chlorophyll forms to cumulative ozone concentration (AOT 40) were found for all the plant species that were examined. The chlorophyll b/a ratio revealed an opposite position to ozone concentration only in the ozone-resistant tobacco cultivar. In all the plant species the highest average chlorophyll content was noted after the 7th day of the experiment. Afterwards, the plants usually revealed various responses. Ozone-sensitive tobacco revealed decrease of chlorophyll content, and after few weeks of decline again an increase was observed. Probably, due to the accommodation for the stress factor. While during first three weeks relatively high levels of chlorophyll contents were noted in ozone-resistant tobacco. Petunia revealed a slow decrease of chlorophyll content and the lowest values at the end of the experiment. A comparison between the plant species revealed the highest level of chlorophyll contents in ozone-resistant tobacco.
Jerez-Timaure, N; Huerta-Leidenz, N; Ortega, J; Rodas-González, A
2013-03-01
A database consisting of 331 beef animals (Brahman-crossbred) was used to determine the multivariate relationships between carcass and beef palatability traits of Venezuelan cattle and to develop prediction equations for Warner-Bratzler shear force (WBSF). The first three principal components (PC) explained 77.53% of the standardized variance. Equations were obtained for each sex class and the total variability observed in WBSF could be explained by its orthogonal regression with carcass weight (CW), fat cover (FC), fat thickness (FT), and skeletal maturity (SM). Prediction equations were: WBSF(steers)=3.566+0.003(CW)-0.033(FC)-0.015(FT)+0.0004(SM); WBSF(heifers)=4.824+0.002(CW)-0.229(FC)+0.096(FT)-0.064(SM); WBSF(bulls)=3.516+0.009(CW)+0.154(FC)-0.129(FT)-0.006(SM). A higher proportion of the variation was explained by the PC when variables of greater weight were selected to define each PC. The equation set presented herein could become an important tool to improve the Venezuelan carcass grading system. PMID:23261538