Airborne electromagnetic data levelling using principal component analysis based on flight line difference

NASA Astrophysics Data System (ADS)

Zhang, Qiong; Peng, Cong; Lu, Yiming; Wang, Hao; Zhu, Kaiguang

2018-04-01

A novel technique is developed to level airborne geophysical data using principal component analysis based on flight line difference. In the paper, flight line difference is introduced to enhance the features of levelling error for airborne electromagnetic (AEM) data and improve the correlation between pseudo tie lines. Thus we conduct levelling to the flight line difference data instead of to the original AEM data directly. Pseudo tie lines are selected distributively cross profile direction, avoiding the anomalous regions. Since the levelling errors of selective pseudo tie lines show high correlations, principal component analysis is applied to extract the local levelling errors by low-order principal components reconstruction. Furthermore, we can obtain the levelling errors of original AEM data through inverse difference after spatial interpolation. This levelling method does not need to fly tie lines and design the levelling fitting function. The effectiveness of this method is demonstrated by the levelling results of survey data, comparing with the results from tie-line levelling and flight-line correlation levelling.

Strain Transient Detection Techniques: A Comparison of Source Parameter Inversions of Signals Isolated through Principal Component Analysis (PCA), Non-Linear PCA, and Rotated PCA

NASA Astrophysics Data System (ADS)

Lipovsky, B.; Funning, G. J.

2009-12-01

We compare several techniques for the analysis of geodetic time series with the ultimate aim to characterize the physical processes which are represented therein. We compare three methods for the analysis of these data: Principal Component Analysis (PCA), Non-Linear PCA (NLPCA), and Rotated PCA (RPCA). We evaluate each method by its ability to isolate signals which may be any combination of low amplitude (near noise level), temporally transient, unaccompanied by seismic emissions, and small scale with respect to the spatial domain. PCA is a powerful tool for extracting structure from large datasets which is traditionally realized through either the solution of an eigenvalue problem or through iterative methods. PCA is an transformation of the coordinate system of our data such that the new "principal" data axes retain maximal variance and minimal reconstruction error (Pearson, 1901; Hotelling, 1933). RPCA is achieved by an orthogonal transformation of the principal axes determined in PCA. In the analysis of meteorological data sets, RPCA has been seen to overcome domain shape dependencies, correct for sampling errors, and to determine principal axes which more closely represent physical processes (e.g., Richman, 1986). NLPCA generalizes PCA such that principal axes are replaced by principal curves (e.g., Hsieh 2004). We achieve NLPCA through an auto-associative feed-forward neural network (Scholz, 2005). We show the geophysical relevance of these techniques by application of each to a synthetic data set. Results are compared by inverting principal axes to determine deformation source parameters. Temporal variability in source parameters, estimated by each method, are also compared.

Classifying Facial Actions

PubMed Central

Donato, Gianluca; Bartlett, Marian Stewart; Hager, Joseph C.; Ekman, Paul; Sejnowski, Terrence J.

2010-01-01

The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions. PMID:21188284

Estimation of surface curvature from full-field shape data using principal component analysis

NASA Astrophysics Data System (ADS)

Sharma, Sameer; Vinuchakravarthy, S.; Subramanian, S. J.

2017-01-01

Three-dimensional digital image correlation (3D-DIC) is a popular image-based experimental technique for estimating surface shape, displacements and strains of deforming objects. In this technique, a calibrated stereo rig is used to obtain and stereo-match pairs of images of the object of interest from which the shapes of the imaged surface are then computed using the calibration parameters of the rig. Displacements are obtained by performing an additional temporal correlation of the shapes obtained at various stages of deformation and strains by smoothing and numerically differentiating the displacement data. Since strains are of primary importance in solid mechanics, significant efforts have been put into computation of strains from the measured displacement fields; however, much less attention has been paid to date to computation of curvature from the measured 3D surfaces. In this work, we address this gap by proposing a new method of computing curvature from full-field shape measurements using principal component analysis (PCA) along the lines of a similar work recently proposed to measure strains (Grama and Subramanian 2014 Exp. Mech. 54 913-33). PCA is a multivariate analysis tool that is widely used to reveal relationships between a large number of variables, reduce dimensionality and achieve significant denoising. This technique is applied here to identify dominant principal components in the shape fields measured by 3D-DIC and these principal components are then differentiated systematically to obtain the first and second fundamental forms used in the curvature calculation. The proposed method is first verified using synthetically generated noisy surfaces and then validated experimentally on some real world objects with known ground-truth curvatures.

Principal component analysis for protein folding dynamics.

PubMed

Maisuradze, Gia G; Liwo, Adam; Scheraga, Harold A

2009-01-09

Protein folding is considered here by studying the dynamics of the folding of the triple beta-strand WW domain from the Formin-binding protein 28. Starting from the unfolded state and ending either in the native or nonnative conformational states, trajectories are generated with the coarse-grained united residue (UNRES) force field. The effectiveness of principal components analysis (PCA), an already established mathematical technique for finding global, correlated motions in atomic simulations of proteins, is evaluated here for coarse-grained trajectories. The problems related to PCA and their solutions are discussed. The folding and nonfolding of proteins are examined with free-energy landscapes. Detailed analyses of many folding and nonfolding trajectories at different temperatures show that PCA is very efficient for characterizing the general folding and nonfolding features of proteins. It is shown that the first principal component captures and describes in detail the dynamics of a system. Anomalous diffusion in the folding/nonfolding dynamics is examined by the mean-square displacement (MSD) and the fractional diffusion and fractional kinetic equations. The collisionless (or ballistic) behavior of a polypeptide undergoing Brownian motion along the first few principal components is accounted for.

Factor Analysis and Counseling Research

ERIC Educational Resources Information Center

Weiss, David J.

1970-01-01

Topics discussed include factor analysis versus cluster analysis, analysis of Q correlation matrices, ipsativity and factor analysis, and tests for the significance of a correlation matrix prior to application of factor analytic techniques. Techniques for factor extraction discussed include principal components, canonical factor analysis, alpha…

Water reuse systems: A review of the principal components

USGS Publications Warehouse

Lucchetti, G.; Gray, G.A.

1988-01-01

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

Mapping brain activity in gradient-echo functional MRI using principal component analysis

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Singh, Manbir; Don, Manuel

1997-05-01

The detection of sites of brain activation in functional MRI has been a topic of immense research interest and many technique shave been proposed to this end. Recently, principal component analysis (PCA) has been applied to extract the activated regions and their time course of activation. This method is based on the assumption that the activation is orthogonal to other signal variations such as brain motion, physiological oscillations and other uncorrelated noises. A distinct advantage of this method is that it does not require any knowledge of the time course of the true stimulus paradigm. This technique is well suited to EPI image sequences where the sampling rate is high enough to capture the effects of physiological oscillations. In this work, we propose and apply tow methods that are based on PCA to conventional gradient-echo images and investigate their usefulness as tools to extract reliable information on brain activation. The first method is a conventional technique where a single image sequence with alternating on and off stages is subject to a principal component analysis. The second method is a PCA-based approach called the common spatial factor analysis technique (CSF). As the name suggests, this method relies on common spatial factors between the above fMRI image sequence and a background fMRI. We have applied these methods to identify active brain ares during visual stimulation and motor tasks. The results from these methods are compared to those obtained by using the standard cross-correlation technique. We found good agreement in the areas identified as active across all three techniques. The results suggest that PCA and CSF methods have good potential in detecting the true stimulus correlated changes in the presence of other interfering signals.

Snapshot hyperspectral imaging probe with principal component analysis and confidence ellipse for classification

NASA Astrophysics Data System (ADS)

Lim, Hoong-Ta; Murukeshan, Vadakke Matham

2017-06-01

Hyperspectral imaging combines imaging and spectroscopy to provide detailed spectral information for each spatial point in the image. This gives a three-dimensional spatial-spatial-spectral datacube with hundreds of spectral images. Probe-based hyperspectral imaging systems have been developed so that they can be used in regions where conventional table-top platforms would find it difficult to access. A fiber bundle, which is made up of specially-arranged optical fibers, has recently been developed and integrated with a spectrograph-based hyperspectral imager. This forms a snapshot hyperspectral imaging probe, which is able to form a datacube using the information from each scan. Compared to the other configurations, which require sequential scanning to form a datacube, the snapshot configuration is preferred in real-time applications where motion artifacts and pixel misregistration can be minimized. Principal component analysis is a dimension-reducing technique that can be applied in hyperspectral imaging to convert the spectral information into uncorrelated variables known as principal components. A confidence ellipse can be used to define the region of each class in the principal component feature space and for classification. This paper demonstrates the use of the snapshot hyperspectral imaging probe to acquire data from samples of different colors. The spectral library of each sample was acquired and then analyzed using principal component analysis. Confidence ellipse was then applied to the principal components of each sample and used as the classification criteria. The results show that the applied analysis can be used to perform classification of the spectral data acquired using the snapshot hyperspectral imaging probe.

Analysis of environmental variation in a Great Plains reservoir using principal components analysis and geographic information systems

USGS Publications Warehouse

Long, J.M.; Fisher, W.L.

2006-01-01

We present a method for spatial interpretation of environmental variation in a reservoir that integrates principal components analysis (PCA) of environmental data with geographic information systems (GIS). To illustrate our method, we used data from a Great Plains reservoir (Skiatook Lake, Oklahoma) with longitudinal variation in physicochemical conditions. We measured 18 physicochemical features, mapped them using GIS, and then calculated and interpreted four principal components. Principal component 1 (PC1) was readily interpreted as longitudinal variation in water chemistry, but the other principal components (PC2-4) were difficult to interpret. Site scores for PC1-4 were calculated in GIS by summing weighted overlays of the 18 measured environmental variables, with the factor loadings from the PCA as the weights. PC1-4 were then ordered into a landscape hierarchy, an emergent property of this technique, which enabled their interpretation. PC1 was interpreted as a reservoir scale change in water chemistry, PC2 was a microhabitat variable of rip-rap substrate, PC3 identified coves/embayments and PC4 consisted of shoreline microhabitats related to slope. The use of GIS improved our ability to interpret the more obscure principal components (PC2-4), which made the spatial variability of the reservoir environment more apparent. This method is applicable to a variety of aquatic systems, can be accomplished using commercially available software programs, and allows for improved interpretation of the geographic environmental variability of a system compared to using typical PCA plots. ?? Copyright by the North American Lake Management Society 2006.

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

PubMed

Appoloni, C R; Melquiades, F L

2014-02-01

Several modern techniques have been applied to prevent counterfeiting of money bills. The objective of this study was to demonstrate the potential of Portable X-ray Fluorescence (PXRF) technique and the multivariate analysis method of Principal Component Analysis (PCA) for classification of bills in order to use it in forensic science. Bills of Dollar, Euro and Real (Brazilian currency) were measured directly at different colored regions, without any previous preparation. Spectra interpretation allowed the identification of Ca, Ti, Fe, Cu, Sr, Y, Zr and Pb. PCA analysis separated the bills in three groups and subgroups among Brazilian currency. In conclusion, the samples were classified according to its origin identifying the elements responsible for differentiation and basic pigment composition. PXRF allied to multivariate discriminate methods is a promising technique for rapid and no destructive identification of false bills in forensic science. Copyright © 2013 Elsevier Ltd. All rights reserved.

Visual enhancement of images of natural resources: Applications in geology

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Neto, G.; Araujo, E. O.; Mascarenhas, N. D. A.; Desouza, R. C. M.

1980-01-01

The principal components technique for use in multispectral scanner LANDSAT data processing results in optimum dimensionality reduction. A powerful tool for MSS IMAGE enhancement, the method provides a maximum impression of terrain ruggedness; this fact makes the technique well suited for geological analysis.

Quantitative structure-activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods.

PubMed

Ahmadi, Mehdi; Shahlaei, Mohsen

2015-01-01

P2X7 antagonist activity for a set of 49 molecules of the P2X7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. The activity of these compounds was estimated by means of combination of principal component analysis (PCA), as a well-known data reduction method, genetic algorithm (GA), as a variable selection technique, and artificial neural network (ANN), as a non-linear modeling method. First, a linear regression, combined with PCA, (principal component regression) was operated to model the structure-activity relationships, and afterwards a combination of PCA and ANN algorithm was employed to accurately predict the biological activity of the P2X7 antagonist. PCA preserves as much of the information as possible contained in the original data set. Seven most important PC's to the studied activity were selected as the inputs of ANN box by an efficient variable selection method, GA. The best computational neural network model was a fully-connected, feed-forward model with 7-7-1 architecture. The developed ANN model was fully evaluated by different validation techniques, including internal and external validation, and chemical applicability domain. All validations showed that the constructed quantitative structure-activity relationship model suggested is robust and satisfactory.

Quantitative structure–activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods

PubMed Central

Ahmadi, Mehdi; Shahlaei, Mohsen

2015-01-01

P2X7 antagonist activity for a set of 49 molecules of the P2X7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. The activity of these compounds was estimated by means of combination of principal component analysis (PCA), as a well-known data reduction method, genetic algorithm (GA), as a variable selection technique, and artificial neural network (ANN), as a non-linear modeling method. First, a linear regression, combined with PCA, (principal component regression) was operated to model the structure–activity relationships, and afterwards a combination of PCA and ANN algorithm was employed to accurately predict the biological activity of the P2X7 antagonist. PCA preserves as much of the information as possible contained in the original data set. Seven most important PC's to the studied activity were selected as the inputs of ANN box by an efficient variable selection method, GA. The best computational neural network model was a fully-connected, feed-forward model with 7−7−1 architecture. The developed ANN model was fully evaluated by different validation techniques, including internal and external validation, and chemical applicability domain. All validations showed that the constructed quantitative structure–activity relationship model suggested is robust and satisfactory. PMID:26600858

Synthetic maps of human gene frequencies in Europeans.

PubMed

Menozzi, P; Piazza, A; Cavalli-Sforza, L

1978-09-01

Multivarate techniques can be used to condense the information for a large number of loci and alleles into one or a few synthetic variables. The geographic distribution of synthetic variables can be plotted by the same technique used in mapping the gene frequency of a single allele. Synthetic maps were constructed for Europe and the Near East, with the use of principal components to condense the information of 38 independent alleles from ten loci. The first principal component summarizes close to 30% of the total information and shows gradients. Maps thus constructed show clines in remarkable agreement with those expected on the basis of the spread of early farming in Europe, thus supporting the hypothesis that this spread was a demic spread rather than a cultural diffusion of farming technology.

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

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

Protein quantification on dendrimer-activated surfaces by using time-of-flight secondary ion mass spectrometry and principal component regression

NASA Astrophysics Data System (ADS)

Kim, Young-Pil; Hong, Mi-Young; Shon, Hyun Kyong; Chegal, Won; Cho, Hyun Mo; Moon, Dae Won; Kim, Hak-Sung; Lee, Tae Geol

2008-12-01

Interaction between streptavidin and biotin on poly(amidoamine) (PAMAM) dendrimer-activated surfaces and on self-assembled monolayers (SAMs) was quantitatively studied by using time-of-flight secondary ion mass spectrometry (ToF-SIMS). The surface protein density was systematically varied as a function of protein concentration and independently quantified using the ellipsometry technique. Principal component analysis (PCA) and principal component regression (PCR) were used to identify a correlation between the intensities of the secondary ion peaks and the surface protein densities. From the ToF-SIMS and ellipsometry results, a good linear correlation of protein density was found. Our study shows that surface protein densities are higher on dendrimer-activated surfaces than on SAMs surfaces due to the spherical property of the dendrimer, and that these surface protein densities can be easily quantified with high sensitivity in a label-free manner by ToF-SIMS.

Exploring patterns enriched in a dataset with contrastive principal component analysis.

PubMed

Abid, Abubakar; Zhang, Martin J; Bagaria, Vivek K; Zou, James

2018-05-30

Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset. However, in many settings we have datasets collected under different conditions, e.g., a treatment and a control experiment, and we are interested in visualizing and exploring patterns that are specific to one dataset. This paper proposes a method, contrastive principal component analysis (cPCA), which identifies low-dimensional structures that are enriched in a dataset relative to comparison data. In a wide variety of experiments, we demonstrate that cPCA with a background dataset enables us to visualize dataset-specific patterns missed by PCA and other standard methods. We further provide a geometric interpretation of cPCA and strong mathematical guarantees. An implementation of cPCA is publicly available, and can be used for exploratory data analysis in many applications where PCA is currently used.

Covariate selection with iterative principal component analysis for predicting physical

USDA-ARS?s Scientific Manuscript database

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 ...

Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

NASA Technical Reports Server (NTRS)

McDonald, G.; Storrie-Lombardi, M.; Nealson, K.

1999-01-01

The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.

Edge Principal Components and Squash Clustering: Using the Special Structure of Phylogenetic Placement Data for Sample Comparison

PubMed Central

Matsen IV, Frederick A.; Evans, Steven N.

2013-01-01

Principal components analysis (PCA) and hierarchical clustering are two of the most heavily used techniques for analyzing the differences between nucleic acid sequence samples taken from a given environment. They have led to many insights regarding the structure of microbial communities. We have developed two new complementary methods that leverage how this microbial community data sits on a phylogenetic tree. Edge principal components analysis enables the detection of important differences between samples that contain closely related taxa. Each principal component axis is a collection of signed weights on the edges of the phylogenetic tree, and these weights are easily visualized by a suitable thickening and coloring of the edges. Squash clustering outputs a (rooted) clustering tree in which each internal node corresponds to an appropriate “average” of the original samples at the leaves below the node. Moreover, the length of an edge is a suitably defined distance between the averaged samples associated with the two incident nodes, rather than the less interpretable average of distances produced by UPGMA, the most widely used hierarchical clustering method in this context. We present these methods and illustrate their use with data from the human microbiome. PMID:23505415

Multivariate classification of small order watersheds in the Quabbin Reservoir Basin, Massachusetts

USGS Publications Warehouse

Lent, R.M.; Waldron, M.C.; Rader, J.C.

1998-01-01

A multivariate approach was used to analyze hydrologic, geologic, geographic, and water-chemistry data from small order watersheds in the Quabbin Reservoir Basin in central Massachusetts. Eighty three small order watersheds were delineated and landscape attributes defining hydrologic, geologic, and geographic features of the watersheds were compiled from geographic information system data layers. Principal components analysis was used to evaluate 11 chemical constituents collected bi-weekly for 1 year at 15 surface-water stations in order to subdivide the basin into subbasins comprised of watersheds with similar water quality characteristics. Three principal components accounted for about 90 percent of the variance in water chemistry data. The principal components were defined as a biogeochemical variable related to wetland density, an acid-neutralization variable, and a road-salt variable related to density of primary roads. Three subbasins were identified. Analysis of variance and multiple comparisons of means were used to identify significant differences in stream water chemistry and landscape attributes among subbasins. All stream water constituents were significantly different among subbasins. Multiple regression techniques were used to relate stream water chemistry to landscape attributes. Important differences in landscape attributes were related to wetlands, slope, and soil type.A multivariate approach was used to analyze hydrologic, geologic, geographic, and water-chemistry data from small order watersheds in the Quabbin Reservoir Basin in central Massachusetts. Eighty three small order watersheds were delineated and landscape attributes defining hydrologic, geologic, and geographic features of the watersheds were compiled from geographic information system data layers. Principal components analysis was used to evaluate 11 chemical constituents collected bi-weekly for 1 year at 15 surface-water stations in order to subdivide the basin into subbasins comprised of watersheds with similar water quality characteristics. Three principal components accounted for about 90 percent of the variance in water chemistry data. The principal components were defined as a biogeochemical variable related to wetland density, an acid-neutralization variable, and a road-salt variable related to density of primary roads. Three subbasins were identified. Analysis of variance and multiple comparisons of means were used to identify significant differences in stream water chemistry and landscape attributes among subbasins. All stream water constituents were significantly different among subbasins. Multiple regression techniques were used to relate stream water chemistry to landscape attributes. Important differences in landscape attributes were related to wetlands, slope, and soil type.

Real time on-chip sequential adaptive principal component analysis for data feature extraction and image compression

NASA Technical Reports Server (NTRS)

Duong, T. A.

2004-01-01

In this paper, we present a new, simple, and optimized hardware architecture sequential learning technique for adaptive Principle Component Analysis (PCA) which will help optimize the hardware implementation in VLSI and to overcome the difficulties of the traditional gradient descent in learning convergence and hardware implementation.

Non-linear principal component analysis applied to Lorenz models and to North Atlantic SLP

NASA Astrophysics Data System (ADS)

Russo, A.; Trigo, R. M.

2003-04-01

A non-linear generalisation of Principal Component Analysis (PCA), denoted Non-Linear Principal Component Analysis (NLPCA), is introduced and applied to the analysis of three data sets. Non-Linear Principal Component Analysis allows for the detection and characterisation of low-dimensional non-linear structure in multivariate data sets. This method is implemented using a 5-layer feed-forward neural network introduced originally in the chemical engineering literature (Kramer, 1991). The method is described and details of its implementation are addressed. Non-Linear Principal Component Analysis is first applied to a data set sampled from the Lorenz attractor (1963). It is found that the NLPCA approximations are more representative of the data than are the corresponding PCA approximations. The same methodology was applied to the less known Lorenz attractor (1984). However, the results obtained weren't as good as those attained with the famous 'Butterfly' attractor. Further work with this model is underway in order to assess if NLPCA techniques can be more representative of the data characteristics than are the corresponding PCA approximations. The application of NLPCA to relatively 'simple' dynamical systems, such as those proposed by Lorenz, is well understood. However, the application of NLPCA to a large climatic data set is much more challenging. Here, we have applied NLPCA to the sea level pressure (SLP) field for the entire North Atlantic area and the results show a slight imcrement of explained variance associated. Finally, directions for future work are presented.%}

Evaluating filterability of different types of sludge by statistical analysis: The role of key organic compounds in extracellular polymeric substances.

PubMed

Xiao, Keke; Chen, Yun; Jiang, Xie; Zhou, Yan

2017-03-01

An investigation was conducted for 20 different types of sludge in order to identify the key organic compounds in extracellular polymeric substances (EPS) that are important in assessing variations of sludge filterability. The different types of sludge varied in initial total solids (TS) content, organic composition and pre-treatment methods. For instance, some of the sludges were pre-treated by acid, ultrasonic, thermal, alkaline, or advanced oxidation technique. The Pearson's correlation results showed significant correlations between sludge filterability and zeta potential, pH, dissolved organic carbon, protein and polysaccharide in soluble EPS (SB EPS), loosely bound EPS (LB EPS) and tightly bound EPS (TB EPS). The principal component analysis (PCA) method was used to further explore correlations between variables and similarities among EPS fractions of different types of sludge. Two principal components were extracted: principal component 1 accounted for 59.24% of total EPS variations, while principal component 2 accounted for 25.46% of total EPS variations. Dissolved organic carbon, protein and polysaccharide in LB EPS showed higher eigenvector projection values than the corresponding compounds in SB EPS and TB EPS in principal component 1. Further characterization of fractionized key organic compounds in LB EPS was conducted with size-exclusion chromatography-organic carbon detection-organic nitrogen detection (LC-OCD-OND). A numerical multiple linear regression model was established to describe relationship between organic compounds in LB EPS and sludge filterability. Copyright © 2016 Elsevier Ltd. All rights reserved.

Cost-effective and rapid concrete repair techniques.

DOT National Transportation Integrated Search

2016-02-08

Concrete is a principal component of many transportation structures. While highly durable, a : variety of processes degrade and damage concrete. Replacement is expensive. Many cases : warrant repair instead of replacement. Since many damage processes...

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clegg, Samuel M; Barefield, James E; Wiens, Roger C

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 whichmore » 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.« less

ERS-2 SAR and IRS-1C LISS III data fusion: A PCA approach to improve remote sensing based geological interpretation

NASA Astrophysics Data System (ADS)

Pal, S. K.; Majumdar, T. J.; Bhattacharya, Amit K.

Fusion of optical and synthetic aperture radar data has been attempted in the present study for mapping of various lithologic units over a part of the Singhbhum Shear Zone (SSZ) and its surroundings. ERS-2 SAR data over the study area has been enhanced using Fast Fourier Transformation (FFT) based filtering approach, and also using Frost filtering technique. Both the enhanced SAR imagery have been then separately fused with histogram equalized IRS-1C LISS III image using Principal Component Analysis (PCA) technique. Later, Feature-oriented Principal Components Selection (FPCS) technique has been applied to generate False Color Composite (FCC) images, from which corresponding geological maps have been prepared. Finally, GIS techniques have been successfully used for change detection analysis in the lithological interpretation between the published geological map and the fusion based geological maps. In general, there is good agreement between these maps over a large portion of the study area. Based on the change detection studies, few areas could be identified which need attention for further detailed ground-based geological studies.

Principal Component Analysis in the Spectral Analysis of the Dynamic Laser Speckle Patterns

NASA Astrophysics Data System (ADS)

Ribeiro, K. M.; Braga, R. A., Jr.; Horgan, G. W.; Ferreira, D. D.; Safadi, T.

2014-02-01

Dynamic laser speckle is a phenomenon that interprets an optical patterns formed by illuminating a surface under changes with coherent light. Therefore, the dynamic change of the speckle patterns caused by biological material is known as biospeckle. Usually, these patterns of optical interference evolving in time are analyzed by graphical or numerical methods, and the analysis in frequency domain has also been an option, however involving large computational requirements which demands new approaches to filter the images in time. Principal component analysis (PCA) works with the statistical decorrelation of data and it can be used as a data filtering. In this context, the present work evaluated the PCA technique to filter in time the data from the biospeckle images aiming the reduction of time computer consuming and improving the robustness of the filtering. It was used 64 images of biospeckle in time observed in a maize seed. The images were arranged in a data matrix and statistically uncorrelated by PCA technique, and the reconstructed signals were analyzed using the routine graphical and numerical methods to analyze the biospeckle. Results showed the potential of the PCA tool in filtering the dynamic laser speckle data, with the definition of markers of principal components related to the biological phenomena and with the advantage of fast computational processing.

Advanced Treatment Monitoring for Olympic-Level Athletes Using Unsupervised Modeling Techniques

PubMed Central

Siedlik, Jacob A.; Bergeron, Charles; Cooper, Michael; Emmons, Russell; Moreau, William; Nabhan, Dustin; Gallagher, Philip; Vardiman, John P.

2016-01-01

Context Analysis of injury and illness data collected at large international competitions provides the US Olympic Committee and the national governing bodies for each sport with information to best prepare for future competitions. Research in which authors have evaluated medical contacts to provide the expected level of medical care and sports medicine services at international competitions is limited. Objective To analyze the medical-contact data for athletes, staff, and coaches who participated in the 2011 Pan American Games in Guadalajara, Mexico, using unsupervised modeling techniques to identify underlying treatment patterns. Design Descriptive epidemiology study. Setting Pan American Games. Patients or Other Participants A total of 618 US athletes (337 males, 281 females) participated in the 2011 Pan American Games. Main Outcome Measure(s) Medical data were recorded from the injury-evaluation and injury-treatment forms used by clinicians assigned to the central US Olympic Committee Sport Medicine Clinic and satellite locations during the operational 17-day period of the 2011 Pan American Games. We used principal components analysis and agglomerative clustering algorithms to identify and define grouped modalities. Lift statistics were calculated for within-cluster subgroups. Results Principal component analyses identified 3 components, accounting for 72.3% of the variability in datasets. Plots of the principal components showed that individual contacts focused on 4 treatment clusters: massage, paired manipulation and mobilization, soft tissue therapy, and general medical. Conclusions Unsupervised modeling techniques were useful for visualizing complex treatment data and provided insights for improved treatment modeling in athletes. Given its ability to detect clinically relevant treatment pairings in large datasets, unsupervised modeling should be considered a feasible option for future analyses of medical-contact data from international competitions. PMID:26794628

Machine learning of frustrated classical spin models. I. Principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Ce; Zhai, Hui

2017-10-01

This work aims at determining whether artificial intelligence can recognize a phase transition without prior human knowledge. If this were successful, it could be applied to, for instance, analyzing data from the quantum simulation of unsolved physical models. Toward this goal, we first need to apply the machine learning algorithm to well-understood models and see whether the outputs are consistent with our prior knowledge, which serves as the benchmark for this approach. In this work, we feed the computer data generated by the classical Monte Carlo simulation for the X Y model in frustrated triangular and union jack lattices, which has two order parameters and exhibits two phase transitions. We show that the outputs of the principal component analysis agree very well with our understanding of different orders in different phases, and the temperature dependences of the major components detect the nature and the locations of the phase transitions. Our work offers promise for using machine learning techniques to study sophisticated statistical models, and our results can be further improved by using principal component analysis with kernel tricks and the neural network method.

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

NASA Astrophysics Data System (ADS)

Schelkanova, Irina; Toronov, Vladislav

2011-07-01

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

Evidence of tampering in watermark identification

NASA Astrophysics Data System (ADS)

McLauchlan, Lifford; Mehrübeoglu, Mehrübe

2009-08-01

In this work, watermarks are embedded in digital images in the discrete wavelet transform (DWT) domain. Principal component analysis (PCA) is performed on the DWT coefficients. Next higher order statistics based on the principal components and the eigenvalues are determined for different sets of images. Feature sets are analyzed for different types of attacks in m dimensional space. The results demonstrate the separability of the features for the tampered digital copies. Different feature sets are studied to determine more effective tamper evident feature sets. The digital forensics, the probable manipulation(s) or modification(s) performed on the digital information can be identified using the described technique.

The potential of statistical shape modelling for geometric morphometric analysis of human teeth in archaeological research

PubMed Central

Fernee, Christianne; Browne, Martin; Zakrzewski, Sonia

2017-01-01

This paper introduces statistical shape modelling (SSM) for use in osteoarchaeology research. SSM is a full field, multi-material analytical technique, and is presented as a supplementary geometric morphometric (GM) tool. Lower mandibular canines from two archaeological populations and one modern population were sampled, digitised using micro-CT, aligned, registered to a baseline and statistically modelled using principal component analysis (PCA). Sample material properties were incorporated as a binary enamel/dentin parameter. Results were assessed qualitatively and quantitatively using anatomical landmarks. Finally, the technique’s application was demonstrated for inter-sample comparison through analysis of the principal component (PC) weights. It was found that SSM could provide high detail qualitative and quantitative insight with respect to archaeological inter- and intra-sample variability. This technique has value for archaeological, biomechanical and forensic applications including identification, finite element analysis (FEA) and reconstruction from partial datasets. PMID:29216199

A Unified Approach to Functional Principal Component Analysis and Functional Multiple-Set Canonical Correlation.

PubMed

Choi, Ji Yeh; Hwang, Heungsun; Yamamoto, Michio; Jung, Kwanghee; Woodward, Todd S

2017-06-01

Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.

Assessment of mechanical properties of isolated bovine intervertebral discs from multi-parametric magnetic resonance imaging.

PubMed

Recuerda, Maximilien; Périé, Delphine; Gilbert, Guillaume; Beaudoin, Gilles

2012-10-12

The treatment planning of spine pathologies requires information on the rigidity and permeability of the intervertebral discs (IVDs). Magnetic resonance imaging (MRI) offers great potential as a sensitive and non-invasive technique for describing the mechanical properties of IVDs. However, the literature reported small correlation coefficients between mechanical properties and MRI parameters. Our hypothesis is that the compressive modulus and the permeability of the IVD can be predicted by a linear combination of MRI parameters. Sixty IVDs were harvested from bovine tails, and randomly separated in four groups (in-situ, digested-6h, digested-18h, digested-24h). Multi-parametric MRI acquisitions were used to quantify the relaxation times T1 and T2, the magnetization transfer ratio MTR, the apparent diffusion coefficient ADC and the fractional anisotropy FA. Unconfined compression, confined compression and direct permeability measurements were performed to quantify the compressive moduli and the hydraulic permeabilities. Differences between groups were evaluated from a one way ANOVA. Multi linear regressions were performed between dependent mechanical properties and independent MRI parameters to verify our hypothesis. A principal component analysis was used to convert the set of possibly correlated variables into a set of linearly uncorrelated variables. Agglomerative Hierarchical Clustering was performed on the 3 principal components. Multilinear regressions showed that 45 to 80% of the Young's modulus E, the aggregate modulus in absence of deformation HA0, the radial permeability kr and the axial permeability in absence of deformation k0 can be explained by the MRI parameters within both the nucleus pulposus and the annulus pulposus. The principal component analysis reduced our variables to two principal components with a cumulative variability of 52-65%, which increased to 70-82% when considering the third principal component. The dendograms showed a natural division into four clusters for the nucleus pulposus and into three or four clusters for the annulus fibrosus. The compressive moduli and the permeabilities of isolated IVDs can be assessed mostly by MT and diffusion sequences. However, the relationships have to be improved with the inclusion of MRI parameters more sensitive to IVD degeneration. Before the use of this technique to quantify the mechanical properties of IVDs in vivo on patients suffering from various diseases, the relationships have to be defined for each degeneration state of the tissue that mimics the pathology. Our MRI protocol associated to principal component analysis and agglomerative hierarchical clustering are promising tools to classify the degenerated intervertebral discs and further find biomarkers and predictive factors of the evolution of the pathologies.

Principal component analysis of TOF-SIMS spectra, images and depth profiles: an industrial perspective

NASA Astrophysics Data System (ADS)

Pacholski, Michaeleen L.

2004-06-01

Principal component analysis (PCA) has been successfully applied to time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra, images and depth profiles. Although SIMS spectral data sets can be small (in comparison to datasets typically discussed in literature from other analytical techniques such as gas or liquid chromatography), each spectrum has thousands of ions resulting in what can be a difficult comparison of samples. Analysis of industrially-derived samples means the identity of most surface species are unknown a priori and samples must be analyzed rapidly to satisfy customer demands. PCA enables rapid assessment of spectral differences (or lack there of) between samples and identification of chemically different areas on sample surfaces for images. Depth profile analysis helps define interfaces and identify low-level components in the system.

Principal components analysis of Jupiter VIMS spectra

USGS Publications Warehouse

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

A comparison of autonomous techniques for multispectral image analysis and classification

NASA Astrophysics Data System (ADS)

Valdiviezo-N., Juan C.; Urcid, Gonzalo; Toxqui-Quitl, Carina; Padilla-Vivanco, Alfonso

2012-10-01

Multispectral imaging has given place to important applications related to classification and identification of objects from a scene. Because of multispectral instruments can be used to estimate the reflectance of materials in the scene, these techniques constitute fundamental tools for materials analysis and quality control. During the last years, a variety of algorithms has been developed to work with multispectral data, whose main purpose has been to perform the correct classification of the objects in the scene. The present study introduces a brief review of some classical as well as a novel technique that have been used for such purposes. The use of principal component analysis and K-means clustering techniques as important classification algorithms is here discussed. Moreover, a recent method based on the min-W and max-M lattice auto-associative memories, that was proposed for endmember determination in hyperspectral imagery, is introduced as a classification method. Besides a discussion of their mathematical foundation, we emphasize their main characteristics and the results achieved for two exemplar images conformed by objects similar in appearance, but spectrally different. The classification results state that the first components computed from principal component analysis can be used to highlight areas with different spectral characteristics. In addition, the use of lattice auto-associative memories provides good results for materials classification even in the cases where some spectral similarities appears in their spectral responses.

PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

PubMed Central

2014-01-01

Background The chemical composition of aerosols and particle size distributions are the most significant factors affecting air quality. In particular, the exposure to finer particles can cause short and long-term effects on human health. In the present paper PM10 (particulate matter with aerodynamic diameter lower than 10 μm), CO, NOx (NO and NO2), Benzene and Toluene trends monitored in six monitoring stations of Bari province are shown. The data set used was composed by bi-hourly means for all parameters (12 bi-hourly means per day for each parameter) and it’s referred to the period of time from January 2005 and May 2007. The main aim of the paper is to provide a clear illustration of how large data sets from monitoring stations can give information about the number and nature of the pollutant sources, and mainly to assess the contribution of the traffic source to PM10 concentration level by using multivariate statistical techniques such as Principal Component Analysis (PCA) and Absolute Principal Component Scores (APCS). Results Comparing the night and day mean concentrations (per day) for each parameter it has been pointed out that there is a different night and day behavior for some parameters such as CO, Benzene and Toluene than PM10. This suggests that CO, Benzene and Toluene concentrations are mainly connected with transport systems, whereas PM10 is mostly influenced by different factors. The statistical techniques identified three recurrent sources, associated with vehicular traffic and particulate transport, covering over 90% of variance. The contemporaneous analysis of gas and PM10 has allowed underlining the differences between the sources of these pollutants. Conclusions The analysis of the pollutant trends from large data set and the application of multivariate statistical techniques such as PCA and APCS can give useful information about air quality and pollutant’s sources. These knowledge can provide useful advices to environmental policies in order to reach the WHO recommended levels. PMID:24555534

Establishing ¹H nuclear magnetic resonance based metabonomics fingerprinting profile for spinal cord injury: a pilot study.

PubMed

Jiang, Hua; Peng, Jin; Zhou, Zhi-yuan; Duan, Yu; Chen, Wei; Cai, Bin; Yang, Hao; Zhang, Wei

2010-09-01

Spinal cord injury (SCI) is a complex trauma that consists of multiple pathological mechanisms involving cytotoxic, oxidation stress and immune-endocrine. This study aimed to establish plasma metabonomics fingerprinting atlas for SCI using (1)H nuclear magnetic resonance (NMR) based metabonomics methodology and principal component analysis techniques. Nine Sprague-Dawley (SD) male rats were randomly divided into SCI, normal and sham-operation control groups. Plasma samples were collected for (1)H NMR spectroscopy 3 days after operation. The NMR data were analyzed using principal component analysis technique with Matlab software. Metabonomics analysis was able to distinguish the three groups (SCI, normal control, sham-operation). The fingerprinting atlas indicated that, compared with those without SCI, the SCI group demonstrated the following characteristics with regard to second principal component: it is made up of fatty acids, myc-inositol, arginine, very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), triglyceride (TG), glucose, and 3-methyl-histamine. The data indicated that SCI results in several significant changes in plasma metabolism early on and that a metabonomics approach based on (1)H NMR spectroscopy can provide a metabolic profile comprising several metabolite classes and allow for relative quantification of such changes. The results also provided support for further development and application of metabonomics technologies for studying SCI and for the utilization of multivariate models for classifying the extent of trauma within an individual.

Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

NASA Astrophysics Data System (ADS)

Shah, Syed Muhammad Saqlain; Batool, Safeera; Khan, Imran; Ashraf, Muhammad Usman; Abbas, Syed Hussnain; Hussain, Syed Adnan

2017-09-01

Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzerland and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.

The Analysis of Dimensionality Reduction Techniques in Cryptographic Object Code Classification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jason L. Wright; Milos Manic

2010-05-01

This paper compares the application of three different dimension reduction techniques to the problem of locating cryptography in compiled object code. A simple classi?er is used to compare dimension reduction via sorted covariance, principal component analysis, and correlation-based feature subset selection. The analysis concentrates on the classi?cation accuracy as the number of dimensions is increased.

Applying Ancestry and Sex Computation as a Quality Control Tool in Targeted Next-Generation Sequencing.

PubMed

Mathias, Patrick C; Turner, Emily H; Scroggins, Sheena M; Salipante, Stephen J; Hoffman, Noah G; Pritchard, Colin C; Shirts, Brian H

2016-03-01

To apply techniques for ancestry and sex computation from next-generation sequencing (NGS) data as an approach to confirm sample identity and detect sample processing errors. We combined a principal component analysis method with k-nearest neighbors classification to compute the ancestry of patients undergoing NGS testing. By combining this calculation with X chromosome copy number data, we determined the sex and ancestry of patients for comparison with self-report. We also modeled the sensitivity of this technique in detecting sample processing errors. We applied this technique to 859 patient samples with reliable self-report data. Our k-nearest neighbors ancestry screen had an accuracy of 98.7% for patients reporting a single ancestry. Visual inspection of principal component plots was consistent with self-report in 99.6% of single-ancestry and mixed-ancestry patients. Our model demonstrates that approximately two-thirds of potential sample swaps could be detected in our patient population using this technique. Patient ancestry can be estimated from NGS data incidentally sequenced in targeted panels, enabling an inexpensive quality control method when coupled with patient self-report. © American Society for Clinical Pathology, 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Integrative sparse principal component analysis of gene expression data.

PubMed

Liu, Mengque; Fan, Xinyan; Fang, Kuangnan; Zhang, Qingzhao; Ma, Shuangge

2017-12-01

In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis. In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance. © 2017 WILEY PERIODICALS, INC.

Principal component analysis of the cytokine and chemokine response to human traumatic brain injury.

PubMed

Helmy, Adel; Antoniades, Chrystalina A; Guilfoyle, Mathew R; Carpenter, Keri L H; Hutchinson, Peter J

2012-01-01

There is a growing realisation that neuro-inflammation plays a fundamental role in the pathology of Traumatic Brain Injury (TBI). This has led to the search for biomarkers that reflect these underlying inflammatory processes using techniques such as cerebral microdialysis. The interpretation of such biomarker data has been limited by the statistical methods used. When analysing data of this sort the multiple putative interactions between mediators need to be considered as well as the timing of production and high degree of statistical co-variance in levels of these mediators. Here we present a cytokine and chemokine dataset from human brain following human traumatic brain injury and use principal component analysis and partial least squares discriminant analysis to demonstrate the pattern of production following TBI, distinct phases of the humoral inflammatory response and the differing patterns of response in brain and in peripheral blood. This technique has the added advantage of making no assumptions about the Relative Recovery (RR) of microdialysis derived parameters. Taken together these techniques can be used in complex microdialysis datasets to summarise the data succinctly and generate hypotheses for future study.

Rotation of EOFs by the Independent Component Analysis: Towards A Solution of the Mixing Problem in the Decomposition of Geophysical Time Series

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Chedin, Alain; Hansen, James E. (Technical Monitor)

2001-01-01

The Independent Component Analysis is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components, a stronger constraint that uses higher-order statistics, instead of the classical decorrelation, a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e. exploratory approach). We demonstrate with a data simulation experiment that, if initialized with a Principal Component Analysis, the Independent Component Analysis performs a rotation of the classical PCA (or EOF) solution. This rotation uses no localization criterion like other Rotation Techniques (RT), only the global generalization of decorrelation by statistical independence is used. This rotation of the PCA solution seems to be able to solve the tendency of PCA to mix several physical phenomena, even when the signal is just their linear sum.

Using Structural Equation Modeling To Fit Models Incorporating Principal Components.

ERIC Educational Resources Information Center

Dolan, Conor; Bechger, Timo; Molenaar, Peter

1999-01-01

Considers models incorporating principal components from the perspectives of structural-equation modeling. These models include the following: (1) the principal-component analysis of patterned matrices; (2) multiple analysis of variance based on principal components; and (3) multigroup principal-components analysis. Discusses fitting these models…

Incorporating principal component analysis into air quality model evaluation

EPA Science Inventory

The efficacy of standard air quality model evaluation techniques is becoming compromised as the simulation periods continue to lengthen in response to ever increasing computing capacity. Accordingly, the purpose of this paper is to demonstrate a statistical approach called Princi...

Performance of Optimized Actuator and Sensor Arrays in an Active Noise Control System

NASA Technical Reports Server (NTRS)

Palumbo, D. L.; Padula, S. L.; Lyle, K. H.; Cline, J. H.; Cabell, R. H.

1996-01-01

Experiments have been conducted in NASA Langley's Acoustics and Dynamics Laboratory to determine the effectiveness of optimized actuator/sensor architectures and controller algorithms for active control of harmonic interior noise. Tests were conducted in a large scale fuselage model - a composite cylinder which simulates a commuter class aircraft fuselage with three sections of trim panel and a floor. Using an optimization technique based on the component transfer functions, combinations of 4 out of 8 piezoceramic actuators and 8 out of 462 microphone locations were evaluated against predicted performance. A combinatorial optimization technique called tabu search was employed to select the optimum transducer arrays. Three test frequencies represent the cases of a strong acoustic and strong structural response, a weak acoustic and strong structural response and a strong acoustic and weak structural response. Noise reduction was obtained using a Time Averaged/Gradient Descent (TAGD) controller. Results indicate that the optimization technique successfully predicted best and worst case performance. An enhancement of the TAGD control algorithm was also evaluated. The principal components of the actuator/sensor transfer functions were used in the PC-TAGD controller. The principal components are shown to be independent of each other while providing control as effective as the standard TAGD.

Characterization and Discrimination of Gram-Positive Bacteria Using Raman Spectroscopy with the Aid of Principal Component Analysis.

PubMed

Colniță, Alia; Dina, Nicoleta Elena; Leopold, Nicolae; Vodnar, Dan Cristian; Bogdan, Diana; Porav, Sebastian Alin; David, Leontin

2017-09-01

Raman scattering and its particular effect, surface-enhanced Raman scattering (SERS), are whole-organism fingerprinting spectroscopic techniques that gain more and more popularity in bacterial detection. In this work, two relevant Gram-positive bacteria species, Lactobacillus casei ( L. casei ) and Listeria monocytogenes ( L. monocytogenes ) were characterized based on their Raman and SERS spectral fingerprints. The SERS spectra were used to identify the biochemical structures of the bacterial cell wall. Two synthesis methods of the SERS-active nanomaterials were used and the recorded spectra were analyzed. L. casei and L. monocytogenes were successfully discriminated by applying Principal Component Analysis (PCA) to their specific spectral data.

Characterization and Discrimination of Gram-Positive Bacteria Using Raman Spectroscopy with the Aid of Principal Component Analysis

PubMed Central

Leopold, Nicolae; Vodnar, Dan Cristian; Bogdan, Diana; Porav, Sebastian Alin; David, Leontin

2017-01-01

Raman scattering and its particular effect, surface-enhanced Raman scattering (SERS), are whole-organism fingerprinting spectroscopic techniques that gain more and more popularity in bacterial detection. In this work, two relevant Gram-positive bacteria species, Lactobacillus casei (L. casei) and Listeria monocytogenes (L. monocytogenes) were characterized based on their Raman and SERS spectral fingerprints. The SERS spectra were used to identify the biochemical structures of the bacterial cell wall. Two synthesis methods of the SERS-active nanomaterials were used and the recorded spectra were analyzed. L. casei and L. monocytogenes were successfully discriminated by applying Principal Component Analysis (PCA) to their specific spectral data. PMID:28862655

Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging.

PubMed

Zuendorf, Gerhard; Kerrouche, Nacer; Herholz, Karl; Baron, Jean-Claude

2003-01-01

Principal component analysis (PCA) is a well-known technique for reduction of dimensionality of functional imaging data. PCA can be looked at as the projection of the original images onto a new orthogonal coordinate system with lower dimensions. The new axes explain the variance in the images in decreasing order of importance, showing correlations between brain regions. We used an efficient, stable and analytical method to work out the PCA of Positron Emission Tomography (PET) images of 74 normal subjects using [(18)F]fluoro-2-deoxy-D-glucose (FDG) as a tracer. Principal components (PCs) and their relation to age effects were investigated. Correlations between the projections of the images on the new axes and the age of the subjects were carried out. The first two PCs could be identified as being the only PCs significantly correlated to age. The first principal component, which explained 10% of the data set variance, was reduced only in subjects of age 55 or older and was related to loss of signal in and adjacent to ventricles and basal cisterns, reflecting expected age-related brain atrophy with enlarging CSF spaces. The second principal component, which accounted for 8% of the total variance, had high loadings from prefrontal, posterior parietal and posterior cingulate cortices and showed the strongest correlation with age (r = -0.56), entirely consistent with previously documented age-related declines in brain glucose utilization. Thus, our method showed that the effect of aging on brain metabolism has at least two independent dimensions. This method should have widespread applications in multivariate analysis of brain functional images. Copyright 2002 Wiley-Liss, Inc.

Decomposing the Apoptosis Pathway Into Biologically Interpretable Principal Components

PubMed Central

Wang, Min; Kornblau, Steven M; Coombes, Kevin R

2018-01-01

Principal component analysis (PCA) is one of the most common techniques in the analysis of biological data sets, but applying PCA raises 2 challenges. First, one must determine the number of significant principal components (PCs). Second, because each PC is a linear combination of genes, it rarely has a biological interpretation. Existing methods to determine the number of PCs are either subjective or computationally extensive. We review several methods and describe a new R package, PCDimension, that implements additional methods, the most important being an algorithm that extends and automates a graphical Bayesian method. Using simulations, we compared the methods. Our newly automated procedure is competitive with the best methods when considering both accuracy and speed and is the most accurate when the number of objects is small compared with the number of attributes. We applied the method to a proteomics data set from patients with acute myeloid leukemia. Proteins in the apoptosis pathway could be explained using 6 PCs. By clustering the proteins in PC space, we were able to replace the PCs by 6 “biological components,” 3 of which could be immediately interpreted from the current literature. We expect this approach combining PCA with clustering to be widely applicable. PMID:29881252

Signal-to-noise contribution of principal component loads in reconstructed near-infrared Raman tissue spectra.

PubMed

Grimbergen, M C M; van Swol, C F P; Kendall, C; Verdaasdonk, R M; Stone, N; Bosch, J L H R

2010-01-01

The overall quality of Raman spectra in the near-infrared region, where biological samples are often studied, has benefited from various improvements to optical instrumentation over the past decade. However, obtaining ample spectral quality for analysis is still challenging due to device requirements and short integration times required for (in vivo) clinical applications of Raman spectroscopy. Multivariate analytical methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), are routinely applied to Raman spectral datasets to develop classification models. Data compression is necessary prior to discriminant analysis to prevent or decrease the degree of over-fitting. The logical threshold for the selection of principal components (PCs) to be used in discriminant analysis is likely to be at a point before the PCs begin to introduce equivalent signal and noise and, hence, include no additional value. Assessment of the signal-to-noise ratio (SNR) at a certain peak or over a specific spectral region will depend on the sample measured. Therefore, the mean SNR over the whole spectral region (SNR(msr)) is determined in the original spectrum as well as for spectra reconstructed from an increasing number of principal components. This paper introduces a method of assessing the influence of signal and noise from individual PC loads and indicates a method of selection of PCs for LDA. To evaluate this method, two data sets with different SNRs were used. The sets were obtained with the same Raman system and the same measurement parameters on bladder tissue collected during white light cystoscopy (set A) and fluorescence-guided cystoscopy (set B). This method shows that the mean SNR over the spectral range in the original Raman spectra of these two data sets is related to the signal and noise contribution of principal component loads. The difference in mean SNR over the spectral range can also be appreciated since fewer principal components can reliably be used in the low SNR data set (set B) compared to the high SNR data set (set A). Despite the fact that no definitive threshold could be found, this method may help to determine the cutoff for the number of principal components used in discriminant analysis. Future analysis of a selection of spectral databases using this technique will allow optimum thresholds to be selected for different applications and spectral data quality levels.

Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.

PubMed

Gao, Hao; Zhang, Yawei; Ren, Lei; Yin, Fang-Fang

2018-01-01

This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images. In the proposed PCR method, the matrix factorization is utilized as an explicit low-rank regularization of 4D images that are represented as a product of spatial principal components and temporal motion coefficients. The key hypothesis of PCR is that temporal coefficients from 4D images can be reasonably approximated by temporal coefficients learned from 2D fluoroscopic training projections. For this purpose, we can acquire fluoroscopic training projections for a few breathing periods at fixed gantry angles that are free from geometric distortion due to gantry rotation, that is, fluoroscopy-based motion learning. Such training projections can provide an effective characterization of the breathing motion. The temporal coefficients can be extracted from these training projections and used as priors for PCR, even though principal components from training projections are certainly not the same for these 4D images to be reconstructed. For this purpose, training data are synchronized with reconstruction data using identical real-time breathing position intervals for projection binning. In terms of image reconstruction, with a priori temporal coefficients, the data fidelity for PCR changes from nonlinear to linear, and consequently, the PCR method is robust and can be solved efficiently. PCR is formulated as a convex optimization problem with the sum of linear data fidelity with respect to spatial principal components and spatiotemporal total variation regularization imposed on 4D image phases. The solution algorithm of PCR is developed based on alternating direction method of multipliers. The implementation is fully parallelized on GPU with NVIDIA CUDA toolbox and each reconstruction takes about a few minutes. The proposed PCR method is validated and compared with a state-of-art method, that is, PICCS, using both simulation and experimental data with the on-board cone-beam CT setting. The results demonstrated the feasibility of PCR for cine CBCT and significantly improved reconstruction quality of PCR from PICCS for cine CBCT. With a priori estimated temporal motion coefficients using fluoroscopic training projections, the PCR method can accurately reconstruct spatial principal components, and then generate cine CT images as a product of temporal motion coefficients and spatial principal components. © 2017 American Association of Physicists in Medicine.

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 contaminants in an aircraft cabin. In addition, experimental data sets are analyzed for a hydrogen peroxide (H2O2) aqueous solution mixture to determine H2O2 concentrations at various levels that could be produced during use of a vapor phase hydrogen peroxide (VPHP) decontamination system. After the PCA application to two and three component systems, the analysis technique is further expanded to include the monitoring of potential bleed air contaminants from engine oil combustion. Simulation data sets created from database spectra were utilized to predict gas components and concentrations in unknown engine oil samples at high temperatures as well as time-evolved gases from the heating of engine oils.

Principal component similarity analysis of Raman spectra to study the effects of pH, heating, and kappa-carrageenan on whey protein structure.

PubMed

Alizadeh-Pasdar, Nooshin; Nakai, Shuryo; Li-Chan, Eunice C Y

2002-10-09

Raman spectroscopy was used to elucidate structural changes of beta-lactoglobulin (BLG), whey protein isolate (WPI), and bovine serum albumin (BSA), at 15% concentration, as a function of pH (5.0, 7.0, and 9.0), heating (80 degrees C, 30 min), and presence of 0.24% kappa-carrageenan. Three data-processing techniques were used to assist in identifying significant changes in Raman spectral data. Analysis of variance showed that of 12 characteristics examined in the Raman spectra, only a few were significantly affected by pH, heating, kappa-carrageenan, and their interactions. These included amide I (1658 cm(-1)) for WPI and BLG, alpha-helix for BLG and BSA, beta-sheet for BSA, CH stretching (2880 cm(-1)) for BLG and BSA, and CH stretching (2930 cm(-1)) for BSA. Principal component analysis reduced dimensionality of the characteristics. Heating and its interaction with kappa-carrageenan were identified as the most influential in overall structure of the whey proteins, using principal component similarity analysis.

Interpretable functional principal component analysis.

PubMed

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. © 2015, The International Biometric Society.

Discrimination of healthy and osteoarthritic articular cartilage by Fourier transform infrared imaging and Fisher’s discriminant analysis

PubMed Central

Mao, Zhi-Hua; Yin, Jian-Hua; Zhang, Xue-Xi; Wang, Xiao; Xia, Yang

2016-01-01

Fourier transform infrared spectroscopic imaging (FTIRI) technique can be used to obtain the quantitative information of content and spatial distribution of principal components in cartilage by combining with chemometrics methods. In this study, FTIRI combining with principal component analysis (PCA) and Fisher’s discriminant analysis (FDA) was applied to identify the healthy and osteoarthritic (OA) articular cartilage samples. Ten 10-μm thick sections of canine cartilages were imaged at 6.25μm/pixel in FTIRI. The infrared spectra extracted from the FTIR images were imported into SPSS software for PCA and FDA. Based on the PCA result of 2 principal components, the healthy and OA cartilage samples were effectively discriminated by the FDA with high accuracy of 94% for the initial samples (training set) and cross validation, as well as 86.67% for the prediction group. The study showed that cartilage degeneration became gradually weak with the increase of the depth. FTIRI combined with chemometrics may become an effective method for distinguishing healthy and OA cartilages in future. PMID:26977354

Identifying overarching excipient properties towards an in-depth understanding of process and product performance for continuous twin-screw wet granulation.

PubMed

Willecke, N; Szepes, A; Wunderlich, M; Remon, J P; Vervaet, C; De Beer, T

2017-04-30

The overall objective of this work is to understand how excipient characteristics influence the process and product performance for a continuous twin-screw wet granulation process. The knowledge gained through this study is intended to be used for a Quality by Design (QbD)-based formulation design approach and formulation optimization. A total of 9 preferred fillers and 9 preferred binders were selected for this study. The selected fillers and binders were extensively characterized regarding their physico-chemical and solid state properties using 21 material characterization techniques. Subsequently, principal component analysis (PCA) was performed on the data sets of filler and binder characteristics in order to reduce the variety of single characteristics to a limited number of overarching properties. Four principal components (PC) explained 98.4% of the overall variability in the fillers data set, while three principal components explained 93.4% of the overall variability in the data set of binders. Both PCA models allowed in-depth evaluation of similarities and differences in the excipient properties. Copyright © 2017. Published by Elsevier B.V.

Discriminating between the vocalizations of Indo-Pacific humpback and Australian snubfin dolphins in Queensland, Australia.

PubMed

Berg Soto, Alvaro; Marsh, Helene; Everingham, Yvette; Smith, Joshua N; Parra, Guido J; Noad, Michael

2014-08-01

Australian snubfin and Indo-Pacific humpback dolphins co-occur throughout most of their range in coastal waters of tropical Australia. Little is known of their ecology or acoustic repertoires. Vocalizations from humpback and snubfin dolphins were recorded in two locations along the Queensland coast during 2008 and 2010 to describe their vocalizations and evaluate the acoustic differences between these two species. Broad vocalization types were categorized qualitatively. Both species produced click trains burst pulses and whistles. Principal component analysis of the nine acoustic variables extracted from the whistles produced nine principal components that were input into discriminant function analyses to classify 96% of humpback dolphin whistles and about 78% of snubfin dolphin calls correctly. Results indicate clear acoustic differences between the vocal whistle repertoires of these two species. A stepwise routine identified two principal components as significantly distinguishable between whistles of each species: frequency parameters and frequency trend ratio. The capacity to identify these species using acoustic monitoring techniques has the potential to provide information on presence/absence, habitat use and relative abundance for each species.

Principal components technique analysis for vegetation and land use discrimination. [Brazilian cerrados

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Formaggio, A. R.; Dossantos, J. R.; Dias, L. A. V.

1984-01-01

Automatic pre-processing technique called Principal Components (PRINCO) in analyzing LANDSAT digitized data, for land use and vegetation cover, on the Brazilian cerrados was evaluated. The chosen pilot area, 223/67 of MSS/LANDSAT 3, was classified on a GE Image-100 System, through a maximum-likehood algorithm (MAXVER). The same procedure was applied to the PRINCO treated image. PRINCO consists of a linear transformation performed on the original bands, in order to eliminate the information redundancy of the LANDSAT channels. After PRINCO only two channels were used thus reducing computer effort. The original channels and the PRINCO channels grey levels for the five identified classes (grassland, "cerrado", burned areas, anthropic areas, and gallery forest) were obtained through the MAXVER algorithm. This algorithm also presented the average performance for both cases. In order to evaluate the results, the Jeffreys-Matusita distance (JM-distance) between classes was computed. The classification matrix, obtained through MAXVER, after a PRINCO pre-processing, showed approximately the same average performance in the classes separability.

Development of neural network techniques for finger-vein pattern classification

NASA Astrophysics Data System (ADS)

Wu, Jian-Da; Liu, Chiung-Tsiung; Tsai, Yi-Jang; Liu, Jun-Ching; Chang, Ya-Wen

2010-02-01

A personal identification system using finger-vein patterns and neural network techniques is proposed in the present study. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis and classification. The biometric system for verification consists of a combination of feature extraction using principal component analysis and pattern classification using both back-propagation network and adaptive neuro-fuzzy inference systems. Finger-vein features are first extracted by principal component analysis method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed adaptive neuro-fuzzy inference system in the pattern classification, the back-propagation network is compared with the proposed system. The experimental results indicated the proposed system using adaptive neuro-fuzzy inference system demonstrated a better performance than the back-propagation network for personal identification using the finger-vein patterns.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 amore » 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.« less

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

USGS Publications Warehouse

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

1981-01-01

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

Multivariate Analysis of Seismic Field Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alam, M. Kathleen

1999-06-01

This report includes the details of the model building procedure and prediction of seismic field data. Principal Components Regression, a multivariate analysis technique, was used to model seismic data collected as two pieces of equipment were cycled on and off. Models built that included only the two pieces of equipment of interest had trouble predicting data containing signals not included in the model. Evidence for poor predictions came from the prediction curves as well as spectral F-ratio plots. Once the extraneous signals were included in the model, predictions improved dramatically. While Principal Components Regression performed well for the present datamore » sets, the present data analysis suggests further work will be needed to develop more robust modeling methods as the data become more complex.« less

Optical perception for detection of cutaneous T-cell lymphoma by multi-spectral imaging

NASA Astrophysics Data System (ADS)

Hsiao, Yu-Ping; Wang, Hsiang-Chen; Chen, Shih-Hua; Tsai, Chung-Hung; Yang, Jen-Hung

2014-12-01

In this study, the spectrum of each picture element of the patient’s skin image was obtained by multi-spectral imaging technology. Spectra of normal or pathological skin were collected from 15 patients. Principal component analysis and principal component scores of skin spectra were employed to distinguish the spectral characteristics with different diseases. Finally, skin regions with suspected cutaneous T-cell lymphoma (CTCL) lesions were successfully predicted by evaluation and classification of the spectra of pathological skin. The sensitivity and specificity of this technique were 89.65% and 95.18% after the analysis of about 109 patients. The probability of atopic dermatitis and psoriasis patients misinterpreted as CTCL were 5.56% and 4.54%, respectively.

Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2015-09-01

The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

A Cognitive Perspective in the Treatment of Incarcerated Clients.

ERIC Educational Resources Information Center

Walsh, Thomas C.

1990-01-01

Proposes a cognitive therapy model as a workable approach in treating incarcerated clients. Reviews principal components and techniques of cognitive theory. Uses case vignettes to illustrate application of this approach. Delineates key features of cognitive model which relate to treatment of incarcerated population. (Author/ABL)

Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

ERIC Educational Resources Information Center

Valero-Mora, Pedro M.; Ledesma, Ruben D.

2011-01-01

This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the…

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Near-infrared Raman spectroscopy to detect anti-Toxoplasma gondii antibodies in blood sera of domestic cats

NASA Astrophysics Data System (ADS)

Duarte, Janaina; Pacheco, Marcos T. T.; Silveira, Landulfo, Jr.; Machado, Rosangela Z.; Martins, Rodrigo A. L.; Zangaro, Renato A.; Villaverde, Antonio G. J. B.

2001-05-01

Near-infrared (NIR) Raman spectroscopy has been studied for the last years for many biomedical applications. It is a powerful tool for biological materials analysis. Toxoplasmosis is an important zoonosis in public health, cats being the principal responsible for the transmission of the disease in Brazil. The objective of this work is to investigate a new method of diagnosis of this disease. NIR Raman spectroscopy was used to detect anti Toxoplasma gondii antibodies in blood sera from domestic cats, without sample preparation. In all, six blood serum samples were used for this study. A previous serological test was done by the Indirect Immunoenzymatic Assay (ELISA) to permit a comparative study between both techniques and it showed that three serum samples were positive and the other three were negative to toxoplasmosis. Raman spectra were taken for all the samples and analyzed by using the principal components analysis (PCA). A diagnosis parameter was defined from the analysis of the second and third principal components of the Raman spectra. It was found that this parameter can detect the infection level of the animal. The results have indicated that NIR Raman spectroscopy, associated to the PCA can be a promising technique for serological analysis, such as toxoplasmosis, allowing a fast and sensitive method of diagnosis.

RP-HPLC method using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate incorporated with normalization technique in principal component analysis to differentiate the bovine, porcine and fish gelatins.

PubMed

Azilawati, M I; Hashim, D M; Jamilah, B; Amin, I

2015-04-01

The amino acid compositions of bovine, porcine and fish gelatin were determined by amino acid analysis using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate as derivatization reagent. Sixteen amino acids were identified with similar spectral chromatograms. Data pre-treatment via centering and transformation of data by normalization were performed to provide data that are more suitable for analysis and easier to be interpreted. Principal component analysis (PCA) transformed the original data matrix into a number of principal components (PCs). Three principal components (PCs) described 96.5% of the total variance, and 2 PCs (91%) explained the highest variances. The PCA model demonstrated the relationships among amino acids in the correlation loadings plot to the group of gelatins in the scores plot. Fish gelatin was correlated to threonine, serine and methionine on the positive side of PC1; bovine gelatin was correlated to the non-polar side chains amino acids that were proline, hydroxyproline, leucine, isoleucine and valine on the negative side of PC1 and porcine gelatin was correlated to the polar side chains amino acids that were aspartate, glutamic acid, lysine and tyrosine on the negative side of PC2. Verification on the database using 12 samples from commercial products gelatin-based had confirmed the grouping patterns and the variables correlations. Therefore, this quantitative method is very useful as a screening method to determine gelatin from various sources. Copyright © 2014 Elsevier Ltd. All rights reserved.

Demixed principal component analysis of neural population data.

PubMed

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

2016-04-12

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.

Application of Principal Component Analysis to NIR Spectra of Phyllosilicates: A Technique for Identifying Phyllosilicates on Mars

NASA Technical Reports Server (NTRS)

Rampe, E. B.; Lanza, N. L.

2012-01-01

Orbital near-infrared (NIR) reflectance spectra of the martian surface from the OMEGA and CRISM instruments have identified a variety of phyllosilicates in Noachian terrains. The types of phyllosilicates present on Mars have important implications for the aqueous environments in which they formed, and, thus, for recognizing locales that may have been habitable. Current identifications of phyllosilicates from martian NIR data are based on the positions of spectral absorptions relative to laboratory data of well-characterized samples and from spectral ratios; however, some phyllosilicates can be difficult to distinguish from one another with these methods (i.e. illite vs. muscovite). Here we employ a multivariate statistical technique, principal component analysis (PCA), to differentiate between spectrally similar phyllosilicate minerals. PCA is commonly used in a variety of industries (pharmaceutical, agricultural, viticultural) to discriminate between samples. Previous work using PCA to analyze raw NIR reflectance data from mineral mixtures has shown that this is a viable technique for identifying mineral types, abundances, and particle sizes. Here, we evaluate PCA of second-derivative NIR reflectance data as a method for classifying phyllosilicates and test whether this method can be used to identify phyllosilicates on Mars.

Detection and tracking of gas plumes in LWIR hyperspectral video sequence data

NASA Astrophysics Data System (ADS)

Gerhart, Torin; Sunu, Justin; Lieu, Lauren; Merkurjev, Ekaterina; Chang, Jen-Mei; Gilles, Jérôme; Bertozzi, Andrea L.

2013-05-01

Automated detection of chemical plumes presents a segmentation challenge. The segmentation problem for gas plumes is difficult due to the diffusive nature of the cloud. The advantage of considering hyperspectral images in the gas plume detection problem over the conventional RGB imagery is the presence of non-visual data, allowing for a richer representation of information. In this paper we present an effective method of visualizing hyperspectral video sequences containing chemical plumes and investigate the effectiveness of segmentation techniques on these post-processed videos. Our approach uses a combination of dimension reduction and histogram equalization to prepare the hyperspectral videos for segmentation. First, Principal Components Analysis (PCA) is used to reduce the dimension of the entire video sequence. This is done by projecting each pixel onto the first few Principal Components resulting in a type of spectral filter. Next, a Midway method for histogram equalization is used. These methods redistribute the intensity values in order to reduce icker between frames. This properly prepares these high-dimensional video sequences for more traditional segmentation techniques. We compare the ability of various clustering techniques to properly segment the chemical plume. These include K-means, spectral clustering, and the Ginzburg-Landau functional.

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nee, K.; Bryan, S.; Levitskaia, T.

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

A Multi-Dimensional Functional Principal Components Analysis of EEG Data

PubMed Central

Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A.; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla

2017-01-01

Summary The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations. PMID:28072468

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

DOE PAGES

Nee, K.; Bryan, S.; Levitskaia, T.; ...

2017-12-28

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

A multi-dimensional functional principal components analysis of EEG data.

PubMed

Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla

2017-09-01

The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations. © 2017, The International Biometric Society.

Color enhancement of landsat agricultural imagery: JPL LACIE image processing support task

NASA Technical Reports Server (NTRS)

Madura, D. P.; Soha, J. M.; Green, W. B.; Wherry, D. B.; Lewis, S. D.

1978-01-01

Color enhancement techniques were applied to LACIE LANDSAT segments to determine if such enhancement can assist analysis in crop identification. The procedure involved increasing the color range by removing correlation between components. First, a principal component transformation was performed, followed by contrast enhancement to equalize component variances, followed by an inverse transformation to restore familiar color relationships. Filtering was applied to lower order components to reduce color speckle in the enhanced products. Use of single acquisition and multiple acquisition statistics to control the enhancement were compared, and the effects of normalization investigated. Evaluation is left to LACIE personnel.

From Periodic Properties to a Periodic Table Arrangement

ERIC Educational Resources Information Center

Besalú, Emili

2013-01-01

A periodic table is constructed from the consideration of periodic properties and the application of the principal components analysis technique. This procedure is useful for objects classification and data reduction and has been used in the field of chemistry for many applications, such as lanthanides, molecules, or conformers classification.…

Detection of counterfeit electronic components through ambient mass spectrometry and chemometrics.

PubMed

Pfeuffer, Kevin P; Caldwell, Jack; Shelley, Jake T; Ray, Steven J; Hieftje, Gary M

2014-09-21

In the last several years, illicit electronic components have been discovered in the inventories of several distributors and even installed in commercial and military products. Illicit or counterfeit electronic components include a broad category of devices that can range from the correct unit with a more recent date code to lower-specification or non-working systems with altered names, manufacturers and date codes. Current methodologies for identification of counterfeit electronics rely on visual microscopy by expert users and, while effective, are very time-consuming. Here, a plasma-based ambient desorption/ionization source, the flowing atmospheric pressure afterglow (FAPA) is used to generate a mass-spectral fingerprint from the surface of a variety of discrete electronic integrated circuits (ICs). Chemometric methods, specifically principal component analysis (PCA) and the bootstrapped error-adjusted single-sample technique (BEAST), are used successfully to differentiate between genuine and counterfeit ICs. In addition, chemical and physical surface-removal techniques are explored and suggest which surface-altering techniques were utilized by counterfeiters.

Confocal Raman imaging for cancer cell classification

NASA Astrophysics Data System (ADS)

Mathieu, Evelien; Van Dorpe, Pol; Stakenborg, Tim; Liu, Chengxun; Lagae, Liesbet

2014-05-01

We propose confocal Raman imaging as a label-free single cell characterization method that can be used as an alternative for conventional cell identification techniques that typically require labels, long incubation times and complex sample preparation. In this study it is investigated whether cancer and blood cells can be distinguished based on their Raman spectra. 2D Raman scans are recorded of 114 single cells, i.e. 60 breast (MCF-7), 5 cervix (HeLa) and 39 prostate (LNCaP) cancer cells and 10 monocytes (from healthy donors). For each cell an average spectrum is calculated and principal component analysis is performed on all average cell spectra. The main features of these principal components indicate that the information for cell identification based on Raman spectra mainly comes from the fatty acid composition in the cell. Based on the second and third principal component, blood cells could be distinguished from cancer cells; and prostate cancer cells could be distinguished from breast and cervix cancer cells. However, it was not possible to distinguish breast and cervix cancer cells. The results obtained in this study, demonstrate the potential of confocal Raman imaging for cell type classification and identification purposes.

Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

PubMed

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Application of principal component analysis in the pollution assessment with heavy metals of vegetable food chain in the old mining areas

PubMed Central

2012-01-01

Background The aim of the paper is to assess by the principal components analysis (PCA) the heavy metal contamination of soil and vegetables widely used as food for people who live in areas contaminated by heavy metals (HMs) due to long-lasting mining activities. This chemometric technique allowed us to select the best model for determining the risk of HMs on the food chain as well as on people's health. Results Many PCA models were computed with different variables: heavy metals contents and some agro-chemical parameters which characterize the soil samples from contaminated and uncontaminated areas, HMs contents of different types of vegetables grown and consumed in these areas, and the complex parameter target hazard quotients (THQ). Results were discussed in terms of principal component analysis. Conclusion There were two major benefits in processing the data PCA: firstly, it helped in optimizing the number and type of data that are best in rendering the HMs contamination of the soil and vegetables. Secondly, it was valuable for selecting the vegetable species which present the highest/minimum risk of a negative impact on the food chain and human health. PMID:23234365

Sources of hydrocarbons in urban road dust: Identification, quantification and prediction.

PubMed

Mummullage, Sandya; Egodawatta, Prasanna; Ayoko, Godwin A; Goonetilleke, Ashantha

2016-09-01

Among urban stormwater pollutants, hydrocarbons are a significant environmental concern due to their toxicity and relatively stable chemical structure. This study focused on the identification of hydrocarbon contributing sources to urban road dust and approaches for the quantification of pollutant loads to enhance the design of source control measures. The study confirmed the validity of the use of mathematical techniques of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for source identification and principal component analysis/absolute principal component scores (PCA/APCS) receptor model for pollutant load quantification. Study outcomes identified non-combusted lubrication oils, non-combusted diesel fuels and tyre and asphalt wear as the three most critical urban hydrocarbon sources. The site specific variabilities of contributions from sources were replicated using three mathematical models. The models employed predictor variables of daily traffic volume (DTV), road surface texture depth (TD), slope of the road section (SLP), effective population (EPOP) and effective impervious fraction (EIF), which can be considered as the five governing parameters of pollutant generation, deposition and redistribution. Models were developed such that they can be applicable in determining hydrocarbon contributions from urban sites enabling effective design of source control measures. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modified neural networks for rapid recovery of tokamak plasma parameters for real time control

NASA Astrophysics Data System (ADS)

Sengupta, A.; Ranjan, P.

2002-07-01

Two modified neural network techniques are used for the identification of the equilibrium plasma parameters of the Superconducting Steady State Tokamak I from external magnetic measurements. This is expected to ultimately assist in a real time plasma control. As different from the conventional network structure where a single network with the optimum number of processing elements calculates the outputs, a multinetwork system connected in parallel does the calculations here in one of the methods. This network is called the double neural network. The accuracy of the recovered parameters is clearly more than the conventional network. The other type of neural network used here is based on the statistical function parametrization combined with a neural network. The principal component transformation removes linear dependences from the measurements and a dimensional reduction process reduces the dimensionality of the input space. This reduced and transformed input set, rather than the entire set, is fed into the neural network input. This is known as the principal component transformation-based neural network. The accuracy of the recovered parameters in the latter type of modified network is found to be a further improvement over the accuracy of the double neural network. This result differs from that obtained in an earlier work where the double neural network showed better performance. The conventional network and the function parametrization methods have also been used for comparison. The conventional network has been used for an optimization of the set of magnetic diagnostics. The effective set of sensors, as assessed by this network, are compared with the principal component based network. Fault tolerance of the neural networks has been tested. The double neural network showed the maximum resistance to faults in the diagnostics, while the principal component based network performed poorly. Finally the processing times of the methods have been compared. The double network and the principal component network involve the minimum computation time, although the conventional network also performs well enough to be used in real time.

Statistical techniques applied to aerial radiometric surveys (STAARS): principal components analysis user's manual. [NURE program

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 thismore » analysis a geologist may begin to interpret the data structure. Insight into geological processes underlying the data may be obtained.« less

Automated cloud screening of AVHRR imagery using split-and-merge clustering

NASA Technical Reports Server (NTRS)

Gallaudet, Timothy C.; Simpson, James J.

1991-01-01

Previous methods to segment clouds from ocean in AVHRR imagery have shown varying degrees of success, with nighttime approaches being the most limited. An improved method of automatic image segmentation, the principal component transformation split-and-merge clustering (PCTSMC) algorithm, is presented and applied to cloud screening of both nighttime and daytime AVHRR data. The method combines spectral differencing, the principal component transformation, and split-and-merge clustering to sample objectively the natural classes in the data. This segmentation method is then augmented by supervised classification techniques to screen clouds from the imagery. Comparisons with other nighttime methods demonstrate its improved capability in this application. The sensitivity of the method to clustering parameters is presented; the results show that the method is insensitive to the split-and-merge thresholds.

Algorithm 971: An Implementation of a Randomized Algorithm for Principal Component Analysis

PubMed Central

LI, HUAMIN; LINDERMAN, GEORGE C.; SZLAM, ARTHUR; STANTON, KELLY P.; KLUGER, YUVAL; TYGERT, MARK

2017-01-01

Recent years have witnessed intense development of randomized methods for low-rank approximation. These methods target principal component analysis and the calculation of truncated singular value decompositions. The present article presents an essentially black-box, foolproof implementation for Mathworks’ MATLAB, a popular software platform for numerical computation. As illustrated via several tests, the randomized algorithms for low-rank approximation outperform or at least match the classical deterministic techniques (such as Lanczos iterations run to convergence) in basically all respects: accuracy, computational efficiency (both speed and memory usage), ease-of-use, parallelizability, and reliability. However, the classical procedures remain the methods of choice for estimating spectral norms and are far superior for calculating the least singular values and corresponding singular vectors (or singular subspaces). PMID:28983138

Efficient three-dimensional resist profile-driven source mask optimization optical proximity correction based on Abbe-principal component analysis and Sylvester equation

NASA Astrophysics Data System (ADS)

Lin, Pei-Chun; Yu, Chun-Chang; Chen, Charlie Chung-Ping

2015-01-01

As one of the critical stages of a very large scale integration fabrication process, postexposure bake (PEB) plays a crucial role in determining the final three-dimensional (3-D) profiles and lessening the standing wave effects. However, the full 3-D chemically amplified resist simulation is not widely adopted during the postlayout optimization due to the long run-time and huge memory usage. An efficient simulation method is proposed to simulate the PEB while considering standing wave effects and resolution enhancement techniques, such as source mask optimization and subresolution assist features based on the Sylvester equation and Abbe-principal component analysis method. Simulation results show that our algorithm is 20× faster than the conventional Gaussian convolution method.

Inspection of baked carbon anodes using a combination of multi-spectral acousto-ultrasonic techniques and principal component analysis.

PubMed

Boubaker, Moez Ben; Picard, Donald; Duchesne, Carl; Tessier, Jayson; Alamdari, Houshang; Fafard, Mario

2018-05-17

This paper reports on the application of an acousto-ultrasonic (AU) scheme for the inspection of industrial-size carbon anode blocks used in the production of primary aluminium by the Hall-Héroult process. A frequency-modulated wave is used to excite the anode blocks at multiple points. The collected attenuated AU signals are decomposed using the Discrete Wavelet Transform (DTW) after which vectors of features are calculated. Principal Component Analysis (PCA) is utilized to cluster the AU responses of the anodes. The approach allows locating cracks in the blocks and the AU features were found sensitive to crack severity. The results are validated using images collected after cutting some anodes. Copyright © 2018 Elsevier B.V. All rights reserved.

Maximally reliable spatial filtering of steady state visual evoked potentials.

PubMed

Dmochowski, Jacek P; Greaves, Alex S; Norcia, Anthony M

2015-04-01

Due to their high signal-to-noise ratio (SNR) and robustness to artifacts, steady state visual evoked potentials (SSVEPs) are a popular technique for studying neural processing in the human visual system. SSVEPs are conventionally analyzed at individual electrodes or linear combinations of electrodes which maximize some variant of the SNR. Here we exploit the fundamental assumption of evoked responses--reproducibility across trials--to develop a technique that extracts a small number of high SNR, maximally reliable SSVEP components. This novel spatial filtering method operates on an array of Fourier coefficients and projects the data into a low-dimensional space in which the trial-to-trial spectral covariance is maximized. When applied to two sample data sets, the resulting technique recovers physiologically plausible components (i.e., the recovered topographies match the lead fields of the underlying sources) while drastically reducing the dimensionality of the data (i.e., more than 90% of the trial-to-trial reliability is captured in the first four components). Moreover, the proposed technique achieves a higher SNR than that of the single-best electrode or the Principal Components. We provide a freely-available MATLAB implementation of the proposed technique, herein termed "Reliable Components Analysis". Copyright © 2015 Elsevier Inc. All rights reserved.

Multi-segmental movements as a function of experience in karate.

PubMed

Zago, Matteo; Codari, Marina; Iaia, F Marcello; Sforza, Chiarella

2017-08-01

Karate is a martial art that partly depends on subjective scoring of complex movements. Principal component analysis (PCA)-based methods can identify the fundamental synergies (principal movements) of motor system, providing a quantitative global analysis of technique. In this study, we aimed at describing the fundamental multi-joint synergies of a karate performance, under the hypothesis that the latter are skilldependent; estimate karateka's experience level, expressed as years of practice. A motion capture system recorded traditional karate techniques of 10 professional and amateur karateka. At any time point, the 3D-coordinates of body markers produced posture vectors that were normalised, concatenated from all karateka and submitted to a first PCA. Five principal movements described both gross movement synergies and individual differences. A second PCA followed by linear regression estimated the years of practice using principal movements (eigenpostures and weighting curves) and centre of mass kinematics (error: 3.71 years; R2 = 0.91, P ≪ 0.001). Principal movements and eigenpostures varied among different karateka and as functions of experience. This approach provides a framework to develop visual tools for the analysis of motor synergies in karate, allowing to detect the multi-joint motor patterns that should be restored after an injury, or to be specifically trained to increase performance.

Intraoperative impaction of total knee replacements: an explicit finite-element-analysis of principal stresses in ceramic vs. cobalt-chromium femoral components.

PubMed

Kluess, Daniel; Mittelmeier, Wolfram; Bader, Rainer

2010-12-01

In connection with technological advances in the manufacturing of medical ceramics, a newly developed ceramic femoral component was introduced in total knee arthroplasty. We generated an explicit finite-element-model to calculate the stresses developed under the highly dynamic intraoperative impaction with regard to cobalt-chromium and ceramic implant material as well as application of a silicone cover in order to reduce stress. The impaction was calculated with the hammer hitting the backside of the impactor at previously measured initial velocities. Subsequently the impactor, consisting of a steel handhold and a polyoxymethylene head, hit the femoral component. Instead of modelling femoral bone, the implant was mounted on four spring elements with spring constants previously determined in an experimental impaction model. The maximum principal stresses in the implants were evaluated at 8000 increments during the first 4 ms of impact. The ceramic implant showed principal stresses 10% to 48% higher than the cobalt chromium femoral component. The simulation of a 5mm thick silicone layer between the impactor and the femoral component showed a strong decrease of vibration resulting in a reduction of 54% to 68% of the maximum stress amounts. The calculated amounts of principal stress were beneath the ultimate bending strengths of each material. Based on the results, intraoperative fracture of femoral components in total knee replacement may not be caused solely by impaction, but also by contributing geometrical factors such as inadequate preparation of the distal femur. In order to minimize the influence of impaction related stress peaks we recommend limiting the velocity as well as the weight of the impaction hammer when inserting femoral components. The silicone cover seems to deliver a strong decrease of implant stress and should be considered in surgery technique in the future. Copyright © 2010 Elsevier Ltd. All rights reserved.

Using Machine Learning Techniques in the Analysis of Oceanographic Data

NASA Astrophysics Data System (ADS)

Falcinelli, K. E.; Abuomar, S.

2017-12-01

Acoustic Doppler Current Profilers (ADCPs) are oceanographic tools capable of collecting large amounts of current profile data. Using unsupervised machine learning techniques such as principal component analysis, fuzzy c-means clustering, and self-organizing maps, patterns and trends in an ADCP dataset are found. Cluster validity algorithms such as visual assessment of cluster tendency and clustering index are used to determine the optimal number of clusters in the ADCP dataset. These techniques prove to be useful in analysis of ADCP data and demonstrate potential for future use in other oceanographic applications.

On the Fallibility of Principal Components in Research

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.; Li, Tenglong

2017-01-01

The measurement error in principal components extracted from a set of fallible measures is discussed and evaluated. It is shown that as long as one or more measures in a given set of observed variables contains error of measurement, so also does any principal component obtained from the set. The error variance in any principal component is shown…

A first application of independent component analysis to extracting structure from stock returns.

PubMed

Back, A D; Weigend, A S

1997-08-01

This paper explores the application of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).

Development and testing of hermetic, laser-ignited pyrotechnic and explosive components

NASA Technical Reports Server (NTRS)

Kramer, Daniel P.; Beckman, Thomas M.; Spangler, Ed M.; Munger, Alan C.; Woods, C. M.

1993-01-01

During the last decade there has been increasing interest in the use of lasers in place of electrical systems to ignite various pyrotechnic and explosive materials. The principal driving force for this work was the requirement for safer energetic components which would be insensitive to electrostatic and electromagnetic radiation. In the last few years this research has accelerated since the basic concepts have proven viable. At the present time it is appropriate to shift the research emphasis in laser initiation from the scientific arena--whether it can be done--to the engineering realm--how it can be put into actual practice in the field. Laser initiation research and development at EG&G Mound was in three principal areas: (1) laser/energetic material interactions; (2) development of novel processing techniques for fabricating hermetic (helium leak rate of less than 1 x 10(exp -8) cu cm/s) laser components; and (3) evaluation and testing of laser-ignited components. Research in these three areas has resulted in the development of high quality, hermetic, laser initiated components. Examples are presented which demonstrate the practicality of fabricating hermetic, laser initiated explosive or pyrotechnic components that can be used in the next generation of ignitors, actuators, and detonators.

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

PubMed

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

2016-05-01

The objective of the present study was to determine if there is a sex-based difference in lifting technique across increasing-load conditions. Eleven male and 14 female participants (n = 25) with no previous history of low back disorder participated in the study. Participants completed freestyle, symmetric lifts of a box with handles from the floor to a table positioned at 50% of their height for five trials under three load conditions (10%, 20%, and 30% of their individual maximum isometric back strength). Joint kinematic data for the ankle, knee, hip, and lumbar and thoracic spine were collected using a two-camera Optotrak motion capture system. Joint angles were calculated using a three-dimensional Euler rotation sequence. Principal component analysis (PCA) and single component reconstruction were applied to assess differences in lifting technique across the entire waveforms. Thirty-two PCs were retained from the five joints and three axes in accordance with the 90% trace criterion. Repeated-measures ANOVA with a mixed design revealed no significant effect of sex for any of the PCs. This is contrary to previous research that used discrete points on the lifting curve to analyze sex-based differences, but agrees with more recent research using more complex analysis techniques. There was a significant effect of load on lifting technique for five PCs of the lower limb (PC1 of ankle flexion, knee flexion, and knee adduction, as well as PC2 and PC3 of hip flexion) (p < 0.005). However, there was no significant effect of load on the thoracic and lumbar spine. It was concluded that when load is standardized to individual back strength characteristics, males and females adopted a similar lifting technique. In addition, as load increased male and female participants changed their lifting technique in a similar manner. Copyright © 2016. Published by Elsevier Ltd.

NIR monitoring of in-service wood structures

Treesearch

Michela Zanetti; Timothy G. Rials; Douglas Rammer

2005-01-01

Near infrared spectroscopy (NIRS) was used to study a set of Southern Yellow Pine boards exposed to natural weathering for different periods of exposure time. This non-destructive spectroscopic technique is a very powerful tool to predict the weathering of wood when used in combination with multivariate analysis (Principal Component Analysis, PCA, and Projection to...

Introducing Chemometrics to the Analytical Curriculum: Combining Theory and Lab Experience

ERIC Educational Resources Information Center

Gilbert, Michael K.; Luttrell, Robert D.; Stout, David; Vogt, Frank

2008-01-01

Beer's law is an ideal technique that works only in certain situations. A method for dealing with more complex conditions needs to be integrated into the analytical chemistry curriculum. For that reason, the capabilities and limitations of two common chemometric algorithms, classical least squares (CLS) and principal component regression (PCR),…

Validation of the Consumer Values versus Perceived Product Attributes Model Measuring the Purchase of Athletic Team Merchandise

ERIC Educational Resources Information Center

Lee, Donghun; Byon, Kevin K.; Schoenstedt, Linda; Johns, Gary; Bussell, Leigh Ann; Choi, Hwansuk

2012-01-01

Various consumer values and perceived product attributes trigger consumptive behaviors of athletic team merchandise (Lee, Trail, Kwon, & Anderson, 2011). Likewise, using a principal component analysis technique on a student sample, a measurement scale was proposed that consisted of nine factors affecting the purchase of athletic team…

Proposed Method for Disaggregation of Secondary Data: The Model for External Reliance of Localities in the Coastal Management Zone (MERLIN-CMZ)

EPA Science Inventory

The Model for External Reliance of Localities In (MERLIN) Coastal Management Zones is a proposed solution to allow scaling of variables to smaller, nested geographies. Utilizing a Principal Components Analysis and data normalization techniques, smaller scale trends are linked to ...

Dynamics and spatio-temporal variability of environmental factors in Eastern Australia using functional principal component analysis

USGS Publications Warehouse

Szabo, J.K.; Fedriani, E.M.; Segovia-Gonzalez, M. M.; Astheimer, L.B.; Hooper, M.J.

2010-01-01

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 19982004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types. ?? 2010 World Scientific Publishing Company.

The use of artificial intelligence techniques to improve the multiple payload integration process

NASA Technical Reports Server (NTRS)

Cutts, Dannie E.; Widgren, Brian K.

1992-01-01

A maximum return of science and products with a minimum expenditure of time and resources is a major goal of mission payload integration. A critical component then, in successful mission payload integration is the acquisition and analysis of experiment requirements from the principal investigator and payload element developer teams. One effort to use artificial intelligence techniques to improve the acquisition and analysis of experiment requirements within the payload integration process is described.

Near-infrared Raman spectroscopy for estimating biochemical changes associated with different pathological conditions of cervix

NASA Astrophysics Data System (ADS)

Daniel, Amuthachelvi; Prakasarao, Aruna; Ganesan, Singaravelu

2018-02-01

The molecular level changes associated with oncogenesis precede the morphological changes in cells and tissues. Hence molecular level diagnosis would promote early diagnosis of the disease. Raman spectroscopy is capable of providing specific spectral signature of various biomolecules present in the cells and tissues under various pathological conditions. The aim of this work is to develop a non-linear multi-class statistical methodology for discrimination of normal, neoplastic and malignant cells/tissues. The tissues were classified as normal, pre-malignant and malignant by employing Principal Component Analysis followed by Artificial Neural Network (PC-ANN). The overall accuracy achieved was 99%. Further, to get an insight into the quantitative biochemical composition of the normal, neoplastic and malignant tissues, a linear combination of the major biochemicals by non-negative least squares technique was fit to the measured Raman spectra of the tissues. This technique confirms the changes in the major biomolecules such as lipids, nucleic acids, actin, glycogen and collagen associated with the different pathological conditions. To study the efficacy of this technique in comparison with histopathology, we have utilized Principal Component followed by Linear Discriminant Analysis (PC-LDA) to discriminate the well differentiated, moderately differentiated and poorly differentiated squamous cell carcinoma with an accuracy of 94.0%. And the results demonstrated that Raman spectroscopy has the potential to complement the good old technique of histopathology.

A new validation technique for estimations of body segment inertia tensors: Principal axes of inertia do matter.

PubMed

Rossi, Marcel M; Alderson, Jacqueline; El-Sallam, Amar; Dowling, James; Reinbolt, Jeffrey; Donnelly, Cyril J

2016-12-08

The aims of this study were to: (i) establish a new criterion method to validate inertia tensor estimates by setting the experimental angular velocity data of an airborne objects as ground truth against simulations run with the estimated tensors, and (ii) test the sensitivity of the simulations to changes in the inertia tensor components. A rigid steel cylinder was covered with reflective kinematic markers and projected through a calibrated motion capture volume. Simulations of the airborne motion were run with two models, using inertia tensor estimated with geometric formula or the compound pendulum technique. The deviation angles between experimental (ground truth) and simulated angular velocity vectors and the root mean squared deviation angle were computed for every simulation. Monte Carlo analyses were performed to assess the sensitivity of simulations to changes in magnitude of principal moments of inertia within ±10% and to changes in orientation of principal axes of inertia within ±10° (of the geometric-based inertia tensor). Root mean squared deviation angles ranged between 2.9° and 4.3° for the inertia tensor estimated geometrically, and between 11.7° and 15.2° for the compound pendulum values. Errors up to 10% in magnitude of principal moments of inertia yielded root mean squared deviation angles ranging between 3.2° and 6.6°, and between 5.5° and 7.9° when lumped with errors of 10° in principal axes of inertia orientation. The proposed technique can effectively validate inertia tensors from novel estimation methods of body segment inertial parameter. Principal axes of inertia orientation should not be neglected when modelling human/animal mechanics. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

PubMed

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

2014-05-06

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

Enhanced Quality Control in Pharmaceutical Applications by Combining Raman Spectroscopy and Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Martinez, J. C.; Guzmán-Sepúlveda, J. R.; Bolañoz Evia, G. R.; Córdova, T.; Guzmán-Cabrera, R.

2018-06-01

In this work, we applied machine learning techniques to Raman spectra for the characterization and classification of manufactured pharmaceutical products. Our measurements were taken with commercial equipment, for accurate assessment of variations with respect to one calibrated control sample. Unlike the typical use of Raman spectroscopy in pharmaceutical applications, in our approach the principal components of the Raman spectrum are used concurrently as attributes in machine learning algorithms. This permits an efficient comparison and classification of the spectra measured from the samples under study. This also allows for accurate quality control as all relevant spectral components are considered simultaneously. We demonstrate our approach with respect to the specific case of acetaminophen, which is one of the most widely used analgesics in the market. In the experiments, commercial samples from thirteen different laboratories were analyzed and compared against a control sample. The raw data were analyzed based on an arithmetic difference between the nominal active substance and the measured values in each commercial sample. The principal component analysis was applied to the data for quantitative verification (i.e., without considering the actual concentration of the active substance) of the difference in the calibrated sample. Our results show that by following this approach adulterations in pharmaceutical compositions can be clearly identified and accurately quantified.

Recovery of a spectrum based on a compressive-sensing algorithm with weighted principal component analysis

NASA Astrophysics Data System (ADS)

Dafu, Shen; Leihong, Zhang; Dong, Liang; Bei, Li; Yi, Kang

2017-07-01

The purpose of this study is to improve the reconstruction precision and better copy the color of spectral image surfaces. A new spectral reflectance reconstruction algorithm based on an iterative threshold combined with weighted principal component space is presented in this paper, and the principal component with weighted visual features is the sparse basis. Different numbers of color cards are selected as the training samples, a multispectral image is the testing sample, and the color differences in the reconstructions are compared. The channel response value is obtained by a Mega Vision high-accuracy, multi-channel imaging system. The results show that spectral reconstruction based on weighted principal component space is superior in performance to that based on traditional principal component space. Therefore, the color difference obtained using the compressive-sensing algorithm with weighted principal component analysis is less than that obtained using the algorithm with traditional principal component analysis, and better reconstructed color consistency with human eye vision is achieved.

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

PubMed Central

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

2009-01-01

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

Incorporating principal component analysis into air quality ...

EPA Pesticide Factsheets

The efficacy of standard air quality model evaluation techniques is becoming compromised as the simulation periods continue to lengthen in response to ever increasing computing capacity. Accordingly, the purpose of this paper is to demonstrate a statistical approach called Principal Component Analysis (PCA) with the intent of motivating its use by the evaluation community. One of the main objectives of PCA is to identify, through data reduction, the recurring and independent modes of variations (or signals) within a very large dataset, thereby summarizing the essential information of that dataset so that meaningful and descriptive conclusions can be made. In this demonstration, PCA is applied to a simple evaluation metric – the model bias associated with EPA's Community Multi-scale Air Quality (CMAQ) model when compared to weekly observations of sulfate (SO42−) and ammonium (NH4+) ambient air concentrations measured by the Clean Air Status and Trends Network (CASTNet). The advantages of using this technique are demonstrated as it identifies strong and systematic patterns of CMAQ model bias across a myriad of spatial and temporal scales that are neither constrained to geopolitical boundaries nor monthly/seasonal time periods (a limitation of many current studies). The technique also identifies locations (station–grid cell pairs) that are used as indicators for a more thorough diagnostic evaluation thereby hastening and facilitating understanding of the prob

Analyzing coastal environments by means of functional data analysis

NASA Astrophysics Data System (ADS)

Sierra, Carlos; Flor-Blanco, Germán; Ordoñez, Celestino; Flor, Germán; Gallego, José R.

2017-07-01

Here we used Functional Data Analysis (FDA) to examine particle-size distributions (PSDs) in a beach/shallow marine sedimentary environment in Gijón Bay (NW Spain). The work involved both Functional Principal Components Analysis (FPCA) and Functional Cluster Analysis (FCA). The grainsize of the sand samples was characterized by means of laser dispersion spectroscopy. Within this framework, FPCA was used as a dimension reduction technique to explore and uncover patterns in grain-size frequency curves. This procedure proved useful to describe variability in the structure of the data set. Moreover, an alternative approach, FCA, was applied to identify clusters and to interpret their spatial distribution. Results obtained with this latter technique were compared with those obtained by means of two vector approaches that combine PCA with CA (Cluster Analysis). The first method, the point density function (PDF), was employed after adapting a log-normal distribution to each PSD and resuming each of the density functions by its mean, sorting, skewness and kurtosis. The second applied a centered-log-ratio (clr) to the original data. PCA was then applied to the transformed data, and finally CA to the retained principal component scores. The study revealed functional data analysis, specifically FPCA and FCA, as a suitable alternative with considerable advantages over traditional vector analysis techniques in sedimentary geology studies.

Two biased estimation techniques in linear regression: Application to aircraft

NASA Technical Reports Server (NTRS)

Klein, Vladislav

1988-01-01

Several ways for detection and assessment of collinearity in measured data are discussed. Because data collinearity usually results in poor least squares estimates, two estimation techniques which can limit a damaging effect of collinearity are presented. These two techniques, the principal components regression and mixed estimation, belong to a class of biased estimation techniques. Detection and assessment of data collinearity and the two biased estimation techniques are demonstrated in two examples using flight test data from longitudinal maneuvers of an experimental aircraft. The eigensystem analysis and parameter variance decomposition appeared to be a promising tool for collinearity evaluation. The biased estimators had far better accuracy than the results from the ordinary least squares technique.

Wavelet packets for multi- and hyper-spectral imagery

NASA Astrophysics Data System (ADS)

Benedetto, J. J.; Czaja, W.; Ehler, M.; Flake, C.; Hirn, M.

2010-01-01

State of the art dimension reduction and classification schemes in multi- and hyper-spectral imaging rely primarily on the information contained in the spectral component. To better capture the joint spatial and spectral data distribution we combine the Wavelet Packet Transform with the linear dimension reduction method of Principal Component Analysis. Each spectral band is decomposed by means of the Wavelet Packet Transform and we consider a joint entropy across all the spectral bands as a tool to exploit the spatial information. Dimension reduction is then applied to the Wavelet Packets coefficients. We present examples of this technique for hyper-spectral satellite imaging. We also investigate the role of various shrinkage techniques to model non-linearity in our approach.

Multivariate Analysis of Solar Spectral Irradiance Measurements

NASA Technical Reports Server (NTRS)

Pilewskie, P.; Rabbette, M.

2001-01-01

Principal component analysis is used to characterize approximately 7000 downwelling solar irradiance spectra retrieved at the Southern Great Plains site during an Atmospheric Radiation Measurement (ARM) shortwave intensive operating period. This analysis technique has proven to be very effective in reducing a large set of variables into a much smaller set of independent variables while retaining the information content. It is used to determine the minimum number of parameters necessary to characterize atmospheric spectral irradiance or the dimensionality of atmospheric variability. It was found that well over 99% of the spectral information was contained in the first six mutually orthogonal linear combinations of the observed variables (flux at various wavelengths). Rotation of the principal components was effective in separating various components by their independent physical influences. The majority of the variability in the downwelling solar irradiance (380-1000 nm) was explained by the following fundamental atmospheric parameters (in order of their importance): cloud scattering, water vapor absorption, molecular scattering, and ozone absorption. In contrast to what has been proposed as a resolution to a clear-sky absorption anomaly, no unexpected gaseous absorption signature was found in any of the significant components.

Independent Component Analysis of Textures

NASA Technical Reports Server (NTRS)

Manduchi, Roberto; Portilla, Javier

2000-01-01

A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on Independent Components Analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product over the marginals most closely approximates the joint probability density function of the filter outputs. The algorithm is implemented using a steerable filter space. Experiments involving both texture classification and synthesis show that compared to Principal Components Analysis, ICA provides superior performance for modeling of natural and synthetic textures.

Demixed principal component analysis of neural population data

PubMed Central

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

2016-01-01

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

Discrimination of gender-, speed-, and shoe-dependent movement patterns in runners using full-body kinematics.

PubMed

Maurer, Christian; Federolf, Peter; von Tscharner, Vinzenz; Stirling, Lisa; Nigg, Benno M

2012-05-01

Changes in gait kinematics have often been analyzed using pattern recognition methods such as principal component analysis (PCA). It is usually just the first few principal components that are analyzed, because they describe the main variability within a dataset and thus represent the main movement patterns. However, while subtle changes in gait pattern (for instance, due to different footwear) may not change main movement patterns, they may affect movements represented by higher principal components. This study was designed to test two hypotheses: (1) speed and gender differences can be observed in the first principal components, and (2) small interventions such as changing footwear change the gait characteristics of higher principal components. Kinematic changes due to different running conditions (speed - 3.1m/s and 4.9 m/s, gender, and footwear - control shoe and adidas MicroBounce shoe) were investigated by applying PCA and support vector machine (SVM) to a full-body reflective marker setup. Differences in speed changed the basic movement pattern, as was reflected by a change in the time-dependent coefficient derived from the first principal. Gender was differentiated by using the time-dependent coefficient derived from intermediate principal components. (Intermediate principal components are characterized by limb rotations of the thigh and shank.) Different shoe conditions were identified in higher principal components. This study showed that different interventions can be analyzed using a full-body kinematic approach. Within the well-defined vector space spanned by the data of all subjects, higher principal components should also be considered because these components show the differences that result from small interventions such as footwear changes. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

Gas chromatography/principal component similarity system for detection of E. coli and S. aureus contaminating salmon and hamburger.

PubMed

Nakai, S; Wang, Z H; Dou, J; Nakamura, S; Ogawa, M; Nakai, E; Vanderstoep, J

1999-02-01

Coho, Atlantic, Spring, and Sockeye salmon and five commercial samples of hamburger patties were analyzed by processing gas chromatography (GC) data of volatile compounds using the principal component similarity (PCS) technique. PCS scattergrams of the samples inoculated with Escherichia coli and Staphylococcus aureus followed by incubation showed the pattern-shift lines moving away from the data point for uninoculated, unincubated reference samples in different directions with increasing incubation time. When the PCS scattergrams were drawn for samples incubated overnight, the samples inoculated with the two bacterial species and the uninoculated samples appeared as three separated groups. This GC/PCS approach has the potential to ensure quality of samples by discriminating good samples from potentially spoiled samples. The latter may require further microbial assays to identify the bacteria species potentially contaminating foods.

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.

Are polychlorinated biphenyl residues adequately describe by aroclor mixture equivalents. Isomer-specific principal components analysis of such residues in fish and turtles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schwartz, T.R.; Stalling, D.L.; Rice, C.L.

1987-01-01

Polychlorinated biphenyl (PCB) residues from fish and turtles were analyzed with SIMCA (Soft Independent Modeling of Class Analogy), a principal components analysis technique. A series of technical Aroclors were also analyzed to provide a reference data set for pattern recognition. Environmental PCB residues are often expressed in terms of relative Aroclor composition. In this work, we assessed the similarity of Aroclors to class models derived for fish and turtles to ascertain if the PCB residues in the samples could be described by an Aroclor or Aroclor mixture. Using PCA, we found that these samples could not be described by anmore » Aroclor or Aroclor mixture and that it would be inappropriate to report these samples as such. 18 references, 3 figures, 3 tables.« less

Raman signatures of ferroic domain walls captured by principal component analysis.

PubMed

Nataf, G F; Barrett, N; Kreisel, J; Guennou, M

2018-01-24

Ferroic domain walls are currently investigated by several state-of-the art techniques in order to get a better understanding of their distinct, functional properties. Here, principal component analysis (PCA) of Raman maps is used to study ferroelectric domain walls (DWs) in LiNbO 3 and ferroelastic DWs in NdGaO 3 . It is shown that PCA allows us to quickly and reliably identify small Raman peak variations at ferroelectric DWs and that the value of a peak shift can be deduced-accurately and without a priori-from a first order Taylor expansion of the spectra. The ability of PCA to separate the contribution of ferroelastic domains and DWs to Raman spectra is emphasized. More generally, our results provide a novel route for the statistical analysis of any property mapped across a DW.

Lipophilicity of oils and fats estimated by TLC.

PubMed

Naşcu-Briciu, Rodica D; Sârbu, Costel

2013-04-01

A representative series of natural toxins belonging to alkaloids and mycotoxins classes was investigated by TLC on classical chemically bonded plates and also on oils- and fats-impregnated plates. Their lipophilicity indices are employed in the characterization and comparison of oils and fats. The retention results allowed an accurate indirect estimation of oils and fats lipophilicity. The investigated fats and oils near classical chemically bonded phases are classified and compared by means of multivariate exploratory techniques, such as cluster analysis, principal component analysis, or fuzzy-principal component analysis. Additionally, a concrete hierarchy of oils and fats derived from the observed lipophilic character is suggested. Human fat seems to be very similar to animal fats, but also possess RP-18, RP-18W, and RP-8. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Application of principal component analysis for improvement of X-ray fluorescence images obtained by polycapillary-based micro-XRF technique

NASA Astrophysics Data System (ADS)

Aida, S.; Matsuno, T.; Hasegawa, T.; Tsuji, K.

2017-07-01

Micro X-ray fluorescence (micro-XRF) analysis is repeated as a means of producing elemental maps. In some cases, however, the XRF images of trace elements that are obtained are not clear due to high background intensity. To solve this problem, we applied principal component analysis (PCA) to XRF spectra. We focused on improving the quality of XRF images by applying PCA. XRF images of the dried residue of standard solution on the glass substrate were taken. The XRF intensities for the dried residue were analyzed before and after PCA. Standard deviations of XRF intensities in the PCA-filtered images were improved, leading to clear contrast of the images. This improvement of the XRF images was effective in cases where the XRF intensity was weak.

Principal Components Analysis on the spectral Bidirectional Reflectance Distribution Function of ceramic colour standards.

PubMed

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. © 2011 Optical Society of America

Principal Component Relaxation Mode Analysis of an All-Atom Molecular Dynamics Simulation of Human Lysozyme

NASA Astrophysics Data System (ADS)

Nagai, Toshiki; Mitsutake, Ayori; Takano, Hiroshi

2013-02-01

A new relaxation mode analysis method, which is referred to as the principal component relaxation mode analysis method, has been proposed to handle a large number of degrees of freedom of protein systems. In this method, principal component analysis is carried out first and then relaxation mode analysis is applied to a small number of principal components with large fluctuations. To reduce the contribution of fast relaxation modes in these principal components efficiently, we have also proposed a relaxation mode analysis method using multiple evolution times. The principal component relaxation mode analysis method using two evolution times has been applied to an all-atom molecular dynamics simulation of human lysozyme in aqueous solution. Slow relaxation modes and corresponding relaxation times have been appropriately estimated, demonstrating that the method is applicable to protein systems.

Application of digital image processing techniques to astronomical imagery, 1979

NASA Technical Reports Server (NTRS)

Lorre, J. J.

1979-01-01

Several areas of applications of image processing to astronomy were identified and discussed. These areas include: (1) deconvolution for atmospheric seeing compensation; a comparison between maximum entropy and conventional Wiener algorithms; (2) polarization in galaxies from photographic plates; (3) time changes in M87 and methods of displaying these changes; (4) comparing emission line images in planetary nebulae; and (5) log intensity, hue saturation intensity, and principal component color enhancements of M82. Examples are presented of these techniques applied to a variety of objects.

Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.

PubMed

Mansouri, Majdi; Nounou, Mohamed N; Nounou, Hazem N

2017-09-01

In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to combine the advantages brought forward by the proposed EWMA-GLRT fault detection chart with the KPCA model. Thus, it is used to enhance fault detection of the Cad System in E. coli model through monitoring some of the key variables involved in this model such as enzymes, transport proteins, regulatory proteins, lysine, and cadaverine. The results demonstrate the effectiveness of the proposed KPCA-based EWMA-GLRT method over Q , GLRT, EWMA, Shewhart, and moving window-GLRT methods. The detection performance is assessed and evaluated in terms of FAR, missed detection rates, and average run length (ARL 1 ) values.

Functional principal component analysis of glomerular filtration rate curves after kidney transplant.

PubMed

Dong, Jianghu J; Wang, Liangliang; Gill, Jagbir; Cao, Jiguo

2017-01-01

This article is motivated by some longitudinal clinical data of kidney transplant recipients, where kidney function progression is recorded as the estimated glomerular filtration rates at multiple time points post kidney transplantation. We propose to use the functional principal component analysis method to explore the major source of variations of glomerular filtration rate curves. We find that the estimated functional principal component scores can be used to cluster glomerular filtration rate curves. Ordering functional principal component scores can detect abnormal glomerular filtration rate curves. Finally, functional principal component analysis can effectively estimate missing glomerular filtration rate values and predict future glomerular filtration rate values.

Experimental design based 3-D QSAR analysis of steroid-protein interactions: Application to human CBG complexes

NASA Astrophysics Data System (ADS)

Norinder, Ulf

1990-12-01

An experimental design based 3-D QSAR analysis using a combination of principal component and PLS analysis is presented and applied to human corticosteroid-binding globulin complexes. The predictive capability of the created model is good. The technique can also be used as guidance when selecting new compounds to be investigated.

The classification of the patients with pulmonary diseases using breath air samples spectral analysis

NASA Astrophysics Data System (ADS)

Kistenev, Yury V.; Borisov, Alexey V.; Kuzmin, Dmitry A.; Bulanova, Anna A.

2016-08-01

Technique of exhaled breath sampling is discussed. The procedure of wavelength auto-calibration is proposed and tested. Comparison of the experimental data with the model absorption spectra of 5% CO2 is conducted. The classification results of three study groups obtained by using support vector machine and principal component analysis methods are presented.

Visual Imagery, Lifecourse Structure and Lifelong Learning

ERIC Educational Resources Information Center

Schuller, Tom

2004-01-01

Imagery could add an extra dimension to analyses of lifelong learning, which need to draw on diverse sources and techniques. This article has two principal components. First I suggest that the use of images might be divided into three categories: as illustration; as evidence; and as heuristic. I go on to explore the latter two categories, first by…

A PRINCIPAL COMPONENT ANALYSIS OF THE CLEAN AIR STATUS AND TRENDS NETWORK (CASTNET) AIR CONCENTRATION DATA

EPA Science Inventory

The spatial and temporal variability of ambient air concentrations of SO₂, SO₄^2-, NO₃, HNO₃, and NH₄⁺ obtained from EPA's CASTNet was examined using an objective, statistically based technique...

Spatial patterns of soil moisture connected to monthly-seasonal precipitation variability in a monsoon region

Treesearch

Yongqiang Liu

2003-01-01

The relations between monthly-seasonal soil moisture and precipitation variability are investigated by identifying the coupled patterns of the two hydrological fields using singular value decomposition (SVD). SVD is a technique of principal component analysis similar to empirical orthogonal knctions (EOF). However, it is applied to two variables simultaneously and is...

Chemical information obtained from Auger depth profiles by means of advanced factor analysis (MLCFA)

NASA Astrophysics Data System (ADS)

De Volder, P.; Hoogewijs, R.; De Gryse, R.; Fiermans, L.; Vennik, J.

1993-01-01

The advanced multivariate statistical technique "maximum likelihood common factor analysis (MLCFA)" is shown to be superior to "principal component analysis (PCA)" for decomposing overlapping peaks into their individual component spectra of which neither the number of components nor the peak shape of the component spectra is known. An examination of the maximum resolving power of both techniques, MLCFA and PCA, by means of artificially created series of multicomponent spectra confirms this finding unambiguously. Substantial progress in the use of AES as a chemical-analysis technique is accomplished through the implementation of MLCFA. Chemical information from Auger depth profiles is extracted by investigating the variation of the line shape of the Auger signal as a function of the changing chemical state of the element. In particular, MLCFA combined with Auger depth profiling has been applied to problems related to steelcord-rubber tyre adhesion. MLCFA allows one to elucidate the precise nature of the interfacial layer of reaction products between natural rubber vulcanized on a thin brass layer. This study reveals many interesting chemical aspects of the oxi-sulfidation of brass undetectable with classical AES.

A Late Pleistocene sea level stack

NASA Astrophysics Data System (ADS)

Spratt, R. M.; Lisiecki, L. E.

2015-08-01

Late Pleistocene sea level has been reconstructed from ocean sediment core data using a wide variety of proxies and models. However, the accuracy of individual reconstructions is limited by measurement error, local variations in salinity and temperature, and assumptions particular to each technique. Here we present a sea level stack (average) which increases the signal-to-noise ratio of individual reconstructions. Specifically, we perform principal component analysis (PCA) on seven records from 0-430 ka and five records from 0-798 ka. The first principal component, which we use as the stack, describes ~80 % of the variance in the data and is similar using either five or seven records. After scaling the stack based on Holocene and Last Glacial Maximum (LGM) sea level estimates, the stack agrees to within 5 m with isostatically adjusted coral sea level estimates for Marine Isotope Stages 5e and 11 (125 and 400 ka, respectively). When we compare the sea level stack with the δ18O of benthic foraminifera, we find that sea level change accounts for about ~40 % of the total orbital-band variance in benthic δ18O, compared to a 65 % contribution during the LGM-to-Holocene transition. Additionally, the second and third principal components of our analyses reflect differences between proxy records associated with spatial variations in the δ18O of seawater.

Boundary layer noise subtraction in hydrodynamic tunnel using robust principal component analysis.

PubMed

Amailland, Sylvain; Thomas, Jean-Hugh; Pézerat, Charles; Boucheron, Romuald

2018-04-01

The acoustic study of propellers in a hydrodynamic tunnel is of paramount importance during the design process, but can involve significant difficulties due to the boundary layer noise (BLN). Indeed, advanced denoising methods are needed to recover the acoustic signal in case of poor signal-to-noise ratio. The technique proposed in this paper is based on the decomposition of the wall-pressure cross-spectral matrix (CSM) by taking advantage of both the low-rank property of the acoustic CSM and the sparse property of the BLN CSM. Thus, the algorithm belongs to the class of robust principal component analysis (RPCA), which derives from the widely used principal component analysis. If the BLN is spatially decorrelated, the proposed RPCA algorithm can blindly recover the acoustical signals even for negative signal-to-noise ratio. Unfortunately, in a realistic case, acoustic signals recorded in a hydrodynamic tunnel show that the noise may be partially correlated. A prewhitening strategy is then considered in order to take into account the spatially coherent background noise. Numerical simulations and experimental results show an improvement in terms of BLN reduction in the large hydrodynamic tunnel. The effectiveness of the denoising method is also investigated in the context of acoustic source localization.

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

PubMed Central

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate. PMID:22736979

Use of multispectral Ikonos imagery for discriminating between conventional and conservation agricultural tillage practices

USGS Publications Warehouse

Vina, Andres; Peters, Albert J.; Ji, Lei

2003-01-01

There is a global concern about the increase in atmospheric concentrations of greenhouse gases. One method being discussed to encourage greenhouse gas mitigation efforts is based on a trading system whereby carbon emitters can buy effective mitigation efforts from farmers implementing conservation tillage practices. These practices sequester carbon from the atmosphere, and such a trading system would require a low-cost and accurate method of verification. Remote sensing technology can offer such a verification technique. This paper is focused on the use of standard image processing procedures applied to a multispectral Ikonos image, to determine whether it is possible to validate that farmers have complied with agreements to implement conservation tillage practices. A principal component analysis (PCA) was performed in order to isolate image variance in cropped fields. Analyses of variance (ANOVA) statistical procedures were used to evaluate the capability of each Ikonos band and each principal component to discriminate between conventional and conservation tillage practices. A logistic regression model was implemented on the principal component most effective in discriminating between conventional and conservation tillage, in order to produce a map of the probability of conventional tillage. The Ikonos imagery, in combination with ground-reference information, proved to be a useful tool for verification of conservation tillage practices.

[Methods of a posteriori identification of food patterns in Brazilian children: a systematic review].

PubMed

Carvalho, Carolina Abreu de; Fonsêca, Poliana Cristina de Almeida; Nobre, Luciana Neri; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2016-01-01

The objective of this study is to provide guidance for identifying dietary patterns using the a posteriori approach, and analyze the methodological aspects of the studies conducted in Brazil that identified the dietary patterns of children. Articles were selected from the Latin American and Caribbean Literature on Health Sciences, Scientific Electronic Library Online and Pubmed databases. The key words were: Dietary pattern; Food pattern; Principal Components Analysis; Factor analysis; Cluster analysis; Reduced rank regression. We included studies that identified dietary patterns of children using the a posteriori approach. Seven studies published between 2007 and 2014 were selected, six of which were cross-sectional and one cohort, Five studies used the food frequency questionnaire for dietary assessment; one used a 24-hour dietary recall and the other a food list. The method of exploratory approach used in most publications was principal components factor analysis, followed by cluster analysis. The sample size of the studies ranged from 232 to 4231, the values of the Kaiser-Meyer-Olkin test from 0.524 to 0.873, and Cronbach's alpha from 0.51 to 0.69. Few Brazilian studies identified dietary patterns of children using the a posteriori approach and principal components factor analysis was the technique most used.

Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

PubMed

Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

2015-03-01

Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

[Identification of varieties of textile fibers by using Vis/NIR infrared spectroscopy technique].

PubMed

Wu, Gui-Fang; He, Yong

2010-02-01

The aim of the present paper was to provide new insight into Vis/NIR spectroscopic analysis of textile fibers. In order to achieve rapid identification of the varieties of fibers, the authors selected 5 kinds of fibers of cotton, flax, wool, silk and tencel to do a study with Vis/NIR spectroscopy. Firstly, the spectra of each kind of fiber were scanned by spectrometer, and principal component analysis (PCA) method was used to analyze the characteristics of the pattern of Vis/NIR spectra. Principal component scores scatter plot (PC1 x PC2 x PC3) of fiber indicated the classification effect of five varieties of fibers. The former 6 principal components (PCs) were selected according to the quantity and size of PCs. The PCA classification model was optimized by using the least-squares support vector machines (LS-SVM) method. The authors used the 6 PCs extracted by PCA as the inputs of LS-SVM, and PCA-LS-SVM model was built to achieve varieties validation as well as mathematical model building and optimization analysis. Two hundred samples (40 samples for each variety of fibers) of five varieties of fibers were used for calibration of PCA-LS-SVM model, and the other 50 samples (10 samples for each variety of fibers) were used for validation. The result of validation showed that Vis/NIR spectroscopy technique based on PCA-LS-SVM had a powerful classification capability. It provides a new method for identifying varieties of fibers rapidly and real time, so it has important significance for protecting the rights of consumers, ensuring the quality of textiles, and implementing rationalization production and transaction of textile materials and its production.

Principal component analysis-based pattern analysis of dose-volume histograms and influence on rectal toxicity.

PubMed

Söhn, Matthias; Alber, Markus; Yan, Di

2007-09-01

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. 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. Rectal wall DVHs in four-field conformal RT can primarily be represented by the first two or three PCs, which describe approximately 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 ( approximately 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. 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.

Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra

NASA Astrophysics Data System (ADS)

Unglert, K.; Radić, V.; Jellinek, A. M.

2016-06-01

Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as self-organizing maps (SOM) and principal component analysis (PCA) can help to quickly and automatically identify important patterns related to impending eruptions. For the first time, we evaluate the performance of SOM and PCA on synthetic volcano seismic spectra constructed from observations during two well-studied eruptions at Klauea Volcano, Hawai'i, that include features observed in many volcanic settings. In particular, our objective is to test which of the techniques can best retrieve a set of three spectral patterns that we used to compose a synthetic spectrogram. We find that, without a priori knowledge of the given set of patterns, neither SOM nor PCA can directly recover the spectra. We thus test hierarchical clustering, a commonly used method, to investigate whether clustering in the space of the principal components and on the SOM, respectively, can retrieve the known patterns. Our clustering method applied to the SOM fails to detect the correct number and shape of the known input spectra. In contrast, clustering of the data reconstructed by the first three PCA modes reproduces these patterns and their occurrence in time more consistently. This result suggests that PCA in combination with hierarchical clustering is a powerful practical tool for automated identification of characteristic patterns in volcano seismic spectra. Our results indicate that, in contrast to PCA, common clustering algorithms may not be ideal to group patterns on the SOM and that it is crucial to evaluate the performance of these tools on a control dataset prior to their application to real data.

Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Gulgundi, Mohammad Shahid; Shetty, Amba

2018-03-01

Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

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.

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)

The Butterflies of Principal Components: A Case of Ultrafine-Grained Polyphase Units

NASA Astrophysics Data System (ADS)

Rietmeijer, F. J. M.

1996-03-01

Dusts in the accretion regions of chondritic interplanetary dust particles [IDPs] consisted of three principal components: carbonaceous units [CUs], carbon-bearing chondritic units [GUs] and carbon-free silicate units [PUs]. Among others, differences among chondritic IDP morphologies and variable bulk C/Si ratios reflect variable mixtures of principal components. The spherical shapes of the initially amorphous principal components remain visible in many chondritic porous IDPs but fusion was documented for CUs, GUs and PUs. The PUs occur as coarse- and ultrafine-grained units that include so called GEMS. Spherical principal components preserved in an IDP as recognisable textural units have unique proporties with important implications for their petrological evolution from pre-accretion processing to protoplanet alteration and dynamic pyrometamorphism. Throughout their lifetime the units behaved as closed-systems without chemical exchange with other units. This behaviour is reflected in their mineralogies while the bulk compositions of principal components define the environments wherein they were formed.

The influence of foot hyperpronation on pelvic biomechanics during stance phase of the gait: A biomechanical simulation study.

PubMed

Yazdani, Farzaneh; Razeghi, Mohsen; Karimi, Mohammad Taghi; Raeisi Shahraki, Hadi; Salimi Bani, Milad

2018-05-01

Despite the theoretical link between foot hyperpronation and biomechanical dysfunction of the pelvis, the literature lacks evidence that confirms this assumption in truly hyperpronated feet subjects during gait. Changes in the kinematic pattern of the pelvic segment were assessed in 15 persons with hyperpronated feet and compared to a control group of 15 persons with normally aligned feet during the stance phase of gait based on biomechanical musculoskeletal simulation. Kinematic and kinetic data were collected while participants walked at a comfortable self-selected speed. A generic OpenSim musculoskeletal model with 23 degrees of freedom and 92 muscles was scaled for each participant. OpenSim inverse kinematic analysis was applied to calculate segment angles in the sagittal, frontal and horizontal planes. Principal component analysis was employed as a data reduction technique, as well as a computational tool to obtain principal component scores. Independent-sample t-test was used to detect group differences. The difference between groups in scores for the first principal component in the sagittal plane was statistically significant (p = 0.01; effect size = 1.06), but differences between principal component scores in the frontal and horizontal planes were not significant. The hyperpronation group had greater anterior pelvic tilt during 20%-80% of the stance phase. In conclusion, in persons with hyperpronation we studied the role of the pelvic segment was mainly to maintain postural balance in the sagittal plane by increasing anterior pelvic inclination. Since anterior pelvic tilt may be associated with low back symptoms, the evaluation of foot posture should be considered in assessing the patients with low back and pelvic dysfunction.

Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression

USGS Publications Warehouse

Tipton, John; Hooten, Mevin B.; Goring, Simon

2017-01-01

Scientific records of temperature and precipitation have been kept for several hundred years, but for many areas, only a shorter record exists. To understand climate change, there is a need for rigorous statistical reconstructions of the paleoclimate using proxy data. Paleoclimate proxy data are often sparse, noisy, indirect measurements of the climate process of interest, making each proxy uniquely challenging to model statistically. We reconstruct spatially explicit temperature surfaces from sparse and noisy measurements recorded at historical United States military forts and other observer stations from 1820 to 1894. One common method for reconstructing the paleoclimate from proxy data is principal component regression (PCR). With PCR, one learns a statistical relationship between the paleoclimate proxy data and a set of climate observations that are used as patterns for potential reconstruction scenarios. We explore PCR in a Bayesian hierarchical framework, extending classical PCR in a variety of ways. First, we model the latent principal components probabilistically, accounting for measurement error in the observational data. Next, we extend our method to better accommodate outliers that occur in the proxy data. Finally, we explore alternatives to the truncation of lower-order principal components using different regularization techniques. One fundamental challenge in paleoclimate reconstruction efforts is the lack of out-of-sample data for predictive validation. Cross-validation is of potential value, but is computationally expensive and potentially sensitive to outliers in sparse data scenarios. To overcome the limitations that a lack of out-of-sample records presents, we test our methods using a simulation study, applying proper scoring rules including a computationally efficient approximation to leave-one-out cross-validation using the log score to validate model performance. The result of our analysis is a spatially explicit reconstruction of spatio-temporal temperature from a very sparse historical record.

Instrumental background in balloon-borne gamma-ray spectrometers and techniques for its reduction

NASA Technical Reports Server (NTRS)

Gehrels, N.

1985-01-01

Instrumental background in balloon-borne gamma-ray spectrometers is presented. The calculations are based on newly available interaction cross sections and new analytic techniques, and are the most detailed and accurate published to date. Results compare well with measurements made in the 20 keV to 10 MeV energy range by the Goddard Low Energy Gamma-ray Spectrometer (LEGS). The principal components of the continuum background in spectrometers with GE detectors and thick active shields are: (1) elastic neutron scattering of atmospheric neutrons on the Ge nuclei; (2) aperture flux of atmospheric and cosmic gamma rays; (3) beta decays of unstable nuclides produced by nuclear interactions of atmospheric protons and neutrons with Ge nuclei; and (4) shield leakage of atmospheric gamma rays. The improved understanding of these components leads to several recommended techniques for reducing the background.

BAYESIAN SEMI-BLIND COMPONENT SEPARATION FOR FOREGROUND REMOVAL IN INTERFEROMETRIC 21 cm OBSERVATIONS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Le; Timbie, Peter T.; Bunn, Emory F.

In this paper, we present a new Bayesian semi-blind approach for foreground removal in observations of the 21 cm signal measured by interferometers. The technique, which we call H i Expectation–Maximization Independent Component Analysis (HIEMICA), is an extension of the Independent Component Analysis technique developed for two-dimensional (2D) cosmic microwave background maps to three-dimensional (3D) 21 cm cosmological signals measured by interferometers. This technique provides a fully Bayesian inference of power spectra and maps and separates the foregrounds from the signal based on the diversity of their power spectra. Relying only on the statistical independence of the components, this approachmore » can jointly estimate the 3D power spectrum of the 21 cm signal, as well as the 2D angular power spectrum and the frequency dependence of each foreground component, without any prior assumptions about the foregrounds. This approach has been tested extensively by applying it to mock data from interferometric 21 cm intensity mapping observations under idealized assumptions of instrumental effects. We also discuss the impact when the noise properties are not known completely. As a first step toward solving the 21 cm power spectrum analysis problem, we compare the semi-blind HIEMICA technique to the commonly used Principal Component Analysis. Under the same idealized circumstances, the proposed technique provides significantly improved recovery of the power spectrum. This technique can be applied in a straightforward manner to all 21 cm interferometric observations, including epoch of reionization measurements, and can be extended to single-dish observations as well.« less

Improving KPCA Online Extraction by Orthonormalization in the Feature Space.

PubMed

Souza Filho, Joao B O; Diniz, Paulo S R

2018-04-01

Recently, some online kernel principal component analysis (KPCA) techniques based on the generalized Hebbian algorithm (GHA) were proposed for use in large data sets, defining kernel components using concise dictionaries automatically extracted from data. This brief proposes two new online KPCA extraction algorithms, exploiting orthogonalized versions of the GHA rule. In both the cases, the orthogonalization of kernel components is achieved by the inclusion of some low complexity additional steps to the kernel Hebbian algorithm, thus not substantially affecting the computational cost of the algorithm. Results show improved convergence speed and accuracy of components extracted by the proposed methods, as compared with the state-of-the-art online KPCA extraction algorithms.

Study of atmospheric dynamics and pollution in the coastal area of English Channel using clustering technique

NASA Astrophysics Data System (ADS)

Sokolov, Anton; Dmitriev, Egor; Delbarre, Hervé; Augustin, Patrick; Gengembre, Cyril; Fourmenten, Marc

2016-04-01

The problem of atmospheric contamination by principal air pollutants was considered in the industrialized coastal region of English Channel in Dunkirk influenced by north European metropolitan areas. MESO-NH nested models were used for the simulation of the local atmospheric dynamics and the online calculation of Lagrangian backward trajectories with 15-minute temporal resolution and the horizontal resolution down to 500 m. The one-month mesoscale numerical simulation was coupled with local pollution measurements of volatile organic components, particulate matter, ozone, sulphur dioxide and nitrogen oxides. Principal atmospheric pathways were determined by clustering technique applied to backward trajectories simulated. Six clusters were obtained which describe local atmospheric dynamics, four winds blowing through the English Channel, one coming from the south, and the biggest cluster with small wind speeds. This last cluster includes mostly sea breeze events. The analysis of meteorological data and pollution measurements allows relating the principal atmospheric pathways with local air contamination events. It was shown that contamination events are mostly connected with a channelling of pollution from local sources and low-turbulent states of the local atmosphere.

The influence of iliotibial band syndrome history on running biomechanics examined via principal components analysis.

PubMed

Foch, Eric; Milner, Clare E

2014-01-03

Iliotibial band syndrome (ITBS) is a common knee overuse injury among female runners. Atypical discrete trunk and lower extremity biomechanics during running may be associated with the etiology of ITBS. Examining discrete data points limits the interpretation of a waveform to a single value. Characterizing entire kinematic and kinetic waveforms may provide additional insight into biomechanical factors associated with ITBS. Therefore, the purpose of this cross-sectional investigation was to determine whether female runners with previous ITBS exhibited differences in kinematics and kinetics compared to controls using a principal components analysis (PCA) approach. Forty participants comprised two groups: previous ITBS and controls. Principal component scores were retained for the first three principal components and were analyzed using independent t-tests. The retained principal components accounted for 93-99% of the total variance within each waveform. Runners with previous ITBS exhibited low principal component one scores for frontal plane hip angle. Principal component one accounted for the overall magnitude in hip adduction which indicated that runners with previous ITBS assumed less hip adduction throughout stance. No differences in the remaining retained principal component scores for the waveforms were detected among groups. A smaller hip adduction angle throughout the stance phase of running may be a compensatory strategy to limit iliotibial band strain. This running strategy may have persisted after ITBS symptoms subsided. © 2013 Published by Elsevier Ltd.

Source Evaluation and Trace Metal Contamination in Benthic Sediments from Equatorial Ecosystems Using Multivariate Statistical Techniques

PubMed Central

Benson, Nsikak U.; Asuquo, Francis E.; Williams, Akan B.; Essien, Joseph P.; Ekong, Cyril I.; Akpabio, Otobong; Olajire, Abaas A.

2016-01-01

Trace metals (Cd, Cr, Cu, Ni and Pb) concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria). The degree of contamination was assessed using the individual contamination factors (ICF) and global contamination factor (GCF). Multivariate statistical approaches including principal component analysis (PCA), cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources. PMID:27257934

Effective use of principal component analysis with high resolution remote sensing data to delineate hydrothermal alteration and carbonate rocks

NASA Technical Reports Server (NTRS)

Feldman, Sandra C.

1987-01-01

Methods of applying principal component (PC) analysis to high resolution remote sensing imagery were examined. Using Airborne Imaging Spectrometer (AIS) data, PC analysis was found to be useful for removing the effects of albedo and noise and for isolating the significant information on argillic alteration, zeolite, and carbonate minerals. An effective technique for using PC analysis using an input the first 16 AIS bands, 7 intermediate bands, and the last 16 AIS bands from the 32 flat field corrected bands between 2048 and 2337 nm. Most of the significant mineralogical information resided in the second PC. PC color composites and density sliced images provided a good mineralogical separation when applied to a AIS data set. Although computer intensive, the advantage of PC analysis is that it employs algorithms which already exist on most image processing systems.

Typification of cider brandy on the basis of cider used in its manufacture.

PubMed

Rodríguez Madrera, Roberto; Mangas Alonso, Juan J

2005-04-20

A study of typification of cider brandies on the basis of the origin of the raw material used in their manufacture was conducted using chemometric techniques (principal component analysis, linear discriminant analysis, and Bayesian analysis) together with their composition in volatile compounds, as analyzed by gas chromatography with flame ionization to detect the major volatiles and by mass spectrometric to detect the minor ones. Significant principal components computed by a double cross-validation procedure allowed the structure of the database to be visualized as a function of the raw material, that is, cider made from fresh apple juice versus cider made from apple juice concentrate. Feasible and robust discriminant rules were computed and validated by a cross-validation procedure that allowed the authors to classify fresh and concentrate cider brandies, obtaining classification hits of >92%. The most discriminating variables for typifying cider brandies according to their raw material were 1-butanol and ethyl hexanoate.

Preliminary Results Of PCA On MRO CRISM Multispectral Images

NASA Astrophysics Data System (ADS)

Klassen, David R.; Smith, M. D.

2008-09-01

Mars Reconnaissance Orbiter arrived at Mars in March 2006 and by September had achieved its science-phase orbit with the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) beginning its visible to near-infrared (VIS/NIR) spectral imaging shortly thereafter. One of the goals of CRISM is to fill in the spatial gaps between the various targeted observations, eventually mapping the entire surface. Due to the large volume of data this would create, the instrument works in a reduced spectral sampling mode creating "multispectral” images. From this data we can create image cubes using 70 wavelengths from 0.410 to 3.504 µm. We present here a preliminary analysis of these multispectral mode data products using the technique of Principal Components Analysis. Previous work with ground-based images has shown that over an entire visible hemisphere, there are only three to four meaningful components out of 32-105 wavelengths over 1.5-4.1 µm. The first two of these components are fairly consistent over all time intervals from day-to-day and season-to-season. [1-4] The preliminary work on the CRISM images cubes implies similar results_three to four significant principal components that are fairly consistent over time. We will show these components and a rough linear mixture modeling based on in-data spectral endmembers derived from the extrema of the principal components [5]. References: [1] Klassen, D. R. and Bell III, J. F. (2001) BAAS 33, 1069. [2] Klassen, D. R. and Bell III, J. F. (2003) BAAS, 35, 936. [3] Klassen, D. R., Wark, T. J., Cugliotta, C. G. (2005) BAAS, 37, 693. [4] Klassen, D. R. and Bell III, J. F. (2007) in preparation. [5] Klassen, D. R. and Bell III, J. F. (2000) BAAS, 32, 1105.

Feature extraction for ultrasonic sensor based defect detection in ceramic components

NASA Astrophysics Data System (ADS)

Kesharaju, Manasa; Nagarajah, Romesh

2014-02-01

High density silicon carbide materials are commonly used as the ceramic element of hard armour inserts used in traditional body armour systems to reduce their weight, while providing improved hardness, strength and elastic response to stress. Currently, armour ceramic tiles are inspected visually offline using an X-ray technique that is time consuming and very expensive. In addition, from X-rays multiple defects are also misinterpreted as single defects. Therefore, to address these problems the ultrasonic non-destructive approach is being investigated. Ultrasound based inspection would be far more cost effective and reliable as the methodology is applicable for on-line quality control including implementation of accept/reject criteria. This paper describes a recently developed methodology to detect, locate and classify various manufacturing defects in ceramic tiles using sub band coding of ultrasonic test signals. The wavelet transform is applied to the ultrasonic signal and wavelet coefficients in the different frequency bands are extracted and used as input features to an artificial neural network (ANN) for purposes of signal classification. Two different classifiers, using artificial neural networks (supervised) and clustering (un-supervised) are supplied with features selected using Principal Component Analysis(PCA) and their classification performance compared. This investigation establishes experimentally that Principal Component Analysis(PCA) can be effectively used as a feature selection method that provides superior results for classifying various defects in the context of ultrasonic inspection in comparison with the X-ray technique.

Support vector machine and principal component analysis for microarray data classification

NASA Astrophysics Data System (ADS)

Astuti, Widi; Adiwijaya

2018-03-01

Cancer is a leading cause of death worldwide although a significant proportion of it can be cured if it is detected early. In recent decades, technology called microarray takes an important role in the diagnosis of cancer. By using data mining technique, microarray data classification can be performed to improve the accuracy of cancer diagnosis compared to traditional techniques. The characteristic of microarray data is small sample but it has huge dimension. Since that, there is a challenge for researcher to provide solutions for microarray data classification with high performance in both accuracy and running time. This research proposed the usage of Principal Component Analysis (PCA) as a dimension reduction method along with Support Vector Method (SVM) optimized by kernel functions as a classifier for microarray data classification. The proposed scheme was applied on seven data sets using 5-fold cross validation and then evaluation and analysis conducted on term of both accuracy and running time. The result showed that the scheme can obtained 100% accuracy for Ovarian and Lung Cancer data when Linear and Cubic kernel functions are used. In term of running time, PCA greatly reduced the running time for every data sets.

Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques

NASA Astrophysics Data System (ADS)

Palacios-Morillo, Ana; Alcázar, Ángela.; de Pablos, Fernando; Jurado, José Marcos

2013-02-01

Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.

Correlation between grade of pearlite spheroidization and laser induced spectra

NASA Astrophysics Data System (ADS)

Yao, Shunchun; Dong, Meirong; Lu, Jidong; Li, Jun; Dong, Xuan

2013-12-01

Laser induced breakdown spectroscopy (LIBS) which is used traditionally as a spectrochemical analytical technique was employed to analyze the grade of pearlite spheroidization. Three 12Cr1MoV steel specimens with different grades of pearlite spheroidization were ablated to produce plasma by pulse laser at 266 nm. In order to determine the optimal temporal condition and plasma parameters for correlating the grade of pearlite spheroidization and laser induced spectra, a set of spectra at different delays were analyzed by the principal component analysis method. Then, the relationship between plasma temperature, intensity ratios of ionic to atomic lines and grade of pearlite spheroidization was studied. The analysis results show that the laser induced spectra of different grades of pearlite spheroidization can be readily identifiable by principal component analysis in the range of 271.941-289.672 nm with 1000 ns delay time. It is also found that a good agreement exists between the Fe ionic to atomic line ratios and the tensile strength, whereas there is no obvious difference in the plasma temperature. Therefore, LIBS may be applied not only as a spectrochemical analytical technique but also as a new way to estimate the grade of pearlite spheroidization.

Characterization of exopolymers of aquatic bacteria by pyrolysis-mass spectrometry

NASA Technical Reports Server (NTRS)

Ford, T.; Sacco, E.; Black, J.; Kelley, T.; Goodacre, R.; Berkeley, R. C.; Mitchell, R.

1991-01-01

Exopolymers from a diverse collection of marine and freshwater bacteria were characterized by pyrolysis-mass spectrometry (Py-MS). Py-MS provides spectra of pyrolysis fragments that are characteristic of the original material. Analysis of the spectra by multivariate statistical techniques (principal component and canonical variate analysis) separated these exopolymers into distinct groups. Py-MS clearly distinguished characteristic fragments, which may be derived from components responsible for functional differences between polymers. The importance of these distinctions and the relevance of pyrolysis information to exopolysaccharide function in aquatic bacteria is discussed.

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

PubMed

Jankovic, Marko; Ogawa, Hidemitsu

2004-10-01

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

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.

Chemometric techniques in oil classification from oil spill fingerprinting.

PubMed

Ismail, Azimah; Toriman, Mohd Ekhwan; Juahir, Hafizan; Kassim, Azlina Md; Zain, Sharifuddin Md; Ahmad, Wan Kamaruzaman Wan; Wong, Kok Fah; Retnam, Ananthy; Zali, Munirah Abdul; Mokhtar, Mazlin; Yusri, Mohd Ayub

2016-10-15

Extended use of GC-FID and GC-MS in oil spill fingerprinting and matching is significantly important for oil classification from the oil spill sources collected from various areas of Peninsular Malaysia and Sabah (East Malaysia). Oil spill fingerprinting from GC-FID and GC-MS coupled with chemometric techniques (discriminant analysis and principal component analysis) is used as a diagnostic tool to classify the types of oil polluting the water. Clustering and discrimination of oil spill compounds in the water from the actual site of oil spill events are divided into four groups viz. diesel, Heavy Fuel Oil (HFO), Mixture Oil containing Light Fuel Oil (MOLFO) and Waste Oil (WO) according to the similarity of their intrinsic chemical properties. Principal component analysis (PCA) demonstrates that diesel, HFO, MOLFO and WO are types of oil or oil products from complex oil mixtures with a total variance of 85.34% and are identified with various anthropogenic activities related to either intentional releasing of oil or accidental discharge of oil into the environment. Our results show that the use of chemometric techniques is significant in providing independent validation for classifying the types of spilled oil in the investigation of oil spill pollution in Malaysia. This, in consequence would result in cost and time saving in identification of the oil spill sources. Copyright © 2016. Published by Elsevier Ltd.

Fusion of Modis and Palsar Principal Component Images Through Curvelet Transform for Land Cover Classification

NASA Astrophysics Data System (ADS)

Singh, Dharmendra; Kumar, Harish

Earth observation satellites provide data that covers different portions of the electromagnetic spectrum at different spatial and spectral resolutions. The increasing availability of information products generated from satellite images are extending the ability to understand the patterns and dynamics of the earth resource systems at all scales of inquiry. In which one of the most important application is the generation of land cover classification from satellite images for understanding the actual status of various land cover classes. The prospect for the use of satel-lite images in land cover classification is an extremely promising one. The quality of satellite images available for land-use mapping is improving rapidly by development of advanced sensor technology. Particularly noteworthy in this regard is the improved spatial and spectral reso-lution of the images captured by new satellite sensors like MODIS, ASTER, Landsat 7, and SPOT 5. For the full exploitation of increasingly sophisticated multisource data, fusion tech-niques are being developed. Fused images may enhance the interpretation capabilities. The images used for fusion have different temporal, and spatial resolution. Therefore, the fused image provides a more complete view of the observed objects. It is one of the main aim of image fusion to integrate different data in order to obtain more information that can be de-rived from each of the single sensor data alone. A good example of this is the fusion of images acquired by different sensors having a different spatial resolution and of different spectral res-olution. Researchers are applying the fusion technique since from three decades and propose various useful methods and techniques. The importance of high-quality synthesis of spectral information is well suited and implemented for land cover classification. More recently, an underlying multiresolution analysis employing the discrete wavelet transform has been used in image fusion. It was found that multisensor image fusion is a tradeoff between the spectral information from a low resolution multi-spectral images and the spatial information from a high resolution multi-spectral images. With the wavelet transform based fusion method, it is easy to control this tradeoff. A new transform, the curvelet transform was used in recent years by Starck. A ridgelet transform is applied to square blocks of detail frames of undecimated wavelet decomposition, consequently the curvelet transform is obtained. Since the ridgelet transform possesses basis functions matching directional straight lines therefore, the curvelet transform is capable of representing piecewise linear contours on multiple scales through few significant coefficients. This property leads to a better separation between geometric details and background noise, which may be easily reduced by thresholding curvelet coefficients before they are used for fusion. The Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instrument provides high radiometric sensitivity (12 bit) in 36 spectral bands ranging in wavelength from 0.4 m to 14.4 m and also it is freely available. Two bands are imaged at a nominal resolution of 250 m at nadir, with five bands at 500 m, and the remaining 29 bands at 1 km. In this paper, the band 1 of spatial resolution 250 m and bandwidth 620-670 nm, and band 2, of spatial resolution of 250m and bandwidth 842-876 nm is considered as these bands has special features to identify the agriculture and other land covers. In January 2006, the Advanced Land Observing Satellite (ALOS) was successfully launched by the Japan Aerospace Exploration Agency (JAXA). The Phased Arraytype L-band SAR (PALSAR) sensor onboard the satellite acquires SAR imagery at a wavelength of 23.5 cm (frequency 1.27 GHz) with capabilities of multimode and multipolarization observation. PALSAR can operate in several modes: the fine-beam single (FBS) polarization mode (HH), fine-beam dual (FBD) polariza-tion mode (HH/HV or VV/VH), polarimetric (PLR) mode (HH/HV/VH/VV), and ScanSAR (WB) mode (HH/VV) [15]. These makes PALSAR imagery very attractive for spatially and temporally consistent monitoring system. The Overview of Principal Component Analysis is that the most of the information within all the bands can be compressed into a much smaller number of bands with little loss of information. It allows us to extract the low-dimensional subspaces that capture the main linear correlation among the high-dimensional image data. This facilitates viewing the explained variance or signal in the available imagery, allowing both gross and more subtle features in the imagery to be seen. In this paper we have explored the fusion technique for enhancing the land cover classification of low resolution satellite data espe-cially freely available satellite data. For this purpose, we have considered to fuse the PALSAR principal component data with MODIS principal component data. Initially, the MODIS band 1 and band 2 is considered, its principal component is computed. Similarly the PALSAR HH, HV and VV polarized data are considered, and there principal component is also computed. con-sequently, the PALSAR principal component image is fused with MODIS principal component image. The aim of this paper is to analyze the effect of classification accuracy on major type of land cover types like agriculture, water and urban bodies with fusion of PALSAR data to MODIS data. Curvelet transformation has been applied for fusion of these two satellite images and Minimum Distance classification technique has been applied for the resultant fused image. It is qualitatively and visually observed that the overall classification accuracy of MODIS image after fusion is enhanced. This type of fusion technique may be quite helpful in near future to use freely available satellite data to develop monitoring system for different land cover classes on the earth.

Differential use of fresh water environments by wintering waterfowl of coastal Texas

USGS Publications Warehouse

White, D.H.; James, D.

1978-01-01

A comparative study of the environmental relationships among 14 species of wintering waterfowl was conducted at the Welder Wildlife Foundation, San Patricia County, near Sinton, Texas during the fall and early winter of 1973. Measurements of 20 environmental factors (social, vegetational, physical, and chemical) were subjected to multivariate statistical methods to determine certain niche characteristics and environmental relationships of waterfowl wintering in the aquatic community.....Each waterfowl species occupied a unique realized niche by responding to distinct combinations of environmental factors identified by principal component analysis. One percent confidence ellipses circumscribing the mean scores plotted for the first and second principal components gave an indication of relative niche width for each species. The waterfowl environments were significantly different interspecifically and water depth at feeding site and % emergent vegetation were most important in the separation. This was shown by subjecting the transformed data to multivariate analysis of variance with an associated step-down procedure. The species were distributed along a community cline extending from shallow water with abundant emergent vegetation to open deep water with little emergent vegetation of any kind. Four waterfowl subgroups were significantly separated along the cline, as indicated by one-way analysis of variance with Duncan?s multiple range test. Clumping of the bird species toward the middle of the available habitat hyperspace was shown in a plot of the principal component scores for the random samples and individual species.....Naturally occurring relationships among waterfowl were clarified using principal comcomponent analysis and related multivariate procedures. These techniques may prove useful in wetland management for particular groups of waterfowl based on habitat preferences.

The Use of Multi-Component Statistical Techniques in Understanding Subduction Zone Arc Granitic Geochemical Data Sets

NASA Astrophysics Data System (ADS)

Pompe, L.; Clausen, B. L.; Morton, D. M.

2015-12-01

Multi-component statistical techniques and GIS visualization are emerging trends in understanding large data sets. Our research applies these techniques to a large igneous geochemical data set from southern California to better understand magmatic and plate tectonic processes. A set of 480 granitic samples collected by Baird from this area were analyzed for 39 geochemical elements. Of these samples, 287 are from the Peninsular Ranges Batholith (PRB) and 164 from part of the Transverse Ranges (TR). Principal component analysis (PCA) summarized the 39 variables into 3 principal components (PC) by matrix multiplication and for the PRB are interpreted as follows: PC1 with about 30% of the variation included mainly compatible elements and SiO2 and indicates extent of differentation; PC2 with about 20% of the variation included HFS elements and may indicate crustal contamination as usually identified by Sri; PC3 with about 20% of the variation included mainly HRE elements and may indicate magma source depth as often diplayed using REE spider diagrams and possibly Sr/Y. Several elements did not fit well in any of the three components: Cr, Ni, U, and Na2O.For the PRB, the PC1 correlation with SiO2 was r=-0.85, the PC2 correlation with Sri was r=0.80, and the PC3 correlation with Gd/Yb was r=-0.76 and with Sr/Y was r=-0.66 . Extending this method to the TR, correlations were r=-0.85, -0.21, -0.06, and -0.64, respectively. A similar extent of correlation for both areas was visually evident using GIS interpolation.PC1 seems to do well at indicating differentiation index for both the PRB and TR and correlates very well with SiO2, Al2O3, MgO, FeO*, CaO, K2O, Sc, V, and Co, but poorly with Na2O and Cr. If the crustal component is represented by Sri, PC2 correlates well and less expesively with this indicator in the PRB, but not in the TR. Source depth has been related to the slope on REE spidergrams, and PC3 based on only the HREE and using the Sr/Y ratios gives a reasonable correlation for both PRB and TR, but the Gd/Yb ratio gives a reasonable correlation for only the PRB. The PRB data provide reasonable correlation between principal components and standard geochemical indicators, perhaps because of the well-recognized monotonic variation from SW to NE. Data sets from the TR give similar results in some cases, but poor correlation in others.

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…

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

USDA-ARS?s Scientific Manuscript database

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...

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

PubMed Central

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

2015-01-01

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

Classification of breast tissue in mammograms using efficient coding.

PubMed

Costa, Daniel D; Campos, Lúcio F; Barros, Allan K

2011-06-24

Female breast cancer is the major cause of death by cancer in western countries. Efforts in Computer Vision have been made in order to improve the diagnostic accuracy by radiologists. Some methods of lesion diagnosis in mammogram images were developed based in the technique of principal component analysis which has been used in efficient coding of signals and 2D Gabor wavelets used for computer vision applications and modeling biological vision. In this work, we present a methodology that uses efficient coding along with linear discriminant analysis to distinguish between mass and non-mass from 5090 region of interest from mammograms. The results show that the best rates of success reached with Gabor wavelets and principal component analysis were 85.28% and 87.28%, respectively. In comparison, the model of efficient coding presented here reached up to 90.07%. Altogether, the results presented demonstrate that independent component analysis performed successfully the efficient coding in order to discriminate mass from non-mass tissues. In addition, we have observed that LDA with ICA bases showed high predictive performance for some datasets and thus provide significant support for a more detailed clinical investigation.

A Late Pleistocene sea level stack

NASA Astrophysics Data System (ADS)

Spratt, Rachel M.; Lisiecki, Lorraine E.

2016-04-01

Late Pleistocene sea level has been reconstructed from ocean sediment core data using a wide variety of proxies and models. However, the accuracy of individual reconstructions is limited by measurement error, local variations in salinity and temperature, and assumptions particular to each technique. Here we present a sea level stack (average) which increases the signal-to-noise ratio of individual reconstructions. Specifically, we perform principal component analysis (PCA) on seven records from 0 to 430 ka and five records from 0 to 798 ka. The first principal component, which we use as the stack, describes ˜ 80 % of the variance in the data and is similar using either five or seven records. After scaling the stack based on Holocene and Last Glacial Maximum (LGM) sea level estimates, the stack agrees to within 5 m with isostatically adjusted coral sea level estimates for Marine Isotope Stages 5e and 11 (125 and 400 ka, respectively). Bootstrapping and random sampling yield mean uncertainty estimates of 9-12 m (1σ) for the scaled stack. Sea level change accounts for about 45 % of the total orbital-band variance in benthic δ18O, compared to a 65 % contribution during the LGM-to-Holocene transition. Additionally, the second and third principal components of our analyses reflect differences between proxy records associated with spatial variations in the δ18O of seawater.

Principal component of explained variance: An efficient and optimal data dimension reduction framework for association studies.

PubMed

Turgeon, Maxime; Oualkacha, Karim; Ciampi, Antonio; Miftah, Hanane; Dehghan, Golsa; Zanke, Brent W; Benedet, Andréa L; Rosa-Neto, Pedro; Greenwood, Celia Mt; Labbe, Aurélie

2018-05-01

The genomics era has led to an increase in the dimensionality of data collected in the investigation of biological questions. In this context, dimension-reduction techniques can be used to summarise high-dimensional signals into low-dimensional ones, to further test for association with one or more covariates of interest. This paper revisits one such approach, previously known as principal component of heritability and renamed here as principal component of explained variance (PCEV). As its name suggests, the PCEV seeks a linear combination of outcomes in an optimal manner, by maximising the proportion of variance explained by one or several covariates of interest. By construction, this method optimises power; however, due to its computational complexity, it has unfortunately received little attention in the past. Here, we propose a general analytical PCEV framework that builds on the assets of the original method, i.e. conceptually simple and free of tuning parameters. Moreover, our framework extends the range of applications of the original procedure by providing a computationally simple strategy for high-dimensional outcomes, along with exact and asymptotic testing procedures that drastically reduce its computational cost. We investigate the merits of the PCEV using an extensive set of simulations. Furthermore, the use of the PCEV approach is illustrated using three examples taken from the fields of epigenetics and brain imaging.

A network view on psychiatric disorders: network clusters of symptoms as elementary syndromes of psychopathology.

PubMed

Goekoop, Rutger; Goekoop, Jaap G

2014-01-01

The vast number of psychopathological syndromes that can be observed in clinical practice can be described in terms of a limited number of elementary syndromes that are differentially expressed. Previous attempts to identify elementary syndromes have shown limitations that have slowed progress in the taxonomy of psychiatric disorders. To examine the ability of network community detection (NCD) to identify elementary syndromes of psychopathology and move beyond the limitations of current classification methods in psychiatry. 192 patients with unselected mental disorders were tested on the Comprehensive Psychopathological Rating Scale (CPRS). Principal component analysis (PCA) was performed on the bootstrapped correlation matrix of symptom scores to extract the principal component structure (PCS). An undirected and weighted network graph was constructed from the same matrix. Network community structure (NCS) was optimized using a previously published technique. In the optimal network structure, network clusters showed a 89% match with principal components of psychopathology. Some 6 network clusters were found, including "Depression", "Mania", "Anxiety", "Psychosis", "Retardation", and "Behavioral Disorganization". Network metrics were used to quantify the continuities between the elementary syndromes. We present the first comprehensive network graph of psychopathology that is free from the biases of previous classifications: a 'Psychopathology Web'. Clusters within this network represent elementary syndromes that are connected via a limited number of bridge symptoms. Many problems of previous classifications can be overcome by using a network approach to psychopathology.

Independent component analysis for automatic note extraction from musical trills

NASA Astrophysics Data System (ADS)

Brown, Judith C.; Smaragdis, Paris

2004-05-01

The method of principal component analysis, which is based on second-order statistics (or linear independence), has long been used for redundancy reduction of audio data. The more recent technique of independent component analysis, enforcing much stricter statistical criteria based on higher-order statistical independence, is introduced and shown to be far superior in separating independent musical sources. This theory has been applied to piano trills and a database of trill rates was assembled from experiments with a computer-driven piano, recordings of a professional pianist, and commercially available compact disks. The method of independent component analysis has thus been shown to be an outstanding, effective means of automatically extracting interesting musical information from a sea of redundant data.

Regional Morphology Analysis Package (RMAP): Empirical Orthogonal Function Analysis, Background and Examples

DTIC Science & Technology

2007-10-01

1984. Complex principal component analysis : Theory and examples. Journal of Climate and Applied Meteorology 23: 1660-1673. Hotelling, H. 1933...Sediments 99. ASCE: 2,566-2,581. Von Storch, H., and A. Navarra. 1995. Analysis of climate variability. Applications of statistical techniques. Berlin...ERDC TN-SWWRP-07-9 October 2007 Regional Morphology Empirical Analysis Package (RMAP): Orthogonal Function Analysis , Background and Examples by

Understanding software faults and their role in software reliability modeling

NASA Technical Reports Server (NTRS)

Munson, John C.

1994-01-01

This study is a direct result of an on-going project to model the reliability of a large real-time control avionics system. In previous modeling efforts with this system, hardware reliability models were applied in modeling the reliability behavior of this system. In an attempt to enhance the performance of the adapted reliability models, certain software attributes were introduced in these models to control for differences between programs and also sequential executions of the same program. As the basic nature of the software attributes that affect software reliability become better understood in the modeling process, this information begins to have important implications on the software development process. A significant problem arises when raw attribute measures are to be used in statistical models as predictors, for example, of measures of software quality. This is because many of the metrics are highly correlated. Consider the two attributes: lines of code, LOC, and number of program statements, Stmts. In this case, it is quite obvious that a program with a high value of LOC probably will also have a relatively high value of Stmts. In the case of low level languages, such as assembly language programs, there might be a one-to-one relationship between the statement count and the lines of code. When there is a complete absence of linear relationship among the metrics, they are said to be orthogonal or uncorrelated. Usually the lack of orthogonality is not serious enough to affect a statistical analysis. However, for the purposes of some statistical analysis such as multiple regression, the software metrics are so strongly interrelated that the regression results may be ambiguous and possibly even misleading. Typically, it is difficult to estimate the unique effects of individual software metrics in the regression equation. The estimated values of the coefficients are very sensitive to slight changes in the data and to the addition or deletion of variables in the regression equation. Since most of the existing metrics have common elements and are linear combinations of these common elements, it seems reasonable to investigate the structure of the underlying common factors or components that make up the raw metrics. The technique we have chosen to use to explore this structure is a procedure called principal components analysis. Principal components analysis is a decomposition technique that may be used to detect and analyze collinearity in software metrics. When confronted with a large number of metrics measuring a single construct, it may be desirable to represent the set by some smaller number of variables that convey all, or most, of the information in the original set. Principal components are linear transformations of a set of random variables that summarize the information contained in the variables. The transformations are chosen so that the first component accounts for the maximal amount of variation of the measures of any possible linear transform; the second component accounts for the maximal amount of residual variation; and so on. The principal components are constructed so that they represent transformed scores on dimensions that are orthogonal. Through the use of principal components analysis, it is possible to have a set of highly related software attributes mapped into a small number of uncorrelated attribute domains. This definitively solves the problem of multi-collinearity in subsequent regression analysis. There are many software metrics in the literature, but principal component analysis reveals that there are few distinct sources of variation, i.e. dimensions, in this set of metrics. It would appear perfectly reasonable to characterize the measurable attributes of a program with a simple function of a small number of orthogonal metrics each of which represents a distinct software attribute domain.

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,…

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

USDA-ARS?s Scientific Manuscript database

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...

Finding Planets in K2: A New Method of Cleaning the Data

NASA Astrophysics Data System (ADS)

Currie, Miles; Mullally, Fergal; Thompson, Susan E.

2017-01-01

We present a new method of removing systematic flux variations from K2 light curves by employing a pixel-level principal component analysis (PCA). This method decomposes the light curves into its principal components (eigenvectors), each with an associated eigenvalue, the value of which is correlated to how much influence the basis vector has on the shape of the light curve. This method assumes that the most influential basis vectors will correspond to the unwanted systematic variations in the light curve produced by K2’s constant motion. We correct the raw light curve by automatically fitting and removing the strongest principal components. The strongest principal components generally correspond to the flux variations that result from the motion of the star in the field of view. Our primary method of calculating the strongest principal components to correct for in the raw light curve estimates the noise by measuring the scatter in the light curve after using an algorithm for Savitsy-Golay detrending, which computes the combined photometric precision value (SG-CDPP value) used in classic Kepler. We calculate this value after correcting the raw light curve for each element in a list of cumulative sums of principal components so that we have as many noise estimate values as there are principal components. We then take the derivative of the list of SG-CDPP values and take the number of principal components that correlates to the point at which the derivative effectively goes to zero. This is the optimal number of principal components to exclude from the refitting of the light curve. We find that a pixel-level PCA is sufficient for cleaning unwanted systematic and natural noise from K2’s light curves. We present preliminary results and a basic comparison to other methods of reducing the noise from the flux variations.

Multivariate image analysis of laser-induced photothermal imaging used for detection of caries tooth

NASA Astrophysics Data System (ADS)

El-Sherif, Ashraf F.; Abdel Aziz, Wessam M.; El-Sharkawy, Yasser H.

2010-08-01

Time-resolved photothermal imaging has been investigated to characterize tooth for the purpose of discriminating between normal and caries areas of the hard tissue using thermal camera. Ultrasonic thermoelastic waves were generated in hard tissue by the absorption of fiber-coupled Q-switched Nd:YAG laser pulses operating at 1064 nm in conjunction with a laser-induced photothermal technique used to detect the thermal radiation waves for diagnosis of human tooth. The concepts behind the use of photo-thermal techniques for off-line detection of caries tooth features were presented by our group in earlier work. This paper illustrates the application of multivariate image analysis (MIA) techniques to detect the presence of caries tooth. MIA is used to rapidly detect the presence and quantity of common caries tooth features as they scanned by the high resolution color (RGB) thermal cameras. Multivariate principal component analysis is used to decompose the acquired three-channel tooth images into a two dimensional principal components (PC) space. Masking score point clusters in the score space and highlighting corresponding pixels in the image space of the two dominant PCs enables isolation of caries defect pixels based on contrast and color information. The technique provides a qualitative result that can be used for early stage caries tooth detection. The proposed technique can potentially be used on-line or real-time resolved to prescreen the existence of caries through vision based systems like real-time thermal camera. Experimental results on the large number of extracted teeth as well as one of the thermal image panoramas of the human teeth voltanteer are investigated and presented.

Surface atrial frequency analysis in patients with atrial fibrillation: a tool for evaluating the effects of intervention.

PubMed

Raine, Dan; Langley, Philip; Murray, Alan; Dunuwille, Asunga; Bourke, John P

2004-09-01

The aims of this study were to evaluate (1) principal component analysis as a technique for extracting the atrial signal waveform from the standard 12-lead ECG and (2) its ability to distinguish changes in atrial fibrillation (AF) frequency parameters over time and in response to pharmacologic manipulation using drugs with different effects on atrial electrophysiology. Twenty patients with persistent AF were studied. Continuous 12-lead Holter ECGs were recorded for 60 minutes, first, in the drug-free state. Mean and variability of atrial waveform frequency were measured using an automated computer technique. This extracted the atrial signal by principal component analysis and identified the main frequency component using Fourier analysis. Patients were then allotted sequentially to receive 1 of 4 drugs intravenously (amiodarone, flecainide, sotalol, or metoprolol), and changes induced in mean and variability of atrial waveform frequency measured. Mean and variability of atrial waveform frequency did not differ within patients between the two 30-minute sections of the drug-free state. As hypothesized, significant changes in mean and variability of atrial waveform frequency were detected after manipulation with amiodarone (mean: 5.77 vs 4.86 Hz; variability: 0.55 vs 0.31 Hz), flecainide (mean: 5.33 vs 4.72 Hz; variability: 0.71 vs 0.31 Hz), and sotalol (mean: 5.94 vs 4.90 Hz; variability: 0.73 vs 0.40 Hz) but not with metoprolol (mean: 5.41 vs 5.17 Hz; variability: 0.81 vs 0.82 Hz). A technique for continuously analyzing atrial frequency characteristics of AF from the surface ECG has been developed and validated.

Detecting transitions in protein dynamics using a recurrence quantification analysis based bootstrap method.

PubMed

Karain, Wael I

2017-11-28

Proteins undergo conformational transitions over different time scales. These transitions are closely intertwined with the protein's function. Numerous standard techniques such as principal component analysis are used to detect these transitions in molecular dynamics simulations. In this work, we add a new method that has the ability to detect transitions in dynamics based on the recurrences in the dynamical system. It combines bootstrapping and recurrence quantification analysis. We start from the assumption that a protein has a "baseline" recurrence structure over a given period of time. Any statistically significant deviation from this recurrence structure, as inferred from complexity measures provided by recurrence quantification analysis, is considered a transition in the dynamics of the protein. We apply this technique to a 132 ns long molecular dynamics simulation of the β-Lactamase Inhibitory Protein BLIP. We are able to detect conformational transitions in the nanosecond range in the recurrence dynamics of the BLIP protein during the simulation. The results compare favorably to those extracted using the principal component analysis technique. The recurrence quantification analysis based bootstrap technique is able to detect transitions between different dynamics states for a protein over different time scales. It is not limited to linear dynamics regimes, and can be generalized to any time scale. It also has the potential to be used to cluster frames in molecular dynamics trajectories according to the nature of their recurrence dynamics. One shortcoming for this method is the need to have large enough time windows to insure good statistical quality for the recurrence complexity measures needed to detect the transitions.

Directly reconstructing principal components of heterogeneous particles from cryo-EM images.

PubMed

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

2015-08-01

Structural heterogeneity of particles can be investigated by their three-dimensional principal components. This paper addresses the question of whether, and with what algorithm, the three-dimensional principal components can be directly recovered from cryo-EM images. The first part of the paper extends the Fourier slice theorem to covariance functions showing that the three-dimensional covariance, and hence the principal components, of a heterogeneous particle can indeed be recovered from two-dimensional cryo-EM images. The second part of the paper proposes a practical algorithm for reconstructing the principal components directly from cryo-EM images without the intermediate step of calculating covariances. This algorithm is based on maximizing the posterior likelihood using the Expectation-Maximization algorithm. The last part of the paper applies this algorithm to simulated data and to two real cryo-EM data sets: a data set of the 70S ribosome with and without Elongation Factor-G (EF-G), and a data set of the influenza virus RNA dependent RNA Polymerase (RdRP). The first principal component of the 70S ribosome data set reveals the expected conformational changes of the ribosome as the EF-G binds and unbinds. The first principal component of the RdRP data set reveals a conformational change in the two dimers of the RdRP. Copyright © 2015 Elsevier Inc. All rights reserved.

Classification of fMRI resting-state maps using machine learning techniques: A comparative study

NASA Astrophysics Data System (ADS)

Gallos, Ioannis; Siettos, Constantinos

2017-11-01

We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

40 CFR 60.2998 - 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... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule... management plan. (c) Operator training and qualification. (d) Emission limitations and operating limits. (e...

40 CFR 60.2570 - 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... Construction On or Before November 30, 1999 Use of Model Rule § 60.2570 What are the principal components of... (k) of this section. (a) Increments of progress toward compliance. (b) Waste management plan. (c...

Assessing Footwear Effects from Principal Features of Plantar Loading during Running.

PubMed

Trudeau, Matthieu B; von Tscharner, Vinzenz; Vienneau, Jordyn; Hoerzer, Stefan; Nigg, Benno M

2015-09-01

The effects of footwear on the musculoskeletal system are commonly assessed by interpreting the resultant force at the foot during the stance phase of running. However, this approach overlooks loading patterns across the entire foot. An alternative technique for assessing foot loading across different footwear conditions is possible using comprehensive analysis tools that extract different foot loading features, thus enhancing the functional interpretation of the differences across different interventions. The purpose of this article was to use pattern recognition techniques to develop and use a novel comprehensive method for assessing the effects of different footwear interventions on plantar loading. A principal component analysis was used to extract different loading features from the stance phase of running, and a support vector machine (SVM) was used to determine whether and how these loading features were different across three shoe conditions. The results revealed distinct loading features at the foot during the stance phase of running. The loading features determined from the principal component analysis allowed successful classification of all three shoe conditions using the SVM. Several differences were found in the location and timing of the loading across each pairwise shoe comparison using the output from the SVM. The analysis approach proposed can successfully be used to compare different loading patterns with a much greater resolution than has been reported previously. This study has several important applications. One such application is that it would not be relevant for a user to select a shoe or for a manufacturer to alter a shoe's construction if the classification across shoe conditions would not have been significant.

[Discrimination of types of polyacrylamide based on near infrared spectroscopy coupled with least square support vector machine].

PubMed

Zhang, Hong-Guang; Yang, Qin-Min; Lu, Jian-Gang

2014-04-01

In this paper, a novel discriminant methodology based on near infrared spectroscopic analysis technique and least square support vector machine was proposed for rapid and nondestructive discrimination of different types of Polyacrylamide. The diffuse reflectance spectra of samples of Non-ionic Polyacrylamide, Anionic Polyacrylamide and Cationic Polyacrylamide were measured. Then principal component analysis method was applied to reduce the dimension of the spectral data and extract of the principal compnents. The first three principal components were used for cluster analysis of the three different types of Polyacrylamide. Then those principal components were also used as inputs of least square support vector machine model. The optimization of the parameters and the number of principal components used as inputs of least square support vector machine model was performed through cross validation based on grid search. 60 samples of each type of Polyacrylamide were collected. Thus a total of 180 samples were obtained. 135 samples, 45 samples for each type of Polyacrylamide, were randomly split into a training set to build calibration model and the rest 45 samples were used as test set to evaluate the performance of the developed model. In addition, 5 Cationic Polyacrylamide samples and 5 Anionic Polyacrylamide samples adulterated with different proportion of Non-ionic Polyacrylamide were also prepared to show the feasibilty of the proposed method to discriminate the adulterated Polyacrylamide samples. The prediction error threshold for each type of Polyacrylamide was determined by F statistical significance test method based on the prediction error of the training set of corresponding type of Polyacrylamide in cross validation. The discrimination accuracy of the built model was 100% for prediction of the test set. The prediction of the model for the 10 mixing samples was also presented, and all mixing samples were accurately discriminated as adulterated samples. The overall results demonstrate that the discrimination method proposed in the present paper can rapidly and nondestructively discriminate the different types of Polyacrylamide and the adulterated Polyacrylamide samples, and offered a new approach to discriminate the types of Polyacrylamide.

Free energy landscape of a biomolecule in dihedral principal component space: sampling convergence and correspondence between structures and minima.

PubMed

Maisuradze, Gia G; Leitner, David M

2007-05-15

Dihedral principal component analysis (dPCA) has recently been developed and shown to display complex features of the free energy landscape of a biomolecule that may be absent in the free energy landscape plotted in principal component space due to mixing of internal and overall rotational motion that can occur in principal component analysis (PCA) [Mu et al., Proteins: Struct Funct Bioinfo 2005;58:45-52]. Another difficulty in the implementation of PCA is sampling convergence, which we address here for both dPCA and PCA using a tetrapeptide as an example. We find that for both methods the sampling convergence can be reached over a similar time. Minima in the free energy landscape in the space of the two largest dihedral principal components often correspond to unique structures, though we also find some distinct minima to correspond to the same structure. 2007 Wiley-Liss, Inc.

The software-cycle model for re-engineering and reuse

NASA Technical Reports Server (NTRS)

Bailey, John W.; Basili, Victor R.

1992-01-01

This paper reports on the progress of a study which will contribute to our ability to perform high-level, component-based programming by describing means to obtain useful components, methods for the configuration and integration of those components, and an underlying economic model of the costs and benefits associated with this approach to reuse. One goal of the study is to develop and demonstrate methods to recover reusable components from domain-specific software through a combination of tools, to perform the identification, extraction, and re-engineering of components, and domain experts, to direct the applications of those tools. A second goal of the study is to enable the reuse of those components by identifying techniques for configuring and recombining the re-engineered software. This component-recovery or software-cycle model addresses not only the selection and re-engineering of components, but also their recombination into new programs. Once a model of reuse activities has been developed, the quantification of the costs and benefits of various reuse options will enable the development of an adaptable economic model of reuse, which is the principal goal of the overall study. This paper reports on the conception of the software-cycle model and on several supporting techniques of software recovery, measurement, and reuse which will lead to the development of the desired economic model.

Fast, Exact Bootstrap Principal Component Analysis for p > 1 million

PubMed Central

Fisher, Aaron; Caffo, Brian; Schwartz, Brian; Zipunnikov, Vadim

2015-01-01

Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subject (p) is much larger than the number of subjects (n), calculating and storing the leading principal components from each bootstrap sample can be computationally infeasible. To address this, we outline methods for fast, exact calculation of bootstrap principal components, eigenvalues, and scores. Our methods leverage the fact that all bootstrap samples occupy the same n-dimensional subspace as the original sample. As a result, all bootstrap principal components are limited to the same n-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that subspace. Several uncertainty metrics can be computed solely based on the bootstrap distribution of these low dimensional coordinates, without calculating or storing the p-dimensional bootstrap components. Fast bootstrap PCA is applied to a dataset of sleep electroencephalogram recordings (p = 900, n = 392), and to a dataset of brain magnetic resonance images (MRIs) (p ≈ 3 million, n = 352). For the MRI dataset, our method allows for standard errors for the first 3 principal components based on 1000 bootstrap samples to be calculated on a standard laptop in 47 minutes, as opposed to approximately 4 days with standard methods. PMID:27616801

Principal Workload: Components, Determinants and Coping Strategies in an Era of Standardization and Accountability

ERIC Educational Resources Information Center

Oplatka, Izhar

2017-01-01

Purpose: In order to fill the gap in theoretical and empirical knowledge about the characteristics of principal workload, the purpose of this paper is to explore the components of principal workload as well as its determinants and the coping strategies commonly used by principals to face this personal state. Design/methodology/approach:…

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

A New 4D Trajectory-Based Approach Unveils Abnormal LV Revolution Dynamics in Hypertrophic Cardiomyopathy

PubMed Central

Madeo, Andrea; Piras, Paolo; Re, Federica; Gabriele, Stefano; Nardinocchi, Paola; Teresi, Luciano; Torromeo, Concetta; Chialastri, Claudia; Schiariti, Michele; Giura, Geltrude; Evangelista, Antonietta; Dominici, Tania; Varano, Valerio; Zachara, Elisabetta; Puddu, Paolo Emilio

2015-01-01

The assessment of left ventricular shape changes during cardiac revolution may be a new step in clinical cardiology to ease early diagnosis and treatment. To quantify these changes, only point registration was adopted and neither Generalized Procrustes Analysis nor Principal Component Analysis were applied as we did previously to study a group of healthy subjects. Here, we extend to patients affected by hypertrophic cardiomyopathy the original approach and preliminarily include genotype positive/phenotype negative individuals to explore the potential that incumbent pathology might also be detected. Using 3D Speckle Tracking Echocardiography, we recorded left ventricular shape of 48 healthy subjects, 24 patients affected by hypertrophic cardiomyopathy and 3 genotype positive/phenotype negative individuals. We then applied Generalized Procrustes Analysis and Principal Component Analysis and inter-individual differences were cleaned by Parallel Transport performed on the tangent space, along the horizontal geodesic, between the per-subject consensuses and the grand mean. Endocardial and epicardial layers were evaluated separately, different from many ecocardiographic applications. Under a common Principal Component Analysis, we then evaluated left ventricle morphological changes (at both layers) explained by first Principal Component scores. Trajectories’ shape and orientation were investigated and contrasted. Logistic regression and Receiver Operating Characteristic curves were used to compare these morphometric indicators with traditional 3D Speckle Tracking Echocardiography global parameters. Geometric morphometrics indicators performed better than 3D Speckle Tracking Echocardiography global parameters in recognizing pathology both in systole and diastole. Genotype positive/phenotype negative individuals clustered with patients affected by hypertrophic cardiomyopathy during diastole, suggesting that incumbent pathology may indeed be foreseen by these methods. Left ventricle deformation in patients affected by hypertrophic cardiomyopathy compared to healthy subjects may be assessed by modern shape analysis better than by traditional 3D Speckle Tracking Echocardiography global parameters. Hypertrophic cardiomyopathy pathophysiology was unveiled in a new manner whereby also diastolic phase abnormalities are evident which is more difficult to investigate by traditional ecocardiographic techniques. PMID:25875818

Next Generation Aura-OMI SO2 Retrieval Algorithm: Introduction and Implementation Status

NASA Technical Reports Server (NTRS)

Li, Can; Joiner, Joanna; Krotkov, Nickolay A.; Bhartia, Pawan K.

2014-01-01

We introduce our next generation algorithm to retrieve SO2 using radiance measurements from the Aura Ozone Monitoring Instrument (OMI). We employ a principal component analysis technique to analyze OMI radiance spectral in 310.5-340 nm acquired over regions with no significant SO2. The resulting principal components (PCs) capture radiance variability caused by both physical processes (e.g., Rayleigh and Raman scattering, and ozone absorption) and measurement artifacts, enabling us to account for these various interferences in SO2 retrievals. By fitting these PCs along with SO2 Jacobians calculated with a radiative transfer model to OMI-measured radiance spectra, we directly estimate SO2 vertical column density in one step. As compared with the previous generation operational OMSO2 PBL (Planetary Boundary Layer) SO2 product, our new algorithm greatly reduces unphysical biases and decreases the noise by a factor of two, providing greater sensitivity to anthropogenic emissions. The new algorithm is fast, eliminates the need for instrument-specific radiance correction schemes, and can be easily adapted to other sensors. These attributes make it a promising technique for producing long-term, consistent SO2 records for air quality and climate research. We have operationally implemented this new algorithm on OMI SIPS for producing the new generation standard OMI SO2 products.

A comparison of robust principal component analysis techniques for buried object detection in downward looking GPR sensor data

NASA Astrophysics Data System (ADS)

Pinar, Anthony; Havens, Timothy C.; Rice, Joseph; Masarik, Matthew; Burns, Joseph; Thelen, Brian

2016-05-01

Explosive hazards are a deadly threat in modern conflicts; hence, detecting them before they cause injury or death is of paramount importance. One method of buried explosive hazard discovery relies on data collected from ground penetrating radar (GPR) sensors. Threat detection with downward looking GPR is challenging due to large returns from non-target objects and clutter. This leads to a large number of false alarms (FAs), and since the responses of clutter and targets can form very similar signatures, classifier design is not trivial. One approach to combat these issues uses robust principal component analysis (RPCA) to enhance target signatures while suppressing clutter and background responses, though there are many versions of RPCA. This work applies some of these RPCA techniques to GPR sensor data and evaluates their merit using the peak signal-to-clutter ratio (SCR) of the RPCA-processed B-scans. Experimental results on government furnished data show that while some of the RPCA methods yield similar results, there are indeed some methods that outperform others. Furthermore, we show that the computation time required by the different RPCA methods varies widely, and the selection of tuning parameters in the RPCA algorithms has a major effect on the peak SCR.

Study of T-wave morphology parameters based on Principal Components Analysis during acute myocardial ischemia

NASA Astrophysics Data System (ADS)

Baglivo, Fabricio Hugo; Arini, Pedro David

2011-12-01

Electrocardiographic repolarization abnormalities can be detected by Principal Components Analysis of the T-wave. In this work we studied the efect of signal averaging on the mean value and reproducibility of the ratio of the 2nd to the 1st eigenvalue of T-wave (T21W) and the absolute and relative T-wave residuum (TrelWR and TabsWR) in the ECG during ischemia induced by Percutaneous Coronary Intervention. Also, the intra-subject and inter-subject variability of T-wave parameters have been analyzed. Results showed that TrelWR and TabsWR evaluated from the average of 10 complexes had lower values and higher reproducibility than those obtained from 1 complex. On the other hand T21W calculated from 10 complexes did not show statistical diferences versus the T21W calculated on single beats. The results of this study corroborate that, with a signal averaging technique, the 2nd and the 1st eigenvalue are not afected by noise while the 4th to 8th eigenvalues are so much afected by this, suggesting the use of the signal averaged technique before calculation of absolute and relative T-wave residuum. Finally, we have shown that T-wave morphology parameters present high intra-subject stability.

Determination of Multiple Near-Surface Residual Stress Components in Laser Peened Aluminum Alloy via the Contour Method

NASA Astrophysics Data System (ADS)

Toparli, M. Burak; Fitzpatrick, Michael E.; Gungor, Salih

2015-09-01

In this study, residual stress fields, including the near-surface residual stresses, were determined for an Al7050-T7451 sample after laser peening. The contour method was applied to measure one component of the residual stress, and the relaxed stresses on the cut surfaces were then measured by X-ray diffraction. This allowed calculation of the three orthogonal stress components using the superposition principle. The near-surface results were validated with results from incremental hole drilling and conventional X-ray diffraction. The results demonstrate that multiple residual stress components can be determined using a combination of the contour method and another technique. If the measured stress components are congruent with the principal stress axes in the sample, then this allows for determination of the complete stress tensor.

Real-Time External Respiratory Motion Measuring Technique Using an RGB-D Camera and Principal Component Analysis †

PubMed Central

Wijenayake, Udaya; Park, Soon-Yong

2017-01-01

Accurate tracking and modeling of internal and external respiratory motion in the thoracic and abdominal regions of a human body is a highly discussed topic in external beam radiotherapy treatment. Errors in target/normal tissue delineation and dose calculation and the increment of the healthy tissues being exposed to high radiation doses are some of the unsolicited problems caused due to inaccurate tracking of the respiratory motion. Many related works have been introduced for respiratory motion modeling, but a majority of them highly depend on radiography/fluoroscopy imaging, wearable markers or surgical node implanting techniques. We, in this article, propose a new respiratory motion tracking approach by exploiting the advantages of an RGB-D camera. First, we create a patient-specific respiratory motion model using principal component analysis (PCA) removing the spatial and temporal noise of the input depth data. Then, this model is utilized for real-time external respiratory motion measurement with high accuracy. Additionally, we introduce a marker-based depth frame registration technique to limit the measuring area into an anatomically consistent region that helps to handle the patient movements during the treatment. We achieved a 0.97 correlation comparing to a spirometer and 0.53 mm average error considering a laser line scanning result as the ground truth. As future work, we will use this accurate measurement of external respiratory motion to generate a correlated motion model that describes the movements of internal tumors. PMID:28792468

Reservoir zonation based on statistical analyses: A case study of the Nubian sandstone, Gulf of Suez, Egypt

NASA Astrophysics Data System (ADS)

El Sharawy, Mohamed S.; Gaafar, Gamal R.

2016-12-01

Both reservoir engineers and petrophysicists have been concerned about dividing a reservoir into zones for engineering and petrophysics purposes. Through decades, several techniques and approaches were introduced. Out of them, statistical reservoir zonation, stratigraphic modified Lorenz (SML) plot and the principal component and clustering analyses techniques were chosen to apply on the Nubian sandstone reservoir of Palaeozoic - Lower Cretaceous age, Gulf of Suez, Egypt, by using five adjacent wells. The studied reservoir consists mainly of sandstone with some intercalation of shale layers with varying thickness from one well to another. The permeability ranged from less than 1 md to more than 1000 md. The statistical reservoir zonation technique, depending on core permeability, indicated that the cored interval of the studied reservoir can be divided into two zones. Using reservoir properties such as porosity, bulk density, acoustic impedance and interval transit time indicated also two zones with an obvious variation in separation depth and zones continuity. The stratigraphic modified Lorenz (SML) plot indicated the presence of more than 9 flow units in the cored interval as well as a high degree of microscopic heterogeneity. On the other hand, principal component and cluster analyses, depending on well logging data (gamma ray, sonic, density and neutron), indicated that the whole reservoir can be divided at least into four electrofacies having a noticeable variation in reservoir quality, as correlated with the measured permeability. Furthermore, continuity or discontinuity of the reservoir zones can be determined using this analysis.

Principal Components Analysis Studies of Martian Clouds

NASA Astrophysics Data System (ADS)

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

2001-11-01

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

Considering Horn's Parallel Analysis from a Random Matrix Theory Point of View.

PubMed

Saccenti, Edoardo; Timmerman, Marieke E

2017-03-01

Horn's parallel analysis is a widely used method for assessing the number of principal components and common factors. We discuss the theoretical foundations of parallel analysis for principal components based on a covariance matrix by making use of arguments from random matrix theory. In particular, we show that (i) for the first component, parallel analysis is an inferential method equivalent to the Tracy-Widom test, (ii) its use to test high-order eigenvalues is equivalent to the use of the joint distribution of the eigenvalues, and thus should be discouraged, and (iii) a formal test for higher-order components can be obtained based on a Tracy-Widom approximation. We illustrate the performance of the two testing procedures using simulated data generated under both a principal component model and a common factors model. For the principal component model, the Tracy-Widom test performs consistently in all conditions, while parallel analysis shows unpredictable behavior for higher-order components. For the common factor model, including major and minor factors, both procedures are heuristic approaches, with variable performance. We conclude that the Tracy-Widom procedure is preferred over parallel analysis for statistically testing the number of principal components based on a covariance matrix.

Use of Raman spectroscopy in the analysis of nickel allergy

NASA Astrophysics Data System (ADS)

Alda, Javier; Castillo-Martinez, Claudio; Valdes-Rodriguez, Rodrigo; Hernández-Blanco, Diana; Moncada, Benjamin; González, Francisco J.

2013-06-01

Raman spectra of the skin of subjects with nickel allergy are analyzed and compared to the spectra of healthy subjects to detect possible biochemical differences in the structure of the skin that could help diagnose metal allergies in a noninvasive manner. Results show differences between the two groups of Raman spectra. These spectral differences can be classified using principal component analysis. Based on these findings, a novel computational technique to make a fast evaluation and classification of the Raman spectra of the skin is presented and proposed as a noninvasive technique for the detection of nickel allergy.

Principal components colour display of ERTS imagery

NASA Technical Reports Server (NTRS)

Taylor, M. M.

1974-01-01

In the technique presented, colours are not derived from single bands, but rather from independent linear combinations of the bands. Using a simple model of the processing done by the visual system, three informationally independent linear combinations of the four ERTS bands are mapped onto the three visual colour dimensions of brightness, redness-greenness and blueness-yellowness. The technique permits user-specific transformations which enhance particular features, but this is not usually needed, since a single transformation provides a picture which conveys much of the information implicit in the ERTS data. Examples of experimental vector images with matched individual band images are shown.

Fast discrimination of traditional Chinese medicine according to geographical origins with FTIR spectroscopy and advanced pattern recognition techniques

NASA Astrophysics Data System (ADS)

Li, Ning; Wang, Yan; Xu, Kexin

2006-08-01

Combined with Fourier transform infrared (FTIR) spectroscopy and three kinds of pattern recognition techniques, 53 traditional Chinese medicine danshen samples were rapidly discriminated according to geographical origins. The results showed that it was feasible to discriminate using FTIR spectroscopy ascertained by principal component analysis (PCA). An effective model was built by employing the Soft Independent Modeling of Class Analogy (SIMCA) and PCA, and 82% of the samples were discriminated correctly. Through use of the artificial neural network (ANN)-based back propagation (BP) network, the origins of danshen were completely classified.

Thermal Inspection of Composite Honeycomb Structures

NASA Technical Reports Server (NTRS)

Zalameda, Joseph N.; Parker, F. Raymond

2014-01-01

Composite honeycomb structures continue to be widely used in aerospace applications due to their low weight and high strength advantages. Developing nondestructive evaluation (NDE) inspection methods are essential for their safe performance. Pulsed thermography is a commonly used technique for composite honeycomb structure inspections due to its large area and rapid inspection capability. Pulsed thermography is shown to be sensitive for detection of face sheet impact damage and face sheet to core disbond. Data processing techniques, using principal component analysis to improve the defect contrast, are presented. In addition, limitations to the thermal detection of the core are investigated. Other NDE techniques, such as computed tomography X-ray and ultrasound, are used for comparison to the thermography results.

Characterizing natural colloidal/particulate-protein interactions using fluorescence-based techniques and principal component analysis.

PubMed

Peiris, Ramila H; Ignagni, Nicholas; Budman, Hector; Moresoli, Christine; Legge, Raymond L

2012-09-15

Characterization of the interactions between natural colloidal/particulate- and protein-like matter is important for understanding their contribution to different physiochemical phenomena like membrane fouling, adsorption of bacteria onto surfaces and various applications of nanoparticles in nanomedicine and nanotoxicology. Precise interpretation of the extent of such interactions is however hindered due to the limitations of most characterization methods to allow rapid, sensitive and accurate measurements. Here we report on a fluorescence-based excitation-emission matrix (EEM) approach in combination with principal component analysis (PCA) to extract information related to the interaction between natural colloidal/particulate- and protein-like matter. Surface plasmon resonance (SPR) analysis and fiber-optic probe based surface fluorescence measurements were used to confirm that the proposed approach can be used to characterize colloidal/particulate-protein interactions at the physical level. This method has potential to be a fundamental measurement of these interactions with the advantage that it can be performed rapidly and with high sensitivity. Copyright © 2012 Elsevier B.V. All rights reserved.

Aroma profile and sensory characteristics of a sulfur dioxide-free mulberry (Morus nigra) wine subjected to non-thermal accelerating aging techniques.

PubMed

Tchabo, William; Ma, Yongkun; Kwaw, Emmanuel; Zhang, Haining; Xiao, Lulu; Tahir, Haroon Elrasheid

2017-10-01

The present study was undertaken to assess accelerating aging effects of high pressure, ultrasound and manosonication on the aromatic profile and sensorial attributes of aged mulberry wines (AMW). A total of 166 volatile compounds were found amongst the AMW. The outcomes of the investigation were presented by means of geometric mean (GM), cluster analysis (CA), principal component analysis (PCA), partial least squares regressions (PLSR) and principal component regression (PCR). GM highlighted 24 organoleptic attributes responsible for the sensorial profile of the AMW. Moreover, CA revealed that the volatile composition of the non-thermal accelerated aged wines differs from that of the conventional aged wines. Besides, PCA discriminated the AMW on the basis of their main sensorial characteristics. Furthermore, PLSR identified 75 aroma compounds which were mainly responsible for the olfactory notes of the AMW. Finally, the overall quality of the AMW was noted to be better predicted by PLSR than PCR. Copyright © 2017 Elsevier Ltd. All rights reserved.

Thermal Inspection of a Composite Fuselage Section Using a Fixed Eigenvector Principal Component Analysis Method

NASA Technical Reports Server (NTRS)

Zalameda, Joseph N.; Bolduc, Sean; Harman, Rebecca

2017-01-01

A composite fuselage aircraft forward section was inspected with flash thermography. The fuselage section is 24 feet long and approximately 8 feet in diameter. The structure is primarily configured with a composite sandwich structure of carbon fiber face sheets with a Nomex(Trademark) honeycomb core. The outer surface area was inspected. The thermal data consisted of 477 data sets totaling in size of over 227 Gigabytes. Principal component analysis (PCA) was used to process the data sets for substructure and defect detection. A fixed eigenvector approach using a global covariance matrix was used and compared to a varying eigenvector approach. The fixed eigenvector approach was demonstrated to be a practical analysis method for the detection and interpretation of various defects such as paint thickness variation, possible water intrusion damage, and delamination damage. In addition, inspection considerations are discussed including coordinate system layout, manipulation of the fuselage section, and the manual scanning technique used for full coverage.

Cross-visit tumor sub-segmentation and registration with outlier rejection for dynamic contrast-enhanced MRI time series data.

PubMed

Buonaccorsi, G A; Rose, C J; O'Connor, J P B; Roberts, C; Watson, Y; Jackson, A; Jayson, G C; Parker, G J M

2010-01-01

Clinical trials of anti-angiogenic and vascular-disrupting agents often use biomarkers derived from DCE-MRI, typically reporting whole-tumor summary statistics and so overlooking spatial parameter variations caused by tissue heterogeneity. We present a data-driven segmentation method comprising tracer-kinetic model-driven registration for motion correction, conversion from MR signal intensity to contrast agent concentration for cross-visit normalization, iterative principal components analysis for imputation of missing data and dimensionality reduction, and statistical outlier detection using the minimum covariance determinant to obtain a robust Mahalanobis distance. After applying these techniques we cluster in the principal components space using k-means. We present results from a clinical trial of a VEGF inhibitor, using time-series data selected because of problems due to motion and outlier time series. We obtained spatially-contiguous clusters that map to regions with distinct microvascular characteristics. This methodology has the potential to uncover localized effects in trials using DCE-MRI-based biomarkers.

Characterization of Hatay honeys according to their multi-element analysis using ICP-OES combined with chemometrics.

PubMed

Yücel, Yasin; Sultanoğlu, Pınar

2013-09-01

Chemical characterisation has been carried out on 45 honey samples collected from Hatay region of Turkey. The concentrations of 17 elements were determined by inductively coupled plasma optical emission spectrometry (ICP-OES). Ca, K, Mg and Na were the most abundant elements, with mean contents of 219.38, 446.93, 49.06 and 95.91 mg kg(-1) respectively. The trace element mean contents ranged between 0.03 and 15.07 mg kg(-1). Chemometric methods such as principal component analysis (PCA) and cluster analysis (CA) techniques were applied to classify honey according to mineral content. The first most important principal component (PC) was strongly associated with the value of Al, B, Cd and Co. CA showed eight clusters corresponding to the eight botanical origins of honey. PCA explained 75.69% of the variance with the first six PC variables. Chemometric analysis of the analytical data allowed the accurate classification of the honey samples according to origin. Copyright © 2013 Elsevier Ltd. All rights reserved.

Characterization of organic and conventional sweet basil leaves using chromatographic and flow-injection mass spectrometric (FIMS) fingerprints combined with principal component analysis

PubMed Central

Lu, Yingjian; Gao, Boyan; Chen, Pei; Charles, Denys; Yu, Liangli (Lucy)

2014-01-01

Sweet basil, Ocimum basilicum., is one of the most important and wildly used spices and has been shown to have antioxidant, antibacterial, and anti-diarrheal activities. In this study, high performance liquid chromatographic (HPLC) and flow-injection mass spectrometric (FIMS) fingerprinting techniques were used to differentiate organic and conventional sweet basil leaf samples. Principal component analysis (PCA) of the fingerprints indicated that both HPLC and FIMS fingerprints could effectively detect the chemical differences in the organic and conventional sweet basil leaf samples. This study suggested that the organic basil sample contained greater concentrations of almost all the major compounds than its conventional counterpart on a per same botanical weight basis. The FIMS method was able to rapidly differentiate the organic and conventional sweet basil leaf samples (1 min analysis time), whereas the HPLC fingerprints provided more information about the chemical composition of the basil samples with a longer analytical time. PMID:24518341

Characterisation of organic and conventional sweet basil leaves using chromatographic and flow-injection mass spectrometric (FIMS) fingerprints combined with principal component analysis.

PubMed

Lu, Yingjian; Gao, Boyan; Chen, Pei; Charles, Denys; Yu, Liangli Lucy

2014-07-01

Sweet basil, Ocimum basilicum, is one of the most important and wildly used spices and has been shown to have antioxidant, antibacterial, and anti-diarrheal activities. In this study, high performance liquid chromatographic (HPLC) and flow-injection mass spectrometric (FIMS) fingerprinting techniques were used to differentiate organic and conventional sweet basil leaf samples. Principal component analysis (PCA) of the fingerprints indicated that both HPLC and FIMS fingerprints could effectively detect the chemical differences in the organic and conventional sweet basil leaf samples. This study suggested that the organic basil sample contained greater concentrations of almost all the major compounds than its conventional counterpart on a per same botanical weight basis. The FIMS method was able to rapidly differentiate the organic and conventional sweet basil leaf samples (1min analysis time), whereas the HPLC fingerprints provided more information about the chemical composition of the basil samples with a longer analytical time. Copyright © 2014 Elsevier Ltd. All rights reserved.

Detection and Characterization of Ground Displacement Sources from Variational Bayesian Independent Component Analysis of GPS Time Series

NASA Astrophysics Data System (ADS)

Gualandi, A.; Serpelloni, E.; Belardinelli, M. E.

2014-12-01

A critical point in the analysis of ground displacements time series is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies. Indeed, PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem, i.e. in recovering and separating the original sources that generated the observed data. This is mainly due to the assumptions on which PCA relies: it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here we present the application of the vbICA technique to GPS position time series. First, we use vbICA on synthetic data that simulate a seismic cycle (interseismic + coseismic + postseismic + seasonal + noise), and study the ability of the algorithm to recover the original (known) sources of deformation. Secondly, we apply vbICA to different tectonically active scenarios, such as earthquakes in central and northern Italy, as well as the study of slow slip events in Cascadia.

Geographical and ethnic variability of finger ridge-counts: biplots of male and female Indian samples.

PubMed

Krishnan, T; Reddy, B M

1994-01-01

The graphical technique of biplot due to Gabriel and others is explained, and is applied to ten finger ridge-count means of 239 populations, mostly Indian. The biplots, together with concentration ellipses based on them, are used to study geographical, gender and ethnic/social group variability, to compare Indian populations with other populations and to study relations between individual counts and populations. The correlation structure of ridge-counts exhibits a tripartite division of digits demonstrated by many other studies, but with a somewhat different combination of digits. Comparisons are also made with the results of Leguebe and Vrydagh, who used principal components, discriminant functions, Andrews functions, etc., to study geographical and gender variations. There is a great deal of homogeneity in Indian populations when compared to populations from the rest of the world. Although broad geographical contiguity is reflected in the biplots, local (states within India) level contiguity is not maintained. Monogoloids and Caucasoids have distinct ridge-count structures. The higher level of homogeneity in females and on the left side observed by Leguebe and Vrydagh is also observed in the biplots. A comparison with principal component plots indicates that biplots yield a graphical representation similar to component plots, and convey more information than component plots.

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

NASA Technical Reports Server (NTRS)

Busch, S. E.; Adams, J. S.; Bandler, S. R.; Chervenak, J. A.; Eckart, M. E.; Finkbeiner, F. M.; Fixsen, D. J.; Kelley, R. L.; Kilbourne, C. A.; Lee, S.-J.;

Door detection in images based on learning by components

NASA Astrophysics Data System (ADS)

Cicirelli, Grazia; D'Orazio, Tiziana; Ancona, Nicola

2001-10-01

In this paper we present a vision-based technique for detecting targets of the environment which has to be reached by an autonomous mobile robot during its navigational task. The targets the robot has to reach are the doors of our office building. Color and shape information are used as identifying features for detecting principal components of the door. In fact in images the door can appear of different dimensions depending on the attitude of the robot with respect to the door, therefore detection of the door is performed by detecting its most significant components in the image. Positive and negative examples, in form of image patterns, are manually selected from real images for training two neural classifiers in order to recognize the single components. Each classifier has been realized by a feed-forward neural network with one hidden layer and sigmoid activation function. Moreover for selecting negative examples, relevant for the problem at hand, a bootstrap technique has been used during the training process. Finally the detecting system has been applied to several test real images for evaluating its performance.

In-tube extraction and GC-MS analysis of volatile components from wild and cultivated sea buckthorn (Hippophae rhamnoides L. ssp. Carpatica) berry varieties and juice.

PubMed

Socaci, Sonia A; Socaciu, Carmen; Tofană, Maria; Raţi, Ioan V; Pintea, Adela

2013-01-01

The health benefits of sea buckthorn (Hippophae rhamnoides L.) are well documented due to its rich content in bioactive phytochemicals (pigments, phenolics and vitamins) as well as volatiles responsible for specific flavours and bacteriostatic action. The volatile compounds are good biomarkers of berry freshness, quality and authenticity. To develop a fast and efficient GC-MS method including a minimal sample preparation technique (in-tube extraction, ITEX) for the discrimination of sea buckthorn varieties based on their chromatographic volatile fingerprint. Twelve sea buckthorn varieties (wild and cultivated) were collected from forestry departments and experimental fields, respectively. The extraction of volatile compounds was performed using the ITEX technique whereas separation and identification was performed using a GC-MS QP-2010. Principal component analysis (PCA) was applied to discriminate the differences among sample composition. Using GC-MS analysis, from the headspace of sea buckthorn samples, 46 volatile compounds were separated with 43 being identified. The most abundant derivatives were ethyl esters of 2-methylbutanoic acid, 3-methylbutanoic acid, hexanoic acid, octanoic acid and butanoic acid, as well as 3-methylbutyl 3-methylbutanoate, 3-methylbutyl 2-methylbutanoate and benzoic acid ethyl ester (over 80% of all volatile compounds). Principal component analysis showed that the first two components explain 79% of data variance, demonstrating a good discrimination between samples. A reliable, fast and eco-friendly ITEX/GC-MS method was applied to fingerprint the volatile profile and to discriminate between wild and cultivated sea buckthorn berries originating from the Carpathians, with relevance to food science and technology. Copyright © 2013 John Wiley & Sons, Ltd.

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.

Source Determination of Red Gel Pen Inks using Raman Spectroscopy and Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy combined with Pearson's Product Moment Correlation Coefficients and Principal Component Analysis.

PubMed

Mohamad Asri, Muhammad Naeim; Mat Desa, Wan Nur Syuhaila; Ismail, Dzulkiflee

2018-01-01

The potential combination of two nondestructive techniques, that is, Raman spectroscopy (RS) and attenuated total reflectance-fourier transform infrared (ATR-FTIR) spectroscopy with Pearson's product moment correlation (PPMC) coefficient (r) and principal component analysis (PCA) to determine the actual source of red gel pen ink used to write a simulated threatening note, was examined. Eighteen (18) red gel pens purchased from Japan and Malaysia from November to December 2014 where one of the pens was used to write a simulated threatening note were analyzed using RS and ATR-FTIR spectroscopy, respectively. The spectra of all the red gel pen inks including the ink deposited on the simulated threatening note gathered from the RS and ATR-FTIR analyses were subjected to PPMC coefficient (r) calculation and principal component analysis (PCA). The coefficients r = 0.9985 and r = 0.9912 for pairwise combination of RS and ATR-FTIR spectra respectively and similarities in terms of PC1 and PC2 scores of one of the inks to the ink deposited on the simulated threatening note substantiated the feasibility of combining RS and ATR-FTIR spectroscopy with PPMC coefficient (r) and PCA for successful source determination of red gel pen inks. The development of pigment spectral library had allowed the ink deposited on the threatening note to be identified as XSL Poppy Red (CI Pigment Red 112). © 2017 American Academy of Forensic Sciences.

An Analysis of the Perceptions and Resources of Large University Classes

PubMed Central

Cash, Ceilidh Barlow; Letargo, Jessa; Graether, Steffen P.; Jacobs, Shoshanah R.

2017-01-01

Large class learning is a reality that is not exclusive to the first-year experience at midsized, comprehensive universities; upper-year courses have similarly high enrollment, with many class sizes greater than 200 students. Research into the efficacy and deficiencies of large undergraduate classes has been ongoing for more than 100 years, with most research associating large classes with weak student engagement, decreased depth of learning, and ineffective interactions. This study used a multidimensional research approach to survey student and instructor perceptions of large biology classes and to characterize the courses offered by a department according to resources and course structure using a categorical principal components analysis. Both student and instructor survey results indicated that a large class begins around 240 students. Large classes were identified as impersonal and classified using extrinsic qualifiers; however, students did identify techniques that made the classes feel smaller. In addition to the qualitative survey, we also attempted to quantify courses by collecting data from course outlines and analyzed the data using categorical principal component analysis. The analysis maps institutional change in resource allocation and teaching structure from 2010 through 2014 and validates the use of categorical principal components analysis in educational research. We examine what perceptions and factors are involved in a large class that is perceived to feel small. Our analysis suggests that it is not the addition of resources or difference in the lecturing method, but it is the instructor that determines whether a large class can feel small. PMID:28495937

A Network View on Psychiatric Disorders: Network Clusters of Symptoms as Elementary Syndromes of Psychopathology

PubMed Central

Goekoop, Rutger; Goekoop, Jaap G.

2014-01-01

Introduction The vast number of psychopathological syndromes that can be observed in clinical practice can be described in terms of a limited number of elementary syndromes that are differentially expressed. Previous attempts to identify elementary syndromes have shown limitations that have slowed progress in the taxonomy of psychiatric disorders. Aim To examine the ability of network community detection (NCD) to identify elementary syndromes of psychopathology and move beyond the limitations of current classification methods in psychiatry. Methods 192 patients with unselected mental disorders were tested on the Comprehensive Psychopathological Rating Scale (CPRS). Principal component analysis (PCA) was performed on the bootstrapped correlation matrix of symptom scores to extract the principal component structure (PCS). An undirected and weighted network graph was constructed from the same matrix. Network community structure (NCS) was optimized using a previously published technique. Results In the optimal network structure, network clusters showed a 89% match with principal components of psychopathology. Some 6 network clusters were found, including "DEPRESSION", "MANIA", “ANXIETY”, "PSYCHOSIS", "RETARDATION", and "BEHAVIORAL DISORGANIZATION". Network metrics were used to quantify the continuities between the elementary syndromes. Conclusion We present the first comprehensive network graph of psychopathology that is free from the biases of previous classifications: a ‘Psychopathology Web’. Clusters within this network represent elementary syndromes that are connected via a limited number of bridge symptoms. Many problems of previous classifications can be overcome by using a network approach to psychopathology. PMID:25427156

Effect of different drying techniques on bioactive components, fatty acid composition, and volatile profile of robusta coffee beans.

PubMed

Dong, Wenjiang; Hu, Rongsuo; Chu, Zhong; Zhao, Jianping; Tan, Lehe

2017-11-01

This study investigated the effect of different drying techniques, namely, room-temperature drying (RTD), solar drying (SD), heat-pump drying (HPD), hot-air drying (HAD), and freeze drying (FD), on bioactive components, fatty acid composition, and the volatile compound profile of robusta coffee beans. The data showed that FD was an effective method to preserve fat, organic acids, and monounsaturated fatty acids. In contrast, HAD was ideal for retaining polyunsaturated fatty acids and amino acids. Sixty-two volatile compounds were identified in the differently dried coffee beans, representing 90% of the volatile compounds. HPD of the coffee beans produced the largest number of volatiles, whereas FD resulted in the highest volatile content. A principal component analysis demonstrated a close relationship between the HPD, SD, and RTD methods whereas the FD and HAD methods were significantly different. Overall, the results provide a basis for potential application to other similar thermal sensitive materials. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Influence Function of Principal Component Analysis by Self-Organizing Rule.

PubMed

Higuchi; Eguchi

1998-07-28

This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.

Metabolomic evaluation of ginsenosides distribution in Panax genus (Panax ginseng and Panax quinquefolius) using multivariate statistical analysis.

PubMed

Pace, Roberto; Martinelli, Ernesto Marco; Sardone, Nicola; D E Combarieu, Eric

2015-03-01

Ginseng is any one of the eleven species belonging to the genus Panax of the family Araliaceae and is found in North America and in eastern Asia. Ginseng is characterized by the presence of ginsenosides. Principally Panax ginseng and Panax quinquefolius are the adaptogenic herbs and are commonly distributed as health food markets. In the present study high performance liquid chromatography has been used to identify and quantify ginsenosides in the two subject species and the different parts of the plant (roots, neck, leaves, flowers, fruits). The power of this chromatographic technique to evaluate the identity of botanical material and to distinguishing different part of the plants has been investigated with metabolomic technique such as principal component analysis. Metabolomics provide a good opportunity for mining useful chemical information from the chromatographic data set resulting an important tool for quality evaluation of medicinal plants in the authenticity, consistency and efficacy. Copyright © 2015 Elsevier B.V. All rights reserved.

High-dimensional inference with the generalized Hopfield model: principal component analysis and corrections.

PubMed

Cocco, S; Monasson, R; Sessak, V

2011-05-01

We consider the problem of inferring the interactions between a set of N binary variables from the knowledge of their frequencies and pairwise correlations. The inference framework is based on the Hopfield model, a special case of the Ising model where the interaction matrix is defined through a set of patterns in the variable space, and is of rank much smaller than N. We show that maximum likelihood inference is deeply related to principal component analysis when the amplitude of the pattern components ξ is negligible compared to √N. Using techniques from statistical mechanics, we calculate the corrections to the patterns to the first order in ξ/√N. We stress the need to generalize the Hopfield model and include both attractive and repulsive patterns in order to correctly infer networks with sparse and strong interactions. We present a simple geometrical criterion to decide how many attractive and repulsive patterns should be considered as a function of the sampling noise. We moreover discuss how many sampled configurations are required for a good inference, as a function of the system size N and of the amplitude ξ. The inference approach is illustrated on synthetic and biological data.

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.

Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous).

PubMed

Hemmateenejad, Bahram; Akhond, Morteza; Miri, Ramin; Shamsipur, Mojtaba

2003-01-01

A QSAR algorithm, principal component-genetic algorithm-artificial neural network (PC-GA-ANN), has been applied to a set of newly synthesized calcium channel blockers, which are of special interest because of their role in cardiac diseases. A data set of 124 1,4-dihydropyridines bearing different ester substituents at the C-3 and C-5 positions of the dihydropyridine ring and nitroimidazolyl, phenylimidazolyl, and methylsulfonylimidazolyl groups at the C-4 position with known Ca(2+) channel binding affinities was employed in this study. Ten different sets of descriptors (837 descriptors) were calculated for each molecule. The principal component analysis was used to compress the descriptor groups into principal components. The most significant descriptors of each set were selected and used as input for the ANN. The genetic algorithm (GA) was used for the selection of the best set of extracted principal components. A feed forward artificial neural network with a back-propagation of error algorithm was used to process the nonlinear relationship between the selected principal components and biological activity of the dihydropyridines. A comparison between PC-GA-ANN and routine PC-ANN shows that the first model yields better prediction ability.

Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data.

PubMed

Salvatore, Stefania; Bramness, Jørgen G; Røislien, Jo

2016-07-12

Wastewater-based epidemiology (WBE) is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA) as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA) and to wavelet principal component analysis (WPCA) which is more flexible temporally. We analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal features of ecstasy (MDMA) were extracted using FPCA using both Fourier and B-spline basis functions with three different smoothing parameters, along with PCA and WPCA with different mother wavelets and shrinkage rules. The stability of FPCA was explored through bootstrapping and analysis of sensitivity to missing data. The first three principal components (PCs), functional principal components (FPCs) and wavelet principal components (WPCs) explained 87.5-99.6 % of the temporal variation between cities, depending on the choice of basis and smoothing. The extracted temporal features from PCA, FPCA and WPCA were consistent. FPCA using Fourier basis and common-optimal smoothing was the most stable and least sensitive to missing data. FPCA is a flexible and analytically tractable method for analysing temporal changes in wastewater data, and is robust to missing data. WPCA did not reveal any rapid temporal changes in the data not captured by FPCA. Overall the results suggest FPCA with Fourier basis functions and common-optimal smoothing parameter as the most accurate approach when analysing WBE data.

Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

NASA Astrophysics Data System (ADS)

Sierra-Pérez, Julián; Torres-Arredondo, M.-A.; Alvarez-Montoya, Joham

2018-01-01

Structural health monitoring consists of using sensors integrated within structures together with algorithms to perform load monitoring, damage detection, damage location, damage size and severity, and prognosis. One possibility is to use strain sensors to infer structural integrity by comparing patterns in the strain field between the pristine and damaged conditions. In previous works, the authors have demonstrated that it is possible to detect small defects based on strain field pattern recognition by using robust machine learning techniques. They have focused on methodologies based on principal component analysis (PCA) and on the development of several unfolding and standardization techniques, which allow dealing with multiple load conditions. However, before a real implementation of this approach in engineering structures, changes in the strain field due to conditions different from damage occurrence need to be isolated. Since load conditions may vary in most engineering structures and promote significant changes in the strain field, it is necessary to implement novel techniques for uncoupling such changes from those produced by damage occurrence. A damage detection methodology based on optimal baseline selection (OBS) by means of clustering techniques is presented. The methodology includes the use of hierarchical nonlinear PCA as a nonlinear modeling technique in conjunction with Q and nonlinear-T 2 damage indices. The methodology is experimentally validated using strain measurements obtained by 32 fiber Bragg grating sensors bonded to an aluminum beam under dynamic bending loads and simultaneously submitted to variations in its pitch angle. The results demonstrated the capability of the methodology for clustering data according to 13 different load conditions (pitch angles), performing the OBS and detecting six different damages induced in a cumulative way. The proposed methodology showed a true positive rate of 100% and a false positive rate of 1.28% for a 99% of confidence.

Classification Techniques for Multivariate Data Analysis.

DTIC Science & Technology

1980-03-28

analysis among biologists, botanists, and ecologists, while some social scientists may refer "typology". Other frequently encountered terms are pattern...the determinantal equation: lB -XW 0 (42) 49 The solutions X. are the eigenvalues of the matrix W-1 B 1 as in discriminant analysis. There are t non...Statistical Package for Social Sciences (SPSS) (14) subprogram FACTOR was used for the principal components analysis. It is designed both for the factor

Quality improvement of diagnosis of the electromyography data based on statistical characteristics of the measured signals

NASA Astrophysics Data System (ADS)

Selivanova, Karina G.; Avrunin, Oleg G.; Zlepko, Sergii M.; Romanyuk, Sergii O.; Zabolotna, Natalia I.; Kotyra, Andrzej; Komada, Paweł; Smailova, Saule

2016-09-01

Research and systematization of motor disorders, taking into account the clinical and neurophysiologic phenomena, are important and actual problem of neurology. The article describes a technique for decomposing surface electromyography (EMG), using Principal Component Analysis. The decomposition is achieved by a set of algorithms that uses a specially developed for analyze EMG. The accuracy was verified by calculation of Mahalanobis distance and Probability error.

40 CFR 62.14505 - What are the principal components of this subpart?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 8 2010-07-01 2010-07-01 false What are the principal components of this subpart? 62.14505 Section 62.14505 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... components of this subpart? This subpart contains the eleven major components listed in paragraphs (a...

Bayesian wavelet PCA methodology for turbomachinery damage diagnosis under uncertainty

NASA Astrophysics Data System (ADS)

Xu, Shengli; Jiang, Xiaomo; Huang, Jinzhi; Yang, Shuhua; Wang, Xiaofang

2016-12-01

Centrifugal compressor often suffers various defects such as impeller cracking, resulting in forced outage of the total plant. Damage diagnostics and condition monitoring of such a turbomachinery system has become an increasingly important and powerful tool to prevent potential failure in components and reduce unplanned forced outage and further maintenance costs, while improving reliability, availability and maintainability of a turbomachinery system. This paper presents a probabilistic signal processing methodology for damage diagnostics using multiple time history data collected from different locations of a turbomachine, considering data uncertainty and multivariate correlation. The proposed methodology is based on the integration of three advanced state-of-the-art data mining techniques: discrete wavelet packet transform, Bayesian hypothesis testing, and probabilistic principal component analysis. The multiresolution wavelet analysis approach is employed to decompose a time series signal into different levels of wavelet coefficients. These coefficients represent multiple time-frequency resolutions of a signal. Bayesian hypothesis testing is then applied to each level of wavelet coefficient to remove possible imperfections. The ratio of posterior odds Bayesian approach provides a direct means to assess whether there is imperfection in the decomposed coefficients, thus avoiding over-denoising. Power spectral density estimated by the Welch method is utilized to evaluate the effectiveness of Bayesian wavelet cleansing method. Furthermore, the probabilistic principal component analysis approach is developed to reduce dimensionality of multiple time series and to address multivariate correlation and data uncertainty for damage diagnostics. The proposed methodology and generalized framework is demonstrated with a set of sensor data collected from a real-world centrifugal compressor with impeller cracks, through both time series and contour analyses of vibration signal and principal components.

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

PubMed Central

2015-01-01

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications. PMID:26267377

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

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

Hierarchical Regularity in Multi-Basin Dynamics on Protein Landscapes

NASA Astrophysics Data System (ADS)

Matsunaga, Yasuhiro; Kostov, Konstatin S.; Komatsuzaki, Tamiki

2004-04-01

We analyze time series of potential energy fluctuations and principal components at several temperatures for two kinds of off-lattice 46-bead models that have two distinctive energy landscapes. The less-frustrated "funnel" energy landscape brings about stronger nonstationary behavior of the potential energy fluctuations at the folding temperature than the other, rather frustrated energy landscape at the collapse temperature. By combining principal component analysis with an embedding nonlinear time-series analysis, it is shown that the fast fluctuations with small amplitudes of 70-80% of the principal components cause the time series to become almost "random" in only 100 simulation steps. However, the stochastic feature of the principal components tends to be suppressed through a wide range of degrees of freedom at the transition temperature.

Exploring the CAESAR database using dimensionality reduction techniques

NASA Astrophysics Data System (ADS)

Mendoza-Schrock, Olga; Raymer, Michael L.

2012-06-01

The Civilian American and European Surface Anthropometry Resource (CAESAR) database containing over 40 anthropometric measurements on over 4000 humans has been extensively explored for pattern recognition and classification purposes using the raw, original data [1-4]. However, some of the anthropometric variables would be impossible to collect in an uncontrolled environment. Here, we explore the use of dimensionality reduction methods in concert with a variety of classification algorithms for gender classification using only those variables that are readily observable in an uncontrolled environment. Several dimensionality reduction techniques are employed to learn the underlining structure of the data. These techniques include linear projections such as the classical Principal Components Analysis (PCA) and non-linear (manifold learning) techniques, such as Diffusion Maps and the Isomap technique. This paper briefly describes all three techniques, and compares three different classifiers, Naïve Bayes, Adaboost, and Support Vector Machines (SVM), for gender classification in conjunction with each of these three dimensionality reduction approaches.

Principals' Perceptions Regarding Their Supervision and Evaluation

ERIC Educational Resources Information Center

Hvidston, David J.; Range, Bret G.; McKim, Courtney Ann

2015-01-01

This study examined the perceptions of principals concerning principal evaluation and supervisory feedback. Principals were asked two open-ended questions. Respondents included 82 principals in the Rocky Mountain region. The emerging themes were "Superintendent Performance," "Principal Evaluation Components," "Specific…

Conformational states and folding pathways of peptides revealed by principal-independent component analyses.

PubMed

Nguyen, Phuong H

2007-05-15

Principal component analysis is a powerful method for projecting multidimensional conformational space of peptides or proteins onto lower dimensional subspaces in which the main conformations are present, making it easier to reveal the structures of molecules from e.g. molecular dynamics simulation trajectories. However, the identification of all conformational states is still difficult if the subspaces consist of more than two dimensions. This is mainly due to the fact that the principal components are not independent with each other, and states in the subspaces cannot be visualized. In this work, we propose a simple and fast scheme that allows one to obtain all conformational states in the subspaces. The basic idea is that instead of directly identifying the states in the subspace spanned by principal components, we first transform this subspace into another subspace formed by components that are independent of one other. These independent components are obtained from the principal components by employing the independent component analysis method. Because of independence between components, all states in this new subspace are defined as all possible combinations of the states obtained from each single independent component. This makes the conformational analysis much simpler. We test the performance of the method by analyzing the conformations of the glycine tripeptide and the alanine hexapeptide. The analyses show that our method is simple and quickly reveal all conformational states in the subspaces. The folding pathways between the identified states of the alanine hexapeptide are analyzed and discussed in some detail. 2007 Wiley-Liss, Inc.

Coarse-to-fine markerless gait analysis based on PCA and Gauss-Laguerre decomposition

NASA Astrophysics Data System (ADS)

Goffredo, Michela; Schmid, Maurizio; Conforto, Silvia; Carli, Marco; Neri, Alessandro; D'Alessio, Tommaso

2005-04-01

Human movement analysis is generally performed through the utilization of marker-based systems, which allow reconstructing, with high levels of accuracy, the trajectories of markers allocated on specific points of the human body. Marker based systems, however, show some drawbacks that can be overcome by the use of video systems applying markerless techniques. In this paper, a specifically designed computer vision technique for the detection and tracking of relevant body points is presented. It is based on the Gauss-Laguerre Decomposition, and a Principal Component Analysis Technique (PCA) is used to circumscribe the region of interest. Results obtained on both synthetic and experimental tests provide significant reduction of the computational costs, with no significant reduction of the tracking accuracy.

TOF-SIMS imaging technique with information entropy

NASA Astrophysics Data System (ADS)

Aoyagi, Satoka; Kawashima, Y.; Kudo, Masahiro

2005-05-01

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is capable of chemical imaging of proteins on insulated samples in principal. However, selection of specific peaks related to a particular protein, which are necessary for chemical imaging, out of numerous candidates had been difficult without an appropriate spectrum analysis technique. Therefore multivariate analysis techniques, such as principal component analysis (PCA), and analysis with mutual information defined by information theory, have been applied to interpret SIMS spectra of protein samples. In this study mutual information was applied to select specific peaks related to proteins in order to obtain chemical images. Proteins on insulated materials were measured with TOF-SIMS and then SIMS spectra were analyzed by means of the analysis method based on the comparison using mutual information. Chemical mapping of each protein was obtained using specific peaks related to each protein selected based on values of mutual information. The results of TOF-SIMS images of proteins on the materials provide some useful information on properties of protein adsorption, optimality of immobilization processes and reaction between proteins. Thus chemical images of proteins by TOF-SIMS contribute to understand interactions between material surfaces and proteins and to develop sophisticated biomaterials.

[Assessment of the strength of tobacco control on creating smoke-free hospitals using principal components analysis].

PubMed

Liu, Hui-lin; Wan, Xia; Yang, Gong-huan

2013-02-01

To explore the relationship between the strength of tobacco control and the effectiveness of creating smoke-free hospital, and summarize the main factors that affect the program of creating smoke-free hospitals. A total of 210 hospitals from 7 provinces/municipalities directly under the central government were enrolled in this study using stratified random sampling method. Principle component analysis and regression analysis were conducted to analyze the strength of tobacco control and the effectiveness of creating smoke-free hospitals. Two principal components were extracted in the strength of tobacco control index, which respectively reflected the tobacco control policies and efforts, and the willingness and leadership of hospital managers regarding tobacco control. The regression analysis indicated that only the first principal component was significantly correlated with the progression in creating smoke-free hospital (P<0.001), i.e. hospitals with higher scores on the first principal component had better achievements in smoke-free environment creation. Tobacco control policies and efforts are critical in creating smoke-free hospitals. The principal component analysis provides a comprehensive and objective tool for evaluating the creation of smoke-free hospitals.

Critical Factors Explaining the Leadership Performance of High-Performing Principals

ERIC Educational Resources Information Center

Hutton, Disraeli M.

2018-01-01

The study explored critical factors that explain leadership performance of high-performing principals and examined the relationship between these factors based on the ratings of school constituents in the public school system. The principal component analysis with the use of Varimax Rotation revealed that four components explain 51.1% of the…

Molecular dynamics in principal component space.

PubMed

Michielssens, Servaas; van Erp, Titus S; Kutzner, Carsten; Ceulemans, Arnout; de Groot, Bert L

2012-07-26

A molecular dynamics algorithm in principal component space is presented. It is demonstrated that sampling can be improved without changing the ensemble by assigning masses to the principal components proportional to the inverse square root of the eigenvalues. The setup of the simulation requires no prior knowledge of the system; a short initial MD simulation to extract the eigenvectors and eigenvalues suffices. Independent measures indicated a 6-7 times faster sampling compared to a regular molecular dynamics simulation.

Optimized principal component analysis on coronagraphic images of the fomalhaut system

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

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 themore » 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.« less

The new ATLAS Fast Calorimeter Simulation

NASA Astrophysics Data System (ADS)

Schaarschmidt, J.; ATLAS Collaboration

2017-10-01

Current and future need for large scale simulated samples motivate the development of reliable fast simulation techniques. The new Fast Calorimeter Simulation is an improved parameterized response of single particles in the ATLAS calorimeter that aims to accurately emulate the key features of the detailed calorimeter response as simulated with Geant4, yet approximately ten times faster. Principal component analysis and machine learning techniques are used to improve the performance and decrease the memory need compared to the current version of the ATLAS Fast Calorimeter Simulation. A prototype of this new Fast Calorimeter Simulation is in development and its integration into the ATLAS simulation infrastructure is ongoing.

Arsenic distribution and valence state variation studied by fast hierarchical length-scale morphological, compositional, and speciation imaging at the Nanoscopium, Synchrotron Soleil

NASA Astrophysics Data System (ADS)

Somogyi, Andrea; Medjoubi, Kadda; Sancho-Tomas, Maria; Visscher, P. T.; Baranton, Gil; Philippot, Pascal

2017-09-01

The understanding of real complex geological, environmental and geo-biological processes depends increasingly on in-depth non-invasive study of chemical composition and morphology. In this paper we used scanning hard X-ray nanoprobe techniques in order to study the elemental composition, morphology and As speciation in complex highly heterogeneous geological samples. Multivariate statistical analytical techniques, such as principal component analysis and clustering were used for data interpretation. These measurements revealed the quantitative and valance state inhomogeneity of As and its relation to the total compositional and morphological variation of the sample at sub-μm scales.

Face Hallucination with Linear Regression Model in Semi-Orthogonal Multilinear PCA Method

NASA Astrophysics Data System (ADS)

Asavaskulkiet, Krissada

2018-04-01

In this paper, we propose a new face hallucination technique, face images reconstruction in HSV color space with a semi-orthogonal multilinear principal component analysis method. This novel hallucination technique can perform directly from tensors via tensor-to-vector projection by imposing the orthogonality constraint in only one mode. In our experiments, we use facial images from FERET database to test our hallucination approach which is demonstrated by extensive experiments with high-quality hallucinated color faces. The experimental results assure clearly demonstrated that we can generate photorealistic color face images by using the SO-MPCA subspace with a linear regression model.

[A study of Boletus bicolor from different areas using Fourier transform infrared spectrometry].

PubMed

Zhou, Zai-Jin; Liu, Gang; Ren, Xian-Pei

2010-04-01

It is hard to differentiate the same species of wild growing mushrooms from different areas by macromorphological features. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis was used to identify 58 samples of boletus bicolor from five different areas. Based on the fingerprint infrared spectrum of boletus bicolor samples, principal component analysis was conducted on 58 boletus bicolor spectra in the range of 1 350-750 cm(-1) using the statistical software SPSS 13.0. According to the result, the accumulated contributing ratio of the first three principal components accounts for 88.87%. They included almost all the information of samples. The two-dimensional projection plot using first and second principal component is a satisfactory clustering effect for the classification and discrimination of boletus bicolor. All boletus bicolor samples were divided into five groups with a classification accuracy of 98.3%. The study demonstrated that wild growing boletus bicolor at species level from different areas can be identified by FTIR spectra combined with principal components analysis.

Chemometric and multivariate statistical analysis of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulfides.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2012-03-20

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of mineral samples are complex, comprised of large mass ranges and many peaks. Consequently, characterization and classification analysis of these systems is challenging. In this study, different chemometric and statistical data evaluation methods, based on monolayer sensitive TOF-SIMS data, have been tested for the characterization and classification of copper-iron sulfide minerals (chalcopyrite, chalcocite, bornite, and pyrite) at different flotation pulp conditions (feed, conditioned feed, and Eh modified). The complex mass spectral data sets were analyzed using the following chemometric and statistical techniques: principal component analysis (PCA); principal component-discriminant functional analysis (PC-DFA); soft independent modeling of class analogy (SIMCA); and k-Nearest Neighbor (k-NN) classification. PCA was found to be an important first step in multivariate analysis, providing insight into both the relative grouping of samples and the elemental/molecular basis for those groupings. For samples exposed to oxidative conditions (at Eh ~430 mV), each technique (PCA, PC-DFA, SIMCA, and k-NN) was found to produce excellent classification. For samples at reductive conditions (at Eh ~ -200 mV SHE), k-NN and SIMCA produced the most accurate classification. Phase identification of particles that contain the same elements but a different crystal structure in a mixed multimetal mineral system has been achieved.

Initial proposition of kinematics model for selected karate actions analysis

NASA Astrophysics Data System (ADS)

Hachaj, Tomasz; Koptyra, Katarzyna; Ogiela, Marek R.

2017-03-01

The motivation for this paper is to initially propose and evaluate two new kinematics models that were developed to describe motion capture (MoCap) data of karate techniques. We decided to develop this novel proposition to create the model that is capable to handle actions description both from multimedia and professional MoCap hardware. For the evaluation purpose we have used 25-joints data with karate techniques recordings acquired with Kinect version 2. It is consisted of MoCap recordings of two professional sport (black belt) instructors and masters of Oyama Karate. We have selected following actions for initial analysis: left-handed furi-uchi punch, right leg hiza-geri kick, right leg yoko-geri kick and left-handed jodan-uke block. Basing on evaluation we made we can conclude that both proposed kinematics models seems to be convenient method for karate actions description. From two proposed variables models it seems that global might be more useful for further usage. We think that because in case of considered punches variables seems to be less correlated and they might also be easier to interpret because of single reference coordinate system. Also principal components analysis proved to be reliable way to examine the quality of kinematics models and with the plot of the variable in principal components space we can nicely present the dependences between variables.

Cocoa bean quality assessment by using hyperspectral images and fuzzy logic techniques

NASA Astrophysics Data System (ADS)

Soto, Juan; Granda, Guillermo; Prieto, Flavio; Ipanaque, William; Machacuay, Jorge

2015-04-01

Nowadays, cocoa bean exportation from Piura-Peru is having a positive international market response due to their inherent high quality. Nevertheless, when using subjective techniques for quality assessment, such as the cut test, a wastefulness of grains is generated, additional to a restriction in the selection as well as improvement approaches in earlier stages for optimizing the quality. Thus, in an attempt to standardize the internal features analyzed by the cut test, for instance, crack formation and internal color changes during the fermentation, this research is submitted as an approach which aims to make use of hyperspectral images, with the purpose of having a quick and accurate analysis. Hyperspectral cube size was reduced by using Principal Component Analysis (PCA). The image generated by principal component PC1 provides enough information to clearly distinguish the internal cracks of the cocoa bean, since the zones where these cracks are, have a negative correlation with PC1. The features taken were processed through a fuzzy block, which is able to describe the cocoa bean quality. Three membership functions were defined in the output: unfermented, partly fermented and well fermented, by using trapezoidal-shaped and triangular-shaped functions. A total of twelve rules were propounded. Furthermore, the bisector method was chosen for the defuzzification. Begin the abstract two lines below author names and addresses.

A robust damage-detection technique with environmental variability combining time-series models with principal components

NASA Astrophysics Data System (ADS)

Lakshmi, K.; Rama Mohan Rao, A.

2014-10-01

In this paper, a novel output-only damage-detection technique based on time-series models for structural health monitoring in the presence of environmental variability and measurement noise is presented. The large amount of data obtained in the form of time-history response is transformed using principal component analysis, in order to reduce the data size and thereby improve the computational efficiency of the proposed algorithm. The time instant of damage is obtained by fitting the acceleration time-history data from the structure using autoregressive (AR) and AR with exogenous inputs time-series prediction models. The probability density functions (PDFs) of damage features obtained from the variances of prediction errors corresponding to references and healthy current data are found to be shifting from each other due to the presence of various uncertainties such as environmental variability and measurement noise. Control limits using novelty index are obtained using the distances of the peaks of the PDF curves in healthy condition and used later for determining the current condition of the structure. Numerical simulation studies have been carried out using a simply supported beam and also validated using an experimental benchmark data corresponding to a three-storey-framed bookshelf structure proposed by Los Alamos National Laboratory. Studies carried out in this paper clearly indicate the efficiency of the proposed algorithm for damage detection in the presence of measurement noise and environmental variability.

[Study of near infrared spectral preprocessing and wavelength selection methods for endometrial cancer tissue].

PubMed

Zhao, Li-Ting; Xiang, Yu-Hong; Dai, Yin-Mei; Zhang, Zhuo-Yong

2010-04-01

Near infrared spectroscopy was applied to measure the tissue slice of endometrial tissues for collecting the spectra. A total of 154 spectra were obtained from 154 samples. The number of normal, hyperplasia, and malignant samples was 36, 60, and 58, respectively. Original near infrared spectra are composed of many variables, for example, interference information including instrument errors and physical effects such as particle size and light scatter. In order to reduce these influences, original spectra data should be performed with different spectral preprocessing methods to compress variables and extract useful information. So the methods of spectral preprocessing and wavelength selection have played an important role in near infrared spectroscopy technique. In the present paper the raw spectra were processed using various preprocessing methods including first derivative, multiplication scatter correction, Savitzky-Golay first derivative algorithm, standard normal variate, smoothing, and moving-window median. Standard deviation was used to select the optimal spectral region of 4 000-6 000 cm(-1). Then principal component analysis was used for classification. Principal component analysis results showed that three types of samples could be discriminated completely and the accuracy almost achieved 100%. This study demonstrated that near infrared spectroscopy technology and chemometrics method could be a fast, efficient, and novel means to diagnose cancer. The proposed methods would be a promising and significant diagnosis technique of early stage cancer.

Principal component analysis acceleration of rovibrational coarse-grain models for internal energy excitation and dissociation

NASA Astrophysics Data System (ADS)

Bellemans, Aurélie; Parente, Alessandro; Magin, Thierry

2018-04-01

The present work introduces a novel approach for obtaining reduced chemistry representations of large kinetic mechanisms in strong non-equilibrium conditions. The need for accurate reduced-order models arises from compression of large ab initio quantum chemistry databases for their use in fluid codes. The method presented in this paper builds on existing physics-based strategies and proposes a new approach based on the combination of a simple coarse grain model with Principal Component Analysis (PCA). The internal energy levels of the chemical species are regrouped in distinct energy groups with a uniform lumping technique. Following the philosophy of machine learning, PCA is applied on the training data provided by the coarse grain model to find an optimally reduced representation of the full kinetic mechanism. Compared to recently published complex lumping strategies, no expert judgment is required before the application of PCA. In this work, we will demonstrate the benefits of the combined approach, stressing its simplicity, reliability, and accuracy. The technique is demonstrated by reducing the complex quantum N2(g+1Σ) -N(S4u ) database for studying molecular dissociation and excitation in strong non-equilibrium. Starting from detailed kinetics, an accurate reduced model is developed and used to study non-equilibrium properties of the N2(g+1Σ) -N(S4u ) system in shock relaxation simulations.

Fractal analysis of scatter imaging signatures to distinguish breast pathologies

NASA Astrophysics Data System (ADS)

Eguizabal, Alma; Laughney, Ashley M.; Krishnaswamy, Venkataramanan; Wells, Wendy A.; Paulsen, Keith D.; Pogue, Brian W.; López-Higuera, José M.; Conde, Olga M.

2013-02-01

Fractal analysis combined with a label-free scattering technique is proposed for describing the pathological architecture of tumors. Clinicians and pathologists are conventionally trained to classify abnormal features such as structural irregularities or high indices of mitosis. The potential of fractal analysis lies in the fact of being a morphometric measure of the irregular structures providing a measure of the object's complexity and self-similarity. As cancer is characterized by disorder and irregularity in tissues, this measure could be related to tumor growth. Fractal analysis has been probed in the understanding of the tumor vasculature network. This work addresses the feasibility of applying fractal analysis to the scattering power map (as a physical modeling) and principal components (as a statistical modeling) provided by a localized reflectance spectroscopic system. Disorder, irregularity and cell size variation in tissue samples is translated into the scattering power and principal components magnitude and its fractal dimension is correlated with the pathologist assessment of the samples. The fractal dimension is computed applying the box-counting technique. Results show that fractal analysis of ex-vivo fresh tissue samples exhibits separated ranges of fractal dimension that could help classifier combining the fractal results with other morphological features. This contrast trend would help in the discrimination of tissues in the intraoperative context and may serve as a useful adjunct to surgeons.

Structure of the principal olfactory tract.

PubMed

Gil-Carcedo, L M; Vallejo, L A; Gil-Carcedo, E

2000-01-01

Although the purpose and importance of the sense of smell in human beings has not been totally clarified, it is one of the principal information channels in macrosmatic animals. It was the first long-distance information system to have appeared in phylogenetic evolution. The objective of this article is to deepen the knowledge of the pathways that join the olfactory epithelium with the cortical olfaction areas, to better understand olfactory dysfunction in human beings. Differential staining and marking techniques were applied to histologic sections obtained from 155 animals of different species, to study the different connections existing among olfactory tract components. Our study of the connections between the olfactory mucosa and the principal olfactory bulb deserves special mention. The distribution of second neuron connections of the olfactory tract with the central nervous system is quite complex and diffuse. This indicates an interrelation between the sense of smell and a multitude of functions. These connections seem to be of different quantitative importance according to species, but qualitatively they exist in both human beings and other macrosmatic animals.

Local Prediction Models on Mid-Atlantic Ridge MORB by Principal Component Regression

NASA Astrophysics Data System (ADS)

Ling, X.; Snow, J. E.; Chin, W.

2017-12-01

The isotopic compositions of the daughter isotopes of long-lived radioactive systems (Sr, Nd, Hf and Pb ) can be used to map the scale and history of mantle heterogeneities beneath mid-ocean ridges. Our goal is to relate the multidimensional structure in the existing isotopic dataset with an underlying physical reality of mantle sources. The numerical technique of Principal Component Analysis is useful to reduce the linear dependence of the data to a minimum set of orthogonal eigenvectors encapsulating the information contained (cf Agranier et al 2005). The dataset used for this study covers almost all the MORBs along mid-Atlantic Ridge (MAR), from 54oS to 77oN and 8.8oW to -46.7oW, including replicating the dataset of Agranier et al., 2005 published plus 53 basalt samples dredged and analyzed since then (data from PetDB). The principal components PC1 and PC2 account for 61.56% and 29.21%, respectively, of the total isotope ratios variability. The samples with similar compositions to HIMU and EM and DM are identified to better understand the PCs. PC1 and PC2 are accountable for HIMU and EM whereas PC2 has limited control over the DM source. PC3 is more strongly controlled by the depleted mantle source than PC2. What this means is that all three principal components have a high degree of significance relevant to the established mantle sources. We also tested the relationship between mantle heterogeneity and sample locality. K-means clustering algorithm is a type of unsupervised learning to find groups in the data based on feature similarity. The PC factor scores of each sample are clustered into three groups. Cluster one and three are alternating on the north and south MAR. Cluster two exhibits on 45.18oN to 0.79oN and -27.9oW to -30.40oW alternating with cluster one. The ridge has been preliminarily divided into 16 sections considering both the clusters and ridge segments. The principal component regression models the section based on 6 isotope ratios and PCs. The prediction residual is about 1-2km. It means that the combined 5 isotopes are a strong predictor of geographic location along the ridge, a slightly surprising result. PCR is a robust and powerful method for both visualizing and manipulating the multidimensional representation of isotope data.

PCA based clustering for brain tumor segmentation of T1w MRI images.

PubMed

Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay

2017-03-01

Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

How multi segmental patterns deviate in spastic diplegia from typical developed.

PubMed

Zago, Matteo; Sforza, Chiarella; Bona, Alessia; Cimolin, Veronica; Costici, Pier Francesco; Condoluci, Claudia; Galli, Manuela

2017-10-01

The relationship between gait features and coordination in children with Cerebral Palsy is not sufficiently analyzed yet. Principal Component Analysis can help in understanding motion patterns decomposing movement into its fundamental components (Principal Movements). This study aims at quantitatively characterizing the functional connections between multi-joint gait patterns in Cerebral Palsy. 65 children with spastic diplegia aged 10.6 (SD 3.7) years participated in standardized gait analysis trials; 31 typically developing adolescents aged 13.6 (4.4) years were also tested. To determine if posture affects gait patterns, patients were split into Crouch and knee Hyperextension group according to knee flexion angle at standing. 3D coordinates of hips, knees, ankles, metatarsal joints, pelvis and shoulders were submitted to Principal Component Analysis. Four Principal Movements accounted for 99% of global variance; components 1-3 explained major sagittal patterns, components 4-5 referred to movements on frontal plane and component 6 to additional movement refinements. Dimensionality was higher in patients than in controls (p<0.01), and the Crouch group significantly differed from controls in the application of components 1 and 4-6 (p<0.05), while the knee Hyperextension group in components 1-2 and 5 (p<0.05). Compensatory strategies of children with Cerebral Palsy (interactions between main and secondary movement patterns), were objectively determined. Principal Movements can reduce the effort in interpreting gait reports, providing an immediate and quantitative picture of the connections between movement components. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

NASA Technical Reports Server (NTRS)

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

1977-01-01

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

Genetic Classification of Populations Using Supervised Learning

PubMed Central

Bridges, Michael; Heron, Elizabeth A.; O'Dushlaine, Colm; Segurado, Ricardo; Morris, Derek; Corvin, Aiden; Gill, Michael; Pinto, Carlos

2011-01-01

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case–control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies. PMID:21589856

[Identification of varieties of cashmere by Vis/NIR spectroscopy technology based on PCA-SVM].

PubMed

Wu, Gui-Fang; He, Yong

2009-06-01

One mixed algorithm was presented to discriminate cashmere varieties with principal component analysis (PCA) and support vector machine (SVM). Cashmere fiber has such characteristics as threadlike, softness, glossiness and high tensile strength. The quality characters and economic value of each breed of cashmere are very different. In order to safeguard the consumer's rights and guarantee the quality of cashmere product, quickly, efficiently and correctly identifying cashmere has significant meaning to the production and transaction of cashmere material. The present research adopts Vis/NIRS spectroscopy diffuse techniques to collect the spectral data of cashmere. The near infrared fingerprint of cashmere was acquired by principal component analysis (PCA), and support vector machine (SVM) methods were used to further identify the cashmere material. The result of PCA indicated that the score map made by the scores of PC1, PC2 and PC3 was used, and 10 principal components (PCs) were selected as the input of support vector machine (SVM) based on the reliabilities of PCs of 99.99%. One hundred cashmere samples were used for calibration and the remaining 75 cashmere samples were used for validation. A one-against-all multi-class SVM model was built, the capabilities of SVM with different kernel function were comparatively analyzed, and the result showed that SVM possessing with the Gaussian kernel function has the best identification capabilities with the accuracy of 100%. This research indicated that the data mining method of PCA-SVM has a good identification effect, and can work as a new method for rapid identification of cashmere material varieties.

Study on pattern recognition of Raman spectrum based on fuzzy neural network

NASA Astrophysics Data System (ADS)

Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing

2017-10-01

Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.

Constrained Principal Component Analysis: Various Applications.

ERIC Educational Resources Information Center

Hunter, Michael; Takane, Yoshio

2002-01-01

Provides example applications of constrained principal component analysis (CPCA) that illustrate the method on a variety of contexts common to psychological research. Two new analyses, decompositions into finer components and fitting higher order structures, are presented, followed by an illustration of CPCA on contingency tables and the CPCA of…

Monitoring land cover changes by remote sensing in north west Egypt

NASA Astrophysics Data System (ADS)

Richards, Timothy Steven

The Mediterratiean coastal strip of Egypt is a semi-arid environment which supports a variety of agricultural practices ranging from irrigated sedentary agriculture to semi-nomadic pastoralism. The sedentarisation of the nomadic Bedouin coupled with an increase in population of both people and livestock and a decrease in the extent of the rangelands, has resulted in severe pressure being exerted upon the environment. Satellite remote sensing of vegetation offers the potential to aid regional management by complementing conventional techniques of vegetation mapping and monitoring. This thesis examines the different techniques available for vegetation mapping using visible and near infrared spectral wave bands. The different techniques available for vegetation mapping using remotely sensed data are reviewed and discussed with reference to semi-arid environments. The underlying similarity of many of the techniques is emphasised and their individual merits discussed. The spectral feature-space of Landsat data of two representative study areas in northern Egypt is explored and examined using graphical techniques and principal components analysis. Hand held radiometric field data are also presented for individual soil types within the region. It is proposed that by using reference data for individual soil types, improved estimates of vegetation cover can be ascertained. A number of radiometric corrections are applied to the digital Landsat data in order to convert the arbitrary digital values of the different spectral bands into physical values of reflectance. The effect of this standardization on the principal components is examined. The stratified approach to vegetation mapping which was explored using the field data is applied in turn to the digital Landsat images. Whilst the stratified approach was not found to offer significant advantages over the non-stratified approach in this case, the analysis does serve to provide an accurate datum against which to measure vegetation. In conclusion a satellite based system for operational vegetation monitoring is proposed.

A measure for objects clustering in principal component analysis biplot: A case study in inter-city buses maintenance cost data

NASA Astrophysics Data System (ADS)

Ginanjar, Irlandia; Pasaribu, Udjianna S.; Indratno, Sapto W.

2017-03-01

This article presents the application of the principal component analysis (PCA) biplot for the needs of data mining. This article aims to simplify and objectify the methods for objects clustering in PCA biplot. The novelty of this paper is to get a measure that can be used to objectify the objects clustering in PCA biplot. Orthonormal eigenvectors, which are the coefficients of a principal component model representing an association between principal components and initial variables. The existence of the association is a valid ground to objects clustering based on principal axes value, thus if m principal axes used in the PCA, then the objects can be classified into 2m clusters. The inter-city buses are clustered based on maintenance costs data by using two principal axes PCA biplot. The buses are clustered into four groups. The first group is the buses with high maintenance costs, especially for lube, and brake canvass. The second group is the buses with high maintenance costs, especially for tire, and filter. The third group is the buses with low maintenance costs, especially for lube, and brake canvass. The fourth group is buses with low maintenance costs, especially for tire, and filter.

A comparison of linear approaches to filter out environmental effects in structural health monitoring

NASA Astrophysics Data System (ADS)

Deraemaeker, A.; Worden, K.

2018-05-01

This paper discusses the possibility of using the Mahalanobis squared-distance to perform robust novelty detection in the presence of important environmental variability in a multivariate feature vector. By performing an eigenvalue decomposition of the covariance matrix used to compute that distance, it is shown that the Mahalanobis squared-distance can be written as the sum of independent terms which result from a transformation from the feature vector space to a space of independent variables. In general, especially when the size of the features vector is large, there are dominant eigenvalues and eigenvectors associated with the covariance matrix, so that a set of principal components can be defined. Because the associated eigenvalues are high, their contribution to the Mahalanobis squared-distance is low, while the contribution of the other components is high due to the low value of the associated eigenvalues. This analysis shows that the Mahalanobis distance naturally filters out the variability in the training data. This property can be used to remove the effect of the environment in damage detection, in much the same way as two other established techniques, principal component analysis and factor analysis. The three techniques are compared here using real experimental data from a wooden bridge for which the feature vector consists in eigenfrequencies and modeshapes collected under changing environmental conditions, as well as damaged conditions simulated with an added mass. The results confirm the similarity between the three techniques and the ability to filter out environmental effects, while keeping a high sensitivity to structural changes. The results also show that even after filtering out the environmental effects, the normality assumption cannot be made for the residual feature vector. An alternative is demonstrated here based on extreme value statistics which results in a much better threshold which avoids false positives in the training data, while allowing detection of all damaged cases.

Survey to Identify Substandard and Falsified Tablets in Several Asian Countries with Pharmacopeial Quality Control Tests and Principal Component Analysis of Handheld Raman Spectroscopy.

PubMed

Kakio, Tomoko; Nagase, Hitomi; Takaoka, Takashi; Yoshida, Naoko; Hirakawa, Junichi; Macha, Susan; Hiroshima, Takashi; Ikeda, Yukihiro; Tsuboi, Hirohito; Kimura, Kazuko

2018-06-01

The World Health Organization has warned that substandard and falsified medical products (SFs) can harm patients and fail to treat the diseases for which they were intended, and they affect every region of the world, leading to loss of confidence in medicines, health-care providers, and health systems. Therefore, development of analytical procedures to detect SFs is extremely important. In this study, we investigated the quality of pharmaceutical tablets containing the antihypertensive candesartan cilexetil, collected in China, Indonesia, Japan, and Myanmar, using the Japanese pharmacopeial analytical procedures for quality control, together with principal component analysis (PCA) of Raman spectrum obtained with handheld Raman spectrometer. Some samples showed delayed dissolution and failed to meet the pharmacopeial specification, whereas others failed the assay test. These products appeared to be substandard. Principal component analysis showed that all Raman spectra could be explained in terms of two components: the amount of the active pharmaceutical ingredient and the kinds of excipients. Principal component analysis score plot indicated one substandard, and the falsified tablets have similar principal components in Raman spectra, in contrast to authentic products. The locations of samples within the PCA score plot varied according to the source country, suggesting that manufacturers in different countries use different excipients. Our results indicate that the handheld Raman device will be useful for detection of SFs in the field. Principal component analysis of that Raman data clarify the difference in chemical properties between good quality products and SFs that circulate in the Asian market.

Principal component analysis and the locus of the Fréchet mean in the space of phylogenetic trees.

PubMed

Nye, Tom M W; Tang, Xiaoxian; Weyenberg, Grady; Yoshida, Ruriko

2017-12-01

Evolutionary relationships are represented by phylogenetic trees, and a phylogenetic analysis of gene sequences typically produces a collection of these trees, one for each gene in the analysis. Analysis of samples of trees is difficult due to the multi-dimensionality of the space of possible trees. In Euclidean spaces, principal component analysis is a popular method of reducing high-dimensional data to a low-dimensional representation that preserves much of the sample's structure. However, the space of all phylogenetic trees on a fixed set of species does not form a Euclidean vector space, and methods adapted to tree space are needed. Previous work introduced the notion of a principal geodesic in this space, analogous to the first principal component. Here we propose a geometric object for tree space similar to the [Formula: see text]th principal component in Euclidean space: the locus of the weighted Fréchet mean of [Formula: see text] vertex trees when the weights vary over the [Formula: see text]-simplex. We establish some basic properties of these objects, in particular showing that they have dimension [Formula: see text], and propose algorithms for projection onto these surfaces and for finding the principal locus associated with a sample of trees. Simulation studies demonstrate that these algorithms perform well, and analyses of two datasets, containing Apicomplexa and African coelacanth genomes respectively, reveal important structure from the second principal components.

Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

PubMed

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

Raman spectra of single cells with autofluorescence suppression by modulated wavelength excitation

NASA Astrophysics Data System (ADS)

Krafft, Christoph; Dochow, Sebastian; Bergner, Norbert; Clement, Joachim H.; Praveen, Bavishna B.; Mazilu, Michael; Marchington, Rob; Dholakia, Kishan; Popp, Jürgen

2012-01-01

Raman spectroscopy is a non-invasive technique offering great potential in the biomedical field for label-free discrimination between normal and tumor cells based on their biochemical composition. First, this contribution describes Raman spectra of lymphocytes after drying, in laser tweezers, and trapped in a microfluidic environment. Second, spectral differences between lymphocytes and acute myeloid leukemia cells (OCI-AML3) are compared for these three experimental conditions. Significant similarities of difference spectra are consistent with the biological relevance of the spectral features. Third, modulated wavelength Raman spectroscopy has been applied to this model system to demonstrate background suppression. Here, the laser excitation wavelength of 785 nm was modulated with a frequency of 40 mHz by 0.6 nm. 40 spectra were accumulated with an exposure time of 5 seconds each. These data were subjected to principal component analysis to calculate modulated Raman signatures. The loading of the principal component shows characteristics of first derivatives with derivative like band shapes. The derivative of this loading corresponds to a pseudo-second derivative spectrum and enables to determine band positions.

Variety identification of brown sugar using short-wave near infrared spectroscopy and multivariate calibration

NASA Astrophysics Data System (ADS)

Yang, Haiqing; Wu, Di; He, Yong

2007-11-01

Near-infrared spectroscopy (NIRS) with the characteristics of high speed, non-destructiveness, high precision and reliable detection data, etc. is a pollution-free, rapid, quantitative and qualitative analysis method. A new approach for variety discrimination of brown sugars using short-wave NIR spectroscopy (800-1050nm) was developed in this work. The relationship between the absorbance spectra and brown sugar varieties was established. The spectral data were compressed by the principal component analysis (PCA). The resulting features can be visualized in principal component (PC) space, which can lead to discovery of structures correlative with the different class of spectral samples. It appears to provide a reasonable variety clustering of brown sugars. The 2-D PCs plot obtained using the first two PCs can be used for the pattern recognition. Least-squares support vector machines (LS-SVM) was applied to solve the multivariate calibration problems in a relatively fast way. The work has shown that short-wave NIR spectroscopy technique is available for the brand identification of brown sugar, and LS-SVM has the better identification ability than PLS when the calibration set is small.

Understanding the pattern of the BSE Sensex

NASA Astrophysics Data System (ADS)

Mukherjee, I.; Chatterjee, Soumya; Giri, A.; Barat, P.

2017-09-01

An attempt is made to understand the pattern of behaviour of the BSE Sensex by analysing the tick-by-tick Sensex data for the years 2006 to 2012 on yearly as well as cumulative basis using Principal Component Analysis (PCA) and its nonlinear variant Kernel Principal Component Analysis (KPCA). The latter technique ensures that the nonlinear character of the interactions present in the system gets captured in the analysis. The analysis is carried out by constructing vector spaces of varying dimensions. The size of the data set ranges from a minimum of 360,000 for one year to a maximum of 2,520,000 for seven years. In all cases the prices appear to be highly correlated and restricted to a very low dimensional subspace of the original vector space. An external perturbation is added to the system in the form of noise. It is observed that while standard PCA is unable to distinguish the behaviour of the noise-mixed data from that of the original, KPCA clearly identifies the effect of the noise. The exercise is extended in case of daily data of other stock markets and similar results are obtained.

Physician performance assessment using a composite quality index.

PubMed

Liu, Kaibo; Jain, Shabnam; Shi, Jianjun

2013-07-10

Assessing physician performance is important for the purposes of measuring and improving quality of service and reducing healthcare delivery costs. In recent years, physician performance scorecards have been used to provide feedback on individual measures; however, one key challenge is how to develop a composite quality index that combines multiple measures for overall physician performance evaluation. A controversy arises over establishing appropriate weights to combine indicators in multiple dimensions, and cannot be easily resolved. In this study, we proposed a generic unsupervised learning approach to develop a single composite index for physician performance assessment by using non-negative principal component analysis. We developed a new algorithm named iterative quadratic programming to solve the numerical issue in the non-negative principal component analysis approach. We conducted real case studies to demonstrate the performance of the proposed method. We provided interpretations from both statistical and clinical perspectives to evaluate the developed composite ranking score in practice. In addition, we implemented the root cause assessment techniques to explain physician performance for improvement purposes. Copyright © 2012 John Wiley & Sons, Ltd.

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

PubMed Central

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

Statistical interpretation of chromatic indicators in correlation to phytochemical profile of a sulfur dioxide-free mulberry (Morus nigra) wine submitted to non-thermal maturation processes.

PubMed

Tchabo, William; Ma, Yongkun; Kwaw, Emmanuel; Zhang, Haining; Xiao, Lulu; Apaliya, Maurice T

2018-01-15

The four different methods of color measurement of wine proposed by Boulton, Giusti, Glories and Commission International de l'Eclairage (CIE) were applied to assess the statistical relationship between the phytochemical profile and chromatic characteristics of sulfur dioxide-free mulberry (Morus nigra) wine submitted to non-thermal maturation processes. The alteration in chromatic properties and phenolic composition of non-thermal aged mulberry wine were examined, aided by the used of Pearson correlation, cluster and principal component analysis. The results revealed a positive effect of non-thermal processes on phytochemical families of wines. From Pearson correlation analysis relationships between chromatic indexes and flavonols as well as anthocyanins were established. Cluster analysis highlighted similarities between Boulton and Giusti parameters, as well as Glories and CIE parameters in the assessment of chromatic properties of wines. Finally, principal component analysis was able to discriminate wines subjected to different maturation techniques on the basis of their chromatic and phenolics characteristics. Copyright © 2017. Published by Elsevier Ltd.

Shape Analysis of 3D Head Scan Data for U.S. Respirator Users

NASA Astrophysics Data System (ADS)

Zhuang, Ziqing; Slice, DennisE; Benson, Stacey; Lynch, Stephanie; Viscusi, DennisJ

2010-12-01

In 2003, the National Institute for Occupational Safety and Health (NIOSH) conducted a head-and-face anthropometric survey of diverse, civilian respirator users. Of the 3,997 subjects measured using traditional anthropometric techniques, surface scans and 26 three-dimensional (3D) landmark locations were collected for 947 subjects. The objective of this study was to report the size and shape variation of the survey participants using the 3D data. Generalized Procrustes Analysis (GPA) was conducted to standardize configurations of landmarks associated with individuals into a common coordinate system. The superimposed coordinates for each individual were used as commensurate variables that describe individual shape and were analyzed using Principal Component Analysis (PCA) to identify population variation. The first four principal components (PC) account for 49% of the total sample variation. The first PC indicates that overall size is an important component of facial variability. The second PC accounts for long and narrow or short and wide faces. Longer narrow orbits versus shorter wider orbits can be described by PC3, and PC4 represents variation in the degree of ortho/prognathism. Geometric Morphometrics provides a detailed and interpretable assessment of morphological variation that may be useful in assessing respirators and devising new test and certification standards.

Detection of micro solder balls using active thermography and probabilistic neural network

NASA Astrophysics Data System (ADS)

He, Zhenzhi; Wei, Li; Shao, Minghui; Lu, Xingning

2017-03-01

Micro solder ball/bump has been widely used in electronic packaging. It has been challenging to inspect these structures as the solder balls/bumps are often embedded between the component and substrates, especially in flip-chip packaging. In this paper, a detection method for micro solder ball/bump based on the active thermography and the probabilistic neural network is investigated. A VH680 infrared imager is used to capture the thermal image of the test vehicle, SFA10 packages. The temperature curves are processed using moving average technique to remove the peak noise. And the principal component analysis (PCA) is adopted to reconstruct the thermal images. The missed solder balls can be recognized explicitly in the second principal component image. Probabilistic neural network (PNN) is then established to identify the defective bump intelligently. The hot spots corresponding to the solder balls are segmented from the PCA reconstructed image, and statistic parameters are calculated. To characterize the thermal properties of solder bump quantitatively, three representative features are selected and used as the input vector in PNN clustering. The results show that the actual outputs and the expected outputs are consistent in identification of the missed solder balls, and all the bumps were recognized accurately, which demonstrates the viability of the PNN in effective defect inspection in high-density microelectronic packaging.

Patching C2n Time Series Data Holes using Principal Component Analysis

DTIC Science & Technology

2007-01-01

characteristic local scale exponent , regardless of dilation of the length examined. THE HURST PARAMETER There are a slew of methods13 available to...fractal dimension D0, which characterises the roughness of the data, and the Hurst parameter, H , which is a measure of the long range dependence (LRD...estimate H . For simplicity, we have opted to use the well known Hurst –Mandelbrot R/S technique, which is also the most elementary. The fitting curve

Recognition of units in coarse, unconsolidated braided-stream deposits from geophysical log data with principal components analysis

USGS Publications Warehouse

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.

Analysis and Evaluation of the Characteristic Taste Components in Portobello Mushroom.

PubMed

Wang, Jinbin; Li, Wen; Li, Zhengpeng; Wu, Wenhui; Tang, Xueming

2018-05-10

To identify the characteristic taste components of the common cultivated mushroom (brown; Portobello), Agaricus bisporus, taste components in the stipe and pileus of Portobello mushroom harvested at different growth stages were extracted and identified, and principal component analysis (PCA) and taste active value (TAV) were used to reveal the characteristic taste components during the each of the growth stages of Portobello mushroom. In the stipe and pileus, 20 and 14 different principal taste components were identified, respectively, and they were considered as the principal taste components of Portobello mushroom fruit bodies, which included most amino acids and 5'-nucleotides. Some taste components that were found at high levels, such as lactic acid and citric acid, were not detected as Portobello mushroom principal taste components through PCA. However, due to their high content, Portobello mushroom could be used as a source of organic acids. The PCA and TAV results revealed that 5'-GMP, glutamic acid, malic acid, alanine, proline, leucine, and aspartic acid were the characteristic taste components of Portobello mushroom fruit bodies. Portobello mushroom was also found to be rich in protein and amino acids, so it might also be useful in the formulation of nutraceuticals and functional food. The results in this article could provide a theoretical basis for understanding and regulating the characteristic flavor components synthesis process of Portobello mushroom. © 2018 Institute of Food Technologists®.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

A rapid ATR-FTIR spectroscopic method for detection of sibutramine adulteration in tea and coffee based on hierarchical cluster and principal component analyses.

PubMed

Cebi, Nur; Yilmaz, Mustafa Tahsin; Sagdic, Osman

2017-08-15

Sibutramine may be illicitly included in herbal slimming foods and supplements marketed as "100% natural" to enhance weight loss. Considering public health and legal regulations, there is an urgent need for effective, rapid and reliable techniques to detect sibutramine in dietetic herbal foods, teas and dietary supplements. This research comprehensively explored, for the first time, detection of sibutramine in green tea, green coffee and mixed herbal tea using ATR-FTIR spectroscopic technique combined with chemometrics. Hierarchical cluster analysis and PCA principle component analysis techniques were employed in spectral range (2746-2656cm -1 ) for classification and discrimination through Euclidian distance and Ward's algorithm. Unadulterated and adulterated samples were classified and discriminated with respect to their sibutramine contents with perfect accuracy without any false prediction. The results suggest that existence of the active substance could be successfully determined at the levels in the range of 0.375-12mg in totally 1.75g of green tea, green coffee and mixed herbal tea by using FTIR-ATR technique combined with chemometrics. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

Dascălu, Cristina Gena; Antohe, Magda Ecaterina

2009-01-01

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

On Using the Average Intercorrelation Among Predictor Variables and Eigenvector Orientation to Choose a Regression Solution.

ERIC Educational Resources Information Center

Mugrage, Beverly; And Others

Three ridge regression solutions are compared with ordinary least squares regression and with principal components regression using all components. Ridge regression, particularly the Lawless-Wang solution, out-performed ordinary least squares regression and the principal components solution on the criteria of stability of coefficient and closeness…

A Note on McDonald's Generalization of Principal Components Analysis

ERIC Educational Resources Information Center

Shine, Lester C., II

1972-01-01

It is shown that McDonald's generalization of Classical Principal Components Analysis to groups of variables maximally channels the totalvariance of the original variables through the groups of variables acting as groups. An equation is obtained for determining the vectors of correlations of the L2 components with the original variables.…

CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles

PubMed Central

Peterson, Leif E

2002-01-01

CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 is a Windows-based computer program for hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles. CLUSFAVOR 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis. PMID:12184816

A combined qualitative-quantitative approach for the identification of highly co-creative technology-driven firms

NASA Astrophysics Data System (ADS)

Milyakov, Hristo; Tanev, Stoyan; Ruskov, Petko

2011-03-01

Value co-creation, is an emerging business and innovation paradigm, however, there is not enough clarity on the distinctive characteristics of value co-creation as compared to more traditional value creation approaches. The present paper summarizes the results from an empirically-derived research study focusing on the development of a systematic procedure for the identification of firms that are active in value co-creation. The study is based on a sample 273 firms that were selected for being representative of the breadth of their value co-creation activities. The results include: i) the identification of the key components of value co-creation based on a research methodology using web search and Principal Component Analysis techniques, and ii) the comparison of two different classification techniques identifying the firms with the highest degree of involvement in value co-creation practices. To the best of our knowledge this is the first study using sophisticated data collection techniques to provide a classification of firms according to the degree of their involvement in value co-creation.

Analysis of indoor air pollutants checklist using environmetric technique for health risk assessment of sick building complaint in nonindustrial workplace

PubMed Central

Syazwan, AI; Rafee, B Mohd; Juahir, Hafizan; Azman, AZF; Nizar, AM; Izwyn, Z; Syahidatussyakirah, K; Muhaimin, AA; Yunos, MA Syafiq; Anita, AR; Hanafiah, J Muhamad; Shaharuddin, MS; Ibthisham, A Mohd; Hasmadi, I Mohd; Azhar, MN Mohamad; Azizan, HS; Zulfadhli, I; Othman, J; Rozalini, M; Kamarul, FT

2012-01-01

Purpose To analyze and characterize a multidisciplinary, integrated indoor air quality checklist for evaluating the health risk of building occupants in a nonindustrial workplace setting. Design A cross-sectional study based on a participatory occupational health program conducted by the National Institute of Occupational Safety and Health (Malaysia) and Universiti Putra Malaysia. Method A modified version of the indoor environmental checklist published by the Department of Occupational Health and Safety, based on the literature and discussion with occupational health and safety professionals, was used in the evaluation process. Summated scores were given according to the cluster analysis and principal component analysis in the characterization of risk. Environmetric techniques was used to classify the risk of variables in the checklist. Identification of the possible source of item pollutants was also evaluated from a semiquantitative approach. Result Hierarchical agglomerative cluster analysis resulted in the grouping of factorial components into three clusters (high complaint, moderate-high complaint, moderate complaint), which were further analyzed by discriminant analysis. From this, 15 major variables that influence indoor air quality were determined. Principal component analysis of each cluster revealed that the main factors influencing the high complaint group were fungal-related problems, chemical indoor dispersion, detergent, renovation, thermal comfort, and location of fresh air intake. The moderate-high complaint group showed significant high loading on ventilation, air filters, and smoking-related activities. The moderate complaint group showed high loading on dampness, odor, and thermal comfort. Conclusion This semiquantitative assessment, which graded risk from low to high based on the intensity of the problem, shows promising and reliable results. It should be used as an important tool in the preliminary assessment of indoor air quality and as a categorizing method for further IAQ investigations and complaints procedures. PMID:23055779

Analysis of indoor air pollutants checklist using environmetric technique for health risk assessment of sick building complaint in nonindustrial workplace.

PubMed

Syazwan, Ai; Rafee, B Mohd; Juahir, Hafizan; Azman, Azf; Nizar, Am; Izwyn, Z; Syahidatussyakirah, K; Muhaimin, Aa; Yunos, Ma Syafiq; Anita, Ar; Hanafiah, J Muhamad; Shaharuddin, Ms; Ibthisham, A Mohd; Hasmadi, I Mohd; Azhar, Mn Mohamad; Azizan, Hs; Zulfadhli, I; Othman, J; Rozalini, M; Kamarul, Ft

2012-01-01

To analyze and characterize a multidisciplinary, integrated indoor air quality checklist for evaluating the health risk of building occupants in a nonindustrial workplace setting. A cross-sectional study based on a participatory occupational health program conducted by the National Institute of Occupational Safety and Health (Malaysia) and Universiti Putra Malaysia. A modified version of the indoor environmental checklist published by the Department of Occupational Health and Safety, based on the literature and discussion with occupational health and safety professionals, was used in the evaluation process. Summated scores were given according to the cluster analysis and principal component analysis in the characterization of risk. Environmetric techniques was used to classify the risk of variables in the checklist. Identification of the possible source of item pollutants was also evaluated from a semiquantitative approach. Hierarchical agglomerative cluster analysis resulted in the grouping of factorial components into three clusters (high complaint, moderate-high complaint, moderate complaint), which were further analyzed by discriminant analysis. From this, 15 major variables that influence indoor air quality were determined. Principal component analysis of each cluster revealed that the main factors influencing the high complaint group were fungal-related problems, chemical indoor dispersion, detergent, renovation, thermal comfort, and location of fresh air intake. The moderate-high complaint group showed significant high loading on ventilation, air filters, and smoking-related activities. The moderate complaint group showed high loading on dampness, odor, and thermal comfort. This semiquantitative assessment, which graded risk from low to high based on the intensity of the problem, shows promising and reliable results. It should be used as an important tool in the preliminary assessment of indoor air quality and as a categorizing method for further IAQ investigations and complaints procedures.

The Complexity of Human Walking: A Knee Osteoarthritis Study

PubMed Central

Kotti, Margarita; Duffell, Lynsey D.; Faisal, Aldo A.; McGregor, Alison H.

2014-01-01

This study proposes a framework for deconstructing complex walking patterns to create a simple principal component space before checking whether the projection to this space is suitable for identifying changes from the normality. We focus on knee osteoarthritis, the most common knee joint disease and the second leading cause of disability. Knee osteoarthritis affects over 250 million people worldwide. The motivation for projecting the highly dimensional movements to a lower dimensional and simpler space is our belief that motor behaviour can be understood by identifying a simplicity via projection to a low principal component space, which may reflect upon the underlying mechanism. To study this, we recruited 180 subjects, 47 of which reported that they had knee osteoarthritis. They were asked to walk several times along a walkway equipped with two force plates that capture their ground reaction forces along 3 axes, namely vertical, anterior-posterior, and medio-lateral, at 1000 Hz. Data when the subject does not clearly strike the force plate were excluded, leaving 1–3 gait cycles per subject. To examine the complexity of human walking, we applied dimensionality reduction via Probabilistic Principal Component Analysis. The first principal component explains 34% of the variance in the data, whereas over 80% of the variance is explained by 8 principal components or more. This proves the complexity of the underlying structure of the ground reaction forces. To examine if our musculoskeletal system generates movements that are distinguishable between normal and pathological subjects in a low dimensional principal component space, we applied a Bayes classifier. For the tested cross-validated, subject-independent experimental protocol, the classification accuracy equals 82.62%. Also, a novel complexity measure is proposed, which can be used as an objective index to facilitate clinical decision making. This measure proves that knee osteoarthritis subjects exhibit more variability in the two-dimensional principal component space. PMID:25232949

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-05-25

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-11-23

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

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;

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

PubMed

Ghosh, Debasree; Chattopadhyay, Parimal

2012-06-01

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

General ultrafast pulse measurement using the cross-correlation single-shot sonogram technique.

PubMed

Reid, Derryck T; Garduno-Mejia, Jesus

2004-03-15

The cross-correlation single-shot sonogram technique offers exact pulse measurement and real-time pulse monitoring via an intuitive time-frequency trace whose shape and orientation directly indicate the spectral chirp of an ultrashort laser pulse. We demonstrate an algorithm that solves a fundamental limitation of the cross-correlation sonogram method, namely, that the time-gating operation is implemented using a replica of the measured pulse rather than the ideal delta-function-like pulse. Using a modified principal-components generalized projections algorithm, we experimentally show accurate pulse retrieval of an asymmetric double pulse, a case that is prone to systematic error when one is using the original sonogram retrieval algorithm.

Judging complex movement performances for excellence: a principal components analysis-based technique applied to competitive diving.

PubMed

Young, Cole; Reinkensmeyer, David J

2014-08-01

Athletes rely on subjective assessment of complex movements from coaches and judges to improve their motor skills. In some sports, such as diving, snowboard half pipe, gymnastics, and figure skating, subjective scoring forms the basis for competition. It is currently unclear whether this scoring process can be mathematically modeled; doing so could provide insight into what motor skill is. Principal components analysis has been proposed as a motion analysis method for identifying fundamental units of coordination. We used PCA to analyze movement quality of dives taken from USA Diving's 2009 World Team Selection Camp, first identifying eigenpostures associated with dives, and then using the eigenpostures and their temporal weighting coefficients, as well as elements commonly assumed to affect scoring - gross body path, splash area, and board tip motion - to identify eigendives. Within this eigendive space we predicted actual judges' scores using linear regression. This technique rated dives with accuracy comparable to the human judges. The temporal weighting of the eigenpostures, body center path, splash area, and board tip motion affected the score, but not the eigenpostures themselves. These results illustrate that (1) subjective scoring in a competitive diving event can be mathematically modeled; (2) the elements commonly assumed to affect dive scoring actually do affect scoring (3) skill in elite diving is more associated with the gross body path and the effect of the movement on the board and water than the units of coordination that PCA extracts, which might reflect the high level of technique these divers had achieved. We also illustrate how eigendives can be used to produce dive animations that an observer can distort continuously from poor to excellent, which is a novel approach to performance visualization. Copyright © 2014 Elsevier B.V. All rights reserved.

Z-Scan Analysis: a New Method to Determine the Oxidative State of Low-Density Lipoprotein and Its Association with Multiple Cardiometabolic Biomarkers

NASA Astrophysics Data System (ADS)

de Freitas, Maria Camila Pruper; Figueiredo Neto, Antonio Martins; Giampaoli, Viviane; da Conceição Quintaneiro Aubin, Elisete; de Araújo Lima Barbosa, Milena Maria; Damasceno, Nágila Raquel Teixeira

2016-04-01

The great atherogenic potential of oxidized low-density lipoprotein has been widely described in the literature. The objective of this study was to investigate whether the state of oxidized low-density lipoprotein in human plasma measured by the Z-scan technique has an association with different cardiometabolic biomarkers. Total cholesterol, high-density lipoprotein cholesterol, triacylglycerols, apolipoprotein A-I and apolipoprotein B, paraoxonase-1, and glucose were analyzed using standard commercial kits, and low-density lipoprotein cholesterol was estimated using the Friedewald equation. A sandwich enzyme-linked immunosorbent assay was used to detect electronegative low-density lipoprotein. Low-density lipoprotein and high-density lipoprotein sizes were determined by Lipoprint® system. The Z-scan technique was used to measure the non-linear optical response of low-density lipoprotein solution. Principal component analysis and correlations were used respectively to resize the data from the sample and test association between the θ parameter, measured with the Z-scan technique, and the principal component. A total of 63 individuals, from both sexes, with mean age 52 years (±11), being overweight and having high levels of total cholesterol and low levels of high-density lipoprotein cholesterol, were enrolled in this study. A positive correlation between the θ parameter and more anti-atherogenic pattern for cardiometabolic biomarkers together with a negative correlation for an atherogenic pattern was found. Regarding the parameters related with an atherogenic low-density lipoprotein profile, the θ parameter was negatively correlated with a more atherogenic pattern. By using Z-scan measurements, we were able to find an association between oxidized low-density lipoprotein state and multiple cardiometabolic biomarkers in samples from individuals with different cardiovascular risk factors.

Pepper seed variety identification based on visible/near-infrared spectral technology

NASA Astrophysics Data System (ADS)

Li, Cuiling; Wang, Xiu; Meng, Zhijun; Fan, Pengfei; Cai, Jichen

2016-11-01

Pepper is a kind of important fruit vegetable, with the expansion of pepper hybrid planting area, detection of pepper seed purity is especially important. This research used visible/near infrared (VIS/NIR) spectral technology to detect the variety of single pepper seed, and chose hybrid pepper seeds "Zhuo Jiao NO.3", "Zhuo Jiao NO.4" and "Zhuo Jiao NO.5" as research sample. VIS/NIR spectral data of 80 "Zhuo Jiao NO.3", 80 "Zhuo Jiao NO.4" and 80 "Zhuo Jiao NO.5" pepper seeds were collected, and the original spectral data was pretreated with standard normal variable (SNV) transform, first derivative (FD), and Savitzky-Golay (SG) convolution smoothing methods. Principal component analysis (PCA) method was adopted to reduce the dimension of the spectral data and extract principal components, according to the distribution of the first principal component (PC1) along with the second principal component(PC2) in the twodimensional plane, similarly, the distribution of PC1 coupled with the third principal component(PC3), and the distribution of PC2 combined with PC3, distribution areas of three varieties of pepper seeds were divided in each twodimensional plane, and the discriminant accuracy of PCA was tested through observing the distribution area of samples' principal components in validation set. This study combined PCA and linear discriminant analysis (LDA) to identify single pepper seed varieties, results showed that with the FD preprocessing method, the discriminant accuracy of pepper seed varieties was 98% for validation set, it concludes that using VIS/NIR spectral technology is feasible for identification of single pepper seed varieties.

Architectural measures of the cancellous bone of the mandibular condyle identified by principal components analysis.

PubMed

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.

Factors associated with successful transition among children with disabilities in eight European countries

PubMed Central

2017-01-01

Introduction This research paper aims to assess factors reported by parents associated with the successful transition of children with complex additional support requirements that have undergone a transition between school environments from 8 European Union member states. Methods Quantitative data were collected from 306 parents within education systems from 8 EU member states (Bulgaria, Cyprus, Greece, Ireland, the Netherlands, Romania, Spain and the UK). The data were derived from an online questionnaire and consisted of 41 questions. Information was collected on: parental involvement in their child’s transition, child involvement in transition, child autonomy, school ethos, professionals’ involvement in transition and integrated working, such as, joint assessment, cooperation and coordination between agencies. Survey questions that were designed on a Likert-scale were included in the Principal Components Analysis (PCA), additional survey questions, along with the results from the PCA, were used to build a logistic regression model. Results Four principal components were identified accounting for 48.86% of the variability in the data. Principal component 1 (PC1), ‘child inclusive ethos,’ contains 16.17% of the variation. Principal component 2 (PC2), which represents child autonomy and involvement, is responsible for 8.52% of the total variation. Principal component 3 (PC3) contains questions relating to parental involvement and contributed to 12.26% of the overall variation. Principal component 4 (PC4), which involves transition planning and coordination, contributed to 11.91% of the overall variation. Finally, the principal components were included in a logistic regression to evaluate the relationship between inclusion and a successful transition, as well as whether other factors that may have influenced transition. All four principal components were significantly associated with a successful transition, with PC1 being having the most effect (OR: 4.04, CI: 2.43–7.18, p<0.0001). Discussion To support a child with complex additional support requirements through transition from special school to mainstream, governments and professionals need to ensure children with additional support requirements and their parents are at the centre of all decisions that affect them. It is important that professionals recognise the educational, psychological, social and cultural contexts of a child with additional support requirements and their families which will provide a holistic approach and remove barriers for learning. PMID:28636649

Factors associated with successful transition among children with disabilities in eight European countries.

PubMed

Ravenscroft, John; Wazny, Kerri; Davis, John M

2017-01-01

This research paper aims to assess factors reported by parents associated with the successful transition of children with complex additional support requirements that have undergone a transition between school environments from 8 European Union member states. Quantitative data were collected from 306 parents within education systems from 8 EU member states (Bulgaria, Cyprus, Greece, Ireland, the Netherlands, Romania, Spain and the UK). The data were derived from an online questionnaire and consisted of 41 questions. Information was collected on: parental involvement in their child's transition, child involvement in transition, child autonomy, school ethos, professionals' involvement in transition and integrated working, such as, joint assessment, cooperation and coordination between agencies. Survey questions that were designed on a Likert-scale were included in the Principal Components Analysis (PCA), additional survey questions, along with the results from the PCA, were used to build a logistic regression model. Four principal components were identified accounting for 48.86% of the variability in the data. Principal component 1 (PC1), 'child inclusive ethos,' contains 16.17% of the variation. Principal component 2 (PC2), which represents child autonomy and involvement, is responsible for 8.52% of the total variation. Principal component 3 (PC3) contains questions relating to parental involvement and contributed to 12.26% of the overall variation. Principal component 4 (PC4), which involves transition planning and coordination, contributed to 11.91% of the overall variation. Finally, the principal components were included in a logistic regression to evaluate the relationship between inclusion and a successful transition, as well as whether other factors that may have influenced transition. All four principal components were significantly associated with a successful transition, with PC1 being having the most effect (OR: 4.04, CI: 2.43-7.18, p<0.0001). To support a child with complex additional support requirements through transition from special school to mainstream, governments and professionals need to ensure children with additional support requirements and their parents are at the centre of all decisions that affect them. It is important that professionals recognise the educational, psychological, social and cultural contexts of a child with additional support requirements and their families which will provide a holistic approach and remove barriers for learning.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Nondestructive Evaluation (NDE) for Inspection of Composite Sandwich Structures

NASA Technical Reports Server (NTRS)

Zalameda, Joseph N.; Parker, F. Raymond

2014-01-01

Composite honeycomb structures are widely used in aerospace applications due to their low weight and high strength advantages. Developing nondestructive evaluation (NDE) inspection methods are essential for their safe performance. Flash thermography is a commonly used technique for composite honeycomb structure inspections due to its large area and rapid inspection capability. Flash thermography is shown to be sensitive for detection of face sheet impact damage and face sheet to core disbond. Data processing techniques, using principal component analysis to improve the defect contrast, are discussed. Limitations to the thermal detection of the core are investigated. In addition to flash thermography, X-ray computed tomography is used. The aluminum honeycomb core provides excellent X-ray contrast compared to the composite face sheet. The X-ray CT technique was used to detect impact damage, core crushing, and skin to core disbonds. Additionally, the X-ray CT technique is used to validate the thermography results.

Patient phenotypes associated with outcomes after aneurysmal subarachnoid hemorrhage: a principal component analysis.

PubMed

Ibrahim, George M; Morgan, Benjamin R; Macdonald, R Loch

2014-03-01

Predictors of outcome after aneurysmal subarachnoid hemorrhage have been determined previously through hypothesis-driven methods that often exclude putative covariates and require a priori knowledge of potential confounders. Here, we apply a data-driven approach, principal component analysis, to identify baseline patient phenotypes that may predict neurological outcomes. Principal component analysis was performed on 120 subjects enrolled in a prospective randomized trial of clazosentan for the prevention of angiographic vasospasm. Correlation matrices were created using a combination of Pearson, polyserial, and polychoric regressions among 46 variables. Scores of significant components (with eigenvalues>1) were included in multivariate logistic regression models with incidence of severe angiographic vasospasm, delayed ischemic neurological deficit, and long-term outcome as outcomes of interest. Sixteen significant principal components accounting for 74.6% of the variance were identified. A single component dominated by the patients' initial hemodynamic status, World Federation of Neurosurgical Societies score, neurological injury, and initial neutrophil/leukocyte counts was significantly associated with poor outcome. Two additional components were associated with angiographic vasospasm, of which one was also associated with delayed ischemic neurological deficit. The first was dominated by the aneurysm-securing procedure, subarachnoid clot clearance, and intracerebral hemorrhage, whereas the second had high contributions from markers of anemia and albumin levels. Principal component analysis, a data-driven approach, identified patient phenotypes that are associated with worse neurological outcomes. Such data reduction methods may provide a better approximation of unique patient phenotypes and may inform clinical care as well as patient recruitment into clinical trials. http://www.clinicaltrials.gov. Unique identifier: NCT00111085.

Principal components of wrist circumduction from electromagnetic surgical tracking.

PubMed

Rasquinha, Brian J; Rainbow, Michael J; Zec, Michelle L; Pichora, David R; Ellis, Randy E

2017-02-01

An electromagnetic (EM) surgical tracking system was used for a functionally calibrated kinematic analysis of wrist motion. Circumduction motions were tested for differences in subject gender and for differences in the sense of the circumduction as clockwise or counter-clockwise motion. Twenty subjects were instrumented for EM tracking. Flexion-extension motion was used to identify the functional axis. Subjects performed unconstrained wrist circumduction in a clockwise and counter-clockwise sense. Data were decomposed into orthogonal flexion-extension motions and radial-ulnar deviation motions. PCA was used to concisely represent motions. Nonparametric Wilcoxon tests were used to distinguish the groups. Flexion-extension motions were projected onto a direction axis with a root-mean-square error of [Formula: see text]. Using the first three principal components, there was no statistically significant difference in gender (all [Formula: see text]). For motion sense, radial-ulnar deviation distinguished the sense of circumduction in the first principal component ([Formula: see text]) and in the third principal component ([Formula: see text]); flexion-extension distinguished the sense in the second principal component ([Formula: see text]). The clockwise sense of circumduction could be distinguished by a multifactorial combination of components; there were no gender differences in this small population. These data constitute a baseline for normal wrist circumduction. The multifactorial PCA findings suggest that a higher-dimensional method, such as manifold analysis, may be a more concise way of representing circumduction in human joints.

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

PubMed Central

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

2014-01-01

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

Application of third molar development and eruption models in estimating dental age in Malay sub-adults.

PubMed

Mohd Yusof, Mohd Yusmiaidil Putera; Cauwels, Rita; Deschepper, Ellen; Martens, Luc

2015-08-01

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03-0.06) and TME (0.2-0.8). In principal component regression, low value of adjusted R(2) and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Use of principal components analysis and protein microarray to explore the association of HIV-1-specific IgG responses with disease progression.

PubMed

Gerns Storey, Helen L; Richardson, Barbra A; Singa, Benson; Naulikha, Jackie; Prindle, Vivian C; Diaz-Ochoa, Vladimir E; Felgner, Phil L; Camerini, David; Horton, Helen; John-Stewart, Grace; Walson, Judd L

2014-01-01

The role of HIV-1-specific antibody responses in HIV disease progression is complex and would benefit from analysis techniques that examine clusterings of responses. Protein microarray platforms facilitate the simultaneous evaluation of numerous protein-specific antibody responses, though excessive data are cumbersome in analyses. Principal components analysis (PCA) reduces data dimensionality by generating fewer composite variables that maximally account for variance in a dataset. To identify clusters of antibody responses involved in disease control, we investigated the association of HIV-1-specific antibody responses by protein microarray, and assessed their association with disease progression using PCA in a nested cohort design. Associations observed among collections of antibody responses paralleled protein-specific responses. At baseline, greater antibody responses to the transmembrane glycoprotein (TM) and reverse transcriptase (RT) were associated with higher viral loads, while responses to the surface glycoprotein (SU), capsid (CA), matrix (MA), and integrase (IN) proteins were associated with lower viral loads. Over 12 months greater antibody responses were associated with smaller decreases in CD4 count (CA, MA, IN), and reduced likelihood of disease progression (CA, IN). PCA and protein microarray analyses highlighted a collection of HIV-specific antibody responses that together were associated with reduced disease progression, and may not have been identified by examining individual antibody responses. This technique may be useful to explore multifaceted host-disease interactions, such as HIV coinfections.

Introduction to uses and interpretation of principal component analyses in forest biology.

Treesearch

J. G. Isebrands; Thomas R. Crow

1975-01-01

The application of principal component analysis for interpretation of multivariate data sets is reviewed with emphasis on (1) reduction of the number of variables, (2) ordination of variables, and (3) applications in conjunction with multiple regression.

Principal component analysis of phenolic acid spectra

USDA-ARS?s Scientific Manuscript database

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...

Optimal pattern synthesis for speech recognition based on principal component analysis

NASA Astrophysics Data System (ADS)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

NASA Astrophysics Data System (ADS)

Gao, Yang; Chen, Maomao; Wu, Junyu; Zhou, Yuan; Cai, Chuangjian; Wang, Daliang; Luo, Jianwen

2017-09-01

Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.

Geochemical differentiation processes for arc magma of the Sengan volcanic cluster, Northeastern Japan, constrained from principal component analysis

NASA Astrophysics Data System (ADS)

Ueki, Kenta; Iwamori, Hikaru

2017-10-01

In this study, with a view of understanding the structure of high-dimensional geochemical data and discussing the chemical processes at work in the evolution of arc magmas, we employed principal component analysis (PCA) to evaluate the compositional variations of volcanic rocks from the Sengan volcanic cluster of the Northeastern Japan Arc. We analyzed the trace element compositions of various arc volcanic rocks, sampled from 17 different volcanoes in a volcanic cluster. The PCA results demonstrated that the first three principal components accounted for 86% of the geochemical variation in the magma of the Sengan region. Based on the relationships between the principal components and the major elements, the mass-balance relationships with respect to the contributions of minerals, the composition of plagioclase phenocrysts, geothermal gradient, and seismic velocity structure in the crust, the first, the second, and the third principal components appear to represent magma mixing, crystallizations of olivine/pyroxene, and crystallizations of plagioclase, respectively. These represented 59%, 20%, and 6%, respectively, of the variance in the entire compositional range, indicating that magma mixing accounted for the largest variance in the geochemical variation of the arc magma. Our result indicated that crustal processes dominate the geochemical variation of magma in the Sengan volcanic cluster.

Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases.

PubMed

Tulay, Emine Elif; Metin, Barış; Tarhan, Nevzat; Arıkan, Mehmet Kemal

2018-06-01

Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers-especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification-especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.

Principal component analysis of normalized full spectrum mass spectrometry data in multiMS-toolbox: An effective tool to identify important factors for classification of different metabolic patterns and bacterial strains.

PubMed

Cejnar, Pavel; Kuckova, Stepanka; Prochazka, Ales; Karamonova, Ludmila; Svobodova, Barbora

2018-06-15

Explorative statistical analysis of mass spectrometry data is still a time-consuming step. We analyzed critical factors for application of principal component analysis (PCA) in mass spectrometry and focused on two whole spectrum based normalization techniques and their application in the analysis of registered peak data and, in comparison, in full spectrum data analysis. We used this technique to identify different metabolic patterns in the bacterial culture of Cronobacter sakazakii, an important foodborne pathogen. Two software utilities, the ms-alone, a python-based utility for mass spectrometry data preprocessing and peak extraction, and the multiMS-toolbox, an R software tool for advanced peak registration and detailed explorative statistical analysis, were implemented. The bacterial culture of Cronobacter sakazakii was cultivated on Enterobacter sakazakii Isolation Agar, Blood Agar Base and Tryptone Soya Agar for 24 h and 48 h and applied by the smear method on an Autoflex speed MALDI-TOF mass spectrometer. For three tested cultivation media only two different metabolic patterns of Cronobacter sakazakii were identified using PCA applied on data normalized by two different normalization techniques. Results from matched peak data and subsequent detailed full spectrum analysis identified only two different metabolic patterns - a cultivation on Enterobacter sakazakii Isolation Agar showed significant differences to the cultivation on the other two tested media. The metabolic patterns for all tested cultivation media also proved the dependence on cultivation time. Both whole spectrum based normalization techniques together with the full spectrum PCA allow identification of important discriminative factors in experiments with several variable condition factors avoiding any problems with improper identification of peaks or emphasis on bellow threshold peak data. The amounts of processed data remain still manageable. Both implemented software utilities are available free of charge from http://uprt.vscht.cz/ms. Copyright © 2018 John Wiley & Sons, Ltd.

Assessment of Supportive, Conflicted, and Controlling Dimensions of Family Functioning: A Principal Components Analysis of Family Environment Scale Subscales in a College Sample.

ERIC Educational Resources Information Center

Kronenberger, William G.; Thompson, Robert J., Jr.; Morrow, Catherine

1997-01-01

A principal components analysis of the Family Environment Scale (FES) (R. Moos and B. Moos, 1994) was performed using 113 undergraduates. Research supported 3 broad components encompassing the 10 FES subscales. These results supported previous research and the generalization of the FES to college samples. (SLD)

Time series analysis of collective motions in proteins

NASA Astrophysics Data System (ADS)

Alakent, Burak; Doruker, Pemra; ćamurdan, Mehmet C.

2004-01-01

The dynamics of α-amylase inhibitor tendamistat around its native state is investigated using time series analysis of the principal components of the Cα atomic displacements obtained from molecular dynamics trajectories. Collective motion along a principal component is modeled as a homogeneous nonstationary process, which is the result of the damped oscillations in local minima superimposed on a random walk. The motion in local minima is described by a stationary autoregressive moving average model, consisting of the frequency, damping factor, moving average parameters and random shock terms. Frequencies for the first 50 principal components are found to be in the 3-25 cm-1 range, which are well correlated with the principal component indices and also with atomistic normal mode analysis results. Damping factors, though their correlation is less pronounced, decrease as principal component indices increase, indicating that low frequency motions are less affected by friction. The existence of a positive moving average parameter indicates that the stochastic force term is likely to disturb the mode in opposite directions for two successive sampling times, showing the modes tendency to stay close to minimum. All these four parameters affect the mean square fluctuations of a principal mode within a single minimum. The inter-minima transitions are described by a random walk model, which is driven by a random shock term considerably smaller than that for the intra-minimum motion. The principal modes are classified into three subspaces based on their dynamics: essential, semiconstrained, and constrained, at least in partial consistency with previous studies. The Gaussian-type distributions of the intermediate modes, called "semiconstrained" modes, are explained by asserting that this random walk behavior is not completely free but between energy barriers.

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

EPA Science Inventory

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

Facile hyphenation of gas chromatography and a microcantilever array sensor for enhanced selectivity.

PubMed

Chapman, Peter J; Vogt, Frank; Dutta, Pampa; Datskos, Panos G; Devault, Gerald L; Sepaniak, Michael J

2007-01-01

The very simple coupling of a standard, packed-column gas chromatograph with a microcantilever array (MCA) is demonstrated for enhanced selectivity and potential analyte identification in the analysis of volatile organic compounds (VOCs). The cantilevers in MCAs are differentially coated on one side with responsive phases (RPs) and produce bending responses of the cantilevers due to analyte-induced surface stresses. Generally, individual components are difficult to elucidate when introduced to MCA systems as mixtures, although pattern recognition techniques are helpful in identifying single components, binary mixtures, or composite responses of distinct mixtures (e.g., fragrances). In the present work, simple test VOC mixtures composed of acetone, ethanol, and trichloroethylene (TCE) in pentane and methanol and acetonitrile in pentane are first separated using a standard gas chromatograph and then introduced into a MCA flow cell. Significant amounts of response diversity to the analytes in the mixtures are demonstrated across the RP-coated cantilevers of the array. Principal component analysis is used to demonstrate that only three components of a four-component VOC mixture could be identified without mixture separation. Calibration studies are performed, demonstrating a good linear response over 2 orders of magnitude for each component in the primary study mixture. Studies of operational parameters including column temperature, column flow rate, and array cell temperature are conducted. Reproducibility studies of VOC peak areas and peak heights are also carried out showing RSDs of less than 4 and 3%, respectively, for intra-assay studies. Of practical significance is the facile manner by which the hyphenation of a mature separation technique and the burgeoning sensing approach is accomplished, and the potential to use pattern recognition techniques with MCAs as a new type of detector for chromatography with analyte-identifying capabilities.

Principal components analysis in clinical studies.

PubMed

Zhang, Zhongheng; Castelló, Adela

2017-09-01

In multivariate analysis, independent variables are usually correlated to each other which can introduce multicollinearity in the regression models. One approach to solve this problem is to apply principal components analysis (PCA) over these variables. This method uses orthogonal transformation to represent sets of potentially correlated variables with principal components (PC) that are linearly uncorrelated. PCs are ordered so that the first PC has the largest possible variance and only some components are selected to represent the correlated variables. As a result, the dimension of the variable space is reduced. This tutorial illustrates how to perform PCA in R environment, the example is a simulated dataset in which two PCs are responsible for the majority of the variance in the data. Furthermore, the visualization of PCA is highlighted.

Complexity of free energy landscapes of peptides revealed by nonlinear principal component analysis.

PubMed

Nguyen, Phuong H

2006-12-01

Employing the recently developed hierarchical nonlinear principal component analysis (NLPCA) method of Saegusa et al. (Neurocomputing 2004;61:57-70 and IEICE Trans Inf Syst 2005;E88-D:2242-2248), the complexities of the free energy landscapes of several peptides, including triglycine, hexaalanine, and the C-terminal beta-hairpin of protein G, were studied. First, the performance of this NLPCA method was compared with the standard linear principal component analysis (PCA). In particular, we compared two methods according to (1) the ability of the dimensionality reduction and (2) the efficient representation of peptide conformations in low-dimensional spaces spanned by the first few principal components. The study revealed that NLPCA reduces the dimensionality of the considered systems much better, than did PCA. For example, in order to get the similar error, which is due to representation of the original data of beta-hairpin in low dimensional space, one needs 4 and 21 principal components of NLPCA and PCA, respectively. Second, by representing the free energy landscapes of the considered systems as a function of the first two principal components obtained from PCA, we obtained the relatively well-structured free energy landscapes. In contrast, the free energy landscapes of NLPCA are much more complicated, exhibiting many states which are hidden in the PCA maps, especially in the unfolded regions. Furthermore, the study also showed that many states in the PCA maps are mixed up by several peptide conformations, while those of the NLPCA maps are more pure. This finding suggests that the NLPCA should be used to capture the essential features of the systems. (c) 2006 Wiley-Liss, Inc.

Spectroscopic and Chemometric Analysis of Binary and Ternary Edible Oil Mixtures: Qualitative and Quantitative Study.

PubMed

Jović, Ozren; Smolić, Tomislav; Primožič, Ines; Hrenar, Tomica

2016-04-19

The aim of this study was to investigate the feasibility of FTIR-ATR spectroscopy coupled with the multivariate numerical methodology for qualitative and quantitative analysis of binary and ternary edible oil mixtures. Four pure oils (extra virgin olive oil, high oleic sunflower oil, rapeseed oil, and sunflower oil), as well as their 54 binary and 108 ternary mixtures, were analyzed using FTIR-ATR spectroscopy in combination with principal component and discriminant analysis, partial least-squares, and principal component regression. It was found that the composition of all 166 samples can be excellently represented using only the first three principal components describing 98.29% of total variance in the selected spectral range (3035-2989, 1170-1140, 1120-1100, 1093-1047, and 930-890 cm(-1)). Factor scores in 3D space spanned by these three principal components form a tetrahedral-like arrangement: pure oils being at the vertices, binary mixtures at the edges, and ternary mixtures on the faces of a tetrahedron. To confirm the validity of results, we applied several cross-validation methods. Quantitative analysis was performed by minimization of root-mean-square error of cross-validation values regarding the spectral range, derivative order, and choice of method (partial least-squares or principal component regression), which resulted in excellent predictions for test sets (R(2) > 0.99 in all cases). Additionally, experimentally more demanding gas chromatography analysis of fatty acid content was carried out for all specimens, confirming the results obtained by FTIR-ATR coupled with principal component analysis. However, FTIR-ATR provided a considerably better model for prediction of mixture composition than gas chromatography, especially for high oleic sunflower oil.

Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

NASA Astrophysics Data System (ADS)

Li, Jiangtong; Luo, Yongdao; Dai, Honglin

2018-01-01

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

Short communication: Discrimination between retail bovine milks with different fat contents using chemometrics and fatty acid profiling.

PubMed

Vargas-Bello-Pérez, Einar; Toro-Mujica, Paula; Enriquez-Hidalgo, Daniel; Fellenberg, María Angélica; Gómez-Cortés, Pilar

2017-06-01

We used a multivariate chemometric approach to differentiate or associate retail bovine milks with different fat contents and non-dairy beverages, using fatty acid profiles and statistical analysis. We collected samples of bovine milk (whole, semi-skim, and skim; n = 62) and non-dairy beverages (n = 27), and we analyzed them using gas-liquid chromatography. Principal component analysis of the fatty acid data yielded 3 significant principal components, which accounted for 72% of the total variance in the data set. Principal component 1 was related to saturated fatty acids (C4:0, C6:0, C8:0, C12:0, C14:0, C17:0, and C18:0) and monounsaturated fatty acids (C14:1 cis-9, C16:1 cis-9, C17:1 cis-9, and C18:1 trans-11); whole milk samples were clearly differentiated from the rest using this principal component. Principal component 2 differentiated semi-skim milk samples by n-3 fatty acid content (C20:3n-3, C20:5n-3, and C22:6n-3). Principal component 3 was related to C18:2 trans-9,trans-12 and C20:4n-6, and its lower scores were observed in skim milk and non-dairy beverages. A cluster analysis yielded 3 groups: group 1 consisted of only whole milk samples, group 2 was represented mainly by semi-skim milks, and group 3 included skim milk and non-dairy beverages. Overall, the present study showed that a multivariate chemometric approach is a useful tool for differentiating or associating retail bovine milks and non-dairy beverages using their fatty acid profile. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Use of multivariate statistics to identify unreliable data obtained using CASA.

PubMed

Martínez, Luis Becerril; Crispín, Rubén Huerta; Mendoza, Maximino Méndez; Gallegos, Oswaldo Hernández; Martínez, Andrés Aragón

2013-06-01

In order to identify unreliable data in a dataset of motility parameters obtained from a pilot study acquired by a veterinarian with experience in boar semen handling, but without experience in the operation of a computer assisted sperm analysis (CASA) system, a multivariate graphical and statistical analysis was performed. Sixteen boar semen samples were aliquoted then incubated with varying concentrations of progesterone from 0 to 3.33 µg/ml and analyzed in a CASA system. After standardization of the data, Chernoff faces were pictured for each measurement, and a principal component analysis (PCA) was used to reduce the dimensionality and pre-process the data before hierarchical clustering. The first twelve individual measurements showed abnormal features when Chernoff faces were drawn. PCA revealed that principal components 1 and 2 explained 63.08% of the variance in the dataset. Values of principal components for each individual measurement of semen samples were mapped to identify differences among treatment or among boars. Twelve individual measurements presented low values of principal component 1. Confidence ellipses on the map of principal components showed no statistically significant effects for treatment or boar. Hierarchical clustering realized on two first principal components produced three clusters. Cluster 1 contained evaluations of the two first samples in each treatment, each one of a different boar. With the exception of one individual measurement, all other measurements in cluster 1 were the same as observed in abnormal Chernoff faces. Unreliable data in cluster 1 are probably related to the operator inexperience with a CASA system. These findings could be used to objectively evaluate the skill level of an operator of a CASA system. This may be particularly useful in the quality control of semen analysis using CASA systems.

[Spatial distribution characteristics of the physical and chemical properties of water in the Kunes River after the supply of snowmelt during spring].

PubMed

Liu, Xiang; Guo, Ling-Peng; Zhang, Fei-Yun; Ma, Jie; Mu, Shu-Yong; Zhao, Xin; Li, Lan-Hai

2015-02-01

Eight physical and chemical indicators related to water quality were monitored from nineteen sampling sites along the Kunes River at the end of snowmelt season in spring. To investigate the spatial distribution characteristics of water physical and chemical properties, cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) are employed. The result of cluster analysis showed that the Kunes River could be divided into three reaches according to the similarities of water physical and chemical properties among sampling sites, representing the upstream, midstream and downstream of the river, respectively; The result of discriminant analysis demonstrated that the reliability of such a classification was high, and DO, Cl- and BOD5 were the significant indexes leading to this classification; Three principal components were extracted on the basis of the principal component analysis, in which accumulative variance contribution could reach 86.90%. The result of principal component analysis also indicated that water physical and chemical properties were mostly affected by EC, ORP, NO3(-) -N, NH4(+) -N, Cl- and BOD5. The sorted results of principal component scores in each sampling sites showed that the water quality was mainly influenced by DO in upstream, by pH in midstream, and by the rest of indicators in downstream. The order of comprehensive scores for principal components revealed that the water quality degraded from the upstream to downstream, i.e., the upstream had the best water quality, followed by the midstream, while the water quality at downstream was the worst. This result corresponded exactly to the three reaches classified using cluster analysis. Anthropogenic activity and the accumulation of pollutants along the river were probably the main reasons leading to this spatial difference.

Evidence for age-associated disinhibition of the wake drive provided by scoring principal components of the resting EEG spectrum in sleep-provoking conditions.

PubMed

Putilov, Arcady A; Donskaya, Olga G

2016-01-01

Age-associated changes in different bandwidths of the human electroencephalographic (EEG) spectrum are well documented, but their functional significance is poorly understood. This spectrum seems to represent summation of simultaneous influences of several sleep-wake regulatory processes. Scoring of its orthogonal (uncorrelated) principal components can help in separation of the brain signatures of these processes. In particular, the opposite age-associated changes were documented for scores on the two largest (1st and 2nd) principal components of the sleep EEG spectrum. A decrease of the first score and an increase of the second score can reflect, respectively, the weakening of the sleep drive and disinhibition of the opposing wake drive with age. In order to support the suggestion of age-associated disinhibition of the wake drive from the antagonistic influence of the sleep drive, we analyzed principal component scores of the resting EEG spectra obtained in sleep deprivation experiments with 81 healthy young adults aged between 19 and 26 and 40 healthy older adults aged between 45 and 66 years. At the second day of the sleep deprivation experiments, frontal scores on the 1st principal component of the EEG spectrum demonstrated an age-associated reduction of response to eyes closed relaxation. Scores on the 2nd principal component were either initially increased during wakefulness or less responsive to such sleep-provoking conditions (frontal and occipital scores, respectively). These results are in line with the suggestion of disinhibition of the wake drive with age. They provide an explanation of why older adults are less vulnerable to sleep deprivation than young adults.

Diagnostics and Active Control of Aircraft Interior Noise

NASA Technical Reports Server (NTRS)

Fuller, C. R.

1998-01-01

This project deals with developing advanced methods for investigating and controlling interior noise in aircraft. The work concentrates on developing and applying the techniques of Near Field Acoustic Holography (NAH) and Principal Component Analysis (PCA) to the aircraft interior noise dynamic problem. This involves investigating the current state of the art, developing new techniques and then applying them to the particular problem being studied. The knowledge gained under the first part of the project was then used to develop and apply new, advanced noise control techniques for reducing interior noise. A new fully active control approach based on the PCA was developed and implemented on a test cylinder. Finally an active-passive approach based on tunable vibration absorbers was to be developed and analytically applied to a range of test structures from simple plates to aircraft fuselages.

Application of principal component analysis to ecodiversity assessment of postglacial landscape (on the example of Debnica Kaszubska commune, Middle Pomerania)

NASA Astrophysics Data System (ADS)

Wojciechowski, Adam

2017-04-01

In order to assess ecodiversity understood as a comprehensive natural landscape factor (Jedicke 2001), it is necessary to apply research methods which recognize the environment in a holistic way. Principal component analysis may be considered as one of such methods as it allows to distinguish the main factors determining landscape diversity on the one hand, and enables to discover regularities shaping the relationships between various elements of the environment under study on the other hand. The procedure adopted to assess ecodiversity with the use of principal component analysis involves: a) determining and selecting appropriate factors of the assessed environment qualities (hypsometric, geological, hydrographic, plant, and others); b) calculating the absolute value of individual qualities for the basic areas under analysis (e.g. river length, forest area, altitude differences, etc.); c) principal components analysis and obtaining factor maps (maps of selected components); d) generating a resultant, detailed map and isolating several classes of ecodiversity. An assessment of ecodiversity with the use of principal component analysis was conducted in the test area of 299,67 km2 in Debnica Kaszubska commune. The whole commune is situated in the Weichselian glaciation area of high hypsometric and morphological diversity as well as high geo- and biodiversity. The analysis was based on topographical maps of the commune area in scale 1:25000 and maps of forest habitats. Consequently, nine factors reflecting basic environment elements were calculated: maximum height (m), minimum height (m), average height (m), the length of watercourses (km), the area of water reservoirs (m2), total forest area (ha), coniferous forests habitats area (ha), deciduous forest habitats area (ha), alder habitats area (ha). The values for individual factors were analysed for 358 grid cells of 1 km2. Based on the principal components analysis, four major factors affecting commune ecodiversity were distinguished: hypsometric component (PC1), deciduous forest habitats component (PC2), river valleys and alder habitats component (PC3), and lakes component (PC4). The distinguished factors characterise natural qualities of postglacial area and reflect well the role of the four most important groups of environment components in shaping ecodiversity of the area under study. The map of ecodiversity of Debnica Kaszubska commune was created on the basis of the first four principal component scores and then five classes of diversity were isolated: very low, low, average, high and very high. As a result of the assessment, five commune regions of very high ecodiversity were separated. These regions are also very attractive for tourists and valuable in terms of their rich nature which include protected areas such as Slupia Valley Landscape Park. The suggested method of ecodiversity assessment with the use of principal component analysis may constitute an alternative methodological proposition to other research methods used so far. Literature Jedicke E., 2001. Biodiversität, Geodiversität, Ökodiversität. Kriterien zur Analyse der Landschaftsstruktur - ein konzeptioneller Diskussionsbeitrag. Naturschutz und Landschaftsplanung, 33(2/3), 59-68.

A HIERARCHIAL STOCHASTIC MODEL OF LARGE SCALE ATMOSPHERIC CIRCULATION PATTERNS AND MULTIPLE STATION DAILY PRECIPITATION

EPA Science Inventory

A stochastic model of weather states and concurrent daily precipitation at multiple precipitation stations is described. our algorithms are invested for classification of daily weather states; k means, fuzzy clustering, principal components, and principal components coupled with ...

Rosacea assessment by erythema index and principal component analysis segmentation maps

NASA Astrophysics Data System (ADS)

Kuzmina, Ilona; Rubins, Uldis; Saknite, Inga; Spigulis, Janis

2017-12-01

RGB images of rosacea were analyzed using segmentation maps of principal component analysis (PCA) and erythema index (EI). Areas of segmented clusters were compared to Clinician's Erythema Assessment (CEA) values given by two dermatologists. The results show that visible blood vessels are segmented more precisely on maps of the erythema index and the third principal component (PC3). In many cases, a distribution of clusters on EI and PC3 maps are very similar. Mean values of clusters' areas on these maps show a decrease of the area of blood vessels and erythema and an increase of lighter skin area after the therapy for the patients with diagnosis CEA = 2 on the first visit and CEA=1 on the second visit. This study shows that EI and PC3 maps are more useful than the maps of the first (PC1) and second (PC2) principal components for indicating vascular structures and erythema on the skin of rosacea patients and therapy monitoring.

Multilevel sparse functional principal component analysis.

PubMed

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

2014-01-29

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

[Content of mineral elements of Gastrodia elata by principal components analysis].

PubMed

Li, Jin-ling; Zhao, Zhi; Liu, Hong-chang; Luo, Chun-li; Huang, Ming-jin; Luo, Fu-lai; Wang, Hua-lei

2015-03-01

To study the content of mineral elements and the principal components in Gastrodia elata. Mineral elements were determined by ICP and the data was analyzed by SPSS. K element has the highest content-and the average content was 15.31 g x kg(-1). The average content of N element was 8.99 g x kg(-1), followed by K element. The coefficient of variation of K and N was small, but the Mn was the biggest with 51.39%. The highly significant positive correlation was found among N, P and K . Three principal components were selected by principal components analysis to evaluate the quality of G. elata. P, B, N, K, Cu, Mn, Fe and Mg were the characteristic elements of G. elata. The content of K and N elements was higher and relatively stable. The variation of Mn content was biggest. The quality of G. elata in Guizhou and Yunnan was better from the perspective of mineral elements.

Visualizing Hyolaryngeal Mechanics in Swallowing Using Dynamic MRI

PubMed Central

Pearson, William G.; Zumwalt, Ann C.

2013-01-01

Introduction Coordinates of anatomical landmarks are captured using dynamic MRI to explore whether a proposed two-sling mechanism underlies hyolaryngeal elevation in pharyngeal swallowing. A principal components analysis (PCA) is applied to coordinates to determine the covariant function of the proposed mechanism. Methods Dynamic MRI (dMRI) data were acquired from eleven healthy subjects during a repeated swallows task. Coordinates mapping the proposed mechanism are collected from each dynamic (frame) of a dynamic MRI swallowing series of a randomly selected subject in order to demonstrate shape changes in a single subject. Coordinates representing minimum and maximum hyolaryngeal elevation of all 11 subjects were also mapped to demonstrate shape changes of the system among all subjects. MophoJ software was used to perform PCA and determine vectors of shape change (eigenvectors) for elements of the two-sling mechanism of hyolaryngeal elevation. Results For both single subject and group PCAs, hyolaryngeal elevation accounted for the first principal component of variation. For the single subject PCA, the first principal component accounted for 81.5% of the variance. For the between subjects PCA, the first principal component accounted for 58.5% of the variance. Eigenvectors and shape changes associated with this first principal component are reported. Discussion Eigenvectors indicate that two-muscle slings and associated skeletal elements function as components of a covariant mechanism to elevate the hyolaryngeal complex. Morphological analysis is useful to model shape changes in the two-sling mechanism of hyolaryngeal elevation. PMID:25090608

Obesity, metabolic syndrome, impaired fasting glucose, and microvascular dysfunction: a principal component analysis approach.

PubMed

Panazzolo, Diogo G; Sicuro, Fernando L; Clapauch, Ruth; Maranhão, Priscila A; Bouskela, Eliete; Kraemer-Aguiar, Luiz G

2012-11-13

We aimed to evaluate the multivariate association between functional microvascular variables and clinical-laboratorial-anthropometrical measurements. Data from 189 female subjects (34.0 ± 15.5 years, 30.5 ± 7.1 kg/m2), who were non-smokers, non-regular drug users, without a history of diabetes and/or hypertension, were analyzed by principal component analysis (PCA). PCA is a classical multivariate exploratory tool because it highlights common variation between variables allowing inferences about possible biological meaning of associations between them, without pre-establishing cause-effect relationships. In total, 15 variables were used for PCA: body mass index (BMI), waist circumference, systolic and diastolic blood pressure (BP), fasting plasma glucose, levels of total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), insulin, C-reactive protein (CRP), and functional microvascular variables measured by nailfold videocapillaroscopy. Nailfold videocapillaroscopy was used for direct visualization of nutritive capillaries, assessing functional capillary density, red blood cell velocity (RBCV) at rest and peak after 1 min of arterial occlusion (RBCV(max)), and the time taken to reach RBCV(max) (TRBCV(max)). A total of 35% of subjects had metabolic syndrome, 77% were overweight/obese, and 9.5% had impaired fasting glucose. PCA was able to recognize that functional microvascular variables and clinical-laboratorial-anthropometrical measurements had a similar variation. The first five principal components explained most of the intrinsic variation of the data. For example, principal component 1 was associated with BMI, waist circumference, systolic BP, diastolic BP, insulin, TG, CRP, and TRBCV(max) varying in the same way. Principal component 1 also showed a strong association among HDL-c, RBCV, and RBCV(max), but in the opposite way. Principal component 3 was associated only with microvascular variables in the same way (functional capillary density, RBCV and RBCV(max)). Fasting plasma glucose appeared to be related to principal component 4 and did not show any association with microvascular reactivity. In non-diabetic female subjects, a multivariate scenario of associations between classic clinical variables strictly related to obesity and metabolic syndrome suggests a significant relationship between these diseases and microvascular reactivity.

The factorial reliability of the Middlesex Hospital Questionnaire in normal subjects.

PubMed

Bagley, C

1980-03-01

The internal reliability of the Middlesex Hospital Questionnaire and its component subscales has been checked by means of principal components analyses of data on 256 normal subjects. The subscales (with the possible exception of Hysteria) were found to contribute to the general underlying factor of psychoneurosis. In general, the principal components analysis points to the reliability of the subscales, despite some item overlap.

The Derivation of Job Compensation Index Values from the Position Analysis Questionnaire (PAQ). Report No. 6.

ERIC Educational Resources Information Center

McCormick, Ernest J.; And Others

The study deals with the job component method of establishing compensation rates. The basic job analysis questionnaire used in the study was the Position Analysis Questionnaire (PAQ) (Form B). On the basis of a principal components analysis of PAQ data for a large sample (2,688) of jobs, a number of principal components (job dimensions) were…

Perceptions of the Principal Evaluation Process and Performance Criteria: A Qualitative Study of the Challenge of Principal Evaluation

ERIC Educational Resources Information Center

Faginski-Stark, Erica; Casavant, Christopher; Collins, William; McCandless, Jason; Tencza, Marilyn

2012-01-01

Recent federal and state mandates have tasked school systems to move beyond principal evaluation as a bureaucratic function and to re-imagine it as a critical component to improve principal performance and compel school renewal. This qualitative study investigated the district leaders' and principals' perceptions of the performance evaluation…

Differentiation of red wines using an electronic nose based on surface acoustic wave devices.

PubMed

García, M; Fernández, M J; Fontecha, J L; Lozano, J; Santos, J P; Aleixandre, M; Sayago, I; Gutiérrez, J; Horrillo, M C

2006-02-15

An electronic nose, utilizing the principle of surface acoustic waves (SAW), was used to differentiate among different wines of the same variety of grapes which come from the same cellar. The electronic nose is based on eight surface acoustic wave sensors, one is a reference sensor and the others are coated by different polymers by spray coating technique. Data analysis was performed by two pattern recognition methods; principal component analysis (PCA) and probabilistic neuronal network (PNN). The results showed that electronic nose was able to identify the tested wines.

Critical comparison of diffuse reflectance spectroscopy and colorimetry as dermatological diagnostic tools for acanthosis nigricans: a chemometric approach.

PubMed

Devpura, Suneetha; Pattamadilok, Bensachee; Syed, Zain U; Vemulapalli, Pranita; Henderson, Marsha; Rehse, Steven J; Hamzavi, Iltefat; Lim, Henry W; Naik, Ratna

2011-06-01

Quantification of skin changes due to acanthosis nigricans (AN), a disorder common among insulin-resistant diabetic and obese individuals, was investigated using two optical techniques: diffuse reflectance spectroscopy (DRS) and colorimetry. Measurements were obtained from AN lesions on the neck and two control sites of eight AN patients. A principal component/discriminant function analysis successfully differentiated between AN lesion and normal skin with 87.7% sensitivity and 94.8% specificity in DRS measurements and 97.2% sensitivity and 96.4% specificity in colorimetry measurements.

Scattering by ensembles of small particles experiment, theory and application

NASA Technical Reports Server (NTRS)

Gustafson, B. A. S.

1980-01-01

A hypothetical self consistent picture of evolution of prestellar intertellar dust through a comet phase leads to predictions about the composition of the circum-solar dust cloud. Scattering properties of thus resulting conglomerates with a bird's-nest type of structure are investigated using a micro-wave analogue technique. Approximate theoretical methods of general interest are developed which compared favorably with the experimental results. The principal features of scattering of visible radiation by zodiacal light particles are reasonably reproduced. A component which is suggestive of (ALPHA)-meteoroids is also predicted.

Spectral compression algorithms for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R.

2007-10-16

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.

A unified development of several techniques for the representation of random vectors and data sets

NASA Technical Reports Server (NTRS)

Bundick, W. T.

1973-01-01

Linear vector space theory is used to develop a general representation of a set of data vectors or random vectors by linear combinations of orthonormal vectors such that the mean squared error of the representation is minimized. The orthonormal vectors are shown to be the eigenvectors of an operator. The general representation is applied to several specific problems involving the use of the Karhunen-Loeve expansion, principal component analysis, and empirical orthogonal functions; and the common properties of these representations are developed.

Chemometric techniques on the analysis of Raman spectra of serum blood samples of breast cancer patients

NASA Astrophysics Data System (ADS)

Rocha-Osornio, L. N.; Pichardo-Molina, J. L.; Barbosa-Garcia, O.; Frausto-Reyes, C.; Araujo-Andrade, C.; Huerta-Franco, R.; Gutiérrez-Juárez, G.

2008-02-01

Raman spectroscopy and Multivariate methods were used to study serum blood samples of control and breast cancer patients. Blood samples were obtained from 11 patients and 12 controls from the central region of Mexico. Our results show that principal component analysis is able to discriminate serum sample of breast cancer patients from those of control group, also the loading vectors of PCA plotted as a function of Raman shift shown which bands permitted to make the maximum discrimination between both groups of samples.

Infrared spectroscopic imaging for noninvasive detection of latent fingerprints.

PubMed

Crane, Nicole J; Bartick, Edward G; Perlman, Rebecca Schwartz; Huffman, Scott

2007-01-01

The capability of Fourier transform infrared (FTIR) spectroscopic imaging to provide detailed images of unprocessed latent fingerprints while also preserving important trace evidence is demonstrated. Unprocessed fingerprints were developed on various porous and nonporous substrates. Data-processing methods used to extract the latent fingerprint ridge pattern from the background material included basic infrared spectroscopic band intensities, addition and subtraction of band intensity measurements, principal components analysis (PCA) and calculation of second derivative band intensities, as well as combinations of these various techniques. Additionally, trace evidence within the fingerprints was recovered and identified.

Genus- and species-level identification of dermatophyte fungi by surface-enhanced Raman spectroscopy.

PubMed

Witkowska, Evelin; Jagielski, Tomasz; Kamińska, Agnieszka

2018-03-05

This paper demonstrates that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) can serve as a fast and reliable technique for detection and identification of dermatophyte fungi at both genus and species level. Dermatophyte infections are the most common mycotic diseases worldwide, affecting a quarter of the human population. Currently, there is no optimal method for detection and identification of fungal diseases, as each has certain limitations. Here, for the first time, we have achieved with a high accuracy, differentiation of dermatophytes representing three major genera, i.e. Trichophyton, Microsporum, and Epidermophyton. Two first principal components (PC), namely PC-1 and PC-2, gave together 97% of total variance. Additionally, species-level identification within the Trichophyton genus has been performed. PC-1 and PC-2, which are the most diagnostically significant, explain 98% of the variance in the data obtained from spectra of: Trichophyton rubrum, Trichophyton menatgrophytes, Trichophyton interdigitale and Trichophyton tonsurans. This study offers a new diagnostic approach for the identification of dermatophytes. Being fast, reliable and cost-effective, it has the potential to be incorporated in the clinical practice to improve diagnostics of medically important fungi. Copyright © 2017 Elsevier B.V. All rights reserved.

A Label-Free Fluorescent Array Sensor Utilizing Liposome Encapsulating Calcein for Discriminating Target Proteins by Principal Component Analysis

PubMed Central

Imamura, Ryota; Murata, Naoki; Shimanouchi, Toshinori; Yamashita, Kaoru; Fukuzawa, Masayuki; Noda, Minoru

2017-01-01

A new fluorescent arrayed biosensor has been developed to discriminate species and concentrations of target proteins by using plural different phospholipid liposome species encapsulating fluorescent molecules, utilizing differences in permeation of the fluorescent molecules through the membrane to modulate liposome-target protein interactions. This approach proposes a basically new label-free fluorescent sensor, compared with the common technique of developed fluorescent array sensors with labeling. We have confirmed a high output intensity of fluorescence emission related to characteristics of the fluorescent molecules dependent on their concentrations when they leak from inside the liposomes through the perturbed lipid membrane. After taking an array image of the fluorescence emission from the sensor using a CMOS imager, the output intensities of the fluorescence were analyzed by a principal component analysis (PCA) statistical method. It is found from PCA plots that different protein species with several concentrations were successfully discriminated by using the different lipid membranes with high cumulative contribution ratio. We also confirmed that the accuracy of the discrimination by the array sensor with a single shot is higher than that of a single sensor with multiple shots. PMID:28714873

A Label-Free Fluorescent Array Sensor Utilizing Liposome Encapsulating Calcein for Discriminating Target Proteins by Principal Component Analysis.

PubMed

Imamura, Ryota; Murata, Naoki; Shimanouchi, Toshinori; Yamashita, Kaoru; Fukuzawa, Masayuki; Noda, Minoru

2017-07-15

A new fluorescent arrayed biosensor has been developed to discriminate species and concentrations of target proteins by using plural different phospholipid liposome species encapsulating fluorescent molecules, utilizing differences in permeation of the fluorescent molecules through the membrane to modulate liposome-target protein interactions. This approach proposes a basically new label-free fluorescent sensor, compared with the common technique of developed fluorescent array sensors with labeling. We have confirmed a high output intensity of fluorescence emission related to characteristics of the fluorescent molecules dependent on their concentrations when they leak from inside the liposomes through the perturbed lipid membrane. After taking an array image of the fluorescence emission from the sensor using a CMOS imager, the output intensities of the fluorescence were analyzed by a principal component analysis (PCA) statistical method. It is found from PCA plots that different protein species with several concentrations were successfully discriminated by using the different lipid membranes with high cumulative contribution ratio. We also confirmed that the accuracy of the discrimination by the array sensor with a single shot is higher than that of a single sensor with multiple shots.

Spatial and spectral analysis of corneal epithelium injury using hyperspectral images

NASA Astrophysics Data System (ADS)

Md Noor, Siti Salwa; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang

2017-12-01

Eye assessment is essential in preventing blindness. Currently, the existing methods to assess corneal epithelium injury are complex and require expert knowledge. Hence, we have introduced a non-invasive technique using hyperspectral imaging (HSI) and an image analysis algorithm of corneal epithelium injury. Three groups of images were compared and analyzed, namely healthy eyes, injured eyes, and injured eyes with stain. Dimensionality reduction using principal component analysis (PCA) was applied to reduce massive data and redundancies. The first 10 principal components (PCs) were selected for further processing. The mean vector of 10 PCs with 45 pairs of all combinations was computed and sent to two classifiers. A quadratic Bayes normal classifier (QDC) and a support vector classifier (SVC) were used in this study to discriminate the eleven eyes into three groups. As a result, the combined classifier of QDC and SVC showed optimal performance with 2D PCA features (2DPCA-QDSVC) and was utilized to classify normal and abnormal tissues, using color image segmentation. The result was compared with human segmentation. The outcome showed that the proposed algorithm produced extremely promising results to assist the clinician in quantifying a cornea injury.

A climatology of total ozone mapping spectrometer data using rotated principal component analysis

NASA Astrophysics Data System (ADS)

Eder, Brian K.; Leduc, Sharon K.; Sickles, Joseph E.

1999-02-01

The spatial and temporal variability of total column ozone (Ω) obtained from the total ozone mapping spectrometer (TOMS version 7.0) during the period 1980-1992 was examined through the use of a multivariate statistical technique called rotated principal component analysis. Utilization of Kaiser's varimax orthogonal rotation led to the identification of 14, mostly contiguous subregions that together accounted for more than 70% of the total Ω variance. Each subregion displayed statistically unique Ω characteristics that were further examined through time series and spectral density analyses, revealing significant periodicities on semiannual, annual, quasi-biennial, and longer term time frames. This analysis facilitated identification of the probable mechanisms responsible for the variability of Ω within the 14 homogeneous subregions. The mechanisms were either dynamical in nature (i.e., advection associated with baroclinic waves, the quasi-biennial oscillation, or El Niño-Southern Oscillation) or photochemical in nature (i.e., production of odd oxygen (O or O3) associated with the annual progression of the Sun). The analysis has also revealed that the influence of a data retrieval artifact, found in equatorial latitudes of version 6.0 of the TOMS data, has been reduced in version 7.0.

Screening of oil sources by using comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry and multivariate statistical analysis.

PubMed

Zhang, Wanfeng; Zhu, Shukui; He, Sheng; Wang, Yanxin

2015-02-06

Using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/TOFMS), volatile and semi-volatile organic compounds in crude oil samples from different reservoirs or regions were analyzed for the development of a molecular fingerprint database. Based on the GC×GC/TOFMS fingerprints of crude oils, principal component analysis (PCA) and cluster analysis were used to distinguish the oil sources and find biomarkers. As a supervised technique, the geological characteristics of crude oils, including thermal maturity, sedimentary environment etc., are assigned to the principal components. The results show that tri-aromatic steroid (TAS) series are the suitable marker compounds in crude oils for the oil screening, and the relative abundances of individual TAS compounds have excellent correlation with oil sources. In order to correct the effects of some other external factors except oil sources, the variables were defined as the content ratio of some target compounds and 13 parameters were proposed for the screening of oil sources. With the developed model, the crude oils were easily discriminated, and the result is in good agreement with the practical geological setting. Copyright © 2014 Elsevier B.V. All rights reserved.

Genus- and species-level identification of dermatophyte fungi by surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Witkowska, Evelin; Jagielski, Tomasz; Kamińska, Agnieszka

2018-03-01

This paper demonstrates that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) can serve as a fast and reliable technique for detection and identification of dermatophyte fungi at both genus and species level. Dermatophyte infections are the most common mycotic diseases worldwide, affecting a quarter of the human population. Currently, there is no optimal method for detection and identification of fungal diseases, as each has certain limitations. Here, for the first time, we have achieved with a high accuracy, differentiation of dermatophytes representing three major genera, i.e. Trichophyton, Microsporum, and Epidermophyton. Two first principal components (PC), namely PC-1 and PC-2, gave together 97% of total variance. Additionally, species-level identification within the Trichophyton genus has been performed. PC-1 and PC-2, which are the most diagnostically significant, explain 98% of the variance in the data obtained from spectra of: Trichophyton rubrum, Trichophyton menatgrophytes, Trichophyton interdigitale and Trichophyton tonsurans. This study offers a new diagnostic approach for the identification of dermatophytes. Being fast, reliable and cost-effective, it has the potential to be incorporated in the clinical practice to improve diagnostics of medically important fungi.

Application of principal component analysis (PCA) and improved joint probability distributions to the inverse first-order reliability method (I-FORM) for predicting extreme sea states

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eckert-Gallup, Aubrey C.; Sallaberry, Cédric J.; Dallman, Ann R.

Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulations as a part of the standard current practice for designing marine structures to survive extreme sea states. These environmental contours are characterized by combinations of significant wave height (H s) and either energy period (T e) or peak period (T p) values calculated for a given recurrence interval using a set of data based on hindcast simulations or buoy observations over a sufficient period of record. The use of the inverse first-order reliability method (I-FORM) is a standard design practice for generating environmentalmore » contours. This paper develops enhanced methodologies for data analysis prior to the application of the I-FORM, including the use of principal component analysis (PCA) to create an uncorrelated representation of the variables under consideration as well as new distribution and parameter fitting techniques. As a result, these modifications better represent the measured data and, therefore, should contribute to the development of more realistic representations of environmental contours of extreme sea states for determining design loads for marine structures.« less

Application of principal component analysis (PCA) and improved joint probability distributions to the inverse first-order reliability method (I-FORM) for predicting extreme sea states

DOE PAGES

Eckert-Gallup, Aubrey C.; Sallaberry, Cédric J.; Dallman, Ann R.; ...

2016-01-06

Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulations as a part of the standard current practice for designing marine structures to survive extreme sea states. These environmental contours are characterized by combinations of significant wave height (H s) and either energy period (T e) or peak period (T p) values calculated for a given recurrence interval using a set of data based on hindcast simulations or buoy observations over a sufficient period of record. The use of the inverse first-order reliability method (I-FORM) is a standard design practice for generating environmentalmore » contours. This paper develops enhanced methodologies for data analysis prior to the application of the I-FORM, including the use of principal component analysis (PCA) to create an uncorrelated representation of the variables under consideration as well as new distribution and parameter fitting techniques. As a result, these modifications better represent the measured data and, therefore, should contribute to the development of more realistic representations of environmental contours of extreme sea states for determining design loads for marine structures.« less

Automated diagnosis of Alzheimer's disease with multi-atlas based whole brain segmentations

NASA Astrophysics Data System (ADS)

Luo, Yuan; Tang, Xiaoying

2017-03-01

Voxel-based analysis is widely used in quantitative analysis of structural brain magnetic resonance imaging (MRI) and automated disease detection, such as Alzheimer's disease (AD). However, noise at the voxel level may cause low sensitivity to AD-induced structural abnormalities. This can be addressed with the use of a whole brain structural segmentation approach which greatly reduces the dimension of features (the number of voxels). In this paper, we propose an automatic AD diagnosis system that combines such whole brain segmen- tations with advanced machine learning methods. We used a multi-atlas segmentation technique to parcellate T1-weighted images into 54 distinct brain regions and extract their structural volumes to serve as the features for principal-component-analysis-based dimension reduction and support-vector-machine-based classification. The relationship between the number of retained principal components (PCs) and the diagnosis accuracy was systematically evaluated, in a leave-one-out fashion, based on 28 AD subjects and 23 age-matched healthy subjects. Our approach yielded pretty good classification results with 96.08% overall accuracy being achieved using the three foremost PCs. In addition, our approach yielded 96.43% specificity, 100% sensitivity, and 0.9891 area under the receiver operating characteristic curve.

Background recovery via motion-based robust principal component analysis with matrix factorization

NASA Astrophysics Data System (ADS)

Pan, Peng; Wang, Yongli; Zhou, Mingyuan; Sun, Zhipeng; He, Guoping

2018-03-01

Background recovery is a key technique in video analysis, but it still suffers from many challenges, such as camouflage, lighting changes, and diverse types of image noise. Robust principal component analysis (RPCA), which aims to recover a low-rank matrix and a sparse matrix, is a general framework for background recovery. The nuclear norm is widely used as a convex surrogate for the rank function in RPCA, which requires computing the singular value decomposition (SVD), a task that is increasingly costly as matrix sizes and ranks increase. However, matrix factorization greatly reduces the dimension of the matrix for which the SVD must be computed. Motion information has been shown to improve low-rank matrix recovery in RPCA, but this method still finds it difficult to handle original video data sets because of its batch-mode formulation and implementation. Hence, in this paper, we propose a motion-assisted RPCA model with matrix factorization (FM-RPCA) for background recovery. Moreover, an efficient linear alternating direction method of multipliers with a matrix factorization (FL-ADM) algorithm is designed for solving the proposed FM-RPCA model. Experimental results illustrate that the method provides stable results and is more efficient than the current state-of-the-art algorithms.

Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

PubMed Central

Hirayama, Jun-ichiro; Hyvärinen, Aapo; Kiviniemi, Vesa; Kawanabe, Motoaki; Yamashita, Okito

2016-01-01

Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated “eigenconnectivity” patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra- and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module-based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods. PMID:28002474

Correspondence Analysis-Theory and Application in Management Accounting Research

NASA Astrophysics Data System (ADS)

Duller, Christine

2010-09-01

Correspondence analysis is an explanatory data analytic technique and is used to identify systematic relations between categorical variables. It is related to principal component analysis and the results provide information on the structure of categorical variables similar to the results given by a principal component analysis in case of metric variables. Classical correspondence analysis is designed two-dimensional, whereas multiple correspondence analysis is an extension to more than two variables. After an introductory overview of the idea and the implementation in standard software packages (PASW, SAS, R) an example in recent research is presented, which deals with strategic management accounting in family and non-family enterprises in Austria, where 70% to 80% of all enterprises can be classified as family firms. Although there is a growing body of literature focusing on various management issues in family firms, so far the state of the art of strategic management accounting in family firms is an empirically under-researched subject. In relevant literature only the (empirically untested) hypothesis can be found, that family firms tend to have less formalized management accounting systems than non-family enterprises. Creating a correspondence analysis will help to identify the underlying structure, which is responsible for differences in strategic management accounting.

Fractal Dimension Change Point Model for Hydrothermal Alteration Anomalies in Silk Road Economic Belt, the Beishan Area, Gansu, China

NASA Astrophysics Data System (ADS)

Han, H. H.; Wang, Y. L.; Ren, G. L.; LI, J. Q.; Gao, T.; Yang, M.; Yang, J. L.

2016-11-01

Remote sensing plays an important role in mineral exploration of “One Belt One Road” plan. One of its applications is extracting and locating hydrothermal alteration zones that are related to mines. At present, the extracting method for alteration anomalies from principal component image mainly relies on the data's normal distribution, without considering the nonlinear characteristics of geological anomaly. In this study, a Fractal Dimension Change Point Model (FDCPM), calculated by the self-similarity and mutability of alteration anomalies, is employed to quantitatively acquire the critical threshold of alteration anomalies. The realization theory and access mechanism of the model are elaborated by an experiment with ASTER data in Beishan mineralization belt, also the results are compared with traditional method (De-Interfered Anomalous Principal Component Thresholding Technique, DIAPCTT). The results show that the findings produced by FDCPM are agree with well with a mounting body of evidence from different perspectives, with the extracting accuracy over 80%, indicating that FDCPM is an effective extracting method for remote sensing alteration anomalies, and could be used as an useful tool for mineral exploration in similar areas in Silk Road Economic Belt.

Spectroscopic study of honey from Apis mellifera from different regions in Mexico

NASA Astrophysics Data System (ADS)

Frausto-Reyes, C.; Casillas-Peñuelas, R.; Quintanar-Stephano, JL; Macías-López, E.; Bujdud-Pérez, JM; Medina-Ramírez, I.

2017-05-01

The objective of this study was to analyze by Raman and UV-Vis-NIR Spectroscopic techniques, Mexican honey from Apis Mellífera, using representative samples with different botanic origins (unifloral and multifloral) and diverse climates. Using Raman spectroscopy together with principal components analysis, the results obtained represent the possibility to use them for determination of floral origin of honey, independently of the region of sampling. For this, the effect of heat up the honey was analyzed in relation that it was possible to greatly reduce the fluorescence background in Raman spectra, which allowed the visualization of fructose and glucose peaks. Using UV-Vis-NIR, spectroscopy, a characteristic spectrum profile of transmittance was obtained for each honey type. In addition, to have an objective characterization of color, a CIE Yxy and CIE L*a*b* colorimetric register was realized for each honey type. Applying the principal component analysis and their correlation with chromaticity coordinates allowed classifying the honey samples in one plot as: cutoff wavelength, maximum transmittance, tones and lightness. The results show that it is possible to obtain a spectroscopic record of honeys with specific characteristics by reducing the effects of fluorescence.

2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.

PubMed

Du, Qi-Shi; Wang, Shu-Qing; Xie, Neng-Zhong; Wang, Qing-Yan; Huang, Ri-Bo; Chou, Kuo-Chen

2017-09-19

A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.

Modified Inverse First Order Reliability Method (I-FORM) for Predicting Extreme Sea States.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eckert-Gallup, Aubrey Celia; Sallaberry, Cedric Jean-Marie; Dallman, Ann Renee

Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulation s as a part of the stand ard current practice for designing marine structure s to survive extreme sea states. Such environmental contours are characterized by combinations of significant wave height ( ) and energy period ( ) values calculated for a given recurrence interval using a set of data based on hindcast simulations or buoy observations over a sufficient period of record. The use of the inverse first - order reliability method (IFORM) i s standard design practice for generating environmental contours.more » In this paper, the traditional appli cation of the IFORM to generating environmental contours representing extreme sea states is described in detail and its merits and drawbacks are assessed. The application of additional methods for analyzing sea state data including the use of principal component analysis (PCA) to create an uncorrelated representation of the data under consideration is proposed. A reexamination of the components of the IFORM application to the problem at hand including the use of new distribution fitting techniques are shown to contribute to the development of more accurate a nd reasonable representations of extreme sea states for use in survivability analysis for marine struc tures. Keywords: In verse FORM, Principal Component Analysis , Environmental Contours, Extreme Sea State Characteri zation, Wave Energy Converters« less

High-level user interfaces for transfer function design with semantics.

PubMed

Salama, Christof Rezk; Keller, Maik; Kohlmann, Peter

2006-01-01

Many sophisticated techniques for the visualization of volumetric data such as medical data have been published. While existing techniques are mature from a technical point of view, managing the complexity of visual parameters is still difficult for non-expert users. To this end, this paper presents new ideas to facilitate the specification of optical properties for direct volume rendering. We introduce an additional level of abstraction for parametric models of transfer functions. The proposed framework allows visualization experts to design high-level transfer function models which can intuitively be used by non-expert users. The results are user interfaces which provide semantic information for specialized visualization problems. The proposed method is based on principal component analysis as well as on concepts borrowed from computer animation.

Face recognition using 3D facial shape and color map information: comparison and combination

NASA Astrophysics Data System (ADS)

Godil, Afzal; Ressler, Sandy; Grother, Patrick

2004-08-01

In this paper, we investigate the use of 3D surface geometry for face recognition and compare it to one based on color map information. The 3D surface and color map data are from the CAESAR anthropometric database. We find that the recognition performance is not very different between 3D surface and color map information using a principal component analysis algorithm. We also discuss the different techniques for the combination of the 3D surface and color map information for multi-modal recognition by using different fusion approaches and show that there is significant improvement in results. The effectiveness of various techniques is compared and evaluated on a dataset with 200 subjects in two different positions.

Recycling of nickel-metal hydride battery scrap

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lyman, J.W.; Palmer, G.R.

1994-12-31

Nickel-metal hydride (Ni-MH) battery technology is being developed as a NiCd replacement for applications in consumer cells and electric vehicle batteries. The U.S. Bureau of Mines is investigating hydrometallurgical recycling technology that separates and recovers individual components from Ni-MH battery scrap. Acid dissolution and metal recovery techniques such as precipitation and solvent extraction produced purified products of rare-earths, nickel, and other metals associated with AB{sub 2} and AB{sub 5} Ni-MH scrap. Tests were conducted on scrap cells of a single chemistry that had been de-canned to reduce iron content. Although recovery techniques have been identified in principal, their applicability tomore » mixed battery waste stream and economic attractiveness remain to be demonstrated. 14 refs.« less

Forensic engineering: applying materials and mechanics principles to the investigation of product failures.

PubMed

Hainsworth, S V; Fitzpatrick, M E

2007-06-01

Forensic engineering is the application of engineering principles or techniques to the investigation of materials, products, structures or components that fail or do not perform as intended. In particular, forensic engineering can involve providing solutions to forensic problems by the application of engineering science. A criminal aspect may be involved in the investigation but often the problems are related to negligence, breach of contract, or providing information needed in the redesign of a product to eliminate future failures. Forensic engineering may include the investigation of the physical causes of accidents or other sources of claims and litigation (for example, patent disputes). It involves the preparation of technical engineering reports, and may require giving testimony and providing advice to assist in the resolution of disputes affecting life or property.This paper reviews the principal methods available for the analysis of failed components and then gives examples of different component failure modes through selected case studies.

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 prediction of the synoptic scale ozone component was found to be the highest when we consider the synoptic scale component of the time series for solar radiation and temperature. KEY WORDS: multivariate analysis; principal component; canonical variate pairs; eigenvalue; eigenvector; ozone; solar radiation; spectral decomposition; Kalman filter; time series prediction

Experimental Researches on the Durability Indicators and the Physiological Comfort of Fabrics using the Principal Component Analysis (PCA) Method

NASA Astrophysics Data System (ADS)

Hristian, L.; Ostafe, M. M.; Manea, L. R.; Apostol, L. L.

2017-06-01

The work pursued the distribution of combed wool fabrics destined to manufacturing of external articles of clothing in terms of the values of durability and physiological comfort indices, using the mathematical model of Principal Component Analysis (PCA). Principal Components Analysis (PCA) applied in this study is a descriptive method of the multivariate analysis/multi-dimensional data, and aims to reduce, under control, the number of variables (columns) of the matrix data as much as possible to two or three. Therefore, based on the information about each group/assortment of fabrics, it is desired that, instead of nine inter-correlated variables, to have only two or three new variables called components. The PCA target is to extract the smallest number of components which recover the most of the total information contained in the initial data.

Psychometric evaluation of the Persian version of the Templer's Death Anxiety Scale in cancer patients.

PubMed

Soleimani, Mohammad Ali; Yaghoobzadeh, Ameneh; Bahrami, Nasim; Sharif, Saeed Pahlevan; Sharif Nia, Hamid

2016-10-01

In this study, 398 Iranian cancer patients completed the 15-item Templer's Death Anxiety Scale (TDAS). Tests of internal consistency, principal components analysis, and confirmatory factor analysis were conducted to assess the internal consistency and factorial validity of the Persian TDAS. The construct reliability statistic and average variance extracted were also calculated to measure construct reliability, convergent validity, and discriminant validity. Principal components analysis indicated a 3-component solution, which was generally supported in the confirmatory analysis. However, acceptable cutoffs for construct reliability, convergent validity, and discriminant validity were not fulfilled for the three subscales that were derived from the principal component analysis. This study demonstrated both the advantages and potential limitations of using the TDAS with Persian-speaking cancer patients.

Characterization of Ground Displacement Sources from Variational Bayesian Independent Component Analysis of Space Geodetic Time Series

NASA Astrophysics Data System (ADS)

Gualandi, Adriano; Serpelloni, Enrico; Elina Belardinelli, Maria; Bonafede, Maurizio; Pezzo, Giuseppe; Tolomei, Cristiano

2015-04-01

A critical point in the analysis of ground displacement time series, as those measured by modern space geodetic techniques (primarly continuous GPS/GNSS and InSAR) is the development of data driven methods that allow to discern and characterize the different sources that generate the observed displacements. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows to reduce the dimensionality of the data space maintaining most of the variance of the dataset explained. It reproduces the original data using a limited number of Principal Components, but it also shows some deficiencies, since PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem. The recovering and separation of the different sources that generate the observed ground deformation is a fundamental task in order to provide a physical meaning to the possible different sources. PCA fails in the BSS problem since it looks for a new Euclidean space where the projected data are uncorrelated. Usually, the uncorrelation condition is not strong enough and it has been proven that the BSS problem can be tackled imposing on the components to be independent. The Independent Component Analysis (ICA) is, in fact, another popular technique adopted to approach this problem, and it can be used in all those fields where PCA is also applied. An ICA approach enables us to explain the displacement time series imposing a fewer number of constraints on the model, and to reveal anomalies in the data such as transient deformation signals. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, we use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here we introduce the vbICA technique and present its application on synthetic data that simulate a GPS network recording ground deformation in a tectonically active region, with synthetic time-series containing interseismic, coseismic, and postseismic deformation, plus seasonal deformation, and white and coloured noise. We study the ability of the algorithm to recover the original (known) sources of deformation, and then apply it to a real scenario: the Emilia seismic sequence (2012, northern Italy), which is an example of seismic sequence occurred in a slowly converging tectonic setting, characterized by several local to regional anthropogenic or natural sources of deformation, mainly subsidence due to fluid withdrawal and sediments compaction. We apply both PCA and vbICA to displacement time-series recorded by continuous GPS and InSAR (Pezzo et al., EGU2015-8950).

Ultra-sensitive high performance liquid chromatography-laser-induced fluorescence based proteomics for clinical applications.

PubMed

Patil, Ajeetkumar; Bhat, Sujatha; Pai, Keerthilatha M; Rai, Lavanya; Kartha, V B; Chidangil, Santhosh

2015-09-08

An ultra-sensitive high performance liquid chromatography-laser induced fluorescence (HPLC-LIF) based technique has been developed by our group at Manipal, for screening, early detection, and staging for various cancers, using protein profiling of clinical samples like, body fluids, cellular specimens, and biopsy-tissue. More than 300 protein profiles of different clinical samples (serum, saliva, cellular samples and tissue homogenates) from volunteers (normal, and different pre-malignant/malignant conditions) were recorded using this set-up. The protein profiles were analyzed using principal component analysis (PCA) to achieve objective detection and classification of malignant, premalignant and healthy conditions with high sensitivity and specificity. The HPLC-LIF protein profiling combined with PCA, as a routine method for screening, diagnosis, and staging of cervical cancer and oral cancer, is discussed in this paper. In recent years, proteomics techniques have advanced tremendously in life sciences and medical sciences for the detection and identification of proteins in body fluids, tissue homogenates and cellular samples to understand biochemical mechanisms leading to different diseases. Some of the methods include techniques like high performance liquid chromatography, 2D-gel electrophoresis, MALDI-TOF-MS, SELDI-TOF-MS, CE-MS and LC-MS techniques. We have developed an ultra-sensitive high performance liquid chromatography-laser induced fluorescence (HPLC-LIF) based technique, for screening, early detection, and staging for various cancers, using protein profiling of clinical samples like, body fluids, cellular specimens, and biopsy-tissue. More than 300 protein profiles of different clinical samples (serum, saliva, cellular samples and tissue homogenates) from healthy and volunteers with different malignant conditions were recorded by using this set-up. The protein profile data were analyzed using principal component analysis (PCA) for objective classification and detection of malignant, premalignant and healthy conditions. The method is extremely sensitive to detect proteins with limit of detection of the order of femto-moles. The HPLC-LIF combined with PCA as a potential proteomic method for the diagnosis of oral cancer and cervical cancer has been discussed in this paper. This article is part of a Special Issue entitled: Proteomics in India. Copyright © 2015 Elsevier B.V. All rights reserved.

Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

ERIC Educational Resources Information Center

Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

2016-01-01

Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

Burst and Principal Components Analyses of MEA Data for 16 Chemicals Describe at Least Three Effects Classes.

EPA Science Inventory

Microelectrode arrays (MEAs) detect drug and chemical induced changes in neuronal network function and have been used for neurotoxicity screening. As a proof-•of-concept, the current study assessed the utility of analytical "fingerprinting" using Principal Components Analysis (P...

Incremental principal component pursuit for video background modeling

DOEpatents

Rodriquez-Valderrama, Paul A.; Wohlberg, Brendt

2017-03-14

An incremental Principal Component Pursuit (PCP) algorithm for video background modeling that is able to process one frame at a time while adapting to changes in background, with a computational complexity that allows for real-time processing, having a low memory footprint and is robust to translational and rotational jitter.

Visual Exploration of Semantic Relationships in Neural Word Embeddings

DOE PAGES

Liu, Shusen; Bremer, Peer-Timo; Thiagarajan, Jayaraman J.; ...

2017-08-29

Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). But, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. Particularly, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or evenmore » misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. We introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.« less

Visual Exploration of Semantic Relationships in Neural Word Embeddings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Shusen; Bremer, Peer-Timo; Thiagarajan, Jayaraman J.

Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). But, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. Particularly, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or evenmore » misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. We introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.« less

Dynamic competitive probabilistic principal components analysis.

PubMed

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.

Investigating the origins of nanostructural variations in differential ethnic hair types using X-ray scattering techniques.

PubMed

Wade, M; Tucker, I; Cunningham, P; Skinner, R; Bell, F; Lyons, T; Patten, K; Gonzalez, L; Wess, T

2013-10-01

Human hair is a major determinant of visual ethnic differentiation. Although hair types are celebrated as part of our ethnic diversity, the approach to hair care has made the assumption that hair types are structurally and chemically similar. Although this is clearly not the case at the macroscopic level, the intervention of many hair treatments is at the nanoscopic and molecular levels. The purpose of the work presented here is to identify the main nanoscopic and molecular hierarchical differences across five different ethnic hair types from hair fibres taken exclusively from the scalp. These are Afro (subdivided into elastic 'rubber' and softer non-elastic 'soft'), Chinese, European and Mullato (mixed race). Small angle X-Ray scattering (SAXS) is a technique capable of resolving nanostructural variations in complex materials. Individual hair fibres from different ethnic hair types were used to investigate structural features found in common and also specific to each type. Simultaneous wide angle X-Ray scattering (WAXS) was used to analyse the submolecular level structure of the fibrous keratin present. The data sets from both techniques were analysed with principal component analysis (PCA) to identify underlying variables. Principal component analysis of both SAXS and WAXS data was shown to discriminate the scattering signal between different hair types. The X-ray scattering results show a common underlying keratin intermediate filament (KIF) structure. However, distinct differences were observed in the preferential orientation and intensity signal from the lipid component of the hair. In addition, differences were observed in the intensity distribution of the very low-angle sample-dependent diffuse scatter surrounding the 'beamstop.' The results indicate that the fibrous keratin scaffold remains consistent between ethnic hair types. The hierarchies made by these may be modulated by variation in the content of keratin-associated proteins (KAPs) and lipids that alter the interfacial structures and lead to macroscopic differences in hair morphology. © 2013 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

A principal components model of soundscape perception.

PubMed

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

2010-11-01

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

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

PubMed

Das, Atanu; Mukhopadhyay, Chaitali

2007-10-28

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

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

NASA Astrophysics Data System (ADS)

Das, Atanu; Mukhopadhyay, Chaitali

2007-10-01

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

SAS program for quantitative stratigraphic correlation by principal components

USGS Publications Warehouse

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.

Implementation of an integrating sphere for the enhancement of noninvasive glucose detection using quantum cascade laser spectroscopy

NASA Astrophysics Data System (ADS)

Werth, Alexandra; Liakat, Sabbir; Dong, Anqi; Woods, Callie M.; Gmachl, Claire F.

2018-05-01

An integrating sphere is used to enhance the collection of backscattered light in a noninvasive glucose sensor based on quantum cascade laser spectroscopy. The sphere enhances signal stability by roughly an order of magnitude, allowing us to use a thermoelectrically (TE) cooled detector while maintaining comparable glucose prediction accuracy levels. Using a smaller TE-cooled detector reduces form factor, creating a mobile sensor. Principal component analysis has predicted principal components of spectra taken from human subjects that closely match the absorption peaks of glucose. These principal components are used as regressors in a linear regression algorithm to make glucose concentration predictions, over 75% of which are clinically accurate.

A novel principal component analysis for spatially misaligned multivariate air pollution data.

PubMed

Jandarov, Roman A; Sheppard, Lianne A; Sampson, Paul D; Szpiro, Adam A

2017-01-01

We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used. We demonstrate the utility of predictive (sparse) PCA in simulated data and apply the approach to annual averages of particulate matter speciation data from national Environmental Protection Agency (EPA) regulatory monitors.

Predicting timing of foot strike during running, independent of striking technique, using principal component analysis of joint angles.

PubMed

Osis, Sean T; Hettinga, Blayne A; Leitch, Jessica; Ferber, Reed

2014-08-22

As 3-dimensional (3D) motion-capture for clinical gait analysis continues to evolve, new methods must be developed to improve the detection of gait cycle events based on kinematic data. Recently, the application of principal component analysis (PCA) to gait data has shown promise in detecting important biomechanical features. Therefore, the purpose of this study was to define a new foot strike detection method for a continuum of striking techniques, by applying PCA to joint angle waveforms. In accordance with Newtonian mechanics, it was hypothesized that transient features in the sagittal-plane accelerations of the lower extremity would be linked with the impulsive application of force to the foot at foot strike. Kinematic and kinetic data from treadmill running were selected for 154 subjects, from a database of gait biomechanics. Ankle, knee and hip sagittal plane angular acceleration kinematic curves were chained together to form a row input to a PCA matrix. A linear polynomial was calculated based on PCA scores, and a 10-fold cross-validation was performed to evaluate prediction accuracy against gold-standard foot strike as determined by a 10 N rise in the vertical ground reaction force. Results show 89-94% of all predicted foot strikes were within 4 frames (20 ms) of the gold standard with the largest error being 28 ms. It is concluded that this new foot strike detection is an improvement on existing methods and can be applied regardless of whether the runner exhibits a rearfoot, midfoot, or forefoot strike pattern. Copyright © 2014 Elsevier Ltd. All rights reserved.

A baseline drift detrending technique for fast scan cyclic voltammetry.

PubMed

DeWaele, Mark; Oh, Yoonbae; Park, Cheonho; Kang, Yu Min; Shin, Hojin; Blaha, Charles D; Bennet, Kevin E; Kim, In Young; Lee, Kendall H; Jang, Dong Pyo

2017-11-06

Fast scan cyclic voltammetry (FSCV) has been commonly used to measure extracellular neurotransmitter concentrations in the brain. Due to the unstable nature of the background currents inherent in FSCV measurements, analysis of FSCV data is limited to very short amounts of time using traditional background subtraction. In this paper, we propose the use of a zero-phase high pass filter (HPF) as the means to remove the background drift. Instead of the traditional method of low pass filtering across voltammograms to increase the signal to noise ratio, a HPF with a low cutoff frequency was applied to the temporal dataset at each voltage point to remove the background drift. As a result, the HPF utilizing cutoff frequencies between 0.001 Hz and 0.01 Hz could be effectively used to a set of FSCV data for removing the drifting patterns while preserving the temporal kinetics of the phasic dopamine response recorded in vivo. In addition, compared to a drift removal method using principal component analysis, this was found to be significantly more effective in reducing the drift (unpaired t-test p < 0.0001, t = 10.88) when applied to data collected from Tris buffer over 24 hours although a drift removal method using principal component analysis also showed the effective background drift reduction. The HPF was also applied to 5 hours of FSCV in vivo data. Electrically evoked dopamine peaks, observed in the nucleus accumbens, were clearly visible even without background subtraction. This technique provides a new, simple, and yet robust, approach to analyse FSCV data with an unstable background.

Principals' Perceptions of Collegial Support as a Component of Administrative Inservice.

ERIC Educational Resources Information Center

Daresh, John C.

To address the problem of increasing professional isolation of building administrators, the Principals' Inservice Project helps establish principals' collegial support groups across the nation. The groups are typically composed of 6 to 10 principals who meet at least once each month over a 2-year period. One collegial support group of seven…

Training the Trainers: Learning to Be a Principal Supervisor

ERIC Educational Resources Information Center

Saltzman, Amy

2017-01-01

While most principal supervisors are former principals themselves, few come to the role with specific training in how to do the job effectively. For this reason, both the Washington, D.C., and Tulsa, Oklahoma, principal supervisor programs include a strong professional development component. In this article, the author takes a look inside these…

Use of multivariate analysis for determining sources of solutes found in wet atmospheric deposition in the United States

USGS Publications Warehouse

Hooper, R.P.; Peters, N.E.

1989-01-01

A principal-components analysis was performed on the major solutes in wet deposition collected from 194 stations in the United States and its territories. Approximately 90% of the components derived could be interpreted as falling into one of three categories - acid, salt, or an agricultural/soil association. The total mass, or the mass of any one solute, was apportioned among these components by multiple linear regression techniques. The use of multisolute components for determining trends or spatial distribution represents a substantial improvement over single-solute analysis in that these components are more directly related to the sources of the deposition. The geographic patterns displayed by the components in this analysis indicate a far more important role for acid deposition in the Southeast and intermountain regions of the United States than would be indicated by maps of sulfate or nitrate deposition alone. In the Northeast and Midwest, the acid component is not declining at most stations, as would be expected from trends in sulfate deposition, but is holding constant or increasing. This is due, in part, to a decline in the agriculture/soil factor throughout this region, which would help to neutralize the acidity.

Motorcyclists safety system to avoid rear end collisions based on acoustic signatures

NASA Astrophysics Data System (ADS)

Muzammel, M.; Yusoff, M. Zuki; Malik, A. Saeed; Mohamad Saad, M. Naufal; Meriaudeau, F.

2017-03-01

In many Asian countries, motorcyclists have a higher fatality rate as compared to other vehicles. Among many other factors, rear end collisions are also contributing for these fatalities. Collision detection systems can be useful to minimize these accidents. However, the designing of efficient and cost effective collision detection system for motorcyclist is still a major challenge. In this paper, an acoustic information based, cost effective and efficient collision detection system is proposed for motorcycle applications. The proposed technique uses the Short time Fourier Transform (STFT) to extract the features from the audio signal and Principal component analysis (PCA) has been used to reduce the feature vector length. The reduction of feature length, further increases the performance of this technique. The proposed technique has been tested on self recorded dataset and gives accuracy of 97.87%. We believe that this method can help to reduce a significant number of motorcycle accidents.

Improved Classification of Orthosiphon stamineus by Data Fusion of Electronic Nose and Tongue Sensors

PubMed Central

Zakaria, Ammar; Shakaff, Ali Yeon Md.; Adom, Abdul Hamid; Ahmad, Mohd Noor; Masnan, Maz Jamilah; Aziz, Abdul Hallis Abdul; Fikri, Nazifah Ahmad; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah

2010-01-01

An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together. PMID:22163381

Improved classification of Orthosiphon stamineus by data fusion of electronic nose and tongue sensors.

PubMed

Zakaria, Ammar; Shakaff, Ali Yeon Md; Adom, Abdul Hamid; Ahmad, Mohd Noor; Masnan, Maz Jamilah; Aziz, Abdul Hallis Abdul; Fikri, Nazifah Ahmad; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah

2010-01-01

An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.

Automated X-Ray Diffraction of Irradiated Materials

DOE PAGES

Rodman, John; Lin, Yuewei; Sprouster, David; ...

2017-10-26

Synchrotron-based X-ray diffraction (XRD) and small-angle Xray scattering (SAXS) characterization techniques used on unirradiated and irradiated reactor pressure vessel steels yield large amounts of data. Machine learning techniques, including PCA, offer a novel method of analyzing and visualizing these large data sets in order to determine the effects of chemistry and irradiation conditions on the formation of radiation induced precipitates. In order to run analysis on these data sets, preprocessing must be carried out to convert the data to a usable format and mask the 2-D detector images to account for experimental variations. Once the data has been preprocessed, itmore » can be organized and visualized using principal component analysis (PCA), multi-dimensional scaling, and k-means clustering. In conclusion, from these techniques, it is shown that sample chemistry has a notable effect on the formation of the radiation induced precipitates in reactor pressure vessel steels.« less

Use of Geochemistry Data Collected by the Mars Exploration Rover Spirit in Gusev Crater to Teach Geomorphic Zonation through Principal Components Analysis

ERIC Educational Resources Information Center

Rodrigue, Christine M.

2011-01-01

This paper presents a laboratory exercise used to teach principal components analysis (PCA) as a means of surface zonation. The lab was built around abundance data for 16 oxides and elements collected by the Mars Exploration Rover Spirit in Gusev Crater between Sol 14 and Sol 470. Students used PCA to reduce 15 of these into 3 components, which,…

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

ERIC Educational Resources Information Center

Ackermann, Margot Elise; Morrow, Jennifer Ann

2008-01-01

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

Wavelet based de-noising of breath air absorption spectra profiles for improved classification by principal component analysis

NASA Astrophysics Data System (ADS)

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

2015-11-01

The comparison results of different mother wavelets used for de-noising of model and experimental data which were presented by profiles of absorption spectra of exhaled air are presented. The impact of wavelets de-noising on classification quality made by principal component analysis are also discussed.

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

NASA Astrophysics Data System (ADS)

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

2013-06-01

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

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

ERIC Educational Resources Information Center

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

2007-01-01

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

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

Code of Federal Regulations, 2012 CFR

2012-07-01

... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule...

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

Code of Federal Regulations, 2014 CFR

2014-07-01

... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule...

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 (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule...

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

Code of Federal Regulations, 2010 CFR

2010-07-01

... 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..., 1999 Use of Model Rule § 60.1580 What are the principal components of the model rule? The model rule...

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

Code of Federal Regulations, 2013 CFR

2013-07-01

... the model rule? 60.2998 Section 60.2998 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule...

Students' Perceptions of Teaching and Learning Practices: A Principal Component Approach

ERIC Educational Resources Information Center

Mukorera, Sophia; Nyatanga, Phocenah

2017-01-01

Students' attendance and engagement with teaching and learning practices is perceived as a critical element for academic performance. Even with stipulated attendance policies, students still choose not to engage. The study employed a principal component analysis to analyze first- and second-year students' perceptions of the importance of the 12…

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

ERIC Educational Resources Information Center

Hunley-Jenkins, Keisha Janine

2012-01-01

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

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,…

Learning Principal Component Analysis by Using Data from Air Quality Networks

ERIC Educational Resources Information Center

Perez-Arribas, Luis Vicente; Leon-González, María Eugenia; Rosales-Conrado, Noelia

2017-01-01

With the final objective of using computational and chemometrics tools in the chemistry studies, this paper shows the methodology and interpretation of the Principal Component Analysis (PCA) using pollution data from different cities. This paper describes how students can obtain data on air quality and process such data for additional information…

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)

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

ERIC Educational Resources Information Center

Hendrix, Dean

2010-01-01

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

Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters.

PubMed

Tao, Dapeng; Lin, Xu; Jin, Lianwen; Li, Xuelong

2016-03-01

Chinese character font recognition (CCFR) has received increasing attention as the intelligent applications based on optical character recognition becomes popular. However, traditional CCFR systems do not handle noisy data effectively. By analyzing in detail the basic strokes of Chinese characters, we propose that font recognition on a single Chinese character is a sequence classification problem, which can be effectively solved by recurrent neural networks. For robust CCFR, we integrate a principal component convolution layer with the 2-D long short-term memory (2DLSTM) and develop principal component 2DLSTM (PC-2DLSTM) algorithm. PC-2DLSTM considers two aspects: 1) the principal component layer convolution operation helps remove the noise and get a rational and complete font information and 2) simultaneously, 2DLSTM deals with the long-range contextual processing along scan directions that can contribute to capture the contrast between character trajectory and background. Experiments using the frequently used CCFR dataset suggest the effectiveness of PC-2DLSTM compared with other state-of-the-art font recognition methods.

Dynamic of consumer groups and response of commodity markets by principal component analysis

NASA Astrophysics Data System (ADS)

Nobi, Ashadun; Alam, Shafiqul; Lee, Jae Woo

2017-09-01

This study investigates financial states and group dynamics by applying principal component analysis to the cross-correlation coefficients of the daily returns of commodity futures. The eigenvalues of the cross-correlation matrix in the 6-month timeframe displays similar values during 2010-2011, but decline following 2012. A sharp drop in eigenvalue implies the significant change of the market state. Three commodity sectors, energy, metals and agriculture, are projected into two dimensional spaces consisting of two principal components (PC). We observe that they form three distinct clusters in relation to various sectors. However, commodities with distinct features have intermingled with one another and scattered during severe crises, such as the European sovereign debt crises. We observe the notable change of the position of two dimensional spaces of groups during financial crises. By considering the first principal component (PC1) within the 6-month moving timeframe, we observe that commodities of the same group change states in a similar pattern, and the change of states of one group can be used as a warning for other group.

[Determination and principal component analysis of mineral elements based on ICP-OES in Nitraria roborowskii fruits from different regions].

PubMed

Yuan, Yuan-Yuan; Zhou, Yu-Bi; Sun, Jing; Deng, Juan; Bai, Ying; Wang, Jie; Lu, Xue-Feng

2017-06-01

The content of elements in fifteen different regions of Nitraria roborowskii samples were determined by inductively coupled plasma-atomic emission spectrometry(ICP-OES), and its elemental characteristics were analyzed by principal component analysis. The results indicated that 18 mineral elements were detected in N. roborowskii of which V cannot be detected. In addition, contents of Na, K and Ca showed high concentration. Ti showed maximum content variance, while K is minimum. Four principal components were gained from the original data. The cumulative variance contribution rate is 81.542% and the variance contribution of the first principal component was 44.997%, indicating that Cr, Fe, P and Ca were the characteristic elements of N. roborowskii.Thus, the established method was simple, precise and can be used for determination of mineral elements in N.roborowskii Kom. fruits. The elemental distribution characteristics among N.roborowskii fruits are related to geographical origins which were clearly revealed by PCA. All the results will provide good basis for comprehensive utilization of N.roborowskii. Copyright© by the Chinese Pharmaceutical Association.

Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

NASA Astrophysics Data System (ADS)

Ji, Yi; Sun, Shanlin; Xie, Hong-Bo

2017-06-01

Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

Hyperspectral optical imaging of human iris in vivo: characteristics of reflectance spectra

NASA Astrophysics Data System (ADS)

Medina, José M.; Pereira, Luís M.; Correia, Hélder T.; Nascimento, Sérgio M. C.

2011-07-01

We report a hyperspectral imaging system to measure the reflectance spectra of real human irises with high spatial resolution. A set of ocular prosthesis was used as the control condition. Reflectance data were decorrelated by the principal-component analysis. The main conclusion is that spectral complexity of the human iris is considerable: between 9 and 11 principal components are necessary to account for 99% of the cumulative variance in human irises. Correcting image misalignments associated with spontaneous ocular movements did not influence this result. The data also suggests a correlation between the first principal component and different levels of melanin present in the irises. It was also found that although the spectral characteristics of the first five principal components were not affected by the radial and angular position of the selected iridal areas, they affect the higher-order ones, suggesting a possible influence of the iris texture. The results show that hyperspectral imaging in the iris, together with adequate spectroscopic analyses provide more information than conventional colorimetric methods, making hyperspectral imaging suitable for the characterization of melanin and the noninvasive diagnosis of ocular diseases and iris color.

Seeing wholes: The concept of systems thinking and its implementation in school leadership

NASA Astrophysics Data System (ADS)

Shaked, Haim; Schechter, Chen

2013-12-01

Systems thinking (ST) is an approach advocating thinking about any given issue as a whole, emphasising the interrelationships between its components rather than the components themselves. This article aims to link ST and school leadership, claiming that ST may enable school principals to develop highly performing schools that can cope successfully with current challenges, which are more complex than ever before in today's era of accountability and high expectations. The article presents the concept of ST - its definition, components, history and applications. Thereafter, its connection to education and its contribution to school management are described. The article concludes by discussing practical processes including screening for ST-skilled principal candidates and developing ST skills among prospective and currently performing school principals, pinpointing three opportunities for skills acquisition: during preparatory programmes; during their first years on the job, supported by veteran school principals as mentors; and throughout their entire career. Such opportunities may not only provide school principals with ST skills but also improve their functioning throughout the aforementioned stages of professional development.

A modified procedure for mixture-model clustering of regional geochemical data

USGS Publications Warehouse

Ellefsen, Karl J.; Smith, David B.; Horton, John D.

2014-01-01

A modified procedure is proposed for mixture-model clustering of regional-scale geochemical data. The key modification is the robust principal component transformation of the isometric log-ratio transforms of the element concentrations. This principal component transformation and the associated dimension reduction are applied before the data are clustered. The principal advantage of this modification is that it significantly improves the stability of the clustering. The principal disadvantage is that it requires subjective selection of the number of clusters and the number of principal components. To evaluate the efficacy of this modified procedure, it is applied to soil geochemical data that comprise 959 samples from the state of Colorado (USA) for which the concentrations of 44 elements are measured. The distributions of element concentrations that are derived from the mixture model and from the field samples are similar, indicating that the mixture model is a suitable representation of the transformed geochemical data. Each cluster and the associated distributions of the element concentrations are related to specific geologic and anthropogenic features. In this way, mixture model clustering facilitates interpretation of the regional geochemical data.

Temporal evolution of financial-market correlations.

PubMed

Fenn, Daniel J; Porter, Mason A; Williams, Stacy; McDonald, Mark; Johnson, Neil F; Jones, Nick S

2011-08-01

We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007-2008 credit and liquidity crisis.

Temporal evolution of financial-market correlations

NASA Astrophysics Data System (ADS)

Fenn, Daniel J.; Porter, Mason A.; Williams, Stacy; McDonald, Mark; Johnson, Neil F.; Jones, Nick S.

2011-08-01

We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components of these correlation matrices and demonstrate that a small number of components accounts for a large proportion of the variability of the markets that we consider. We characterize the time-evolving relationships between the different assets by investigating the correlations between the asset price time series and principal components. Using this approach, we uncover notable changes that occurred in financial markets and identify the assets that were significantly affected by these changes. We show in particular that there was an increase in the strength of the relationships between several different markets following the 2007-2008 credit and liquidity crisis.

The Application of Principal Component Analysis Using Fixed Eigenvectors to the Infrared Thermographic Inspection of the Space Shuttle Thermal Protection System

NASA Technical Reports Server (NTRS)

Cramer, K. Elliott; Winfree, William P.

2006-01-01

The Nondestructive Evaluation Sciences Branch at NASA s Langley Research Center has been actively involved in the development of thermographic inspection techniques for more than 15 years. Since the Space Shuttle Columbia accident, NASA has focused on the improvement of advanced NDE techniques for the Reinforced Carbon-Carbon (RCC) panels that comprise the orbiter s wing leading edge. Various nondestructive inspection techniques have been used in the examination of the RCC, but thermography has emerged as an effective inspection alternative to more traditional methods. Thermography is a non-contact inspection method as compared to ultrasonic techniques which typically require the use of a coupling medium between the transducer and material. Like radiographic techniques, thermography can be used to inspect large areas, but has the advantage of minimal safety concerns and the ability for single-sided measurements. Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. A typical implementation of PCA is when the eigenvectors are generated from the data set being analyzed. Although it is a powerful tool 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 when a fixed set of eigenvectors is used to process the thermal data from the RCC materials. These eigen vectors can be generated either from an analytic model of the thermal response of the material under examination, or from a large cross section of experimental data. This paper will provide the details of the analytic model; an overview of the PCA process; as well as a quantitative signal-to-noise comparison of the results of performing both embodiments of PCA on thermographic data from various RCC specimens. Details of a system that has been developed to allow insitu inspection of a majority of shuttle RCC components will be presented along with the acceptance test results for this system. Additionally, the results of applying this technology to the Space Shuttle Discovery after its return from flight will be presented.

QSAR modeling of flotation collectors using principal components extracted from topological indices.

PubMed

Natarajan, R; Nirdosh, Inderjit; Basak, Subhash C; Mills, Denise R

2002-01-01

Several topological indices were calculated for substituted-cupferrons that were tested as collectors for the froth flotation of uranium. The principal component analysis (PCA) was used for data reduction. Seven principal components (PC) were found to account for 98.6% of the variance among the computed indices. The principal components thus extracted were used in stepwise regression analyses to construct regression models for the prediction of separation efficiencies (Es) of the collectors. A two-parameter model with a correlation coefficient of 0.889 and a three-parameter model with a correlation coefficient of 0.913 were formed. PCs were found to be better than partition coefficient to form regression equations, and inclusion of an electronic parameter such as Hammett sigma or quantum mechanically derived electronic charges on the chelating atoms did not improve the correlation coefficient significantly. The method was extended to model the separation efficiencies of mercaptobenzothiazoles (MBT) and aminothiophenols (ATP) used in the flotation of lead and zinc ores, respectively. Five principal components were found to explain 99% of the data variability in each series. A three-parameter equation with correlation coefficient of 0.985 and a two-parameter equation with correlation coefficient of 0.926 were obtained for MBT and ATP, respectively. The amenability of separation efficiencies of chelating collectors to QSAR modeling using PCs based on topological indices might lead to the selection of collectors for synthesis and testing from a virtual database.

Pattern Analysis of Dynamic Susceptibility Contrast-enhanced MR Imaging Demonstrates Peritumoral Tissue Heterogeneity

PubMed Central

Akbari, Hamed; Macyszyn, Luke; Da, Xiao; Wolf, Ronald L.; Bilello, Michel; Verma, Ragini; O’Rourke, Donald M.

2014-01-01

Purpose To augment the analysis of dynamic susceptibility contrast material–enhanced magnetic resonance (MR) images to uncover unique tissue characteristics that could potentially facilitate treatment planning through a better understanding of the peritumoral region in patients with glioblastoma. Materials and Methods Institutional review board approval was obtained for this study, with waiver of informed consent for retrospective review of medical records. Dynamic susceptibility contrast-enhanced MR imaging data were obtained for 79 patients, and principal component analysis was applied to the perfusion signal intensity. The first six principal components were sufficient to characterize more than 99% of variance in the temporal dynamics of blood perfusion in all regions of interest. The principal components were subsequently used in conjunction with a support vector machine classifier to create a map of heterogeneity within the peritumoral region, and the variance of this map served as the heterogeneity score. Results The calculated principal components allowed near-perfect separability of tissue that was likely highly infiltrated with tumor and tissue that was unlikely infiltrated with tumor. The heterogeneity map created by using the principal components showed a clear relationship between voxels judged by the support vector machine to be highly infiltrated and subsequent recurrence. The results demonstrated a significant correlation (r = 0.46, P < .0001) between the heterogeneity score and patient survival. The hazard ratio was 2.23 (95% confidence interval: 1.4, 3.6; P < .01) between patients with high and low heterogeneity scores on the basis of the median heterogeneity score. Conclusion Analysis of dynamic susceptibility contrast-enhanced MR imaging data by using principal component analysis can help identify imaging variables that can be subsequently used to evaluate the peritumoral region in glioblastoma. These variables are potentially indicative of tumor infiltration and may become useful tools in guiding therapy, as well as individualized prognostication. © RSNA, 2014 PMID:24955928

Reliable screening of various foodstuffs with respect to their irradiation status: A comparative study of different analytical techniques

NASA Astrophysics Data System (ADS)

Ahn, Jae-Jun; Akram, Kashif; Kwak, Ji-Young; Jeong, Mi-Seon; Kwon, Joong-Ho

2013-10-01

Cost-effective and time-efficient analytical techniques are required to screen large food lots in accordance to their irradiation status. Gamma-irradiated (0-10 kGy) cinnamon, red pepper, black pepper, and fresh paprika were investigated using photostimulated luminescence (PSL), direct epifluorescent filter technique/the aerobic plate count (DEFT/APC), and electronic-nose (e-nose) analyses. The screening results were also confirmed with thermoluminescence analysis. PSL analysis discriminated between irradiated (positive, >5000 PCs) and non-irradiated (negative, <700 PCs) cinnamon and red peppers. Black pepper had intermediate results (700-5000 PCs), while paprika had low sensitivity (negative results) upon irradiation. The DEFT/APC technique also showed clear screening results through the changes in microbial profiles, where the best results were found in paprika, followed by red pepper and cinnamon. E-nose analysis showed a dose-dependent discrimination in volatile profiles upon irradiation through principal component analysis. These methods can be used considering their potential applications for the screening analysis of irradiated foods.

Streamflow characterization using functional data analysis of the Potomac River

NASA Astrophysics Data System (ADS)

Zelmanow, A.; Maslova, I.; Ticlavilca, A. M.; McKee, M.

2013-12-01

Flooding and droughts are extreme hydrological events that affect the United States economically and socially. The severity and unpredictability of flooding has caused billions of dollars in damage and the loss of lives in the eastern United States. In this context, there is an urgent need to build a firm scientific basis for adaptation by developing and applying new modeling techniques for accurate streamflow characterization and reliable hydrological forecasting. The goal of this analysis is to use numerical streamflow characteristics in order to classify, model, and estimate the likelihood of extreme events in the eastern United States, mainly the Potomac River. Functional data analysis techniques are used to study yearly streamflow patterns, with the extreme streamflow events characterized via functional principal component analysis. These methods are merged with more classical techniques such as cluster analysis, classification analysis, and time series modeling. The developed functional data analysis approach is used to model continuous streamflow hydrographs. The forecasting potential of this technique is explored by incorporating climate factors to produce a yearly streamflow outlook.

Biometric Authentication for Gender Classification Techniques: A Review

NASA Astrophysics Data System (ADS)

Mathivanan, P.; Poornima, K.

2017-12-01

One of the challenging biometric authentication applications is gender identification and age classification, which captures gait from far distance and analyze physical information of the subject such as gender, race and emotional state of the subject. It is found that most of the gender identification techniques have focused only with frontal pose of different human subject, image size and type of database used in the process. The study also classifies different feature extraction process such as, Principal Component Analysis (PCA) and Local Directional Pattern (LDP) that are used to extract the authentication features of a person. This paper aims to analyze different gender classification techniques that help in evaluating strength and weakness of existing gender identification algorithm. Therefore, it helps in developing a novel gender classification algorithm with less computation cost and more accuracy. In this paper, an overview and classification of different gender identification techniques are first presented and it is compared with other existing human identification system by means of their performance.

Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications. © 2014 American Academy of Forensic Sciences.

Cultivating an Environment that Contributes to Teaching and Learning in Schools: High School Principals' Actions

ERIC Educational Resources Information Center

Lin, Mind-Dih

2012-01-01

Improving principal leadership is a vital component to the success of educational reform initiatives that seek to improve whole-school performance, as principal leadership often exercises positive but indirect effects on student learning. Because of the importance of principals within the field of school improvement, this article focuses on…

Measuring Principals' Effectiveness: Results from New Jersey's First Year of Statewide Principal Evaluation. REL 2016-156

ERIC Educational Resources Information Center

Herrmann, Mariesa; Ross, Christine

2016-01-01

States and districts across the country are implementing new principal evaluation systems that include measures of the quality of principals' school leadership practices and measures of student achievement growth. Because these evaluation systems will be used for high-stakes decisions, it is important that the component measures of the evaluation…

The Views of Novice and Late Career Principals Concerning Instructional and Organizational Leadership within Their Evaluation

ERIC Educational Resources Information Center

Hvidston, David J.; Range, Bret G.; McKim, Courtney Ann; Mette, Ian M.

2015-01-01

This study examined the perspectives of novice and late career principals concerning instructional and organizational leadership within their performance evaluations. An online survey was sent to 251 principals with a return rate of 49%. Instructional leadership components of the evaluation that were most important to all principals were:…

Profiling of Piper betle Linn. cultivars by direct analysis in real time mass spectrometric technique.

PubMed

Bajpai, Vikas; Sharma, Deepty; Kumar, Brijesh; Madhusudanan, K P

2010-12-01

Piper betle Linn. is a traditional plant associated with the Asian and southeast Asian cultures. Its use is also recorded in folk medicines in these regions. Several of its medicinal properties have recently been proven. Phytochemical analysis showed the presence of mainly terpenes and phenols in betel leaves. These constituents vary in the different cultivars of Piper betle. In this paper we have attempted to profile eight locally available betel cultivars using the recently developed mass spectral ionization technique of direct analysis in real time (DART). Principal component analysis has also been employed to analyze the DART MS data of these betel cultivars. The results show that the cultivars of Piper betle could be differentiated using DART MS data. Copyright © 2010 John Wiley & Sons, Ltd.

Assessment of computer techniques for processing digital LANDSAT MSS data for lithological discrimination of Serra do Ramalho, State of Bahia

NASA Technical Reports Server (NTRS)

Paradella, W. R. (Principal Investigator); Vitorello, I.; Monteiro, M. D.

1984-01-01

Enhancement techniques and thematic classifications were applied to the metasediments of Bambui Super Group (Upper Proterozoic) in the Region of Serra do Ramalho, SW of the state of Bahia. Linear contrast stretch, band-ratios with contrast stretch, and color-composites allow lithological discriminations. The effects of human activities and of vegetation cover mask and limit, in several ways, the lithological discrimination with digital MSS data. Principal component images and color composite of linear contrast stretch of these products, show lithological discrimination through tonal gradations. This set of products allows the delineations of several metasedimentary sequences to a level superior to reconnaissance mapping. Supervised (maximum likelihood classifier) and nonsupervised (K-Means classifier) classification of the limestone sequence, host to fluorite mineralization show satisfactory results.

FPA-FTIR Microspectroscopy for Monitoring Chemotherapy Efficacy in Triple-Negative Breast Cancer

NASA Astrophysics Data System (ADS)

Zawlik, Izabela; Kaznowska, Ewa; Cebulski, Jozef; Kolodziej, Magdalena; Depciuch, Joanna; Vongsvivut, Jitraporn; Cholewa, Marian

2016-11-01

Triple-negative breast cancer is the most aggressive breast cancer subtype with limited treatment options and a poor prognosis. Approximately 70% of triple-negative breast cancer patients fail to achieve a pathologic complete response (pCR) after chemotherapy due to the lack of targeted therapies for this subtype. We report here the development of a focal-plane-array Fourier transform infrared (FPA-FTIR) microspectroscopic technique combined with principal component analysis (PCA) for monitoring chemotherapy effects in triple-negative breast cancer patients. The PCA results obtained using the FPA-FTIR spectral data collected from the same patients before and after the chemotherapy revealed discriminatory features that were consistent with the pathologic and clinical responses to chemotherapy, indicating the potential of the technique as a monitoring tool for observing chemotherapy efficacy.

Visualization of Global Sensitivity Analysis Results Based on a Combination of Linearly Dependent and Independent Directions

NASA Technical Reports Server (NTRS)

Davies, Misty D.; Gundy-Burlet, Karen

2010-01-01

A useful technique for the validation and verification of complex flight systems is Monte Carlo Filtering -- a global sensitivity analysis that tries to find the inputs and ranges that are most likely to lead to a subset of the outputs. A thorough exploration of the parameter space for complex integrated systems may require thousands of experiments and hundreds of controlled and measured variables. Tools for analyzing this space often have limitations caused by the numerical problems associated with high dimensionality and caused by the assumption of independence of all of the dimensions. To combat both of these limitations, we propose a technique that uses a combination of the original variables with the derived variables obtained during a principal component analysis.

Resonant loading of aircraft secondary structure panels for use with thermoelastic stress analysis and digital image correlation

NASA Astrophysics Data System (ADS)

Waugh, Rachael C.; Dulieu-Barton, Janice M.; Quinn, S.

2015-03-01

Thermoelastic stress analysis (TSA) is an established active thermographic approach which uses the thermoelastic effect to correlate the temperature change that occurs as a material is subjected to elastic cyclic loading to the sum of the principal stresses on the surface of the component. Digital image correlation (DIC) tracks features on the surface of a material to establish a displacement field of a component subjected to load, which can then be used to calculate the strain field. The application of both DIC and TSA on a composite plate representative of aircraft secondary structure subject to resonant frequency loading using a portable loading device, i.e. `remote loading' is described. Laboratory based loading for TSA and DIC is typically imparted using a test machine, however in the current work a vibration loading system is used which is able to excite the component of interest at resonant frequency which enables TSA and DIC to be carried out. The accuracy of the measurements made under remote loading of both of the optical techniques applied is discussed. The data are compared to extract complimentary information from the two techniques. This work forms a step towards a combined strain based non-destructive evaluation procedure able to identify and quantify the effect of defects more fully, particularly when examining component performance in service applications.

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

ERIC Educational Resources Information Center

Chou, Yeh-Tai; Wang, Wen-Chung

2010-01-01

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

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

ERIC Educational Resources Information Center

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

2009-01-01

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

Relaxation mode analysis of a peptide system: comparison with principal component analysis.

PubMed

Mitsutake, Ayori; Iijima, Hiromitsu; Takano, Hiroshi

2011-10-28

This article reports the first attempt to apply the relaxation mode analysis method to a simulation of a biomolecular system. In biomolecular systems, the principal component analysis is a well-known method for analyzing the static properties of fluctuations of structures obtained by a simulation and classifying the structures into some groups. On the other hand, the relaxation mode analysis has been used to analyze the dynamic properties of homopolymer systems. In this article, a long Monte Carlo simulation of Met-enkephalin in gas phase has been performed. The results are analyzed by the principal component analysis and relaxation mode analysis methods. We compare the results of both methods and show the effectiveness of the relaxation mode analysis.

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

NASA Technical Reports Server (NTRS)

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

1984-01-01

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

Fast principal component analysis for stacking seismic data

NASA Astrophysics Data System (ADS)

Wu, Juan; Bai, Min

2018-04-01

Stacking seismic data plays an indispensable role in many steps of the seismic data processing and imaging workflow. Optimal stacking of seismic data can help mitigate seismic noise and enhance the principal components to a great extent. Traditional average-based seismic stacking methods cannot obtain optimal performance when the ambient noise is extremely strong. We propose a principal component analysis (PCA) algorithm for stacking seismic data without being sensitive to noise level. Considering the computational bottleneck of the classic PCA algorithm in processing massive seismic data, we propose an efficient PCA algorithm to make the proposed method readily applicable for industrial applications. Two numerically designed examples and one real seismic data are used to demonstrate the performance of the presented method.

Multivariate analyses of salt stress and metabolite sensing in auto- and heterotroph Chenopodium cell suspensions.

PubMed

Wongchai, C; Chaidee, A; Pfeiffer, W

2012-01-01

Global warming increases plant salt stress via evaporation after irrigation, but how plant cells sense salt stress remains unknown. Here, we searched for correlation-based targets of salt stress sensing in Chenopodium rubrum cell suspension cultures. We proposed a linkage between the sensing of salt stress and the sensing of distinct metabolites. Consequently, we analysed various extracellular pH signals in autotroph and heterotroph cell suspensions. Our search included signals after 52 treatments: salt and osmotic stress, ion channel inhibitors (amiloride, quinidine), salt-sensing modulators (proline), amino acids, carboxylic acids and regulators (salicylic acid, 2,4-dichlorphenoxyacetic acid). Multivariate analyses revealed hirarchical clusters of signals and five principal components of extracellular proton flux. The principal component correlated with salt stress was an antagonism of γ-aminobutyric and salicylic acid, confirming involvement of acid-sensing ion channels (ASICs) in salt stress sensing. Proline, short non-substituted mono-carboxylic acids (C2-C6), lactic acid and amiloride characterised the four uncorrelated principal components of proton flux. The proline-associated principal component included an antagonism of 2,4-dichlorphenoxyacetic acid and a set of amino acids (hydrophobic, polar, acidic, basic). The five principal components captured 100% of variance of extracellular proton flux. Thus, a bias-free, functional high-throughput screening was established to extract new clusters of response elements and potential signalling pathways, and to serve as a core for quantitative meta-analysis in plant biology. The eigenvectors reorient research, associating proline with development instead of salt stress, and the proof of existence of multiple components of proton flux can help to resolve controversy about the acid growth theory. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

Classification of white wine aromas with an electronic nose.

PubMed

Lozano, J; Santos, J P; Horrillo, M C

2005-09-15

This paper reports the use of a tin dioxide multisensor array based electronic nose for recognition of 29 typical aromas in white wine. Headspace technique has been used to extract aroma of the wine. Multivariate analysis, including principal component analysis (PCA) as well as probabilistic neural networks (PNNs), has been used to identify the main aroma added to the wine. The results showed that in spite of the strong influence of ethanol and other majority compounds of wine, the system could discriminate correctly the aromatic compounds added to the wine with a minimum accuracy of 97.2%.

Critical comparison of diffuse reflectance spectroscopy and colorimetry as dermatological diagnostic tools for acanthosis nigricans: a chemometric approach

PubMed Central

Devpura, Suneetha; Pattamadilok, Bensachee; Syed, Zain U.; Vemulapalli, Pranita; Henderson, Marsha; Rehse, Steven J.; Hamzavi, Iltefat; Lim, Henry W.; Naik, Ratna

2011-01-01

Quantification of skin changes due to acanthosis nigricans (AN), a disorder common among insulin-resistant diabetic and obese individuals, was investigated using two optical techniques: diffuse reflectance spectroscopy (DRS) and colorimetry. Measurements were obtained from AN lesions on the neck and two control sites of eight AN patients. A principal component/discriminant function analysis successfully differentiated between AN lesion and normal skin with 87.7% sensitivity and 94.8% specificity in DRS measurements and 97.2% sensitivity and 96.4% specificity in colorimetry measurements. PMID:21698027

EM in high-dimensional spaces.

PubMed

Draper, Bruce A; Elliott, Daniel L; Hayes, Jeremy; Baek, Kyungim

2005-06-01

This paper considers fitting a mixture of Gaussians model to high-dimensional data in scenarios where there are fewer data samples than feature dimensions. Issues that arise when using principal component analysis (PCA) to represent Gaussian distributions inside Expectation-Maximization (EM) are addressed, and a practical algorithm results. Unlike other algorithms that have been proposed, this algorithm does not try to compress the data to fit low-dimensional models. Instead, it models Gaussian distributions in the (N - 1)-dimensional space spanned by the N data samples. We are able to show that this algorithm converges on data sets where low-dimensional techniques do not.

The bio-optical properties of CDOM as descriptor of lake stratification.

PubMed

Bracchini, Luca; Dattilo, Arduino Massimo; Hull, Vincent; Loiselle, Steven Arthur; Martini, Silvia; Rossi, Claudio; Santinelli, Chiara; Seritti, Alfredo

2006-11-01

Multivariate statistical techniques are used to demonstrate the fundamental role of CDOM optical properties in the description of water masses during the summer stratification of a deep lake. PC1 was linked with dissolved species and PC2 with suspended particles. In the first principal component that the role of CDOM bio-optical properties give a better description of the stratification of the Salto Lake with respect to temperature. The proposed multivariate approach can be used for the analysis of different stratified aquatic ecosystems in relation to interaction between bio-optical properties and stratification of the water body.

[The application of the multidimensional statistical methods in the evaluation of the influence of atmospheric pollution on the population's health].

PubMed

Surzhikov, V D; Surzhikov, D V

2014-01-01

The search and measurement of causal relationships between exposure to air pollution and health state of the population is based on the system analysis and risk assessment to improve the quality of research. With this purpose there is applied the modern statistical analysis with the use of criteria of independence, principal component analysis and discriminate function analysis. As a result of analysis out of all atmospheric pollutants there were separated four main components: for diseases of the circulatory system main principal component is implied with concentrations of suspended solids, nitrogen dioxide, carbon monoxide, hydrogen fluoride, for the respiratory diseases the main c principal component is closely associated with suspended solids, sulfur dioxide and nitrogen dioxide, charcoal black. The discriminant function was shown to be used as a measure of the level of air pollution.

Priority of VHS Development Based in Potential Area using Principal Component Analysis

NASA Astrophysics Data System (ADS)

Meirawan, D.; Ana, A.; Saripudin, S.

2018-02-01

The current condition of VHS is still inadequate in quality, quantity and relevance. The purpose of this research is to analyse the development of VHS based on the development of regional potential by using principal component analysis (PCA) in Bandung, Indonesia. This study used descriptive qualitative data analysis using the principle of secondary data reduction component. The method used is Principal Component Analysis (PCA) analysis with Minitab Statistics Software tool. The results of this study indicate the value of the lowest requirement is a priority of the construction of development VHS with a program of majors in accordance with the development of regional potential. Based on the PCA score found that the main priority in the development of VHS in Bandung is in Saguling, which has the lowest PCA value of 416.92 in area 1, Cihampelas with the lowest PCA value in region 2 and Padalarang with the lowest PCA value.

Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.

PubMed

Azevedo, C F; Nascimento, M; Silva, F F; Resende, M D V; Lopes, P S; Guimarães, S E F; Glória, L S

2015-10-09

A significant contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. With this approach, genome-wide selection (GWS) can be used for this purpose. GWS consists of analyzing a large number of single nucleotide polymorphism markers widely distributed in the genome; however, because the number of markers is much larger than the number of genotyped individuals, and such markers are highly correlated, special statistical methods are widely required. Among these methods, independent component regression, principal component regression, partial least squares, and partial principal components stand out. Thus, the aim of this study was to propose an application of the methods of dimensionality reduction to GWS of carcass traits in an F2 (Piau x commercial line) pig population. The results show similarities between the principal and the independent component methods and provided the most accurate genomic breeding estimates for most carcass traits in pigs.

Tailored multivariate analysis for modulated enhanced diffraction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni

2015-10-21

Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scoresmore » and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. The multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). When applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. To develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.« less

The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

NASA Astrophysics Data System (ADS)

Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

2015-08-01

The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

Direct Analysis in Real Time by Mass Spectrometric Technique for Determining the Variation in Metabolite Profiles of Cinnamomum tamala Nees and Eberm Genotypes

PubMed Central

Singh, Vineeta; Gupta, Atul Kumar; Singh, S. P.; Kumar, Anil

2012-01-01

Cinnamomum tamala Nees & Eberm. is an important traditional medicinal plant, mentioned in various ancient literatures such as Ayurveda. Several of its medicinal properties have recently been proved. To characterize diversity in terms of metabolite profiles of Cinnamomum tamala Nees and Eberm genotypes, a newly emerging mass spectral ionization technique direct time in real time (DART) is very helpful. The DART ion source has been used to analyze an extremely wide range of phytochemicals present in leaves of Cinnamomum tamala. Ten genotypes were assessed for the presence of different phytochemicals. Phytochemical analysis showed the presence of mainly terpenes and phenols. These constituents vary in the different genotypes of Cinnamomum tamala. Principal component analysis has also been employed to analyze the DART data of these Cinnamomum genotypes. The result shows that the genotype of Cinnamomum tamala could be differentiated using DART MS data. The active components present in Cinnamomum tamala may be contributing significantly to high amount of antioxidant property of leaves and, in turn, conditional effects for diabetic patients. PMID:22701361

ENDOR-Induced EPR of Disordered Systems: Application to X-Irradiated Alanine.

PubMed

Kusakovskij, Jevgenij; Maes, Kwinten; Callens, Freddy; Vrielinck, Henk

2018-02-15

The electron paramagnetic resonance (EPR) spectra of radiation-induced radicals in organic solids are generally composed of multiple components that largely overlap due to their similar weak g anisotropy and a large number of hyperfine (HF) interactions. Such properties make these systems difficult to study using standard cw EPR spectroscopy even in single crystals. Electron-nuclear double-resonance (ENDOR) spectroscopy is a powerful and widely used complementary technique. In particular, ENDOR-induced EPR (EIE) experiments are useful for separating the overlapping contributions. In the present work, these techniques were employed to study the EPR spectrum of stable radicals in X-irradiated alanine, which is widely used in dosimetric applications. The principal values of all major proton HF interactions of the dominant radicals were determined by analyzing the magnetic field dependence of the ENDOR spectrum at 50 K, where the rotation of methyl groups is frozen. Accurate simulations of the EPR spectrum were performed after the major components were separated using an EIE analysis. As a result, new evidence in favor of the model of the second dominant radical was obtained.

A Direct Approach to In-Plane Stress Separation using Photoelastic Ptychography

NASA Astrophysics Data System (ADS)

Anthony, Nicholas; Cadenazzi, Guido; Kirkwood, Henry; Huwald, Eric; Nugent, Keith; Abbey, Brian

2016-08-01

The elastic properties of materials, either under external load or in a relaxed state, influence their mechanical behaviour. Conventional optical approaches based on techniques such as photoelasticity or thermoelasticity can be used for full-field analysis of the stress distribution within a specimen. The circular polariscope in combination with holographic photoelasticity allows the sum and difference of principal stress components to be determined by exploiting the temporary birefringent properties of materials under load. Phase stepping and interferometric techniques have been proposed as a method for separating the in-plane stress components in two-dimensional photoelasticity experiments. In this paper we describe and demonstrate an alternative approach based on photoelastic ptychography which is able to obtain quantitative stress information from far fewer measurements than is required for interferometric based approaches. The complex light intensity equations based on Jones calculus for this setup are derived. We then apply this approach to the problem of a disc under diametrical compression. The experimental results are validated against the analytical solution derived by Hertz for the theoretical displacement fields for an elastic disc subject to point loading.

Authentication of fattening diet of Iberian pigs according to their volatile compounds profile from raw subcutaneous fat.

PubMed

Narváez-Rivas, M; Pablos, F; Jurado, J M; León-Camacho, M

2011-02-01

The composition of volatile components of subcutaneous fat from Iberian pig has been studied. Purge and trap gas chromatography-mass spectrometry has been used. The composition of the volatile fraction of subcutaneous fat has been used for authentication purposes of different types of Iberian pig fat. Three types of this product have been considered, montanera, extensive cebo and intensive cebo. With classification purposes, several pattern recognition techniques have been applied. In order to find out possible tendencies in the sample distribution as well as the discriminant power of the variables, principal component analysis was applied as visualisation technique. Linear discriminant analysis (LDA) and soft independent modelling by class analogy (SIMCA) were used to obtain suitable classification models. LDA and SIMCA allowed the differentiation of three fattening diets by using the contents in 2,2,4,6,6-pentamethyl-heptane, m-xylene, 2,4-dimethyl-heptane, 6-methyl-tridecane, 1-methoxy-2-propanol, isopropyl alcohol, o-xylene, 3-ethyl-2,2-dimethyl-oxirane, 2,6-dimethyl-undecane, 3-methyl-3-pentanol and limonene.

A Direct Approach to In-Plane Stress Separation using Photoelastic Ptychography

PubMed Central

Anthony, Nicholas; Cadenazzi, Guido; Kirkwood, Henry; Huwald, Eric; Nugent, Keith; Abbey, Brian

2016-01-01

The elastic properties of materials, either under external load or in a relaxed state, influence their mechanical behaviour. Conventional optical approaches based on techniques such as photoelasticity or thermoelasticity can be used for full-field analysis of the stress distribution within a specimen. The circular polariscope in combination with holographic photoelasticity allows the sum and difference of principal stress components to be determined by exploiting the temporary birefringent properties of materials under load. Phase stepping and interferometric techniques have been proposed as a method for separating the in-plane stress components in two-dimensional photoelasticity experiments. In this paper we describe and demonstrate an alternative approach based on photoelastic ptychography which is able to obtain quantitative stress information from far fewer measurements than is required for interferometric based approaches. The complex light intensity equations based on Jones calculus for this setup are derived. We then apply this approach to the problem of a disc under diametrical compression. The experimental results are validated against the analytical solution derived by Hertz for the theoretical displacement fields for an elastic disc subject to point loading. PMID:27488605

A perspective on two chemometrics tools: PCA and MCR, and introduction of a new one: Pattern recognition entropy (PRE), as applied to XPS and ToF-SIMS depth profiles of organic and inorganic materials

NASA Astrophysics Data System (ADS)

Chatterjee, Shiladitya; Singh, Bhupinder; Diwan, Anubhav; Lee, Zheng Rong; Engelhard, Mark H.; Terry, Jeff; Tolley, H. Dennis; Gallagher, Neal B.; Linford, Matthew R.

2018-03-01

X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) are much used analytical techniques that provide information about the outermost atomic and molecular layers of materials. In this work, we discuss the application of multivariate spectral techniques, including principal component analysis (PCA) and multivariate curve resolution (MCR), to the analysis of XPS and ToF-SIMS depth profiles. Multivariate analyses often provide insight into data sets that is not easily obtained in a univariate fashion. Pattern recognition entropy (PRE), which has its roots in Shannon's information theory, is also introduced. This approach is not the same as the mutual information/entropy approaches sometimes used in data processing. A discussion of the theory of each technique is presented. PCA, MCR, and PRE are applied to four different data sets obtained from: a ToF-SIMS depth profile through ca. 100 nm of plasma polymerized C3F6 on Si, a ToF-SIMS depth profile through ca. 100 nm of plasma polymerized PNIPAM (poly (N-isopropylacrylamide)) on Si, an XPS depth profile through a film of SiO2 on Si, and an XPS depth profile through a film of Ta2O5 on Ta. PCA, MCR, and PRE reveal the presence of interfaces in the films, and often indicate that the first few scans in the depth profiles are different from those that follow. PRE and backward difference PRE provide this information in a straightforward fashion. Rises in the PRE signals at interfaces suggest greater complexity to the corresponding spectra. Results from PCA, especially for the higher principal components, were sometimes difficult to understand. MCR analyses were generally more interpretable.

Performance-Based Preparation of Principals: A Framework for Improvement. A Special Report of the NASSP Consortium for the Performance-Based Preparation of Principals.

ERIC Educational Resources Information Center

National Association of Secondary School Principals, Reston, VA.

Preparation programs for principals should have excellent academic and performance based components. In examining the nature of performance based principal preparation this report finds that school administration programs must bridge the gap between conceptual learning in the classroom and the requirements of professional practice. A number of…

Real time damage detection using recursive principal components and time varying auto-regressive modeling

NASA Astrophysics Data System (ADS)

Krishnan, M.; Bhowmik, B.; Hazra, B.; Pakrashi, V.

2018-02-01

In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using Recursive Principal Component Analysis (RPCA) in conjunction with Time Varying Auto-Regressive Modeling (TVAR) is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by TVAR modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/non-linear-states that indicate damage. Most of the works available in the literature deal with algorithms that require windowing of the gathered data owing to their data-driven nature which renders them ineffective for online implementation. Algorithms focussed on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection is missing, which motivates the development of the present framework that is amenable for online implementation which could be utilized along with suite experimental and numerical investigations. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. TVAR modeling on the principal component explaining maximum variance is utilized and the damage is identified by tracking the TVAR coefficients. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Numerical simulations performed on a 5-dof nonlinear system under white noise excitation and El Centro (also known as 1940 Imperial Valley earthquake) excitation, for different damage scenarios, demonstrate the robustness of the proposed algorithm. The method is further validated on results obtained from case studies involving experiments performed on a cantilever beam subjected to earthquake excitation; a two-storey benchscale model with a TMD and, data from recorded responses of UCLA factor building demonstrate the efficacy of the proposed methodology as an ideal candidate for real time, reference free structural health monitoring.

EMPCA and Cluster Analysis of Quasar Spectra: Construction and Application to Simulated Spectra

NASA Astrophysics Data System (ADS)

Marrs, Adam; Leighly, Karen; Wagner, Cassidy; Macinnis, Francis

2017-01-01

Quasars have complex spectra with emission lines influenced by many factors. Therefore, to fully describe the spectrum requires specification of a large number of parameters, such as line equivalent width, blueshift, and ratios. Principal Component Analysis (PCA) aims to construct eigenvectors-or principal components-from the data with the goal of finding a few key parameters that can be used to predict the rest of the spectrum fairly well. Analysis of simulated quasar spectra was used to verify and justify our modified application of PCA.We used a variant of PCA called Weighted Expectation Maximization PCA (EMPCA; Bailey 2012) along with k-means cluster analysis to analyze simulated quasar spectra. Our approach combines both analytical methods to address two known problems with classical PCA. EMPCA uses weights to account for uncertainty and missing points in the spectra. K-means groups similar spectra together to address the nonlinearity of quasar spectra, specifically variance in blueshifts and widths of the emission lines.In producing and analyzing simulations, we first tested the effects of varying equivalent widths and blueshifts on the derived principal components, and explored the differences between standard PCA and EMPCA. We also tested the effects of varying signal-to-noise ratio. Next we used the results of fits to composite quasar spectra (see accompanying poster by Wagner et al.) to construct a set of realistic simulated spectra, and subjected those spectra to the EMPCA /k-means analysis. We concluded that our approach was validated when we found that the mean spectra from our k-means clusters derived from PCA projection coefficients reproduced the trends observed in the composite spectra.Furthermore, our method needed only two eigenvectors to identify both sets of correlations used to construct the simulations, as well as indicating the linear and nonlinear segments. Comparing this to regular PCA, which can require a dozen or more components, or to direct spectral analysis that may need measurement of 20 fit parameters, shows why the dual application of these two techniques is such a powerful tool.

Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data

USGS Publications Warehouse

Myint, S.W.; Yuan, M.; Cerveny, R.S.; Giri, C.P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and objectoriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. ?? 2008 by MDPI.

Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data

PubMed Central

Myint, Soe W.; Yuan, May; Cerveny, Randall S.; Giri, Chandra P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. PMID:27879757

Application of the principal component analysis (PCA) to HVSR data aimed at the seismic characterization of earthquake prone areas

NASA Astrophysics Data System (ADS)

Paolucci, Enrico; Lunedei, Enrico; Albarello, Dario

2017-10-01

In this work, we propose a procedure based on principal component analysis on data sets consisting of many horizontal to vertical spectral ratio (HVSR or H/V) curves obtained by single-station ambient vibration acquisitions. This kind of analysis aimed at the seismic characterization of the investigated area by identifying sites characterized by similar HVSR curves. It also allows to extract the typical HVSR patterns of the explored area and to establish their relative importance, providing an estimate of the level of heterogeneity under the seismic point of view. In this way, an automatic explorative seismic characterization of the area becomes possible by only considering ambient vibration data. This also implies that the relevant outcomes can be safely compared with other available information (geological data, borehole measurements, etc.) without any conceptual trade-off. The whole algorithm is remarkably fast: on a common personal computer, the processing time takes few seconds for a data set including 100-200 HVSR measurements. The procedure has been tested in three study areas in the Central-Northern Italy characterized by different geological settings. Outcomes demonstrate that this technique is effective and well correlates with most significant seismostratigraphical heterogeneities present in each of the study areas.

Improvements of the Vis-NIRS Model in the Prediction of Soil Organic Matter Content Using Spectral Pretreatments, Sample Selection, and Wavelength Optimization

NASA Astrophysics Data System (ADS)

Lin, Z. D.; Wang, Y. B.; Wang, R. J.; Wang, L. S.; Lu, C. P.; Zhang, Z. Y.; Song, L. T.; Liu, Y.

2017-07-01

A total of 130 topsoil samples collected from Guoyang County, Anhui Province, China, were used to establish a Vis-NIR model for the prediction of organic matter content (OMC) in lime concretion black soils. Different spectral pretreatments were applied for minimizing the irrelevant and useless information of the spectra and increasing the spectra correlation with the measured values. Subsequently, the Kennard-Stone (KS) method and sample set partitioning based on joint x-y distances (SPXY) were used to select the training set. Successive projection algorithm (SPA) and genetic algorithm (GA) were then applied for wavelength optimization. Finally, the principal component regression (PCR) model was constructed, in which the optimal number of principal components was determined using the leave-one-out cross validation technique. The results show that the combination of the Savitzky-Golay (SG) filter for smoothing and multiplicative scatter correction (MSC) can eliminate the effect of noise and baseline drift; the SPXY method is preferable to KS in the sample selection; both the SPA and the GA can significantly reduce the number of wavelength variables and favorably increase the accuracy, especially GA, which greatly improved the prediction accuracy of soil OMC with Rcc, RMSEP, and RPD up to 0.9316, 0.2142, and 2.3195, respectively.

Volatile Organic Compounds: Characteristics, distribution and sources in urban schools

NASA Astrophysics Data System (ADS)

Mishra, Nitika; Bartsch, Jennifer; Ayoko, Godwin A.; Salthammer, Tunga; Morawska, Lidia

2015-04-01

Long term exposure to organic pollutants, both inside and outside school buildings may affect children's health and influence their learning performance. Since children spend significant amount of time in school, air quality, especially in classrooms plays a key role in determining the health risks associated with exposure at schools. Within this context, the present study investigated the ambient concentrations of Volatile Organic Compounds (VOCs) in 25 primary schools in Brisbane with the aim to quantify the indoor and outdoor VOCs concentrations, identify VOCs sources and their contribution, and based on these; propose mitigation measures to reduce VOCs exposure in schools. One of the most important findings is the occurrence of indoor sources, indicated by the I/O ratio >1 in 19 schools. Principal Component Analysis with Varimax rotation was used to identify common sources of VOCs and source contribution was calculated using an Absolute Principal Component Scores technique. The result showed that outdoor 47% of VOCs were contributed by petrol vehicle exhaust but the overall cleaning products had the highest contribution of 41% indoors followed by air fresheners and art and craft activities. These findings point to the need for a range of basic precautions during the selection, use and storage of cleaning products and materials to reduce the risk from these sources.

Failure of Standard Training Sets in the Analysis of Fast-Scan Cyclic Voltammetry Data.

PubMed

Johnson, Justin A; Rodeberg, Nathan T; Wightman, R Mark

2016-03-16

The use of principal component regression, a multivariate calibration method, in the analysis of in vivo fast-scan cyclic voltammetry data allows for separation of overlapping signal contributions, permitting evaluation of the temporal dynamics of multiple neurotransmitters simultaneously. To accomplish this, the technique relies on information about current-concentration relationships across the scan-potential window gained from analysis of training sets. The ability of the constructed models to resolve analytes depends critically on the quality of these data. Recently, the use of standard training sets obtained under conditions other than those of the experimental data collection (e.g., with different electrodes, animals, or equipment) has been reported. This study evaluates the analyte resolution capabilities of models constructed using this approach from both a theoretical and experimental viewpoint. A detailed discussion of the theory of principal component regression is provided to inform this discussion. The findings demonstrate that the use of standard training sets leads to misassignment of the current-concentration relationships across the scan-potential window. This directly results in poor analyte resolution and, consequently, inaccurate quantitation, which may lead to erroneous conclusions being drawn from experimental data. Thus, it is strongly advocated that training sets be obtained under the experimental conditions to allow for accurate data analysis.

Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

PubMed Central

Ciucci, Sara; Ge, Yan; Durán, Claudio; Palladini, Alessandra; Jiménez-Jiménez, Víctor; Martínez-Sánchez, Luisa María; Wang, Yuting; Sales, Susanne; Shevchenko, Andrej; Poser, Steven W.; Herbig, Maik; Otto, Oliver; Androutsellis-Theotokis, Andreas; Guck, Jochen; Gerl, Mathias J.; Cannistraci, Carlo Vittorio

2017-01-01

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics. PMID:28287094

Anomaly Detection in Gamma-Ray Vehicle Spectra with Principal Components Analysis and Mahalanobis Distances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tardiff, Mark F.; Runkle, Robert C.; Anderson, K. K.

2006-01-23

The goal of primary radiation monitoring in support of routine screening and emergency response is to detect characteristics in vehicle radiation signatures that indicate the presence of potential threats. Two conceptual approaches to analyzing gamma-ray spectra for threat detection are isotope identification and anomaly detection. While isotope identification is the time-honored method, an emerging technique is anomaly detection that uses benign vehicle gamma ray signatures to define an expectation of the radiation signature for vehicles that do not pose a threat. Newly acquired spectra are then compared to this expectation using statistical criteria that reflect acceptable false alarm rates andmore » probabilities of detection. The gamma-ray spectra analyzed here were collected at a U.S. land Port of Entry (POE) using a NaI-based radiation portal monitor (RPM). The raw data were analyzed to develop a benign vehicle expectation by decimating the original pulse-height channels to 35 energy bins, extracting composite variables via principal components analysis (PCA), and estimating statistically weighted distances from the mean vehicle spectrum with the mahalanobis distance (MD) metric. This paper reviews the methods used to establish the anomaly identification criteria and presents a systematic analysis of the response of the combined PCA and MD algorithm to modeled mono-energetic gamma-ray sources.« less

Stochastic analysis of transverse dispersion in density‐coupled transport in aquifers

USGS Publications Warehouse

Welty, Claire; Kane, Allen C.; Kauffman, Leon J.

2003-01-01

Spectral perturbation techniques have been used previously to derive integral expressions for dispersive mixing in concentration‐dependent transport in three‐dimensional, heterogeneous porous media, where fluid density and viscosity are functions of solute concentration. Whereas earlier work focused on evaluating longitudinal dispersivity in isotropic media and incorporating the result in a mean one‐dimensional transport model, the emphasis of this paper is on evaluation of the complete dispersion tensor, including the more general case of anisotropic media. Approximate analytic expressions for all components of the macroscopic dispersivity tensor are derived, and the tensor is shown to be asymmetric. The tensor is separated into its symmetric and antisymmetric parts, where the symmetric part is used to calculate the principal components and principal directions of dispersivity, and the antisymmetric part of the tensor is shown to modify the velocity of the solute body compared to that of the background fluid. An example set of numerical simulations incorporating the tensor illustrates the effect of density‐coupled dispersivity on a sinking plume in an aquifer. The simulations show that the effective transverse vertical spreading in a sinking plume to be significantly greater than would be predicted by a standard density‐coupled transport model that does not incorporate the coupling in the dispersivity tensor.

Seasonal forecasting of high wind speeds over Western Europe

NASA Astrophysics Data System (ADS)

Palutikof, J. P.; Holt, T.

2003-04-01

As financial losses associated with extreme weather events escalate, there is interest from end users in the forestry and insurance industries, for example, in the development of seasonal forecasting models with a long lead time. This study uses exceedences of the 90th, 95th, and 99th percentiles of daily maximum wind speed over the period 1958 to present to derive predictands of winter wind extremes. The source data is the 6-hourly NCEP Reanalysis gridded surface wind field. Predictor variables include principal components of Atlantic sea surface temperature and several indices of climate variability, including the NAO and SOI. Lead times of up to a year are considered, in monthly increments. Three regression techniques are evaluated; multiple linear regression (MLR), principal component regression (PCR), and partial least squares regression (PLS). PCR and PLS proved considerably superior to MLR with much lower standard errors. PLS was chosen to formulate the predictive model since it offers more flexibility in experimental design and gave slightly better results than PCR. The results indicate that winter windiness can be predicted with considerable skill one year ahead for much of coastal Europe, but that this deteriorates rapidly in the hinterland. The experiment succeeded in highlighting PLS as a very useful method for developing more precise forecasting models, and in identifying areas of high predictability.

Identification of fungal phytopathogens using Fourier transform infrared-attenuated total reflection spectroscopy and advanced statistical methods

NASA Astrophysics Data System (ADS)

Salman, Ahmad; Lapidot, Itshak; Pomerantz, Ami; Tsror, Leah; Shufan, Elad; Moreh, Raymond; Mordechai, Shaul; Huleihel, Mahmoud

2012-01-01

The early diagnosis of phytopathogens is of a great importance; it could save large economical losses due to crops damaged by fungal diseases, and prevent unnecessary soil fumigation or the use of fungicides and bactericides and thus prevent considerable environmental pollution. In this study, 18 isolates of three different fungi genera were investigated; six isolates of Colletotrichum coccodes, six isolates of Verticillium dahliae and six isolates of Fusarium oxysporum. Our main goal was to differentiate these fungi samples on the level of isolates, based on their infrared absorption spectra obtained using the Fourier transform infrared-attenuated total reflection (FTIR-ATR) sampling technique. Advanced statistical and mathematical methods: principal component analysis (PCA), linear discriminant analysis (LDA), and k-means were applied to the spectra after manipulation. Our results showed significant spectral differences between the various fungi genera examined. The use of k-means enabled classification between the genera with a 94.5% accuracy, whereas the use of PCA [3 principal components (PCs)] and LDA has achieved a 99.7% success rate. However, on the level of isolates, the best differentiation results were obtained using PCA (9 PCs) and LDA for the lower wavenumber region (800-1775 cm-1), with identification success rates of 87%, 85.5%, and 94.5% for Colletotrichum, Fusarium, and Verticillium strains, respectively.