Relevant bibliographies by topics / Multivariate analysis – Data processing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multivariate analysis – Data processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Multivariate analysis – Data processing"

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Guccione, Pietro, Mattia Lopresti, Marco Milanesio, and Rocco Caliandro. "Multivariate Analysis Applications in X-ray Diffraction." Crystals 11, no. 1 (December 25, 2020): 12. http://dx.doi.org/10.3390/cryst11010012.

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Multivariate analysis (MA) is becoming a fundamental tool for processing in an efficient way the large amount of data collected in X-ray diffraction experiments. Multi-wedge data collections can increase the data quality in case of tiny protein crystals; in situ or operando setups allow investigating changes on powder samples occurring during repeated fast measurements; pump and probe experiments at X-ray free-electron laser (XFEL) sources supply structural characterization of fast photo-excitation processes. In all these cases, MA can facilitate the extraction of relevant information hidden in data, disclosing the possibility of automatic data processing even in absence of a priori structural knowledge. MA methods recently used in the field of X-ray diffraction are here reviewed and described, giving hints about theoretical background and possible applications. The use of MA in the framework of the modulated enhanced diffraction technique is described in detail.
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Li, Xiu Min. "Multivariate Regression Analysis Using Statistics with R." Advanced Materials Research 765-767 (September 2013): 1572–75. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1572.

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Multiple regression analysis is a useful model in econometrics. It can be applied in many fields. Statistics software plays an important role in processing data. This paper gives a method to use R, constructs regression model, and explains the result.
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Bartolacci, Gianni, and Ahmed Bouajila. "Application of multivariate tools to mineral processing data analysis and modeling: Flotation case." IFAC Proceedings Volumes 33, no. 22 (August 2000): 179–84. http://dx.doi.org/10.1016/s1474-6670(17)36988-4.

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Kümmel, Anne, Paul Selzer, Martin Beibel, Hanspeter Gubler, Christian N. Parker, and Daniela Gabriel. "Comparison of Multivariate Data Analysis Strategies for High-Content Screening." Journal of Biomolecular Screening 16, no. 3 (February 18, 2011): 338–47. http://dx.doi.org/10.1177/1087057110395390.

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High-content screening (HCS) is increasingly used in biomedical research generating multivariate, single-cell data sets. Before scoring a treatment, the complex data sets are processed (e.g., normalized, reduced to a lower dimensionality) to help extract valuable information. However, there has been no published comparison of the performance of these methods. This study comparatively evaluates unbiased approaches to reduce dimensionality as well as to summarize cell populations. To evaluate these different data-processing strategies, the prediction accuracies and the Z′ factors of control compounds of a HCS cell cycle data set were monitored. As expected, dimension reduction led to a lower degree of discrimination between control samples. A high degree of classification accuracy was achieved when the cell population was summarized on well level using percentile values. As a conclusion, the generic data analysis pipeline described here enables a systematic review of alternative strategies to analyze multiparametric results from biological systems.
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Wang, Lijun, Yu Lei, Ying Zeng, Li Tong, and Bin Yan. "Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data." Computational and Mathematical Methods in Medicine 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/645921.

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Brain decoding with functional magnetic resonance imaging (fMRI) requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA) has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise) or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.
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Parachalil, Drishya Rajan, Brenda Brankin, Jennifer McIntyre, and Hugh J. Byrne. "Raman spectroscopic analysis of high molecular weight proteins in solution – considerations for sample analysis and data pre-processing." Analyst 143, no. 24 (2018): 5987–98. http://dx.doi.org/10.1039/c8an01701h.

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This study explores the potential of Raman spectroscopy, coupled with multivariate regression techniques and ion exchange chromatography, to quantitatively monitor diagnostically relevant changes in high molecular weight proteins in liquid plasma.
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Apruzzese, Francesca, Ramin Reshadat, and Stephen T. Balke. "In-Line Monitoring of Polymer Processing. II: Spectral Data Analysis." Applied Spectroscopy 56, no. 10 (October 2002): 1268–74. http://dx.doi.org/10.1366/000370202760354713.

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The objective of this work was to examine the application of various multivariate methods to determine the composition of a flowing, molten, immiscible, polyethylene–polypropylene blend from near-infrared spectra. These spectra were acquired during processing by monitoring the melt with a fiber-optic-assisted in-line spectrometer. Undesired differences in spectra obtained from identical compositions were attributed to additive and multiplicative light scattering effects. Duplicate blend composition data were obtained over a range of 0 to 100% polyethylene. On the basis of previously published approaches, three data preprocessing methods were investigated: second derivative of absorbance with respect to wavelength (d2), multiplicative scatter correction (MSC), and a combination consisting of MSC followed by d2. The latter method was shown to substantially improve superposition of spectra and principal component analysis (PCA) scores. Also, fewer latent variables were required. The continuum regression (CR) approach, a method that encompasses ordinary least squares (OLS), partial least squares (PLS), and principle component regression (PCR) models, was then implemented and provided the best prediction model as one based on characteristics between those of PLS and OLS models.
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Yajie, Li, Lv Zhengdong, and Wang Maonan. "Visualization Investigation on the Marine Data with Multivariate Statistical Analysis Methods." Polish Maritime Research 24, s2 (August 28, 2017): 89–94. http://dx.doi.org/10.1515/pomr-2017-0069.

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Abstract Marine information is an important way for us to know and study more about the ocean. Marine data makes the basic of marine information. Because of the huge quantity and diversity of marine data, and at the same time marine data is polyatomic variable, we start with statistical analysis methods to search for the regularity of the marine data. On one hand, we get the aggregate variation functions of the marine data by factor analyzing in aspect of the spatiality. Then we visually describe the marine status of the studied sea area with pre variogram function and post variogram function. On the other hand, we used cluster analysis method to get the verifying rule in time and make visible graphs of the marine data. In this way, we can also supply with the suggestions in classifying the sea seawater quality. The data processing result shows that the suggested methods in this article are both operable and effective. At the same time some reasonable suggestions are given in the article.
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Riani, Marco, Anthony C. Atkinson, Andrea Cerioli, and Aldo Corbellini. "Efficient robust methods via monitoring for clustering and multivariate data analysis." Pattern Recognition 88 (April 2019): 246–60. http://dx.doi.org/10.1016/j.patcog.2018.11.016.

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Rodríguez-Ruiz, Julieta G., Carlos Eric Galván-Tejada, Sodel Vázquez-Reyes, Jorge Issac Galván-Tejada, and Hamurabi Gamboa-Rosales. "Classification of Depressive Episodes Using Nighttime Data: Multivariate and Univariate Analysis." Proceedings of the Institute for System Programming of the RAS 33, no. 2 (2021): 115–24. http://dx.doi.org/10.15514/ispras-2021-33(2)-6.

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Mental disorders like depression represent 28% of global disability, it affects around 7.5% percent of global disability. Depression is a common disorder that affects the state of mind, normal activities, emotions, and produces sleep disorders. It is estimated that approximately 50% of depressive patients suffering from sleep disturbances. In this paper, a data mining process to classify depressive and not depressive episodes during nighttime is carried out based on a formal method of data mining called Knowledge Discovery in Databases (KDD). KDD guides the process of data mining with stages well established: Pre-KDD, Selection, Pre-processing, Transformation, Data Mining, Evaluation, and Post-KDD. The dataset used for the classification is the DEPRESJON dataset, which contains the motor activity of 23 unipolar and bipolar depressed patients and 32 healthy controls. The classification is carried out with two different approaches; a multivariate and univariate analysis to classify depressive and non-depressive episodes. For the multivariate analysis, the Random Forest algorithm is implemented with a model construct of 8 features, the results of the classification are specificity equal to 0.9927 and sensitivity equal to 0.9991. The univariate analysis shows that the maximum of the activity is the most descriptive characteristic of the model with 0.908 in accuracy for the classification of depressive episodes.
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Dissertations / Theses on the topic "Multivariate analysis – Data processing"

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Jonsson, Pär. "Multivariate processing and modelling of hyphenated metabolite data." Doctoral thesis, Umeå universitet, Kemi, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-663.

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One trend in the ‘omics’ sciences is the generation of increasing amounts of data, describing complex biological samples. To cope with this and facilitate progress towards reliable diagnostic tools, it is crucial to develop methods for extracting representative and predictive information. In global metabolite analysis (metabolomics and metabonomics) NMR, GC/MS and LC/MS are the main platforms for data generation. Multivariate projection methods (e.g. PCA, PLS and O-PLS) have been recognized as efficient tools for data analysis within subjects such as biology and chemistry due to their ability to provide interpretable models based on many, correlated variables. In global metabolite analysis, these methods have been successfully applied in areas such as toxicology, disease diagnosis and plant functional genomics. This thesis describes the development of processing methods for the unbiased extraction of representative and predictive information from metabolic GC/MS and LC/MS data characterizing biofluids, e.g. plant extracts, urine and blood plasma. In order to allow the multivariate projections to detect and highlight differences between samples, one requirement of the processing methods is that they must extract a common set of descriptors from all samples and still retain the metabolically relevant information in the data. In Papers I and II this was done by applying a hierarchical multivariate compression approach to both GC/MS and LC/MS data. In the study described in Paper III a hierarchical multivariate curve resolution strategy (H-MCR) was developed for simultaneously resolving multiple GC/MS samples into pure profiles. In Paper IV the H-MCR method was applied to a drug toxicity study in rats, where the method’s potential for biomarker detection and identification was exemplified. Finally, the H-MCR method was extended, as described in Paper V, allowing independent samples to be processed and predicted using a model based on an existing set of representative samples. The fact that these processing methods proved to be valid for predicting the properties of new independent samples indicates that it is now possible for global metabolite analysis to be extended beyond isolated studies. In addition, the results facilitate high through-put analysis, because predicting the nature of samples is rapid compared to the actual processing. In summary this research highlights the possibilities for using global metabolite analysis in diagnosis.
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Siluyele, Ian John. "Power studies of multivariate two-sample tests of comparison." Thesis, University of the Western Cape, 2007. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_6355_1255091702.

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The multivariate two-sample tests provide a means to test the match between two multivariate distributions. Although many tests exist in the literature, relatively little is known about the relative power of these procedures. The studies reported in the thesis contrasts the effectiveness, in terms of power, of seven such tests with a Monte Carlo study. The relative power of the tests was investigated against location, scale, and correlation alternatives.

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Vitale, Raffaele. "Novel chemometric proposals for advanced multivariate data analysis, processing and interpretation." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/90442.

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The present Ph.D. thesis, primarily conceived to support and reinforce the relation between academic and industrial worlds, was developed in collaboration with Shell Global Solutions (Amsterdam, The Netherlands) in the endeavour of applying and possibly extending well-established latent variable-based approaches (i.e. Principal Component Analysis - PCA - Partial Least Squares regression - PLS - or Partial Least Squares Discriminant Analysis - PLSDA) for complex problem solving not only in the fields of manufacturing troubleshooting and optimisation, but also in the wider environment of multivariate data analysis. To this end, novel efficient algorithmic solutions are proposed throughout all chapters to address very disparate tasks, from calibration transfer in spectroscopy to real-time modelling of streaming flows of data. The manuscript is divided into the following six parts, focused on various topics of interest: Part I - Preface, where an overview of this research work, its main aims and justification is given together with a brief introduction on PCA, PLS and PLSDA; Part II - On kernel-based extensions of PCA, PLS and PLSDA, where the potential of kernel techniques, possibly coupled to specific variants of the recently rediscovered pseudo-sample projection, formulated by the English statistician John C. Gower, is explored and their performance compared to that of more classical methodologies in four different applications scenarios: segmentation of Red-Green-Blue (RGB) images, discrimination of on-/off-specification batch runs, monitoring of batch processes and analysis of mixture designs of experiments; Part III - On the selection of the number of factors in PCA by permutation testing, where an extensive guideline on how to accomplish the selection of PCA components by permutation testing is provided through the comprehensive illustration of an original algorithmic procedure implemented for such a purpose; Part IV - On modelling common and distinctive sources of variability in multi-set data analysis, where several practical aspects of two-block common and distinctive component analysis (carried out by methods like Simultaneous Component Analysis - SCA - DIStinctive and COmmon Simultaneous Component Analysis - DISCO-SCA - Adapted Generalised Singular Value Decomposition - Adapted GSVD - ECO-POWER, Canonical Correlation Analysis - CCA - and 2-block Orthogonal Projections to Latent Structures - O2PLS) are discussed, a new computational strategy for determining the number of common factors underlying two data matrices sharing the same row- or column-dimension is described, and two innovative approaches for calibration transfer between near-infrared spectrometers are presented; Part V - On the on-the-fly processing and modelling of continuous high-dimensional data streams, where a novel software system for rational handling of multi-channel measurements recorded in real time, the On-The-Fly Processing (OTFP) tool, is designed; Part VI - Epilogue, where final conclusions are drawn, future perspectives are delineated, and annexes are included.
La presente tesis doctoral, concebida principalmente para apoyar y reforzar la relación entre la academia y la industria, se desarrolló en colaboración con Shell Global Solutions (Amsterdam, Países Bajos) en el esfuerzo de aplicar y posiblemente extender los enfoques ya consolidados basados en variables latentes (es decir, Análisis de Componentes Principales - PCA - Regresión en Mínimos Cuadrados Parciales - PLS - o PLS discriminante - PLSDA) para la resolución de problemas complejos no sólo en los campos de mejora y optimización de procesos, sino también en el entorno más amplio del análisis de datos multivariados. Con este fin, en todos los capítulos proponemos nuevas soluciones algorítmicas eficientes para abordar tareas dispares, desde la transferencia de calibración en espectroscopia hasta el modelado en tiempo real de flujos de datos. El manuscrito se divide en las seis partes siguientes, centradas en diversos temas de interés: Parte I - Prefacio, donde presentamos un resumen de este trabajo de investigación, damos sus principales objetivos y justificaciones junto con una breve introducción sobre PCA, PLS y PLSDA; Parte II - Sobre las extensiones basadas en kernels de PCA, PLS y PLSDA, donde presentamos el potencial de las técnicas de kernel, eventualmente acopladas a variantes específicas de la recién redescubierta proyección de pseudo-muestras, formulada por el estadista inglés John C. Gower, y comparamos su rendimiento respecto a metodologías más clásicas en cuatro aplicaciones a escenarios diferentes: segmentación de imágenes Rojo-Verde-Azul (RGB), discriminación y monitorización de procesos por lotes y análisis de diseños de experimentos de mezclas; Parte III - Sobre la selección del número de factores en el PCA por pruebas de permutación, donde aportamos una guía extensa sobre cómo conseguir la selección de componentes de PCA mediante pruebas de permutación y una ilustración completa de un procedimiento algorítmico original implementado para tal fin; Parte IV - Sobre la modelización de fuentes de variabilidad común y distintiva en el análisis de datos multi-conjunto, donde discutimos varios aspectos prácticos del análisis de componentes comunes y distintivos de dos bloques de datos (realizado por métodos como el Análisis Simultáneo de Componentes - SCA - Análisis Simultáneo de Componentes Distintivos y Comunes - DISCO-SCA - Descomposición Adaptada Generalizada de Valores Singulares - Adapted GSVD - ECO-POWER, Análisis de Correlaciones Canónicas - CCA - y Proyecciones Ortogonales de 2 conjuntos a Estructuras Latentes - O2PLS). Presentamos a su vez una nueva estrategia computacional para determinar el número de factores comunes subyacentes a dos matrices de datos que comparten la misma dimensión de fila o columna y dos planteamientos novedosos para la transferencia de calibración entre espectrómetros de infrarrojo cercano; Parte V - Sobre el procesamiento y la modelización en tiempo real de flujos de datos de alta dimensión, donde diseñamos la herramienta de Procesamiento en Tiempo Real (OTFP), un nuevo sistema de manejo racional de mediciones multi-canal registradas en tiempo real; Parte VI - Epílogo, donde presentamos las conclusiones finales, delimitamos las perspectivas futuras, e incluimos los anexos.
La present tesi doctoral, concebuda principalment per a recolzar i reforçar la relació entre l'acadèmia i la indústria, es va desenvolupar en col·laboració amb Shell Global Solutions (Amsterdam, Països Baixos) amb l'esforç d'aplicar i possiblement estendre els enfocaments ja consolidats basats en variables latents (és a dir, Anàlisi de Components Principals - PCA - Regressió en Mínims Quadrats Parcials - PLS - o PLS discriminant - PLSDA) per a la resolució de problemes complexos no solament en els camps de la millora i optimització de processos, sinó també en l'entorn més ampli de l'anàlisi de dades multivariades. A aquest efecte, en tots els capítols proposem noves solucions algorítmiques eficients per a abordar tasques dispars, des de la transferència de calibratge en espectroscopia fins al modelatge en temps real de fluxos de dades. El manuscrit es divideix en les sis parts següents, centrades en diversos temes d'interès: Part I - Prefaci, on presentem un resum d'aquest treball de recerca, es donen els seus principals objectius i justificacions juntament amb una breu introducció sobre PCA, PLS i PLSDA; Part II - Sobre les extensions basades en kernels de PCA, PLS i PLSDA, on presentem el potencial de les tècniques de kernel, eventualment acoblades a variants específiques de la recentment redescoberta projecció de pseudo-mostres, formulada per l'estadista anglés John C. Gower, i comparem el seu rendiment respecte a metodologies més clàssiques en quatre aplicacions a escenaris diferents: segmentació d'imatges Roig-Verd-Blau (RGB), discriminació i monitorització de processos per lots i anàlisi de dissenys d'experiments de mescles; Part III - Sobre la selecció del nombre de factors en el PCA per proves de permutació, on aportem una guia extensa sobre com aconseguir la selecció de components de PCA a través de proves de permutació i una il·lustració completa d'un procediment algorítmic original implementat per a la finalitat esmentada; Part IV - Sobre la modelització de fonts de variabilitat comuna i distintiva en l'anàlisi de dades multi-conjunt, on discutim diversos aspectes pràctics de l'anàlisis de components comuns i distintius de dos blocs de dades (realitzat per mètodes com l'Anàlisi Simultània de Components - SCA - Anàlisi Simultània de Components Distintius i Comuns - DISCO-SCA - Descomposició Adaptada Generalitzada en Valors Singulars - Adapted GSVD - ECO-POWER, Anàlisi de Correlacions Canòniques - CCA - i Projeccions Ortogonals de 2 blocs a Estructures Latents - O2PLS). Presentem al mateix temps una nova estratègia computacional per a determinar el nombre de factors comuns subjacents a dues matrius de dades que comparteixen la mateixa dimensió de fila o columna, i dos plantejaments nous per a la transferència de calibratge entre espectròmetres d'infraroig proper; Part V - Sobre el processament i la modelització en temps real de fluxos de dades d'alta dimensió, on dissenyem l'eina de Processament en Temps Real (OTFP), un nou sistema de tractament racional de mesures multi-canal registrades en temps real; Part VI - Epíleg, on presentem les conclusions finals, delimitem les perspectives futures, i incloem annexos.
Vitale, R. (2017). Novel chemometric proposals for advanced multivariate data analysis, processing and interpretation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90442
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Doshi, Punit Rameshchandra. "Adaptive prefetching for visual data exploration." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0131103-203307.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: Adaptive prefetching; Large-scale multivariate data visualization; Semantic caching; Hierarchical data exploration; Exploratory data analysis. Includes bibliographical references (p.66-70).
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Cannon, Paul C. "Extending the information partition function : modeling interaction effects in highly multivariate, discrete data /." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2263.pdf.

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Forshed, Jenny. "Processing and analysis of NMR data : Impurity determination and metabolic profiling." Doctoral thesis, Stockholm : Dept. of analytical chemistry, Stockholm university, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-712.

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Guamán, Novillo Ana Verónica. "Multivariate Signal Processing for Quantitative and Qualitative Analysis of Ion Mobility Spectrometry data, applied to Biomedical Applications and Food Related Applications." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/349210.

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There are several applications where the measurement of VOC results to be useful, such as: toxic leaks, air quality measurements, explosive detection, monitoring of food and beverages quality, diagnosis of diseases, etc. Some of this applications claim for fast responses or even real time responses. In this context, there are few analytical techniques for performing gas phase analysis, among of them Ion Mobility Spectrometry (IMS). IMS is a fast analytical device based on the time of flight of ions in a drift tube. The response of IMS lasts typically few seconds, but it can be even less than a second. This fast response has drifted its use towards novel applications, such as biomedical and food applications (bio-related applications). Nonetheless, it has also brought the need to analyze complex spectra with hundreds of compounds. In fact, tackling this disadvantage is the main focus of this thesis, where new algorithms for enhancing the IMS performance are investigated when are applied to bio-related applications. Nonlinear behavior and charge competitions of IMS responses are important issues that need to be addressed. Both effects have a direct impact in the IMS spectra interpretation —especially when real dataset are studied. Additionally, the use of univariate spectra analysis, where peaks information is extracted manually, becomes unfeasible in bio-related applications. In this context, this work introduces multivariate methodologies focused on quantitative and qualitative analysis. In the case of quantitative analysis, calibration models were built using univariate methodology, Partial Leas Squares (PLS) and Multivariate Curve Resolution techniques (MCR). The quantitative analysis aims tackling the main issues of IMS such as non linearities and mixture effect. Definitely, univariate techniques provides poor or overoptimistic results that minimize the impact of the IMS use. The results show a really improvement on the performance when multivariate techniques were used. Regarding the results between MCR and PLS, the main difference is the interpretability that offers MCR. In the case of qualitative analysis, two different approaches were planned for building models for classes' discrimination. The first approach consisted on building a model through principal component analysis and linear discriminant analysis, besides of using robust cross validation methodology for obtaining reliable results. This methodology were implemented in samples of wine, where main motivation was found discrimination regarding to their origin. The results were fully satisfactory because the model was able to separate four groups with a high accuracy rate. The second approach involves the use of Multivarite Curve Resolution — Lasso algorithm for extracting pure components of samples from rats' breath and then use a feature selection technique for obtaining the most representative features subset. In this case, the objective of the application was to find a model that discriminate rats with sepsis from control rats. The results shows there were few pure components of IMS that generate a discriminatory model that means there are specific compounds in the breath linked with the disease. Summarizing, the following proposal has as main objective resolving open issues in stand-alone IMS that are applied to the analysis of bio-related applications. Two major investigation lines were proposed in this thesis: (i) qualitative analysis and (ii) quantitative analysis. The qualitative analysis covers pre-processing algorithms and the developing of new methodologies for building models in bio-related applications. The quantitative analysis are focused on highlighting the importance of the use of multivariate techniques instead of univariate techniques. In order to reach the objectives of this thesis, a set of datasets were created, which are detailed on the content of this thesis. The results and main conclusions are deeply explained in the extended proposal.
El objetivo de esta tesis es el desarrollo de nuevas metodologías en el procesado de señal multivariante en espectros IMS. En este trabajo se ha realizado una comparación entre tres espectrómetros IMS. Esta labor comparativa, mediante procesado multivariante, es prácticamente inédita en este ámbito. En este caso se realizó un estudio con 3 aminas y se determinó el límite de detección. Los resultados mostraron que los 3 espectrómetros tuvieron un rendimiento similar, a pesar de que sus condiciones de operación son distintas. Se propuso una técnica específica para eliminar ruido de baja frecuencia acoplado al espectro de IMS. Se observó que utilizar PCA o ICA (métodos multivariantes) mejora notablemente la relación señal ruido si se compara con las técnicas convencionales. Se ha estudiado el alineamiento de los espectros y se han propuesto soluciones basadas en los diferentes métodos del estado del arte. Se ha evidenciado que incluir compuestos de referencia para garantizar que el proceso de alineamiento es el adecuado es ventajoso. En el caso de que esto no fuese posible se aconseja realizar el alineamiento por etapas, primero un alineamiento en una misma muestra, y luego entre muestras. Se realizaron modelos cualitativos para diferenciar o discriminar clases a partir de medidas de IMS. Se propusieron dos modelos multivariantes con técnicas de validación cruzada. Los resultados obtenidos muestran el gran potencial de IMS en este sentido. Se evaluó el rendimiento cuantitativo de los IMS al utilizar métodos multivariantes y fueron comparados con métodos univariantes habituales en el ámbito de IMS. De los resultados obtenidos se observó que los modelos univariantes no son capaces de resolver comportamientos típicos de IMS como son el comportamiento no lineal y el efecto en mezclas. En este sentido las técnicas multivariantes mostraron mejores prestaciones. Se comparó la utilización de técnicas multivariantes que proyectan los datos en un nuevo subespacio como lo es PLS con técnicas de deconvolución como lo es MCR en sus dos versiones ALS y Lasso. Los resultados obtenidos fueron bastante similares, sin embargo MCR ofrece una ventaja importante ya que permite interpretar de mejor manera los resultados.
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Cannon, Paul C. "Extending the Information Partition Function: Modeling Interaction Effects in Highly Multivariate, Discrete Data." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/1234.

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Because of the huge amounts of data made available by the technology boom in the late twentieth century, new methods are required to turn data into usable information. Much of this data is categorical in nature, which makes estimation difficult in highly multivariate settings. In this thesis we review various multivariate statistical methods, discuss various statistical methods of natural language processing (NLP), and discuss a general class of models described by Erosheva (2002) called generalized mixed membership models. We then propose extensions of the information partition function (IPF) derived by Engler (2002), Oliphant (2003), and Tolley (2006) that will allow modeling of discrete, highly multivariate data in linear models. We report results of the modified IPF model on the World Health Organization's Survey on Global Aging (SAGE).
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Oller, Moreno Sergio. "Data processing for Life Sciences measurements with hyphenated Gas Chromatography-Ion Mobility Spectrometry." Doctoral thesis, Universitat de Barcelona, 2018. http://hdl.handle.net/10803/523539.

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Recent progress in analytical chemistry instrumentation has increased the amount of data available for analysis. This progress has been encompassed by computational improvements, that have enabled new possibilities to analyze larger amounts of data. These two factors have allowed to analyze more complex samples in multiple life science fields, such as biology, medicine, pharmacology, or food science. One of the techniques that has benefited from these improvements is Gas Chromatography - Ion Mobility Spectrometry (GC-IMS). This technique is useful for the detection of Volatile Organic Compounds (VOCs) in complex samples. Ion Mobility Spectrometry is an analytical technique for characterizing chemical substances based on the velocity of gas-phase ions in an electric field. It is able to detect trace levels of volatile chemicals reaching for some analytes ppb concentrations. While the instrument has moderate selectivity it is very fast in the analysis, as an ion mobility spectrum can be acquired in tenths of milliseconds. As it operates at ambient pressure, it is found not only as laboratory instrumentation but also in-site, to perform screening applications. For instance it is often used in airports for the detection of drugs and explosives. To enhance the selectivity of the IMS, especially for the analysis of complex samples, a gas chromatograph can be used for sample pre-separation at the expense of the length of the analysis. While there is better instrumentation and more computational power, better algorithms are still needed to exploit and extract all the information present in the samples. In particular, GC-IMS has not received much attention compared to other analytical techniques. In this work we address some of the data analysis issues for GC-IMS: With respect to the pre-processing, we explore several baseline estimation methods and we suggest a variation of Asymmetric Least Squares, a popular baseline estimation technique, that is able to cope with signals that present large peaks or large dynamic range. This baseline estimation method is used in Gas Chromatography - Mass Spectrometry signals as well, as it suits both techniques. Furthermore, we also characterize spectral misalignments in a several months long study, and propose an alignment method based on monotonic cubic splines for its correction. Based on the misalignment characterization we propose an optimal time span between consecutive calibrant samples. We the explore the usage of Multivariate Curve Resolution methods for the deconvolution of overlapped peaks and their extraction into pure components. We propose the use of a sliding window in the retention time axis to extract the pure components from smaller windows. The pure components are tracked through the windows. This approach is able to extract analytes with lower response with respect to MCR, compounds that have a low variance in the overall matrix Finally we apply some of these developments to real world applications, on a dataset for the prevention of fraud and quality control in the classification of olive oils, measured with GC-IMS, and on data for biomarker discovery of prostate cancer by analyzing the headspace of urine samples with a GC-MS instrument.
Els avenços recents en instrumentació química i el progrés en les capacitats computacionals obren noves possibilitats per l’anàlisi de dades provinents de diversos camps en l’àmbit de les ciències de la vida, com la biologia, la medicina o la ciència de l’alimentació. Una de les tècniques que s’ha beneficiat d’aquests avenços és la cromatografia de gasos – espectrometria de mobilitat d’ions (GC-IMS). Aquesta tècnica és útil per detectar compostos orgànics volàtils en mostres complexes. L’IMS és una tècnica analítica per caracteritzar substàncies químiques basada en la velocitat d’ions en fase gasosa en un camp elèctric, capaç de detectar traces d’alguns volàtils en concentracions de ppb ràpidament. Per augmentar-ne la selectivitat, un cromatògraf de gasos pot emprar-se per pre-separar la mostra, a expenses de la durada de l’anàlisi. Tot i disposar de millores en la instrumentació i més poder computacional, calen millors algoritmes per extreure tota la informació de les mostres. En particular, GC-IMS no ha rebut molta atenció en comparació amb altres tècniques analítiques. En aquest treball, tractem alguns problemes de l’anàlisi de dades de GC-IMS: Pel que fa al pre-processat, explorem algoritmes d’estimació de la línia de base i en proposem una millora, adaptada a les necessitats de l’instrument. Aquest algoritme també s’utilitza en mostres de cromatografia de gasos espectrometria de masses (GC-MS), en tant que s’adapta correctament a ambdues tècniques. Caracteritzem els desalineaments espectrals que es produeixen en un estudi de diversos mesos de durada, i proposem un mètode d’alineat basat en splines cúbics monotònics per a la seva correcció i un interval de temps òptim entre dues mostres calibrants. Explorem l’ús de mètodes de resolució multivariant de corbes (MCR) per a la deconvolució de pics solapats i la seva extracció en components purs. Proposem l’ús d’una finestra mòbil en el temps de retenció. Aquesta millora permet extreure més informació d’analits. Finalment utilitzem alguns d’aquests desenvolupaments a dues aplicacions: la prevenció de frau en la classificació d’olis d’oliva, mesurada amb GC-IMS i la cerca de biomarcadors de càncer de pròstata en volàtils de la orina, feta amb GC-MS.
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Alexander, Miranda Abhilash. "Spectral factor model for time series learning." Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209812.

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Today's computerized processes generate

massive amounts of streaming data.

In many applications, data is collected for modeling the processes. The process model is hoped to drive objectives such as decision support, data visualization, business intelligence, automation and control, pattern recognition and classification, etc. However, we face significant challenges in data-driven modeling of processes. Apart from the errors, outliers and noise in the data measurements, the main challenge is due to a large dimensionality, which is the number of variables each data sample measures. The samples often form a long temporal sequence called a multivariate time series where any one sample is influenced by the others.

We wish to build a model that will ensure robust generation, reviewing, and representation of new multivariate time series that are consistent with the underlying process.

In this thesis, we adopt a modeling framework to extract characteristics from multivariate time series that correspond to dynamic variation-covariation common to the measured variables across all the samples. Those characteristics of a multivariate time series are named its 'commonalities' and a suitable measure for them is defined. What makes the multivariate time series model versatile is the assumption regarding the existence of a latent time series of known or presumed characteristics and much lower dimensionality than the measured time series; the result is the well-known 'dynamic factor model'.

Original variants of existing methods for estimating the dynamic factor model are developed: The estimation is performed using the frequency-domain equivalent of the dynamic factor model named the 'spectral factor model'. To estimate the spectral factor model, ideas are sought from the asymptotic theory of spectral estimates. This theory is used to attain a probabilistic formulation, which provides maximum likelihood estimates for the spectral factor model parameters. Then, maximum likelihood parameters are developed with all the analysis entirely in the spectral-domain such that the dynamically transformed latent time series inherits the commonalities maximally.

The main contribution of this thesis is a learning framework using the spectral factor model. We term learning as the ability of a computational model of a process to robustly characterize the data the process generates for purposes of pattern matching, classification and prediction. Hence, the spectral factor model could be claimed to have learned a multivariate time series if the latent time series when dynamically transformed extracts the commonalities reliably and maximally. The spectral factor model will be used for mainly two multivariate time series learning applications: First, real-world streaming datasets obtained from various processes are to be classified; in this exercise, human brain magnetoencephalography signals obtained during various cognitive and physical tasks are classified. Second, the commonalities are put to test by asking for reliable prediction of a multivariate time series given its past evolution; share prices in a portfolio are forecasted as part of this challenge.

For both spectral factor modeling and learning, an analytical solution as well as an iterative solution are developed. While the analytical solution is based on low-rank approximation of the spectral density function, the iterative solution is based on the expectation-maximization algorithm. For the human brain signal classification exercise, a strategy for comparing similarities between the commonalities for various classes of multivariate time series processes is developed. For the share price prediction problem, a vector autoregressive model whose parameters are enriched with the maximum likelihood commonalities is designed. In both these learning problems, the spectral factor model gives commendable performance with respect to competing approaches.

Les processus informatisés actuels génèrent des quantités massives de flux de données. Dans nombre d'applications, ces flux de données sont collectées en vue de modéliser les processus. Les modèles de processus obtenus ont pour but la réalisation d'objectifs tels que l'aide à la décision, la visualisation de données, l'informatique décisionnelle, l'automatisation et le contrôle, la reconnaissance de formes et la classification, etc. La modélisation de processus sur la base de données implique cependant de faire face à d’importants défis. Outre les erreurs, les données aberrantes et le bruit, le principal défi provient de la large dimensionnalité, i.e. du nombre de variables dans chaque échantillon de données mesurées. Les échantillons forment souvent une longue séquence temporelle appelée série temporelle multivariée, où chaque échantillon est influencé par les autres. Notre objectif est de construire un modèle robuste qui garantisse la génération, la révision et la représentation de nouvelles séries temporelles multivariées cohérentes avec le processus sous-jacent.

Dans cette thèse, nous adoptons un cadre de modélisation capable d’extraire, à partir de séries temporelles multivariées, des caractéristiques correspondant à des variations - covariations dynamiques communes aux variables mesurées dans tous les échantillons. Ces caractéristiques sont appelées «points communs» et une mesure qui leur est appropriée est définie. Ce qui rend le modèle de séries temporelles multivariées polyvalent est l'hypothèse relative à l'existence de séries temporelles latentes de caractéristiques connues ou présumées et de dimensionnalité beaucoup plus faible que les séries temporelles mesurées; le résultat est le bien connu «modèle factoriel dynamique». Des variantes originales de méthodes existantes pour estimer le modèle factoriel dynamique sont développées :l'estimation est réalisée en utilisant l'équivalent du modèle factoriel dynamique au niveau du domaine de fréquence, désigné comme le «modèle factoriel spectral». Pour estimer le modèle factoriel spectral, nous nous basons sur des idées relatives à la théorie des estimations spectrales. Cette théorie est utilisée pour aboutir à une formulation probabiliste, qui fournit des estimations de probabilité maximale pour les paramètres du modèle factoriel spectral. Des paramètres de probabilité maximale sont alors développés, en plaçant notre analyse entièrement dans le domaine spectral, de façon à ce que les séries temporelles latentes transformées dynamiquement héritent au maximum des points communs.

La principale contribution de cette thèse consiste en un cadre d'apprentissage utilisant le modèle factoriel spectral. Nous désignons par apprentissage la capacité d'un modèle de processus à caractériser de façon robuste les données générées par le processus à des fins de filtrage par motif, classification et prédiction. Dans ce contexte, le modèle factoriel spectral est considéré comme ayant appris une série temporelle multivariée si la série temporelle latente, une fois dynamiquement transformée, permet d'extraire les points communs de façon fiable et maximale. Le modèle factoriel spectral sera utilisé principalement pour deux applications d'apprentissage de séries multivariées :en premier lieu, des ensembles de données sous forme de flux venant de différents processus du monde réel doivent être classifiés; lors de cet exercice, la classification porte sur des signaux magnétoencéphalographiques obtenus chez l'homme au cours de différentes tâches physiques et cognitives; en second lieu, les points communs obtenus sont testés en demandant une prédiction fiable d'une série temporelle multivariée étant donnée l'évolution passée; les prix d'un portefeuille d'actions sont prédits dans le cadre de ce défi.

À la fois pour la modélisation et pour l'apprentissage factoriel spectral, une solution analytique aussi bien qu'une solution itérative sont développées. Tandis que la solution analytique est basée sur une approximation de rang inférieur de la fonction de densité spectrale, la solution itérative est basée, quant à elle, sur l'algorithme de maximisation des attentes. Pour l'exercice de classification des signaux magnétoencéphalographiques humains, une stratégie de comparaison des similitudes entre les points communs des différentes classes de processus de séries temporelles multivariées est développée. Pour le problème de prédiction des prix des actions, un modèle vectoriel autorégressif dont les paramètres sont enrichis avec les points communs de probabilité maximale est conçu. Dans ces deux problèmes d’apprentissage, le modèle factoriel spectral atteint des performances louables en regard d’approches concurrentes.
Doctorat en Sciences
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Books on the topic "Multivariate analysis – Data processing"

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Exploratory and multivariate data analysis. Boston: Academic Press, 1991.

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Susanne, May, and Clark Virginia 1928-, eds. Practical multivariate analysis. 5th ed. Boca Raton: Taylor & Francis, 2012.

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1949-, Dunn G., and Everitt Brian, eds. Applied multivariate data analysis. London: E. Arnold, 1991.

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1949-, Dunn G., ed. Applied multivariate data analysis. New York: Oxford University Press, 1992.

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1949-, Dunn G., ed. Applied multivariate data analysis. 2nd ed. London: Arnold, 2001.

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Multivariate statistical simulation. New York: Wiley, 1987.

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1928-, Clark Virginia, ed. Computer-aided multivariate analysis. 2nd ed. New York: Van Nostrand Reinhold, 1990.

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Afifi, A. A. Computer-aided multivariate analysis. 4th ed. Boca Raton, Fla: Chapman & Hall/CRC, 2004.

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Afifi, A. A. Computer-aided multivariate analysis. Boca Raton: Chapman & Hall/CRC, 1999.

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Afifi, A. A. Computer-aided multivariate analysis. 2nd ed. New York: Chapman & Hall, 1990.

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Book chapters on the topic "Multivariate analysis – Data processing"

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Everitt, Brian S., and Graham Dunn. "Multivariate Data and Multivariate Statistics." In Applied Multivariate Data Analysis, 1–8. West Sussex, United Kingdom: John Wiley & Sons, Ltd,., 2013. http://dx.doi.org/10.1002/9781118887486.ch1.

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Bürgel, Oliver. "Multivariate Data Analysis." In The Internationalisation of British Start-up Companies in High-Technology Industries, 141–85. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57671-3_6.

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Haslwanter, Thomas. "Multivariate Data Analysis." In An Introduction to Statistics with Python, 221–25. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28316-6_12.

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Vehkalahti, Kimmo, and Brian S. Everitt. "Multivariate Data and Multivariate Analysis." In Multivariate Analysis for the Behavioral Sciences, 225–37. Second edition. | Boca Raton, Florida : CRC Press [2019] | Earlier edition published as: Multivariable modeling and multivariate analysis for the behavioral sciences / [by] Brian S. Everitt.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351202275-12.

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Everitt, Brian Sidney. "Multivariate Data and Multivariate Analysis." In Springer Texts in Statistics, 1–15. London: Springer London, 2005. http://dx.doi.org/10.1007/1-84628-124-5_1.

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Everitt, Brian, and Torsten Hothorn. "Multivariate Data and Multivariate Analysis." In An Introduction to Applied Multivariate Analysis with R, 1–24. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9650-3_1.

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Murtagh, Fionn, and André Heck. "Cluster Analysis." In Multivariate Data Analysis, 55–109. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3789-5_3.

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Murtagh, Fionn, and André Heck. "Discriminant Analysis." In Multivariate Data Analysis, 111–54. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3789-5_4.

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Murtagh, Fionn, and André Heck. "Principal Components Analysis." In Multivariate Data Analysis, 13–53. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3789-5_2.

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Härdle, Wolfgang Karl, and Léopold Simar. "Data." In Applied Multivariate Statistical Analysis, 547–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26006-4_22.

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Conference papers on the topic "Multivariate analysis – Data processing"

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Ahmed, M. U., N. Rehman, D. Looney, T. M. Rutkowski, P. Kidmose, and D. P. Mandic. "Multivariate entropy analysis with data-driven scales." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288770.

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Er, Wenjun, and Danilo P. Mandic. "Dynamical complexity analysis of multivariate financial data." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6639371.

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Filianin, Kirill, Satu-Pia Reinikainen, and Tuomo Sainio. "Detection of current inefficiencies in copper electrowinning with multivariate data analysis." In 2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2016. http://dx.doi.org/10.1109/icicip.2016.7885879.

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Pao, Y. H., B. F. Duan, Y. L. Zhao, and S. R. LeClair. "Analysis and visualization of category membership distribution in multivariate data." In Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296). IEEE, 1999. http://dx.doi.org/10.1109/ipmm.1999.791565.

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Zhesi He, R. Ruddle, and L. Caves. "Strategies and tools for multivariate biology: interactive analysis of high dimensional postgenomic data." In IET Seminar on Signal Processing for Genomics. IEE, 2006. http://dx.doi.org/10.1049/ic:20060375.

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Cucci, Costanza, Andrea Barucci, Lorenzo Stefani, Marcello Picollo, Reyes Jiménez-Garnica, and Laura Fuster-Lopez. "Reflectance hyperspectral data processing on a set of Picasso paintings: which algorithm provides what? A comparative analysis of multivariate, statistical and artificial intelligence methods." In Optics for Arts, Architecture, and Archaeology (O3A) VIII, edited by Roger Groves and Haida Liang. SPIE, 2021. http://dx.doi.org/10.1117/12.2593838.

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Lu, Jun, Zhenfei Zhan, Pan Wang, Yudong Fang, and Junqi Yang. "A Stochastic Multivariate Validation Method for Dynamic Systems." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67690.

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As computer models become more powerful and popular, the complexity of input and output data raises new computational challenges. One of the key difficulties for model validation is to evaluate the quality of a computer model with multivariate, highly correlated and non-normal data, the direct application of traditional validation approaches does not appear to be suitable. This paper proposes a stochastic method to validate the dynamic systems. Firstly, a dimension reduction utilizing kernel principal component analysis (KPCA) is used to improve the computational efficiency. A probability model is then established by non-parametric kernel density estimation (KDE) method, and differences between the test data and simulation results are finally extracted to further comparative validation. This new approach resolves some critical drawbacks of the previous methods and improves the processing ability to nonlinear problem to validation the dynamic model. The proposed method and process are successfully illustrated through a real-world vehicle dynamic system example. The results demonstrate that the method of incorporate with KPCA and KDE is an effective approach to solve the dynamic model validation problem.
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Naveed, Khuram, Sidra Mukhtar, and Naveed Ur Rehman. "Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis." In 2021 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2021. http://dx.doi.org/10.1109/ssp49050.2021.9513823.

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Nair, Shruti, Sungsoo Ha, and Wei Xu. "Data Analysis on Multivariate Image Set." In 2018 New York Scientific Data Summit (NYSDS). IEEE, 2018. http://dx.doi.org/10.1109/nysds.2018.8538941.

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Lainscsek, Claudia, Manuel E. Hernandez, Howard Poizner, and Terrence J. Sejnowski. "Multivariate spectral analysis of electroencephalography data." In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2013. http://dx.doi.org/10.1109/ner.2013.6696142.

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Reports on the topic "Multivariate analysis – Data processing"

1

Alam, M. Kathleen. Multivariate Analysis of Seismic Field Data. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/8993.

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Cheng, Qiuming. Spatially and geographically weighted multivariate analysis methods for mineral image processing. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0169.

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Chen, Maximillian Gene, Kristin Marie Divis, James D. Morrow, and Laura A. McNamara. Visualizing Clustering and Uncertainty Analysis with Multivariate Longitudinal Data. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1472228.

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Boyd, Thomas J., and Richard B. Coffin. Isotope Ratio Spectrometry Data Processing Software: Multivariate Statistical Methods for Hydrocarbon Source Identification and Comparison. Fort Belvoir, VA: Defense Technical Information Center, April 2004. http://dx.doi.org/10.21236/ada422798.

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Fowler, Kimberly M., Alison H. A. Colotelo, Janelle L. Downs, Kenneth D. Ham, Jordan W. Henderson, Sadie A. Montgomery, Christopher R. Vernon, and Steven A. Parker. Simplified Processing Method for Meter Data Analysis. Office of Scientific and Technical Information (OSTI), November 2015. http://dx.doi.org/10.2172/1255411.

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Wong, George Y. Statistical Analysis of Multivariate Interval-Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada418647.

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Wong, George Y. Statistical Analysis of Multivariate Interval Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada409921.

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Grunsky, E. Spatial factor analysis: a technique to assess the spatial relationships of multivariate data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128074.

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Wong, George. Statistical Analysis of Multivariate Interval-Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada390768.

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Hodgkiss, W. S. Shallow Water Adaptive Array Processing and Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada306525.

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