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‣ Extração de características de imagens de faces humanas através de wavelets, PCA e IMPCA; Features extraction of human faces images through wavelets, PCA and IMPCA

Bianchi, Marcelo Franceschi de
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 10/04/2006 Português
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Reconhecimento de padrões em imagens é uma área de grande interesse no mundo científico. Os chamados métodos de extração de características, possuem as habilidades de extrair características das imagens e também de reduzir a dimensionalidade dos dados gerando assim o chamado vetor de características. Considerando uma imagem de consulta, o foco de um sistema de reconhecimento de imagens de faces humanas é pesquisar em um banco de imagens, a imagem mais similar à imagem de consulta, de acordo com um critério dado. Este trabalho de pesquisa foi direcionado para a geração de vetores de características para um sistema de reconhecimento de imagens, considerando bancos de imagens de faces humanas, para propiciar tal tipo de consulta. Um vetor de características é uma representação numérica de uma imagem ou parte dela, descrevendo seus detalhes mais representativos. O vetor de características é um vetor n-dimensional contendo esses valores. Essa nova representação da imagem propicia vantagens ao processo de reconhecimento de imagens, pela redução da dimensionalidade dos dados. Uma abordagem alternativa para caracterizar imagens para um sistema de reconhecimento de imagens de faces humanas é a transformação do domínio. A principal vantagem de uma transformação é a sua efetiva caracterização das propriedades locais da imagem. As wavelets diferenciam-se das tradicionais técnicas de Fourier pela forma de localizar a informação no plano tempo-freqüência; basicamente...

‣ Utilização de análise de componentes principais em séries temporais; Use of principal component analysis in time series

Teixeira, Sérgio Coichev
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 12/04/2013 Português
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Um dos principais objetivos da análise de componentes principais consiste em reduzir o número de variáveis observadas em um conjunto de variáveis não correlacionadas, fornecendo ao pesquisador subsídios para entender a variabilidade e a estrutura de correlação dos dados observados com uma menor quantidade de variáveis não correlacionadas chamadas de componentes principais. A técnica é muito simples e amplamente utilizada em diversos estudos de diferentes áreas. Para construção, medimos a relação linear entre as variáveis observadas pela matriz de covariância ou pela matriz de correlação. Entretanto, as matrizes de covariância e de correlação podem deixar de capturar importante informações para dados correlacionados sequencialmente no tempo, autocorrelacionados, desperdiçando parte importante dos dados para interpretação das componentes. Neste trabalho, estudamos a técnica de análise de componentes principais que torna possível a interpretação ou análise da estrutura de autocorrelação dos dados observados. Para isso, exploramos a técnica de análise de componentes principais para o domínio da frequência que fornece para dados autocorrelacionados um resultado mais específico e detalhado do que a técnica de componentes principais clássica. Pelos métodos SSA (Singular Spectrum Analysis) e MSSA (Multichannel Singular Spectrum Analysis)...

‣ Reduction capability of soil humic substances from the Rio Negro basin, Brazil, towards Hg(II) studied by a multimethod approach and principal component analysis (PCA)

Serudo, Ricardo Lima; de Oliveira, Luciana Camargo; Rocha, Julio Cesar; Paterlini, William Cesar; Rosa, Andre Henrique; da Silva, Heliandro Cordovil; Botero, Wander Gustavo
Fonte: Elsevier B.V. Publicador: Elsevier B.V.
Tipo: Artigo de Revista Científica Formato: 229-236
Português
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This paper characterizes humic substances (HS) extracted from soil samples collected in the Rio Negro basin in the state of Amazonas, Brazil, particularly investigating their reduction capabilities towards Hg(II) in order to elucidate potential mercury cycling/volatilization in this environment. For this reason, a multimethod approach was used, consisting of both instrumental methods (elemental analysis, EPR, solid-state NMR, FIA combined with cold-vapor AAS of Hg(0)) and statistical methods such as principal component analysis (PCA) and a central composite factorial planning method. The HS under study were divided into groups, complexing and reducing ones, owing to different distribution of their functionalities. The main functionalities (cor)related with reduction of Hg(II) were phenolic, carboxylic and amide groups, while the groups related with complexation of Hg(II) were ethers, hydroxyls, aldehydes and ketones. The HS extracted from floodable regions of the Rio Negro basin presented a greater capacity to retain (to complex, to adsorb physically and/or chemically) Hg(II), while nonfloodable regions showed a greater capacity to reduce Hg(II), indicating that HS extracted from different types of regions contribute in different ways to the biogeochemical mercury cycle in the basin of the mid-Rio Negro...

‣ Coprocessador neuro-genetico para analise de componentes principais; Neuro-Genetic Coprocessor for Principal Component Analysis

George Emmanuel Bozinis
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 31/07/2007 Português
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O propósito deste trabalho é estudar em detalhe a implementação em hardware de algoritmos neuro-genéticos. Uma representação numérica inédita com características neurais e genéticas e um algoritmo para sua utilização são apresentados e usados no desenvolvimento de um coprocessador com uma seção neural baseada na análise de componentes principais (PCA). As operações genéticas recombinação, mutação, mutação de máscara e intercâmbio, específicas para este modelo, são apresentadas. Também foi criada e implementada uma metodologia de cálculo da curva de ativação neural usando apenas lógica combinacional. Como resultado adicional a implementação, realizada na linguagem VHDL e seguindo a norma Wishbone, pode ser facilmente reutilizada; The intention of this work is to study the hardware implementation of neuro-genetic algorithms in detail. A novel numerical representation with neural and genetic characteristics and an algorithm for its utilization are presented and used in the development of a coprocessor with a neural section based on the principal component analysis (PCA). The genetic operations: crossover, mutation, mask mutation and swap, specific for this mode!, are presented. Also, a methodology for the calculation of the neural activation curve was created and implemented using only combinational logic. Additionally...

‣ Predicting effects of toxic events to anaerobic granular sludge with quantitative image analysis and principal component analysis

Costa, J. C.; Alves, M. M.; Ferreira, E. C.
Fonte: Universidade do Minho Publicador: Universidade do Minho
Tipo: Conferência ou Objeto de Conferência
Publicado em 24/06/2008 Português
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Detergents and solvents are included in the list of compounds that can be inhibitory or toxic to anaerobic digestion processes. Industrial cleaning stages/processes produce vast amounts of contaminated wastewater. In order to optimize the control of these wastewaters it is important to know and predict the effects on the activity and physical properties of anaerobic aggregates in an early stage. Datasets gathering morphological, physiological and reactor performance information were created from three toxic shock loads (SL1 – 1.6 mgdetergent/L; SL2 – 3.1 mgdetergent/L; SL3 – 40 mgsolvent/L). The use of Principal Component Analysis (PCA) allowed the visualization of the main effects caused by the toxics, by clustering the samples according to its operational phase, exposure or recovery. The morphological parameters showed to be sensitive enough to detect the operational problems even before the COD removal efficiency decreased. Its high loadings in the plane defined by the first and second principal components, which gathers the higher variability in datasets, express the usefulness of monitor the biomass morphology in order to achieve a suitable control of the process. PCA defined a new latent variable t[1], gathering the most relevant variability in dataset...

‣ Principal component analysis and quantitative image analysis to predict effects of toxics in anaerobic granular sludge

Costa, J. C.; Alves, M. M.; Ferreira, E. C.
Fonte: Elsevier Ltd. Publicador: Elsevier Ltd.
Tipo: Artigo de Revista Científica
Publicado em /02/2009 Português
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Principal component analysis (PCA) was applied to datasets gathering morphological, physiological and reactor performance information, from three toxic shock loads (SL1 – 1.6 mgdetergent/L; SL2 – 3.1 mgdetergent/L; SL3 – 40 mgsolvent/L) applied in an expanded granular sludge bed (EGSB) reactor. The PCA allowed the visualization of the main effects caused by the toxics, by clustering the samples according to its operational phase, exposure or recovery. The aim was to investigate the variables or group of variables that mostly contribute for the early detection of operational problems. The morphological parameters showed to be sensitive enough to detect the operational problems even before the COD removal efficiency decreased. As observed by the high loadings in the plane defined by the first and second principal components. PCA defined a new latent variable t[1], gathering the most relevant variability in dataset, that showed an immediate variation after the toxics were fed to the reactors. t[1] varied 262%, 254% and 80%, respectively, in SL1, SL2 and SL3. The high loadings/weights of the morphological parameters associated with this new variable express its influence in shock load monitoring and control, and consequently in operational problems recognition.; Fundação para a Ciência e a Tecnologia (FCT) -Bolsa SFRH/BD/13317/2003...

‣ Antioxidant capacity, total phenolic content, fatty acids and correlation by principal component analysis of exotic and native fruits from Brazil

Ribeiro,Alessandra B.; Bonafé,Elton G.; Silva,Beatriz C.; Montanher,Paula F.; Santos Júnior,Oscar O.; Boeing,Joana S.; Visentainer,Jesuí V.
Fonte: Sociedade Brasileira de Química Publicador: Sociedade Brasileira de Química
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/05/2013 Português
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The antioxidant capacities of seven exotic and native fruits from Brazil were evaluated using DPPH•, ABTS•+ and FRAP assays, in addition to their total phenolic content and fatty acid composition. Murici and dovialis presented the highest total phenolic contents (243.42 and 205.98 mg GAE 100 g-1, respectively), and the highest antioxidant capacities by the FRAP assay (24.97 and 23.70 µmol Fe2+ g-1, respectively). In the DPPH• and ABTS•+ assays, dovialis presented the highest antioxidant capacity, 9.59 and 10.41 TE g-1, respectively. The highest alpha-linolenic and linoleic acid contents were found in siriguela (107.86 mg FA g-1 TL) and tomatinho do mato (215.50 mg FA g-1 TL), respectively. The principal component analysis (PCA) of fatty acids yielded three significant PCs, which accounted for 99.75% of the data set total variance. The PCA data of the antioxidant analyses yielded two significant PCs, which accounted for 97.00% of the total variance.

‣ Assessment of walker-assisted gait based on Principal Component Analysis and wireless inertial sensors

Martins,Maria; Elias,Arlindo; Cifuentes,Carlos; Alfonso,Manuel; Frizera,Anselmo; Santos,Cristina; Ceres,Ramón
Fonte: SBEB - Sociedade Brasileira de Engenharia Biomédica Publicador: SBEB - Sociedade Brasileira de Engenharia Biomédica
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/09/2014 Português
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INTRODUCTION:This study investigates a gait research protocol to assess the impact of a walker model with forearm supports on the kinematic parameters of the lower limb during locomotion. METHODS: Thirteen healthy participants without any history of gait dysfunction were enrolled in the experimental procedure. Spatiotemporal and kinematic gait parameters were calculated by using wireless inertial sensors and analyzed with Principal Component Analysis (PCA). The PCA method was selected to achieve dimension reduction and evaluate the main effects in gait performance during walker-assisted gait. Additionally, the interaction among the variables included in each Principal Component (PCs) derived from PCA is exposed to expand the understanding of the main differences between walker-assisted and unassisted gait conditions. RESULTS:The results of the statistical analysis identified four PCs that retained 65% of the data variability. These components were associated with spatiotemporal information, knee joint, hip joint and ankle joint motion, respectively. CONCLUSION: Assisted gait by a walker model with forearm supports was characterized by slower gait, shorter steps, larger double support phase and lower body vertical acceleration when compared with normal...

‣ Principal Component Analysis applied to digital image compression

Santo,Rafael do Espírito
Fonte: Instituto Israelita de Ensino e Pesquisa Albert Einstein Publicador: Instituto Israelita de Ensino e Pesquisa Albert Einstein
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/06/2012 Português
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OBJECTIVE: To describe the use of a statistical tool (Principal Component Analysis – PCA) for the recognition of patterns and compression, applying these concepts to digital images used in Medicine. METHODS: The description of Principal Component Analysis is made by means of the explanation of eigenvalues and eigenvectors of a matrix. This concept is presented on a digital image collected in the clinical routine of a hospital, based on the functional aspects of a matrix. The analysis of potential for recovery of the original image was made in terms of the rate of compression obtained. RESULTS: The compressed medical images maintain the principal characteristics until approximately one-fourth of their original size, highlighting the use of Principal Component Analysis as a tool for image compression. Secondarily, the parameter obtained may reflect the complexity and potentially, the texture of the original image. CONCLUSION: The quantity of principal components used in the compression influences the recovery of the original image from the final (compacted) image.

‣ Automated pattern-guided principal component analysis vs expert-based immunophenotypic classification of B-cell chronic lymphoproliferative disorders: a step forward in the standardization of clinical immunophenotyping; Leukemia

Costa ES., Pedreira CE.; Barrena S., Lecrevisse Q.; Flores J., Quijano S.; Almeida J., García M.; Bottcher S., Van Dongen JJ.; Orfao A.
Fonte: Pontifícia Universidade Javeriana Publicador: Pontifícia Universidade Javeriana
Formato: 1927-1933
Português
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24-11; Immunophenotypic characterization of B-cell chronic lympho- proliferative disorders (B-CLPD) is becoming increasingly complex due to usage of progressively larger panels of reagents and a high number of World Health Organization (WHO) entities. Typically, data analysis is performed separately for each stained aliquot of a sample; subsequently, an expert interprets the overall immunophenotypic profile (IP) of neoplastic B-cells and assigns it to specific diagnostic categories. We constructed a principal component analysis (PCA)-based tool to guide immunophenotypic classification of B-CLPD. Three reference groups of immuno- phenotypic data filesFB-cell chronic lymphocytic leukemias (B-CLL; n 1⁄4 10), mantle cell (MCL; n 1⁄4 10) and follicular lympho- mas (FL; n1⁄410)Fwere built. Subsequently, each of the 175 cases studied was evaluated and assigned to either one of the three reference groups or to none of them (other B-CLPD). Most cases (89%) were correctly assigned to their corre- sponding WHO diagnostic group with overall positive and negative predictive values of 89 and 96%, respectively. The efficiency of the PCA-based approach was particularly high among typical B-CLL, MCL and FL vs other B-CLPD cases. In summary, PCA-guided immunophenotypic classification of B-CLPD is a promising tool for standardized interpretation of tumor IP...

‣ Incremental kernel principal component analysis

Chin, T.J.; Suter, D.
Fonte: IEEE-Inst Electrical Electronics Engineers Inc Publicador: IEEE-Inst Electrical Electronics Engineers Inc
Tipo: Artigo de Revista Científica
Publicado em //2007 Português
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The kernel principal component analysis (KPCA) has been applied in numerous image-related machine learning applications and it has exhibited superior performance over previous approaches, such as PCA. However, the standard implementation of KPCA scales badly with the problem size, making computations for large problems infeasible. Also, the "batch" nature of the standard KPCA computation method does not allow for applications that require online processing. This has somewhat restricted the domains in which KPCA can potentially be applied. This paper introduces an incremental computation algorithm for KPCA to address these two problems. The basis of the proposed solution lies in computing incremental linear PCA in the kernel induced feature space, and constructing reduced-set expansions to maintain constant update speed and memory usage. We also provide experimental results which demonstrate the effectiveness of the approach.

‣ Interpreting variability in global SST data using independent component analysis and principal component analysis

Westra, S.; Brown, C.; Lall, U.; Koch, I.; Sharma, A.
Fonte: John Wiley & Sons Ltd Publicador: John Wiley & Sons Ltd
Tipo: Artigo de Revista Científica
Publicado em //2010 Português
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Component extraction techniques are used widely in the analysis and interpretation of high-dimensional climate datasets such as global sea surface temperatures (SSTs). Principal component analysis (PCA), a frequently used component extraction technique, provides an orthogonal representation of the multivariate dataset and maximizes the variance explained by successive components. A disadvantage of PCA, however, is that the interpretability of the second and higher components may be limited. For this reason, a Varimax rotation is often applied to the PCA solution to enhance the interpretability of the components by maximizing a simple structure. An alternative rotational approach is known as independent component analysis (ICA), which finds a set of underlying ‘source signals’ which drive the multivariate ‘mixed’ dataset. Here we compare the capacity of PCA, the Varimax rotation and ICA in explaining climate variability present in globally distributed SST anomaly (SSTA) data. We find that phenomena which are global in extent, such as the global warming trend and the El Niño-Southern Oscillation (ENSO), are well represented using PCA. In contrast, the Varimax rotation provides distinct advantages in interpreting more localized phenomena such as variability in the tropical Atlantic. Finally...

‣ Modeling multivariable hydrological series: principal component analysis or independent component analysis?

Westra, S.; Brown, C.; Lall, U.; Sharma, A.
Fonte: Amer Geophysical Union Publicador: Amer Geophysical Union
Tipo: Artigo de Revista Científica
Publicado em //2007 Português
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The generation of synthetic multivariate rainfall and/or streamflow time series that accurately simulate both the spatial and temporal dependence of the original multivariate series remains a challenging problem in hydrology and frequently requires either the estimation of a large number of model parameters or significant simplifying assumptions on the model structure. As an alternative, we propose a relatively parsimonious two-step approach to generating synthetic multivariate time series at monthly or longer timescales, by first transforming the data to a set of statistically independent univariate time series and then applying a univariate time series model to the transformed data. The transformation is achieved through a technique known as independent component analysis (ICA), which uses an approximation of mutual information to maximize the independence between the transformed series. We compare this with principal component analysis (PCA), which merely removes the covariance (or spatial correlation) of the multivariate time series, without necessarily ensuring complete independence. Both methods are tested using a monthly multivariate data set of reservoir inflows from Colombia. We show that the discrepancy between the synthetically generated data and the original data...

‣ Data processing method applying Principal Component Analysis and Spectral Angle Mapper for imaging spectroscopic sensors

García Allende, Pilar Beatriz; Conde Portilla, Olga María; Mirapeix Serrano, Jesús María; Cubillas de Cos, Ana María; López Higuera, José Miguel
Fonte: SPIE Society of Photo-Optical Instrumentation Engineers Publicador: SPIE Society of Photo-Optical Instrumentation Engineers
Tipo: info:eu-repo/semantics/conferenceObject; publishedVersion
Português
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A data processing method for hyperspectral images is presented. Each image contains the whole diffuse reflectance spectra of the analyzed material for all the spatial positions along a specific line of vision. This data processing method is composed of two blocks: data compression and classification unit. Data compression is performed by means of Principal Component Analysis (PCA) and the spectral interpretation algorithm for classification is the Spectral Angle Mapper (SAM). This strategy of classification applying PCA and SAM has been successfully tested on the raw material on-line characterization in the tobacco industry. In this application case the desired raw material (tobacco leaves) should be discriminated from other unwanted spurious materials, such as plastic, cardboard, leather, candy paper, etc. Hyperspectral images are recorded by a spectroscopic sensor consisting of a monochromatic camera and a passive Prism- Grating-Prism device. Performance results are compared with a spectral interpretation algorithm based on Artificial Neural Networks (ANN).

‣ Hyperspectral data processing algorithm combining principal component analysis and K nearest neighbours

García Allende, Pilar Beatriz; Conde Portilla, Olga María; Amado González, Marta; Quintela Incera, Antonio; López Higuera, José Miguel
Fonte: SPIE Society of Photo-Optical Instrumentation Engineers Publicador: SPIE Society of Photo-Optical Instrumentation Engineers
Tipo: info:eu-repo/semantics/conferenceObject; publishedVersion
Português
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A processing algorithm to classify hyperspectral images from an imaging spectroscopic sensor is investigated in this paper. In this research two approaches are followed. First, the feasibility of an analysis scheme consisting of spectral feature extraction and classification is demonstrated. Principal component analysis (PCA) is used to perform data dimensionality reduction while the spectral interpretation algorithm for classification is the K nearest neighbour (KNN). The performance of the KNN method, in terms of accuracy and classification time, is determined as a function of the compression rate achieved in the PCA pre-processing stage. Potential applications of these hyperspectral sensors for foreign object detection in industrial scenarios are enormous, for example in raw material quality control. KNN classifier provides an enormous improvement in this particular case, since as no training is required, new products can be added in any time. To reduce the high computational load of the KNN classifier, a generalization of the binary tree employed in sorting and searching, kd-tree , has been implemented in a second approach. Finally, the performance of both strategies, with or without the inclusion of the kd-tree, has been successfully tested and their properties compared in the raw material quality control of the tobacco industry.

‣ Quince (Cydonia oblonga Miller) Fruit Characterization Using Principal Component Analysis

Silva, Branca M.; Andrade, Paula B.; Martins, Rui C.; Valentão, Patricia; Ferreres, Federico; Seabra, Rosa M.; Ferreira, Margarida A.
Fonte: American Chemical Society Publicador: American Chemical Society
Tipo: Artículo Formato: 259768 bytes; application/pdf
Português
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12 pages, 4 figures, 6 tables.-- Printed version published 2005.; This paper presents a large amount of data on the composition of quince fruit with regard to phenolic compounds, organic acids, and free amino acids. Subsequently, principal component analysis (PCA) is carried out to characterize this fruit. The main purposes of this study were (i) the clarification of the interactions among three factorsquince fruit part, geographical origin of the fruits, and harvesting yearand the phenolic, organic acid, and free amino acid profiles; (ii) the classification of the possible differences; and (iii) the possible correlation among the contents of phenolics, organic acids, and free amino acids in quince fruit. With these aims, quince pulp and peel from nine geographical origins of Portugal, harvested in three consecutive years, for a total of 48 samples, were studied. PCA was performed to assess the relationship among the different components of quince fruit phenolics, organic acids, and free amino acids. Phenolics determination was the most interesting. The difference between pulp and peel phenolic profiles was more apparent during PCA. Two PCs accounted for 81.29% of the total variability, PC1 (74.14%) and PC2 (7.15%). PC1 described the difference between the contents of caffeoylquinic acids (3-O-...

‣ Perceptual audio classification using principal component analysis

Burka, Zak
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
Português
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The development of robust algorithms for the recognition and classification of sensory data is one of the central topics in the area of intelligent systems and computational vision research. In order to build better intelligent systems capable of processing environmental data accurately, current research is focusing on algorithms which try to model the types of processing that occur naturally in the human brain. In the domain of computer vision, these approaches to classification are being applied to areas such as facial recognition, object detection, motion tracking, and others. This project investigates the extension of these types of perceptual classification techniques to the realm of acoustic data. As part of this effort, an algorithm for audio fingerprinting using principal component analysis for feature extraction and classification was developed and tested. The results of these experiments demonstrate the feasibility of such a system, and suggestions for future implementation enhancements are examined and proposed.

‣ Uso de Imagens Digitais e Análise de Componentes Principais na Identificação dos Níveis de Cr (VI) em Amostras de Solos; Use of Digital Images and Principal Component Analysis for the Identification of Cr (VI) Levels in Soil Samples

Luciana F. Oliveira; Natália T. Canevari; Amanda Jesus; Edenir R. Pereira Filho; UFSCar
Fonte: Revista Virtual de Química Publicador: Revista Virtual de Química
Tipo: ; Formato: binary/octet-stream
Publicado em 06/05/2013 Português
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A proposição de métodos simples e rápidos para a identificação dos níveis de Cr (VI) em amostras de solos é desejável para nortear estratégias de remediação. O presente trabalho teve como objetivo desenvolver um procedimento para a identificação de amostras de solos com concentrações de Cr (VI) superiores aos valores estabelecidos pelas legislações internacionais. Uma amostra de solo foi fortificada com concentrações de Cr (VI) que variaram de 0 a 20 mg kg-1 (total de 61 fortificações) e posteriormente submetidas a extração alcalina. Os extratos foram colocados em placas de Petri, aos quais se adicionou difenilcarbazida 0,2 % (m v-1) como reagente colorimétrico e H2SO4 (5 mol L-1) para o ajuste do pH. Após o desenvolvimento da coloração, as placas foram posicionadas em um scanner comercial e obtidas imagens da parte inferior. As imagens foram tratadas com programas computacionais para cálculo dos seguintes descritores de cores (R, G, B, H, S, V, r, g, b e L) e, efetuou-se uma análise por ACP (Análise de componentes principais - Principal Component Analysis). Houve uma boa separação entre os valores acima e abaixo da legislação italiana, a qual define um valor máximo de 2,0 mg kg-1 para Cr (VI). Também foram utilizados os valores de Cr (VI) das legislações do Canadá e da Suécia e...

‣ Determination of Phase Transition by Principal Component Analysis Applied to Raman Spectra of Polycristalline BATIO3 at Low and High Temperature

Mejía-Uriarte,E.V.; Sato-Berrú,R.Y.; Navarrete,M.; Kolokoltsev,O.; Saniger,J. M.
Fonte: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico Publicador: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/02/2012 Português
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This work describes the principal component analysis (PCA) applied to Raman spectra of polycrystalline BaTiO3 as a function of temperature. During each experiment the samples was continuously heated and the Raman spectrum was registered every 0.5 °C at a rate of 0.1 °C min-1. This procedure is applied on samples BaTiO3 compact powder to obtain their thermal behavior from -190 °C to 230 °C. The PCA method was able to distinguish spectral features to determine the phase transition temperature and the whole thermal history including the structural phase transition from rhombohedra to orthorhombic at -100 °C, orthorhombic to tetragonal at -5 °C and tetragonal to cubic at 121 °C.

‣ Quantification of not-dipolar components of atrial depolarization by principal component analysis of the P-wave

Censi,Federica; Calcagnini,Giovanni; Bartolini,Pietro; Ricci,Renato Pietro; Santini,Massimo
Fonte: Istituto Superiore di Sanità Publicador: Istituto Superiore di Sanità
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/06/2012 Português
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BACKGROUND: Principal component analysis (PCA) of the T-wave has been demonstrated to quantify the dipolar and not-dipolar components of the ventricular activation, the latter reflecting repolarization heterogeneity. Accordingly, the PCA of the P-wave could help in analyzing the heterogeneous propagation of sinus impulses in the atria, which seems to predispose to fibrillation. AIM: The aim of this study is to perform the PCA of the P-wave in patients prone to atrial fibrillation (AF). METHODS: PCA is performed on P-waves extracted by averaging technique from ECG recordings acquired using a 32-lead mapping system (2048 Hz, 24 bit, 0-400 Hz bandwidth). We extracted PCA parameters related to the dipolar and not dipolar components of the P-wave using the first 3 eigenvalues and the cumulative percent of variance explained by the first 3 PCs (explained variance EV). RESULTS AND CONCLUSIONS: We found that the EV associated to the low risk patients is higher than that associated to the high risk patients, and that, correspondingly, the first eigenvalue is significantly lower while the second one is significantly higher in the high risk patients respect to the low risk group. Factor loadings showed that on average all leads contribute to the first principal component.